DESC:Field of the Invention
The present invention relates to intelligent asset monitoring and protection systems for power distribution infrastructure, and more particularly to adaptive monitoring, movement validation, and theft-risk detection of transformers and field infrastructure assets using sensor-based telemetry, location tracking, and event-driven operational control.

Background of the Invention
Power utilities and infrastructure operators manage extensive networks of field infrastructure assets including distribution transformers, feeder pillar units, junction boxes, solar inverters, remote monitoring devices, and associated electrical infrastructure deployed across geographically dispersed and often remote environments. Such assets are frequently installed in unattended outdoor locations exposed to harsh environmental conditions, thereby creating operational challenges associated with asset visibility, monitoring, maintenance, protection, and lifecycle management.
Distribution transformers and related field infrastructure assets represent substantial operational and capital investments and are vulnerable to theft, unauthorized relocation, vandalism, tampering, environmental degradation, and physical damage. In many deployment scenarios, utilities and infrastructure operators lack continuous visibility into movement, deployment status, operational condition, and installation history associated with such assets, thereby making it difficult to maintain accurate asset inventories, validate deployment activities, and perform reliable maintenance planning, particularly for assets deployed across remote or inaccessible regions.
Existing asset tracking and monitoring systems generally utilize global positioning system (GPS)-based location monitoring in combination with wireless communication technologies for transmitting asset information to centralized monitoring platforms. Certain conventional systems additionally incorporate sensors such as accelerometers, vibration sensors, tilt sensors, temperature sensors, or geofence monitoring mechanisms for detecting environmental conditions and abnormal events associated with monitored assets. However, such conventional systems present multiple technical and operational limitations when deployed in power distribution infrastructure environments.
Many existing tracking systems rely on continuous wireless communication and periodic location acquisition, resulting in substantial energy consumption and reduced operational lifespan of battery-powered devices. In remote field deployments where reliable wired power infrastructure is unavailable, frequent maintenance intervention may be required for battery replacement or servicing, thereby increasing operational cost and reducing system reliability. Further, conventional monitoring systems generally utilize fixed threshold-based event detection mechanisms for generating alerts associated with vibration, tilt, movement, or geofence conditions without considering operational context associated with the monitored asset. Consequently, authorized transportation activities, maintenance operations, installation handling, or environmental disturbances may be incorrectly classified as theft-related events, thereby generating false alarms, unnecessary operational responses, and reduced monitoring reliability.
Existing systems further lack intelligence for differentiating legitimate transportation activity from unauthorized movement or theft-related conditions associated with transformers and field infrastructure assets. In many instances, both authorized transportation and theft-related activity involve physical movement of the monitored asset, thereby making conventional threshold-based movement detection insufficient for reliable classification. Known systems generally fail to validate continuity of operational-state transitions or analyze whether detected movement behavior corresponds to an expected transport lifecycle associated with authorized deployment workflows. Moreover, conventional systems do not adequately correlate multiple sensor-event conditions for contextual interpretation of asset behavior and therefore lack coordinated event characterization mechanisms capable of generating reliable movement legitimacy assessments or theft-risk evaluations.
In addition, existing monitoring systems generally operate using static communication and sensing configurations irrespective of event criticality, operational state, or available energy conditions, thereby resulting in inefficient bandwidth utilization, excessive power consumption, and reduced operational lifespan. Further, conventional systems lack adaptive protection intelligence for responding to theft-risk conditions or tamper-related events and generally provide only basic alert transmission mechanisms without supporting adaptive monitoring escalation, covert tracking behavior, secure event logging, backup communication activation, or dynamic modification of reporting behavior based on evolving threat conditions.
Accordingly, there exists a need for an improved intelligent asset monitoring and protection system capable of context-aware movement validation, adaptive event characterization, operational-state-based monitoring, autonomous energy-aware operation, and intelligent differentiation between authorized transportation activity and theft-related movement associated with transformers and field infrastructure assets deployed in remote and distributed environments.

Objective of the Invention
The principal objective of the present invention is to provide an intelligent asset monitoring and protection system for transformers and field infrastructure assets capable of adaptive monitoring, contextual event analysis, and autonomous operation in remote and distributed deployment environments.
Another objective of the present invention is to provide a context-aware movement validation mechanism configured to intelligently differentiate authorized transportation activity from unauthorized movement or theft-related conditions associated with transformers and field infrastructure assets.
Another objective of the present invention is to provide a coordinated event characterization mechanism capable of correlating multiple detected conditions including movement-related conditions, vibration conditions, tilt conditions, positional displacement conditions, geofence conditions, and tamper-related conditions to generate reliable event confidence assessments and theft-risk evaluations.
Another objective of the present invention is to provide an adaptive operational control mechanism configured to dynamically modify sensing operations, location tracking operations, communication operations, reporting frequency, and power usage based on operational state, event severity level, event confidence score, and available energy condition associated with an intelligent tracking device.
Another objective of the present invention is to provide an energy-aware monitoring and communication mechanism capable of supporting long-term autonomous operation of field-deployed infrastructure assets using intelligent power management and solar-assisted energy replenishment.
Another objective of the present invention is to provide an adaptive protection and response mechanism configured to activate emergency tracking, covert tracking, secure event logging, backup communication operations, and adaptive alert transmission in response to theft-risk conditions, tamper-related conditions, or abnormal movement behavior associated with monitored infrastructure assets.

Summary of the Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
The present invention provides an intelligent asset monitoring and protection system for transformers and field infrastructure assets configured to perform adaptive monitoring, context-aware event characterization, operational-state-based movement validation, and autonomous protection operations in remote and distributed deployment environments. The system integrates sensor-based telemetry, location tracking, adaptive communication control, and intelligent energy management through a coordinated controller-driven architecture configured to support long-term autonomous operation and reliable theft-risk detection.
In an aspect, the system includes an intelligent tracking device comprising a location tracking module, a plurality of environmental and movement-related sensors, a processing subsystem, a communication subsystem, and a power management subsystem. The plurality of sensors may include one or more of accelerometers, vibration sensors, tilt sensors, positional displacement sensors, impact sensors, tamper sensors, temperature sensors, or geofence monitoring mechanisms configured to collectively monitor operational conditions associated with transformers and field infrastructure assets. The processing subsystem is configured to analyze collected location data and sensor data to identify event conditions associated with monitored assets.
In another aspect, the processing subsystem is configured to determine an operational state associated with a monitored asset, wherein the operational state includes at least one of a warehouse state, transport state, installed state, maintenance state, or theft-risk state. The processing subsystem dynamically assigns different priorities and weighting values to multiple detected conditions based on the determined operational state and generates an event confidence score using correlated movement-related conditions, location-related conditions, positional displacement conditions, vibration conditions, tilt conditions, temperature conditions, geofence conditions, and tamper-related conditions.
In another aspect, the system performs context-aware movement validation configured to differentiate authorized transportation activity from unauthorized movement or theft-related conditions. The processing subsystem validates movement associated with the monitored asset by correlating operational-state continuity, positional displacement behavior, movement-related conditions, geofence transition behavior, and event confidence information with an expected transport lifecycle behavior associated with authorized deployment workflows. Based on the correlation analysis, movement satisfying expected transport lifecycle continuity is classified as an authorized transport condition, whereas movement associated with operational-state discontinuity, abnormal movement behavior, or abnormal geofence transition is classified as an unauthorized movement condition or a theft-risk condition.
In another aspect, the system dynamically determines an event severity level and an energy condition associated with the intelligent tracking device and automatically selects an operational mode based on the determined event severity level, generated event confidence score, determined operational state, and available energy condition. The operational mode may include one or more of a normal monitoring mode, low-power mode, emergency tracking mode, adaptive reporting mode, or covert tracking mode. The system further dynamically modifies sensing operations, communication operations, location tracking frequency, reporting intervals, transmission behavior, and power consumption behavior based on the selected operational mode.
In another aspect, the communication subsystem selectively transmits monitoring information or alert information based on event criticality, operational state, and available energy condition. During normal operating conditions, the system may transmit summarized monitoring information using low-power communication behavior, whereas during theft-risk conditions or tamper-related events, the system activates adaptive protection operations including emergency tracking, backup communication activation, covert transmission of location information, secure event logging, irregular or variable transmission intervals, and high-priority alert communication.
In another aspect, the power management subsystem includes a rechargeable battery and a solar-assisted charging arrangement configured to support sustained autonomous operation in remote field environments without continuous wired power availability. The processing subsystem continuously evaluates the available energy condition and intelligently regulates sensing activity, communication activity, event prioritization, and operational behavior to optimize power consumption while preserving emergency tracking and protection capabilities associated with theft-risk conditions.
In another aspect, the system stores historical operational data including location history, movement history, vibration history, temperature history, event severity history, tamper-related event history, and operational-state transition history associated with monitored assets. The processing subsystem analyzes the stored historical operational data to identify abnormal operational patterns, generate maintenance recommendations, generate dynamic risk levels, and support predictive monitoring and asset protection operations associated with transformers and field infrastructure assets.

Brief description of the drawings
The figures described below depict various aspects of the system and methods disclosed herein. It should be understood that each figure depicts an embodiment of a particular aspect of the disclosed system and methods, and that each of the figures is intended to accord with a possible embodiment thereof. Further, wherever possible, the following description refers to the reference numerals included in the following figures, in which features depicted in multiple figures are designated with consistent reference numerals.
Figure 1 illustrates an example intelligent asset monitoring and protection system architecture (100) for transformers and field infrastructure assets, according to one embodiment of the present invention.
Figure 2 illustrates an example architecture of an intelligent tracking device (200) configured for adaptive monitoring, event characterization, movement validation, and protection of transformers and field infrastructure assets, according to one embodiment of the present invention.
Figure 3 illustrates an adaptive monitoring and protection workflow (300) for context-aware event characterization, operational-state-based monitoring, adaptive communication control, and energy-aware operation associated with transformers and field infrastructure assets, according to one embodiment of the present invention.
Figure 4 illustrates a transport and theft differentiation workflow (400) for validating movement legitimacy and differentiating authorized transportation activity from unauthorized movement or theft-related conditions associated with transformers and field infrastructure assets, according to one embodiment of the present invention.
Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may have not been drawn to scale. For example, the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure.
Throughout the drawings, it should be noted that like reference numbers are used to depict the same or similar elements, features, and structures.

Detailed Description of the Invention
The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the invention as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary.
Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used by the inventor to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention as defined by the appended claims and their equivalents.
It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a component surface” includes reference to one or more of such surfaces.
Figures discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way that would limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged system. The terms used to describe various embodiments are exemplary. It should be understood that these are provided to merely aid the understanding of the description, and that their use and definitions, in no way limit the scope of the invention. Terms first, second, and the like are used to differentiate between objects having the same terminology and are in no way intended to represent a chronological order, unless where explicitly stated otherwise. A set is defined as a non-empty set including at least one element.
FIG. 1 illustrates an example intelligent asset monitoring and protection system architecture (100) configured for monitoring, movement validation, adaptive protection, and operational management associated with transformers and field infrastructure assets, according to one embodiment of the present invention.
As illustrated in FIG. 1, the system architecture (100) includes a remote transformer or field infrastructure asset (100), an intelligent tracking device (105), a communication network (110), an optional satellite communication arrangement (115), a cloud platform (120), and one or more utility or authorized user interfaces (130).
In an embodiment, the remote transformer or field infrastructure asset (100) may include one or more of a distribution transformer, feeder pillar unit, junction box, solar inverter, electrical cabinet, pole-mounted infrastructure unit, remote monitoring device, or other power distribution infrastructure deployed in geographically distributed or remote environments. The intelligent tracking device (105) is operatively associated with the transformer or field infrastructure asset (100) and is configured to continuously monitor operational conditions, movement behavior, location information, environmental conditions, and tamper-related activity associated with the monitored asset.
In an embodiment, the intelligent tracking device (105) may include one or more sensing modules, location tracking modules, communication modules, processing modules, memory modules, and power management modules configured to collectively perform adaptive monitoring and protection operations. The intelligent tracking device (105) continuously collects sensor data and location data associated with the transformer or field infrastructure asset (100) and processes the collected information to identify event conditions, movement-related conditions, positional displacement conditions, geofence conditions, and tamper-related conditions associated with the monitored asset.
In an embodiment, the intelligent tracking device (105) communicates with the cloud platform (120) through the communication network (110). The communication network (110) may include one or more wireless communication technologies including cellular communication networks, fourth-generation (4G) communication networks, Narrowband Internet of Things (NB-IoT) communication networks, Long-Term Evolution for Machines (LTE-M) communication networks, low-power wide-area communication networks, or combinations thereof. In certain embodiments, the communication network (110) supports bidirectional communication between the intelligent tracking device (105) and the cloud platform (120) for transmission of monitoring information, configuration information, operational commands, alert notifications, firmware updates, and device management information.
In another embodiment, the system architecture (100) may additionally include a satellite communication arrangement (115) configured to provide backup communication capability in remote deployment environments where terrestrial communication coverage associated with the communication network (110) is unavailable, degraded, interrupted, or intentionally jammed. The intelligent tracking device (105) may automatically switch between primary communication modes and backup communication modes based on signal availability, communication quality, event criticality, or detection of tamper-related conditions.
In an embodiment, the cloud platform (120) is configured to receive monitoring data, location data, sensor-event information, and operational-state information associated with the transformer or field infrastructure asset (100). The cloud platform (120) may include a data ingestion and storage module (120A), an event processing engine (120B), an analytics engine (120C), an alert and notification engine (120D), a device management module (120E), and a security and encryption module (120F).
In an embodiment, the data ingestion and storage module (120A) stores historical operational data associated with monitored assets including location history, movement history, vibration history, temperature history, event severity history, tamper-event history, and operational-state transition history. The stored historical operational data may be utilized for generating maintenance recommendations, identifying abnormal operational patterns, generating dynamic risk levels, and supporting predictive monitoring operations.
In another embodiment, the event processing engine (120B) and the analytics engine (120C) are configured to analyze collected sensor-event conditions and determine event severity levels, event confidence scores, operational states, movement legitimacy information, and theft-risk conditions associated with monitored assets. In some embodiments, the event severity level represents a relative severity associated with detected operational events, movement conditions, tamper-related conditions, or abnormal asset behavior, while the event confidence score represents a contextual confidence measure generated based on correlation of multiple detected conditions including movement-related conditions, positional displacement conditions, vibration conditions, geofence conditions, and tamper-related conditions associated with the monitored asset.
In certain embodiments, the analytics engine (120C) further correlates movement-related conditions, positional displacement conditions, geofence transition behavior, operational-state continuity, route continuity information, and tamper-related conditions to determine movement legitimacy information associated with authorized transportation activity and to differentiate authorized transportation activity from unauthorized movement or theft-related conditions associated with the transformer or field infrastructure asset.
In an embodiment, the alert and notification engine (120D) is configured to generate adaptive notifications, emergency alerts, maintenance alerts, geofence alerts, theft-risk alerts, and tamper-related alerts based on analyzed event conditions and operational priorities. The alert and notification engine (120D) may selectively transmit summary monitoring information during normal operational conditions and detailed monitoring information during theft-risk conditions or abnormal operational conditions.
In another embodiment, the device management module (120E) is configured to manage operational parameters associated with one or more intelligent tracking devices (105). The device management module (120E) may support remote configuration, firmware updates, adaptive operational-mode management, communication scheduling, reporting interval modification, sensor calibration, and device health monitoring.
In an embodiment, the security and encryption module (120F) is configured to support secure communication, encrypted data storage, authentication operations, tamper-response protection, and secure event logging associated with monitored assets and intelligent tracking devices (105).
In an embodiment, utility personnel or authorized users (130) may access monitoring information associated with monitored assets through one or more user interfaces including a web dashboard (130A), a mobile application (130B), SMS or email alert systems (130C), and control center or supervisory control and data acquisition (SCADA) systems (130D). The user interfaces enable authorized personnel to monitor real-time asset status, location information, operational-state information, theft-risk conditions, event alerts, maintenance recommendations, and historical operational data associated with the transformer or field infrastructure asset (100).
In an embodiment, the intelligent tracking device (105), the cloud platform (120), and the utility or authorized user interfaces (130) collectively support adaptive monitoring and intelligent protection operations associated with transformers and field infrastructure assets. The system architecture (100) enables context-aware movement validation, operational-state-based event characterization, adaptive communication control, autonomous energy-aware operation, and intelligent differentiation between authorized transportation activity and theft-related movement conditions associated with monitored infrastructure assets.
In an example operational scenario, when the transformer or field infrastructure asset (100) is undergoing authorized transportation, the intelligent tracking device (105) may identify a valid transport operational state and correlate movement-related conditions, positional displacement conditions, and geofence transition behavior with expected transport lifecycle continuity. Under such conditions, the cloud platform (120) classifies the detected movement as an authorized transport condition and transmits summary operational information to authorized users (130).
In another example operational scenario, when abnormal movement behavior, unauthorized geofence deviation, operational-state discontinuity, tamper-related conditions, or communication interruption conditions are detected, the intelligent tracking device (105) and the cloud platform (120) may collectively classify the detected activity as a theft-risk condition. Under such conditions, the system architecture (100) automatically activates adaptive protection operations including emergency tracking, backup communication activation, covert tracking behavior, secure event logging, adaptive alert escalation, and transmission of high-priority monitoring information to authorized users (130).
FIG. 2 illustrates an example architecture of an intelligent tracking device (105) configured for adaptive monitoring, movement validation, event characterization, energy-aware operation, and protection of transformers and field infrastructure assets, according to one embodiment of the present invention.
As illustrated in FIG. 2, the intelligent tracking device (105) includes a sensor subsystem (105A), a processing unit (105B), a power subsystem (105C), a communication subsystem (105D), a storage subsystem (105E), a security and protection subsystem (105F), and an auxiliary interface subsystem (105G). The various subsystems are communicatively coupled and collectively operate to perform context-aware monitoring, operational-state analysis, adaptive communication control, movement legitimacy validation, and theft-risk detection associated with transformers and field infrastructure assets.
In an embodiment, the sensor subsystem (105A) includes one or more sensing modules configured to monitor environmental conditions, movement behavior, positional displacement conditions, and tamper-related conditions associated with a monitored transformer or field infrastructure asset. The sensor subsystem (105A) may include a global positioning system (GPS) or global navigation satellite system (GNSS) module, a vibration sensor or accelerometer, a tilt sensor, a temperature sensor, an impact sensor, and a tamper detection unit.
In an embodiment, the GPS or GNSS module is configured to determine location information, route continuity information, geofence transition behavior, and positional displacement information associated with the monitored asset. The vibration sensor and accelerometer detect vibration patterns, movement continuity behavior, lifting activity, and abnormal handling behavior, while the tilt sensor detects orientation changes and abnormal displacement conditions associated with the monitored asset. The impact sensor detects shock conditions or abrupt disturbances, and the tamper detection unit identifies enclosure opening conditions, unauthorized device removal, communication interruption attempts, signal jamming conditions, or tamper-related activity associated with the intelligent tracking device (105).
In an embodiment, the processing unit (105B) includes a microcontroller and processor arrangement, a memory subsystem, and an event characterization and control engine configured to collectively perform intelligent monitoring and adaptive operational control operations. The processing unit (105B) receives sensor-event information from the sensor subsystem (105A), communication information from the communication subsystem (105D), stored operational data from the storage subsystem (105E), and energy-condition information from the power subsystem (105C).
In another embodiment, the event characterization and control engine dynamically analyzes collected sensor-event conditions including movement-related conditions, vibration conditions, tilt conditions, positional displacement conditions, geofence conditions, temperature conditions, and tamper-related conditions to determine operational states, event severity levels, event confidence scores, movement legitimacy information, and theft-risk conditions associated with the monitored asset.
In an embodiment, the processing unit (105B) dynamically assigns weighting values to multiple detected conditions based on operational-state information associated with the monitored asset. For example, during an installed operational state, positional displacement conditions and tilt conditions may be assigned higher weighting values, whereas during an authorized transport operational state, movement continuity conditions and route continuity conditions may be weighted differently to reduce false theft classifications associated with legitimate transportation activity.
In another embodiment, the event characterization and control engine generates an event confidence score using correlated sensor-event conditions and dynamically assigned weighting values. In an exemplary implementation, the event confidence score (ECS) may be determined as follows:
ECS=?_(i=1)^n¦W_i ×C_i
where:
ECS represents the event confidence score,
Wi represents a dynamically assigned weighting value associated with a detected condition, and
Ci represents a detected condition value associated with movement-related conditions, vibration conditions, geofence conditions, tilt conditions, positional displacement conditions, or tamper-related conditions.
In an embodiment, the processing unit (105B) further determines an event severity level based on the generated event confidence score, operational-state information, and energy-condition information associated with the intelligent tracking device (105). In an exemplary implementation, the event severity level (ESL) may be determined as follows:
ESL=f(ECS,O,E)
where:
ESL represents the event severity level,
ECS represents the generated event confidence score,
represents operational-state information, and
E represents an energy condition associated with the intelligent tracking device (105).
In another embodiment, the processing unit (105B) performs context-aware movement validation configured to differentiate authorized transportation activity from unauthorized movement or theft-related conditions. The event characterization and control engine correlates operational-state continuity, positional displacement behavior, vibration patterns, movement continuity behavior, and geofence transition behavior with an expected transport lifecycle associated with authorized deployment workflows.
In an example operational scenario, when the monitored transformer or field infrastructure asset is undergoing authorized transportation, the processing unit (105B) identifies a valid transport operational state and detects expected transport lifecycle continuity including controlled lifting behavior, stable movement continuity, predictable geofence transitions, and authorized route progression. Under such conditions, the detected movement is classified as an authorized transport condition.
In another example operational scenario, when abrupt tilt conditions, abnormal positional displacement conditions, unauthorized geofence deviation, operational-state discontinuity, abnormal vibration patterns, or tamper-related conditions are detected, the processing unit (105B) classifies the detected activity as an unauthorized movement condition or a theft-risk condition and activates adaptive protection operations.
In an embodiment, the communication subsystem (105D) includes one or more cellular communication modules, antennas, SIM or eSIM arrangements, and optional satellite communication arrangements configured to support bidirectional communication between the intelligent tracking device (105) and remote monitoring platforms. The communication subsystem (105D) may support communication technologies including 4G, NB-IoT, LTE-M, satellite communication, or low-power wireless communication technologies.
In another embodiment, the communication subsystem (105D) dynamically modifies communication behavior based on operational-state information, event severity levels, event confidence scores, communication quality conditions, and available energy conditions associated with the intelligent tracking device (105). During normal operational conditions, summarized monitoring information may be periodically transmitted using low-power communication behavior. During theft-risk conditions or tamper-related events, the communication subsystem (105D) may activate emergency tracking communication, adaptive alert transmission, covert transmission behavior, irregular transmission intervals, or backup communication activation.
In an embodiment, the power subsystem (105C) includes a solar panel arrangement, a charge controller, a rechargeable battery pack, and a power management unit configured to support autonomous long-term operation of the intelligent tracking device (105) in remote deployment environments.
In another embodiment, the power management unit continuously monitors available energy conditions and dynamically regulates sensing frequency, location tracking frequency, communication frequency, reporting intervals, and operational behavior associated with the intelligent tracking device (105). During low-energy conditions, the processing unit (105B) may activate low-power operational modes while preserving energy reserves for emergency tracking operations and theft-risk response activities.
In an embodiment, the storage subsystem (105E) stores historical operational data, sensor-event history, location history, operational-state history, event severity history, tamper-event history, firmware information, and operational parameters associated with the intelligent tracking device (105).
In another embodiment, the security and protection subsystem (105F) includes encryption modules, tamper-response circuits, and secure event logging mechanisms configured to protect operational information and support adaptive protection operations associated with the monitored asset. Upon detection of tamper-related conditions or communication interference conditions, the security and protection subsystem (105F) may activate covert tracking behavior, secure event logging, emergency communication operations, or adaptive protection escalation.
In an embodiment, the auxiliary interface subsystem (105G) includes firmware update interfaces, debugging interfaces, and external input/output interfaces configured to support device configuration, maintenance operations, firmware management, diagnostics, and integration with external infrastructure monitoring systems.
Accordingly, the intelligent tracking device (105) operates as a coordinated adaptive monitoring platform wherein the sensor subsystem (105A), processing unit (105B), communication subsystem (105D), power subsystem (105C), storage subsystem (105E), security and protection subsystem (105F), and auxiliary interface subsystem (105G) collectively support intelligent movement validation, adaptive monitoring escalation, operational-state-aware event characterization, energy-aware communication control, autonomous protection operations, and theft-risk differentiation associated with transformers and field infrastructure assets.
In another embodiment, the intelligent asset monitoring and protection system includes a sensor subsystem configured to collect location data and sensor data associated with a transformer or field infrastructure asset, a processing subsystem communicatively coupled with the sensor subsystem, a communication subsystem, a power subsystem, and a protection subsystem collectively configured to perform adaptive monitoring and protection operations associated with the transformer or field infrastructure asset. The processing subsystem analyzes collected location data and sensor data, determines operational states associated with the transformer or field infrastructure asset, determines event severity levels based on multiple detected conditions, generates event confidence scores associated with the monitored asset, and dynamically assigns weighting values to the multiple detected conditions based on determined operational states. In certain embodiments, the processing subsystem further generates a transport legitimacy score associated with authorized transportation behavior and a theft anomaly score associated with abnormal movement behavior, operational-state discontinuity, abnormal geofence deviation, route deviation, or tamper-related conditions, and compares the transport legitimacy score and the theft anomaly score to classify movement as an authorized transport condition or a theft-risk condition associated with the transformer or field infrastructure asset.
In another embodiment, the processing subsystem dynamically selects operational modes based on determined operational states, determined event severity levels, generated event confidence scores, generated transport legitimacy scores, generated theft anomaly scores, and determined energy conditions associated with the system, and automatically adjusts sensing activity, location tracking operations, communication operations, reporting frequency, power consumption behavior, and adaptive protection operations based on the selected operational modes. The communication subsystem selectively transmits monitoring information or alert information associated with the transformer or field infrastructure asset, while the power subsystem supplies power using at least a rechargeable battery and a solar-assisted charging arrangement. Upon detection of theft-risk conditions or tamper-related conditions, the protection subsystem activates emergency tracking operations, backup communication activation, covert tracking behavior, secure event logging, covert transmission behavior using variable or irregular transmission intervals, or tamper-response operations associated with the transformer or field infrastructure asset.
FIG. 3 illustrates an adaptive monitoring and protection workflow (300) configured for context-aware event characterization, operational-state-based monitoring, movement validation, adaptive communication control, and intelligent protection of transformers and field infrastructure assets, according to one embodiment of the present invention.
In particular, FIG. 3 illustrates a method for monitoring and protecting a transformer or a field infrastructure asset, wherein the method comprises collecting, by an intelligent tracking device, location data and sensor data associated with the transformer or the field infrastructure asset; analyzing the collected location data and sensor data to identify event conditions associated with the transformer or the field infrastructure asset; determining event severity levels based on movement-related conditions, location-related conditions, and sensor-related conditions; determining energy conditions associated with the intelligent tracking device; dynamically selecting operational modes based on the determined event severity levels and energy conditions; dynamically adjusting sensing operations, location tracking operations, communication operations, reporting frequency, and power usage based on selected operational modes; validating movement associated with the transformer or field infrastructure asset using operational-state information, route continuity behavior, geofence conditions, and event confidence information; detecting theft-risk conditions or tamper-related conditions; activating adaptive protection operations including emergency tracking, covert transmission behavior, secure event logging, backup communication activation, and covert tracking operations; storing historical operational data; generating maintenance recommendations and dynamic risk levels; and transmitting monitoring information or alert information associated with the transformer or field infrastructure asset.
In another embodiment, FIG. 3 further illustrates determining operational states associated with the transformer or field infrastructure asset, wherein the operational states include one or more of a warehouse state, transport state, installed state, maintenance state, or theft-risk state; dynamically assigning weighting values and monitoring priorities to multiple detected conditions based on the determined operational states; progressively increasing monitoring activity and communication priority based on determined event severity levels; generating event confidence scores based on vibration conditions, tilt conditions, positional displacement conditions, temperature conditions, geofence conditions, and tamper-related conditions; dynamically modifying sensing frequency, location tracking frequency, communication frequency, reporting interval, and power consumption behavior based on determined operational states, generated event confidence scores, determined event severity levels, and determined energy conditions; validating movement by correlating operational-state continuity, positional displacement behavior, movement continuity behavior, geofence transition behavior, route continuity information, and expected transport lifecycle behavior associated with authorized transportation activity; generating transport legitimacy scores associated with authorized transportation behavior; generating theft anomaly scores associated with abnormal movement behavior, operational-state discontinuity, abnormal geofence deviation, route deviation, or tamper-related conditions; and comparing the transport legitimacy scores and theft anomaly scores to classify movement as an authorized transport condition, unauthorized movement condition, or theft-risk condition associated with the transformer or field infrastructure asset.
The adaptive monitoring and protection workflow (300) illustrated in FIG. 3 is explained in greater detail below.
As illustrated in FIG. 3, the workflow (300) begins at step (305) by initializing subsystem verification and operational readiness associated with a transformer or field infrastructure asset. In an embodiment, the intelligent tracking device verifies readiness of sensing modules, communication modules, power management modules, storage modules, and security modules prior to initiating monitoring operations.
At step (310), the workflow includes collecting location data and sensor data associated with the transformer or field infrastructure asset. The collected data may include location information, vibration conditions, tilt conditions, positional displacement conditions, temperature conditions, impact conditions, movement-related conditions, and tamper-related conditions associated with the monitored asset.
At step (315), the workflow includes determining an operational state associated with the transformer or field infrastructure asset and analyzing movement-related conditions, location-related conditions, and sensor-related conditions. In an embodiment, the operational state may include one or more of a warehouse state, transport state, installed state, maintenance state, or theft-risk state.
At step (320), the workflow includes generating an event confidence score based on vibration conditions, tilt conditions, positional displacement conditions, temperature conditions, geofence conditions, and tamper-related conditions associated with the monitored asset. In an exemplary implementation, the event confidence score (ECS) may be determined as follows:
ECS=?_(i=1)^n¦W_i ×C_i
where:
ECS represents the event confidence score,
Wi represents a weighting value associated with a detected condition, and
Ci represents a detected condition value associated with movement-related conditions, vibration conditions, geofence conditions, positional displacement conditions, or tamper-related conditions.
In certain embodiments, the event confidence score may be normalized within a range of 0 to 100, wherein event confidence scores below 30 correspond to normal operational conditions associated with stable asset behavior, event confidence scores between 30 and 70 correspond to moderate-risk conditions associated with abnormal movement behavior, environmental disturbances, or transitional operational conditions, and event confidence scores above 70 correspond to elevated theft-risk conditions, tamper-related conditions, unauthorized movement activity, or abnormal geofence transition behavior associated with the transformer or field infrastructure asset. The event confidence score may be dynamically modified based on operational-state information, weighting values associated with detected conditions, and contextual correlation of multiple sensor-event conditions associated with the monitored asset.
In some embodiments, tilt variation exceeding threshold values between 15 degrees and 45 degrees during an installed operational state may indicate abnormal displacement conditions, unauthorized movement activity, tamper-related conditions, or theft-risk conditions associated with the transformer or field infrastructure asset. The tilt threshold values may be dynamically modified based on operational-state information, installation orientation, asset type, environmental conditions, or transportation status associated with the monitored asset.
At step (325), the workflow includes assigning dynamic weighting values to detected conditions and determining an event severity level and an energy condition associated with the intelligent tracking device. In an embodiment, weighting values associated with detected conditions are dynamically modified based on operational-state information associated with the monitored asset. For example, during an installed operational state, positional displacement conditions and tilt conditions may be assigned higher weighting values, whereas during a transport operational state, movement continuity conditions and route continuity conditions may be weighted differently to reduce false theft classifications associated with authorized transportation activity.
In another embodiment, the event severity level (ESL) may be determined based on the event confidence score, operational-state information, and available energy condition associated with the intelligent tracking device as follows:
ESL=f(ECS,O,E)
where:
ESL represents the event severity level,
ECS represents the generated event confidence score,
represents operational-state information, and
E represents an energy condition associated with the intelligent tracking device.
At step (330), the workflow includes selecting an operational mode and dynamically adjusting sensing operations, location tracking operations, communication operations, reporting frequency, and power usage based on the selected operational mode. The operational mode may include one or more of a normal monitoring mode, low-power mode, adaptive reporting mode, emergency tracking mode, or covert tracking mode.
In certain embodiments, monitoring information may be transmitted at intervals between 15 minutes and 6 hours during normal operational conditions to reduce communication overhead and power consumption associated with the intelligent tracking device. During theft-risk conditions, tamper-related conditions, abnormal movement conditions, or emergency tracking conditions, location information and sensor-event information may be transmitted at intervals between 5 seconds and 2 minutes to support adaptive monitoring, rapid alert escalation, and enhanced tracking accuracy associated with the transformer or field infrastructure asset.
At step (335), the workflow includes validating movement associated with the transformer or field infrastructure asset using operational-state information, positional displacement conditions, geofence conditions, and event confidence information. In an embodiment, the processing unit correlates operational-state continuity, movement continuity behavior, positional displacement behavior, and geofence transition behavior with an expected transport lifecycle associated with authorized deployment workflows to differentiate authorized transportation activity from unauthorized movement or theft-related conditions.
In another embodiment, the movement validation process reduces false theft classifications by correlating operational-state continuity, route progression behavior, geofence transition patterns, and movement continuity information associated with the monitored asset. Such contextual correlation enables differentiation between legitimate transportation activity and unauthorized movement conditions, thereby improving monitoring reliability and reducing unnecessary alert escalation.
In certain embodiments, deviation of an actual transport route from an authorized transport corridor by a threshold distance between 100 meters and 5 kilometers may increase the theft anomaly score associated with the transformer or field infrastructure asset. The threshold deviation distance may be dynamically modified based on operational-state information, transport-route classification, geographic deployment conditions, or authorized transportation requirements associated with the monitored asset.
At step (340), the workflow includes detecting a theft-risk condition or tamper-related condition and activating adaptive protection operations including emergency tracking, backup communication activation, secure event logging, covert transmission behavior, or covert tracking operation. Upon detection of communication interruption conditions, signal jamming attempts, unauthorized enclosure opening conditions, or abnormal movement behavior, the intelligent tracking device dynamically escalates monitoring and protection operations based on event severity level, operational-state information, and communication availability conditions.
In another embodiment, the intelligent tracking device dynamically modifies reporting intervals and transmission behavior based on event severity level, operational-state information, communication availability, and detected theft-risk conditions. During normal operational conditions, summarized monitoring information may be transmitted at periodic intervals to reduce energy consumption, whereas during theft-risk conditions or tamper-related events, location information and sensor-event information may be transmitted at adaptive, variable, or irregular transmission intervals to reduce predictability and improve covert monitoring capability.
At step (345), the workflow includes transmitting monitoring information or alert information, storing historical operational data, generating maintenance recommendations and dynamic risk levels, and continuing adaptive monitoring and protection operations associated with the transformer or field infrastructure asset.
In an example operational scenario, when the monitored transformer is undergoing authorized transportation, the workflow (300) identifies a valid transport operational state, expected route continuity behavior, and stable movement continuity behavior, thereby classifying the detected activity as an authorized transport condition while reducing false theft alerts. In another example operational scenario, when abrupt tilt conditions, abnormal positional displacement conditions, unauthorized geofence deviation, communication interruption conditions, or tamper-related conditions are detected during an installed operational state, the workflow (300) classifies the detected activity as a theft-risk condition and activates emergency tracking operations, covert communication behavior, adaptive alert escalation, and secure event logging operations.
Accordingly, the adaptive monitoring and protection workflow (300) enables coordinated context-aware monitoring, operational-state-based movement validation, adaptive event characterization, intelligent theft-risk differentiation, energy-aware communication control, autonomous protection operations, reduced false theft classifications, adaptive covert monitoring behavior, and long-term monitoring of transformers and field infrastructure assets deployed in remote and distributed environments.
FIG. 4 illustrates an example adaptive movement validation and theft differentiation workflow (400) associated with a transformer or field infrastructure asset, according to one embodiment of the present invention. In particular, FIG. 4 illustrates a context-aware movement classification framework configured to distinguish authorized transportation activity from unauthorized movement or theft-related activity through coordinated analysis of operational-state continuity, movement behavior, geofence transition behavior, and anomaly scoring.
At step 405, the system performs detecting movement associated with a transformer or field infrastructure asset. In some embodiments, movement detection may be initiated using one or more movement-related sensors including an accelerometer, vibration sensor, tilt sensor, inertial measurement unit (IMU), or positional displacement monitoring mechanism. The movement detection process may identify initiation of asset displacement, lifting activity, vibration activity, directional movement, translational displacement, or abnormal physical interaction associated with the transformer or field infrastructure asset.
In certain embodiments, movement detection may be triggered when acceleration magnitude exceeds a predefined threshold value represented as:
A_mag=v(a_x^2+a_y^2+a_z^2 )
where A_mag represents resultant acceleration magnitude and a_x, a_y, and a_z represent acceleration components detected across multiple axes. The controller may classify the detected movement as stationary movement, transport movement, vibration disturbance, impact movement, or abnormal displacement based on detected acceleration characteristics.
At step 410, the system performs determining an operational state associated with the transformer or field infrastructure asset. In some embodiments, the operational state may include at least one of a warehouse state, transport state, installed state, maintenance state, service state, temporary relocation state, or theft-risk state. The operational-state determination may be based on deployment records, historical movement data, geofence association, installation records, maintenance schedules, authorized transport instructions, and contextual sensor conditions.
In certain embodiments, the operational state may dynamically transition according to an expected operational lifecycle sequence including:
Warehouse ? Authorized Dispatch ? Transport ? Installation ? Operational Service ? Maintenance ? Reinstallation.
The controller may continuously verify whether detected movement behavior aligns with an expected operational-state transition sequence.
At step 415, the system performs verifying operational-state transition continuity associated with the transformer or field infrastructure asset. In some embodiments, the system may determine whether the detected operational-state transition corresponds to a valid and authorized asset lifecycle progression. For example, movement detected immediately after an authorized dispatch instruction may be associated with a legitimate transportation condition, whereas movement detected without a preceding operational-state transition authorization may increase theft-risk probability.
In certain embodiments, operational-state continuity validation may include analyzing dispatch authorization records associated with authorized movement of the transformer or field infrastructure asset, scheduled installation windows corresponding to expected deployment activities, maintenance activity logs associated with authorized servicing operations, technician authentication information corresponding to personnel assigned for asset handling or transportation, route authorization information associated with approved transportation paths, expected destination geofence information corresponding to authorized installation or storage locations, and historical deployment continuity information associated with prior operational-state transitions of the monitored asset.
In some embodiments, discontinuity between expected operational-state progression and detected movement behavior may generate an operational-state anomaly indicator represented as:
OSA=|S_expected-S_detected|
where OSA represents operational-state anomaly magnitude, S_expected represents an expected operational state, and S_detected represents a detected operational state associated with the asset.
At step 420, the system performs analyzing movement behavior patterns including lift behavior, tilt progression, vibration patterns, movement continuity, and positional displacement conditions. In some embodiments, the controller may correlate multiple movement-related parameters to characterize physical movement behavior associated with the transformer or field infrastructure asset.
In certain embodiments, the system may distinguish crane-assisted authorized loading behavior, smooth transportation vibration patterns, and temporary installation handling associated with legitimate transportation or deployment activity from unauthorized dragging movement, impact-associated displacement, forced enclosure removal, and abrupt extraction activity associated with theft-risk conditions, tampering events, or unauthorized movement of the transformer or field infrastructure asset.
In some embodiments, movement continuity analysis may include determining whether movement occurs in a continuous route-consistent pattern or in fragmented irregular movement segments. Authorized transportation activity may exhibit relatively stable directional continuity and progressive displacement behavior, while theft-related activity may exhibit abnormal stoppage intervals, abrupt directional deviation, or irregular displacement characteristics.
In certain embodiments, tilt progression analysis may evaluate progressive orientation changes using:
?=?tan??^(-1) (v(a_x^2+a_y^2 )/a_z )
where ? represents detected tilt angle associated with the transformer or field infrastructure asset.
At step 425, the system performs analyzing geofence transition behavior and route continuity associated with the transformer or field infrastructure asset. In some embodiments, the system may evaluate whether movement of the transformer or field infrastructure asset follows an expected authorized transport route between authorized geofence regions. In certain embodiments, the controller may compare warehouse geofence exit timing associated with departure of the transformer or field infrastructure asset from an authorized storage location, route progression continuity associated with ongoing transportation activity, expected transit corridors corresponding to authorized transport paths, intermediate waypoint validation associated with predefined transport checkpoints, installation-site entry timing associated with arrival at an authorized deployment location, and authorized destination alignment corresponding to an expected installation or delivery destination associated with the monitored asset.
In some embodiments, route continuity analysis may determine whether movement deviates from an expected transportation path beyond an allowable threshold distance. The route deviation magnitude may be represented as:
D_route=|P_actual-P_expected|
where D_route represents route deviation magnitude, P_actual represents an actual movement path, and P_expected represents an expected transport path.
At step 430, the system performs generating a transport legitimacy score based on operational-state continuity and analyzed movement behavior patterns. In some embodiments, the transport legitimacy score may represent a probability that detected movement corresponds to authorized transportation activity. In certain embodiments, the transport legitimacy score may be generated using weighted correlation of operational-state continuity associated with the transformer or field infrastructure asset, route consistency corresponding to an authorized transportation path, movement smoothness associated with expected transportation behavior, authorized timing alignment corresponding to scheduled dispatch and installation activities, vibration signature correlation associated with legitimate transportation conditions, geofence continuity corresponding to authorized movement between predefined locations, and authenticated dispatch validation associated with approved transportation or deployment operations.
In some embodiments, the transport legitimacy score may be represented as:
TLS=?_(i=1)^n¦w_i X_i
where TLSrepresents transport legitimacy score, w_irepresents adaptive weighting coefficients, and X_irepresents validated transport-behavior parameters.
At step 435, the system performs generating a theft anomaly score based on abnormal movement behavior, abnormal geofence transition, and operational-state discontinuity. In some embodiments, the theft anomaly score may represent a probability that detected movement corresponds to unauthorized displacement, theft activity, tampering activity, or illegal relocation.
In certain embodiments, theft anomaly generation may include analysis of abnormal lifting behavior associated with unauthorized extraction of the transformer or field infrastructure asset, forced movement signatures corresponding to dragging or abrupt displacement conditions, nighttime unauthorized movement occurring outside scheduled operational windows, communication interruption conditions associated with signal blocking or jamming attempts, enclosure tampering conditions associated with unauthorized access to the intelligent tracking device, abrupt geofence escape behavior corresponding to unauthorized removal from an approved location, route discontinuity associated with deviation from an authorized transportation path, unauthorized stationary intervals occurring at unexpected locations, and covert displacement behavior associated with concealed or irregular transportation activity.
In some embodiments, the theft anomaly score may be dynamically computed as:
TAS=aA_m+ßG_a+?O_d+dT_c
where:
TAS represents theft anomaly score,
A_m represents abnormal movement magnitude,
G_a represents geofence anomaly magnitude,
O_d represents operational-state discontinuity magnitude,
T_c represents tamper-condition severity,
and a,ß,?,d represent adaptive weighting coefficients.
At step 440, the system performs comparing the transport legitimacy score and the theft anomaly score associated with the transformer or field infrastructure asset. In some embodiments, the controller may dynamically evaluate relative confidence associated with legitimate transportation activity versus theft-risk activity.
In certain embodiments:
when the transport legitimacy score exceeds the theft anomaly score beyond a predefined threshold margin, the movement may be classified as authorized transport activity;
and when the theft anomaly score exceeds the transport legitimacy score beyond a predefined threshold margin, the movement may be classified as unauthorized movement or theft-risk activity.
In some embodiments, an adaptive confidence comparison relationship may be represented as:
C=TAS-TLS
where:
C>0indicates elevated theft probability,
and C<0indicates elevated transport legitimacy probability.
At step 445, the system performs classifying movement as an authorized transport condition or an unauthorized movement condition and activating adaptive monitoring and protection operations. In some embodiments, when the movement is classified as authorized transportation activity, the system may maintain normal monitoring operation including periodic reporting, low-priority communication, and energy-optimized sensing behavior.
In certain embodiments, when the movement is classified as unauthorized movement activity or theft-risk activity, the system may automatically activate one or more protection operations including emergency tracking activation, high-frequency GPS monitoring, backup communication activation, covert tracking operation, secure event logging, irregular transmission interval operation, high-priority alert transmission, remote notification dispatch, and adaptive monitoring escalation associated with the transformer or field infrastructure asset.
In some embodiments, the adaptive monitoring escalation may dynamically increase sensing frequency, communication priority, location acquisition frequency, reporting interval granularity, and threat-monitoring sensitivity based on the theft anomaly score and the determined event severity level associated with the transformer or field infrastructure asset. Under elevated theft-risk conditions, the intelligent tracking device may increase monitoring intensity and communication responsiveness to support enhanced tracking accuracy, rapid alert transmission, and adaptive protection operations.
In an example scenario, a transformer transported from a warehouse to an authorized installation site may exhibit an authorized dispatch-state transition, continuous route progression corresponding to an approved transportation path, smooth vibration signatures associated with normal transportation activity, expected geofence transition behavior between authorized locations, and valid installation-site entry corresponding to an authorized deployment operation. Under such conditions, the system may generate a high transport legitimacy score and a low theft anomaly score, thereby classifying the detected movement as authorized transportation activity. In contrast, unauthorized nighttime movement associated with abrupt lifting activity, geofence escape, route discontinuity, communication interruption, and abnormal vibration patterns may generate a high theft anomaly score, thereby causing the system to activate covert tracking, emergency monitoring, and theft-response operations.
In another embodiment, the intelligent tracking device may implement adaptive movement-behavior profiling configured to dynamically refine movement classification associated with the transformer or field infrastructure asset. The processing unit may analyze historical transportation patterns, vibration signatures, geofence transition behavior, operational-state transition history, and movement continuity information to dynamically modify weighting coefficients associated with transport legitimacy scoring and theft anomaly scoring. Such adaptive movement-behavior profiling improves contextual movement validation accuracy and reduces false theft classifications associated with recurring authorized transportation operations.
In another embodiment, the intelligent tracking device may implement energy-prioritized adaptive monitoring configured to preserve operational continuity during theft-risk conditions, low-energy conditions, or communication outage scenarios. The processing unit may dynamically prioritize critical monitoring operations including emergency location tracking, tamper-event logging, theft-risk communication operations, and protection-response operations while selectively reducing non-critical sensing activity and background communication operations. Such adaptive energy prioritization improves long-term operational survivability and enables sustained monitoring operation during remote deployment conditions or intentional communication disruption attempts.
In another embodiment, the intelligent tracking device may support distributed infrastructure monitoring across a plurality of transformers or field infrastructure assets deployed within a common utility network. The cloud platform may correlate operational-state information, transportation activity, route progression behavior, maintenance schedules, geofence transition behavior, and tamper-related conditions associated with the plurality of monitored assets to identify coordinated theft activity, unauthorized relocation patterns, abnormal infrastructure movement behavior, or region-specific tampering events. Such distributed infrastructure monitoring improves large-scale asset visibility and enhances predictive risk assessment associated with geographically distributed utility infrastructure deployments.
In another embodiment, the intelligent tracking device may implement adaptive covert protection operation configured to maintain concealed monitoring capability during theft-risk conditions or tamper-related events. Upon detection of signal jamming attempts, unauthorized enclosure access conditions, communication interruption conditions, or abnormal displacement behavior, the intelligent tracking device may dynamically activate covert tracking behavior including irregular transmission intervals, hidden low-power communication operation, delayed transmission scheduling, backup communication activation, or stealth location reporting behavior. Such adaptive covert protection operation improves recovery probability associated with stolen transformers or field infrastructure assets while reducing detectability of the intelligent tracking device during unauthorized movement conditions.
In another embodiment, the intelligent tracking device may implement predictive maintenance intelligence configured to identify progressive degradation conditions associated with transformers or field infrastructure assets. The processing unit may analyze vibration history, abnormal tilt progression, temperature variation patterns, repeated impact conditions, operational anomalies, and historical event conditions to generate maintenance recommendations, infrastructure health indicators, and dynamic asset-risk levels associated with the monitored asset. Such predictive maintenance intelligence improves operational reliability, reduces unexpected infrastructure failures, and enables proactive maintenance scheduling associated with geographically distributed field infrastructure assets.
A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims. ,CLAIMS:We Claim:
1. A method for monitoring and protecting a transformer or a field infrastructure asset, the method comprising:
collecting, by an intelligent tracking device, location data and sensor data associated with the transformer or the field infrastructure asset;
analyzing the collected location data and sensor data to identify an event condition associated with the transformer or the field infrastructure asset;
determining an event severity level based on multiple detected conditions including movement-related conditions, location-related conditions, and sensor-related conditions;
determining an energy condition associated with the intelligent tracking device;
automatically selecting an operational mode based on the determined event severity level and the determined energy condition;
automatically adjusting at least one of sensing operations, location tracking operations, communication operations, reporting frequency, or power usage based on the selected operational mode; and
transmitting monitoring information or alert information based on the determined event severity level.

2. The method as claimed in claim 1, further comprising determining an operational state of the transformer or the field infrastructure asset, wherein the operational state includes at least one of a warehouse state, a transport state, an installed state, a maintenance state, or a theft-risk state, dynamically assigning different priorities to the multiple detected conditions based on the determined operational state, and progressively increasing monitoring activity and communication priority when the determined event severity level exceeds one or more threshold conditions.

3. The method as claimed in claim 2, further comprising analyzing multiple detected conditions including vibration conditions, tilt conditions, positional displacement conditions, temperature conditions, geofence conditions, and tamper-related conditions to generate an event confidence score associated with the transformer or the field infrastructure asset, wherein the event severity level is determined based on the generated event confidence score and the determined operational state.

4. The method as claimed in claim 3, wherein different weighting values are dynamically assigned to the multiple detected conditions based on the determined operational state, and wherein the event confidence score is generated using the dynamically assigned weighting values.

5. The method as claimed in claim 3, further comprising dynamically modifying at least one of sensing frequency, location tracking frequency, communication frequency, reporting interval, or power consumption behavior based on the determined operational state, the determined event severity level, the generated event confidence score, and the determined energy condition associated with the intelligent tracking device.

6. The method as claimed in claim 1, further comprising detecting a tamper-related condition including at least one of unauthorized enclosure opening, device removal, communication interruption, signal jamming, abnormal movement, or unauthorized displacement associated with the transformer or the field infrastructure asset, and automatically activating one or more protection operations including emergency tracking activation, high-priority alert transmission, backup communication activation, secure event logging, covert transmission of location information using variable or irregular transmission intervals, or covert tracking operation configured to reduce communication detectability during a theft-risk condition or a tamper-related condition.

7. The method as claimed in claim 3, wherein movement associated with the transformer or the field infrastructure asset is validated by correlating the determined operational state, positional displacement conditions, movement-related conditions, geofence conditions, route continuity information, and the generated event confidence score with an expected transport lifecycle behavior associated with an authorized transport condition, wherein validating movement further comprises:
generating a transport legitimacy score associated with authorized transportation behavior;
generating a theft anomaly score associated with abnormal movement behavior, operational-state discontinuity, abnormal geofence deviation, or tamper-related conditions; and
comparing the transport legitimacy score and the theft anomaly score to classify movement satisfying the expected transport lifecycle behavior as a normal movement condition and movement exhibiting operational-state discontinuity, abnormal movement behavior, route deviation, or abnormal geofence transition as an unauthorized movement condition or a theft-risk condition.

8. The method as claimed in claim 1, further comprising storing historical operational data associated with the transformer or the field infrastructure asset, the historical operational data including at least one of location history, movement history, vibration history, temperature history, event severity history, or tamper-related event history, and analyzing the historical operational data to identify abnormal operational patterns, generate a maintenance recommendation or a risk alert associated with the transformer or the field infrastructure asset, and generate a dynamic risk level associated with the transformer or the field infrastructure asset.

9. The method as claimed in claim 5, further comprising automatically modifying transmission of monitoring data based on the determined operational state, the determined event severity level, the generated event confidence score, and the determined energy condition, wherein summary monitoring information is transmitted during a normal operational condition, detailed monitoring information including live location data and sensor-event information is transmitted during a theft-risk condition or a tamper-related condition, and location information is transmitted at variable or irregular transmission intervals during the theft-risk condition or the tamper-related condition.

10. An intelligent asset monitoring and protection system for monitoring a transformer or a field infrastructure asset, the system comprising:
a sensor subsystem configured to collect location data and sensor data associated with the transformer or the field infrastructure asset;
a processing subsystem communicatively coupled with the sensor subsystem and configured to:
analyze the collected location data and sensor data;
determine an operational state associated with the transformer or the field infrastructure asset;
determine an event severity level based on multiple detected conditions;
generate an event confidence score associated with the transformer or the field infrastructure asset;
dynamically assign weighting values to the multiple detected conditions based on the determined operational state;
generate a transport legitimacy score associated with authorized transportation behavior and a theft anomaly score associated with abnormal movement behavior, operational-state discontinuity, abnormal geofence deviation, or tamper-related conditions;
compare the transport legitimacy score and the theft anomaly score to classify movement as an authorized transport condition or a theft-risk condition;
dynamically select an operational mode based on the determined operational state, the determined event severity level, the generated event confidence score, and an energy condition associated with the system; and
automatically adjust monitoring operations based on the selected operational mode;
a communication subsystem configured to selectively transmit monitoring information or alert information associated with the transformer or the field infrastructure asset;
a power subsystem configured to supply power using at least a rechargeable battery and a solar-assisted charging arrangement; and
a protection subsystem configured to activate at least one of emergency tracking, backup communication, covert tracking, secure event logging, covert transmission using variable or irregular transmission intervals, or tamper-response operation upon detection of a theft-risk condition or a tamper-related condition associated with the transformer or the field infrastructure asset,
wherein the processing subsystem is configured to dynamically modify sensing activity, communication activity, reporting frequency, power consumption behavior, and protection operations based on the determined operational state, the determined event severity level, the generated event confidence score, the transport legitimacy score, the theft anomaly score, and the determined energy condition.