1. A system (100) for detecting and mapping leaks in a pressurized irrigation pipeline, the system (100) comprising: acoustic sensing units (102a-102n) adapted to capture acoustic signals generated within the pressurized irrigation pipeline; a preprocessing unit (104) adapted to generate frequency-domain data from the acoustic signals using signal transformation and noise reduction techniques; a feature extraction unit (106) adapted to derive characteristic parameters including spectral entropy, root mean square energy, and zero-crossing rate from the frequency-domain data; a localization unit (108) adapted to provide time-synchronized acoustic data associated with the adjacent acoustic sensing units (102a-102n); a communication unit (110) adapted to enable transmission of data through a wireless communication network; and a controller (112), characterized in that controller (112) is configured to: receive the acoustic signals from the acoustic sensing units (102a-102n); receive the frequency-domain data from the preprocessing unit (104); receive the characteristic parameters from the feature extraction unit (106); process the characteristic parameters using a trained artificial intelligence model to identify an acoustic anomaly corresponding to a leak condition; determine a location of the leak using triangulation based on the time-synchronized acoustic data received from the localization unit (108); generate data indicative of the leak condition and the location of the leak; transmit the generated data through the communication unit (110); and control a visualization unit (200) to generate a visual representation of the pressurized irrigation pipeline including the location of the leak and an alert notification.

2. The system (100) as claimed in claim 1, comprising a power unit (114) adapted to supply energy using a solar energy source for continuous operation in remote agricultural environments.

3. The system (100) as claimed in claim 1, wherein the visualization unit (200) is installed with a user interface (202) configured to enable presentation of the generated visual representation of the pressurized irrigation pipeline including the location of the leak and the alert notification.

4. The system (100) as claimed in claim 1, wherein the acoustic sensing units (102a-102n) comprise hydrophones, piezoelectric microphones, or a combination thereof adapted to detect vibration and sound signals generated by fluid flow within the pressurized irrigation pipeline.

5. The system (100) as claimed in claim 1, wherein the preprocessing unit (104) is adapted to perform a fast Fourier transform and apply denoising techniques to isolate leak-related frequency components.

6. The system (100) as claimed in claim 1, wherein the controller (112) is configured to process the characteristic parameters using a convolutional neural network or a recurrent neural network to classify acoustic patterns associated with leak events.

7. The system (100) as claimed in claim 1, wherein the controller (112) is configured to determine the location of the leak by correlating time differences of arrival of acoustic signals between the adjacent acoustic sensing units (102a-102n).

8. The system (100) as claimed in claim 1, wherein the communication unit (110) comprises a low-power wide-area communication interface including a Long-Range Wide Area Network (LoRaWAN) radio.

9. The system (100) as claimed in claim 1, wherein the controller (112) is configured to perform real-time inference without reliance on cloud-based computation.

10. A method (400) for detecting and mapping leaks in a pressurized irrigation pipeline, the method (400) is characterized by steps of: receiving acoustic signals from acoustic sensing units (102a-102n); receiving frequency-domain data from a preprocessing unit (104); receiving characteristic parameters from a feature extraction unit (106); processing the characteristic parameters using a trained artificial intelligence model to identify an acoustic anomaly corresponding to a leak condition; determining a location of the leak using triangulation based on time-synchronized acoustic data received from a localization unit (108); generating data indicative of the leak condition and the location of the leak; transmitting the generated data through a communication unit (110); and controlling a visualization unit (200) to generate a visual representation of the pressurized irrigation pipeline including the location of the leak and an alert notification. Date: April 14, 2026 Place: Noida Nainsi Rastogi Patent Agent (IN/PA-2372) Agent for the Applicant

Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to an irrigation system and particularly to a system and method for detecting and mapping leaks in pressurized irrigation pipeline.
Description of Related Art
[002] Pressurized irrigation pipeline networks form a critical component of modern agricultural infrastructure, where efficient water distribution directly influences crop productivity and resource conservation. However, such systems often suffer from leakage issues that remain undetected for extended durations. Leakage results in substantial water loss, increased energy consumption, and degradation of system performance. In regions with limited water availability, such losses create severe agricultural and economic challenges. Furthermore, lack of precise leak localization leads to delayed corrective measures and potential pipeline failure, that disrupts irrigation schedules and affects crop yield.
[003] Existing approaches for leak detection in irrigation pipelines include manual inspection techniques, pressure monitoring systems, and flow rate analysis methods. Manual inspection involves periodic field surveys by trained personnel to identify visible signs of leakage. Pressure-based systems rely on detection of pressure drops within the pipeline network to infer possible leak presence. Flow monitoring techniques compare input and output flow rates to estimate discrepancies. In addition, certain acoustic-based tools exist, that utilize sound signals to detect anomalies within pipelines, often with centralized data processing frameworks or external analytical systems.
[004] Despite availability of these solutions, several limitations persist in practical deployment. Manual inspection requires significant labour effort and fails to provide continuous monitoring capability. Pressure and flow-based methods lack sensitivity toward small or early-stage leaks and do not provide accurate localization. Acoustic tools without advanced processing capability face difficulty in interpretation of complex signal patterns and often produce unreliable results. Moreover, centralized processing systems depend on stable communication infrastructure, that remains unavailable in many agricultural regions. These shortcomings result in delayed detection, inefficient maintenance practices, and continued resource loss across irrigation systems.
[005] There is thus a need for an improved and advanced system and method for detecting and mapping leaks in a pressurized irrigation pipeline that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a system for detecting and mapping leaks in a pressurized irrigation pipeline. The system comprising acoustic sensing units adapted to capture acoustic signals generated within the pressurized irrigation pipeline. The system further comprising a preprocessing unit adapted to generate frequency-domain data from the acoustic signals using signal transformation and noise reduction techniques. The system further comprising a feature extraction unit adapted to derive characteristic parameters including spectral entropy, root mean square energy, and zero-crossing rate from the frequency-domain data. The system further comprising a localization unit adapted to provide time-synchronized acoustic data associated with the adjacent acoustic sensing units. The system further comprising a communication unit adapted to enable transmission of data through a wireless communication network. The system further comprising a controller. The controller is configured to receive the acoustic signals from the acoustic sensing units, receive the frequency-domain data from the preprocessing unit, receive the characteristic parameters from the feature extraction unit, process the characteristic parameters using a trained artificial intelligence model to identify an acoustic anomaly corresponding to a leak condition, determine a location of the leak using triangulation based on the time-synchronized acoustic data received from the localization unit, generate data indicative of the leak condition and the location of the leak, transmit the generated data through the communication unit, and control a visualization unit to generate a visual representation of the pressurized irrigation pipeline including the location of the leak and an alert notification.
[007] Embodiments in accordance with the present invention further provide a method for detecting and mapping leaks in a pressurized irrigation pipeline. The method comprising steps of: receiving acoustic signals from acoustic sensing units; receiving frequency-domain data from a preprocessing unit; receiving characteristic parameters from a feature extraction unit; processing the characteristic parameters using a trained artificial intelligence model to identify an acoustic anomaly corresponding to a leak condition; determining a location of the leak using triangulation based on time-synchronized acoustic data received from a localization unit; generating data indicative of the leak condition and the location of the leak; transmitting the generated data through a communication unit; and controlling a visualization unit to generate a visual representation of the pressurized irrigation pipeline including the location of the leak and an alert notification.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for detecting and mapping leaks in a pressurized irrigation pipeline.
[009] Next, embodiments of the present application may provide a system for detecting and mapping leaks in a pressurized irrigation pipeline that enables real-time leak detection through localized data processing, thereby reducing response time and preventing prolonged water loss.
[0010] Next, embodiments of the present application may provide a system for detecting and mapping leaks in a pressurized irrigation pipeline that improves detection accuracy by identifying subtle acoustic variations associated with early-stage leaks, thereby facilitating timely intervention.
[0011] Next, embodiments of the present application may provide a system for detecting and mapping leaks in a pressurized irrigation pipeline that reduces dependency on external communication infrastructure by supporting on-site analysis, thereby ensuring reliable operation in remote agricultural regions.
[0012] These and other advantages will be apparent from the present application of the embodiments described herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 illustrates a block diagram of a system for detecting and mapping leaks in pressurized irrigation pipeline, according to an embodiment of the present invention;
[0014] FIG. 2 illustrates a connectivity diagram of the system for detecting and mapping leaks in pressurized irrigation pipeline with a visualization unit, according to an embodiment of the present invention;
[0015] FIG. 3 illustrates components of a processing unit of the system for detecting and mapping leaks in pressurized irrigation pipeline, according to an embodiment of the present invention; and
[0016] FIG. 4 depicts a flowchart of a method for detecting and mapping leaks in pressurized irrigation pipeline, according to an embodiment of the present invention.
DETAILED DESCRIPTION
[0017] FIG. 1 illustrates a block diagram of a system 100 for detecting and mapping leaks in pressurized irrigation pipeline, according to an embodiment of the present application. In an embodiment of the present invention, the system 100 may be adapted to operate as an integrated, distributed, and real-time monitoring framework configured to continuously analyse acoustic behaviour of fluid flow within the pressurized irrigation pipeline. The system 100 may be deployed across agricultural environments wherein pipeline networks extend over large geographical areas and require autonomous monitoring without continuous human intervention.
[0018] In operation, the system 100 may be adapted to acquire acoustic signals generated within the pressurized irrigation pipeline during normal flow conditions as well as during leak events. The acquired acoustic signals may be processed through a signal conditioning pipeline to transform raw time-domain signals into frequency-domain representations and extract characteristic parameters indicative of pipeline behaviour. The system 100 may be further adapted to analyse the extracted characteristic parameters using a trained artificial intelligence model to identify deviations from normal operating conditions corresponding to leak-induced acoustic anomalies.
[0019] Further, the system 100 may be configured to determine a spatial location of a detected leak by correlating time-synchronized acoustic data obtained from multiple sensing points distributed along the pressurized irrigation pipeline. The location determination may be performed using triangulation techniques based on time differences of arrival of acoustic signals. Thus, enabling precise geolocation of leak events even in buried or inaccessible pipeline sections. The system 100 may generate data indicative of leak presence and corresponding location in real time.
[0020] Subsequently, the system 100 may be adapted to transmit the generated data through a wireless communication network to a visualization interface for presentation and monitoring. The visualization interface may provide graphical representation of the pressurized irrigation pipeline, detected leak locations, and associated alerts. The system 100 may be further configured to generate alert notifications to enable timely maintenance actions. The overall architecture of the system 100 may be adapted to perform on-device processing using edge computing. Thus, reducing dependency on cloud infrastructure, minimizing latency, and ensuring reliable operation in low-connectivity agricultural environments.
[0021] The system 100 may be specifically adapted for agricultural environments characterized by limited communication infrastructure, variable environmental conditions, and large-scale distributed pipeline networks. The system 100 may be adapted to operate under conditions including fluctuating pressure levels, soil-buried pipelines, and exposure to environmental noise. Thus, ensuring reliable leak detection performance in real-world agricultural deployments.
[0022] In an embodiment of the present invention, the system 100 may be robust, scalable, and adaptive for real-time leak detection and localization that can process acoustic signals, analyse characteristic parameters, and provide reliable output suitable for visualization, alert generation, and maintenance actions with minimal delay.
[0023] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance the processing speed and efficiency such as the system 100 may comprise acoustic sensing units 102a-102n (hereinafter referred individually to as the acoustic sensing units 102, and plurally to as the acoustic sensing units 102), a preprocessing unit 104, a feature extraction unit 106, a localization unit 108, a communication unit 110, a controller 112, and a power unit 114. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0024] In an embodiment of the present invention, the acoustic sensing units 102 may be adapted to capture acoustic signals generated within the pressurized irrigation pipeline. The acoustic signals may comprise parameters indicative of fluid behaviour selected from turbulence, vibration patterns, pressure variations, leak-induced disturbances, and so forth. The acoustic sensing units 102 may be, but not limited to, hydrophones, piezoelectric microphones, vibration sensors, and so forth. Embodiments of the present application are intended to include or otherwise cover any type of the acoustic sensing units 102, including known, related art, and/or later developed technologies. The acoustic sensing units 102 may be configured to sense signals continuously or periodically depending on pipeline conditions.
[0025] The acoustic sensing units 102 may be deployed as part of a distributed sensor network arranged along the pressurized irrigation pipeline at predetermined spatial intervals. The distributed sensor network may be adapted to provide synchronized acoustic data acquisition from multiple sensing points. The spacing between adjacent acoustic sensing units 102 may be determined based on pipeline length, fluid pressure characteristics, and expected leak detection sensitivity. The distributed sensor network may be configured to enable cooperative sensing. The acoustic data from multiple acoustic sensing units 102 is temporally aligned to support accurate leak localization through multi-point correlation.
[0026] The acoustic signals may further include leak-induced acoustic signatures comprising turbulence noise, high-frequency vibration patterns, pressure-induced oscillations, cavitation effects, and whistling sound characteristics generated due to fluid escape through structural discontinuities in the pressurized irrigation pipeline. The acoustic sensing units 102 may be adapted to capture variations in amplitude, frequency distribution, and temporal patterns associated with such leak-induced disturbances,. Thus, enabling early-stage leak detection prior to manifestation of visible or pressure-based anomalies.
[0027] In an embodiment of the present invention, the pressurized irrigation pipeline may be indicative of a fluid transport infrastructure deployed in agricultural environments for distribution of water across cultivated areas. The pressurized irrigation pipeline may include, but not limited to, drip irrigation networks, sprinkler systems, subsurface pipelines, surface pipelines, and so forth. Embodiments of the present application are intended to include or otherwise cover any type of the pressurized irrigation pipeline, including known, related art, and/or later developed technologies.
[0028] The system 100 may be adapted to operate in a modular architecture wherein additional acoustic sensing units 102 may be integrated without modification of core system functionality. The modular architecture may enable scalability across varying pipeline lengths and configurations. The system 100 may further be adapted for retrofit deployment in existing irrigation infrastructures without requiring structural modifications to the pressurized irrigation pipeline.
[0029] The acoustic sensing units 102 may be, but not limited to, hydrophones, piezoelectric microphones, vibration sensors, pressure transducers, fibre optic acoustic sensors, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the acoustic sensing units 102, including known, related art, and/or later developed technologies.
[0030] In an embodiment of the present invention, the preprocessing unit 104 may be adapted to receive the detected acoustic signals and generate frequency-domain data using signal transformation and noise reduction techniques. The preprocessing unit 104 may be configured to preprocess the received acoustic signals to generate conditioned signal parameters. The preprocessing of the received acoustic signals may include, but not limited to, signal transformation using fast Fourier transform, noise filtering, signal normalization, denoising, and so forth. Embodiments of the present application are intended to include or otherwise cover any type of preprocessing of the acoustic signals, including known, related art, and/or later developed technologies.
[0031] The preprocessing unit 104 may be adapted to execute a sequential signal conditioning pipeline comprising signal transformation, normalization, filtering, and denoising operations. The signal transformation may include application of Fast Fourier Transform (FFT) to convert time-domain acoustic signals into frequency-domain representations. The denoising operations may include adaptive filtering, band-pass filtering, and noise suppression techniques to isolate leak-relevant frequency components. The preprocessing unit 104 may generate conditioned frequency-domain data suitable for downstream feature extraction.
[0032] The preprocessing unit 104 may be, but not limited to, digital signal processors (DSP), microcontrollers, embedded processing circuits, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the preprocessing unit 104, including known, related art, and/or later developed technologies.
[0033] In an embodiment of the present invention, the frequency-domain data may be transmitted to the feature extraction unit 106 operatively coupled to the preprocessing unit 104. The feature extraction unit 106 may be configured to derive characteristic parameters from the frequency-domain data. The characteristic parameters may include, but not limited to, spectral entropy, root mean square energy, zero-crossing rate, and other statistical or spectral descriptors associated with acoustic patterns. These characteristic parameters may represent distinguishing indicators of normal operation and leak conditions.
[0034] The characteristic parameters may further include higher-order statistical descriptors, spectral centroid, spectral bandwidth, kurtosis, skewness, and temporal energy distribution metrics associated with the acoustic signals. The feature extraction unit 106 may be adapted to construct a feature vector representing operational and anomalous states of the pressurized irrigation pipeline. The feature vector may be utilized by the trained artificial intelligence model for classification and anomaly detection.
[0035] The feature extraction unit 106 may be, but not limited to, statistical analysis engines, spectral analysis processors, embedded signal processing units, machine learning feature computation engines, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the feature extraction unit 106, including known, related art, and/or later developed technologies.
[0036] In an embodiment of the present invention, the localization unit 108 may be adapted to provide time-synchronized acoustic data associated with the adjacent acoustic sensing units 102. The localization unit 108 may enable temporal alignment and correlation of acoustic signals received from multiple sensing points distributed along the pressurized irrigation pipeline. The localization unit 108 may support spatial analysis for determination of leak position based on time differences of acoustic signal arrival.
[0037] The localization unit 108 may be, but not limited to, triangulation engines, time difference of arrival (TDOA) processing systems, signal correlation processors, spatial computation units, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the localization unit 108, including known, related art, and/or later developed technologies.
[0038] In an embodiment of the present invention, the generated data may be transmitted to the communication unit 110 adapted to enable transmission through a wireless communication network. The communication unit 110 may be adapted to operate using a Low-Power Wide-Area Network (LPWAN) communication protocol including Long-Range Wide Area Network (LoAaWAN). The Long-Range Wide Area Network (LoRaWAN) may be implemented using communication modules compliant with standards defined by the LoRa Alliance. The communication unit 110 may further include cellular communication interfaces, including Fourth Generation (4G) Long Term Evolution (LTE) modules and Fifth Generation (5G) communication modules, as well as short-range communication interfaces such as Wi-Fi (Wireless Fidelity) and Bluetooth. The communication unit 110 may be adapted to optimize data transmission for low power consumption and long-range communication in rural agricultural environments.
[0039] The communication unit 110 may be, but not limited to, Low-Power Wide-Area Network (LPWAN) communication modules, Long-Range Wide Area Network (LoRaWAN) transceivers, cellular communication modules including Fourth Generation (4G) Long Term Evolution (LTE) and Fifth Generation (5G), Wi-Fi (Wireless Fidelity) modules, Bluetooth modules, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the communication unit 110, including known, related art, and/or later developed technologies.
[0040] In an embodiment of the present invention, the characteristic parameters and time-synchronized acoustic data may be transmitted to the controller 112 operatively coupled to the feature extraction unit 106, the localization unit 108, and a memory (not shown) storing executable instructions. The controller 112 may be configured to receive the acoustic signals from the acoustic sensing units 102, the frequency-domain data from the preprocessing unit 104, and the characteristic parameters from the feature extraction unit 106. The controller 112 may process the characteristic parameters using a trained artificial intelligence model to identify an acoustic anomaly corresponding to a leak condition. The trained artificial intelligence model may include, but not limited to, a convolutional neural network, a recurrent neural network, similar analytical models, and so forth. Embodiments of the present application are intended to include or otherwise cover any type of artificial intelligence model, including known, related art, and/or later developed technologies.
[0041] The controller 112 may be configured to execute the trained artificial intelligence model on an edge processing hardware platform to enable real-time, on-device inference. The edge processing hardware platform may include, but not limited to, an NVIDIA Jetson Nano (manufactured by NVIDIA Corporation), a Google Coral Edge Tensor Processing Unit (TPU) (manufactured by Google LLC), an ARM Cortex-based microcontroller (manufactured by Arm Holdings plc), or equivalent embedded artificial intelligence acceleration hardware. The edge processing hardware platform may be adapted to perform local computation of acoustic signal analysis without dependency on remote cloud infrastructure. Thus, reducing latency, minimizing bandwidth usage, and enabling continuous operation in low-connectivity agricultural environments.
[0042] In an embodiment of the present invention, the controller 112 may be further configured to determine a location of the leak using triangulation based on the time-synchronized acoustic data. The triangulation may refer to a spatial determination technique in which the leak position is computed based on correlation of time differences of arrival of acoustic signals between the adjacent acoustic sensing units 102. The controller 112 may generate data indicative of the leak condition and the location of the leak.
[0043] The triangulation may be based on Time Difference of Arrival (TDOA) estimation of acoustic signals captured at multiple of the acoustic sensing units 102. The localization unit 108 may be adapted to compute propagation delay between the acoustic sensing units 102 and estimate the leak position using geometric triangulation algorithms. The localization unit 108 may further incorporate signal correlation techniques and cross-correlation functions to improve localization accuracy under varying noise conditions.
[0044] The controller 112 may be, but not limited to, microprocessors, microcontrollers, central processing units (CPU), graphics processing units (GPU), edge artificial intelligence processors, system-on-chip (SoC) architectures, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the controller 112, including known, related art, and/or later developed technologies. The controller 112 may further be explained in detail in conjunction with FIG. 3.
[0045] In an embodiment of the present invention, the communication unit 110 may transmit the generated data to a visualization unit 200 (as shown in FIG. 2). The visualization unit 200 may provide graphical mapping, alert indicators, historical data representation, and monitoring dashboards for user interaction. The visualization unit 200 may further be explained in detail in conjunction with FIG. 2.
[0046] In an embodiment of the present invention, the system 100 may further comprise the power unit 114 adapted to supply energy for operation of the system 100. The power unit 114 may utilize a solar energy source to support continuous operation in remote agricultural environments. The power unit 114 may ensure uninterrupted operation of sensing, processing, communication, and visualization functions. The power unit 114 may be adapted to supply energy for operation of the system 100 using a solar energy harvesting mechanism. The power unit 114 may include photovoltaic panels, charge controllers, and energy storage elements including rechargeable battery systems. The power unit 114 may be adapted to provide continuous and autonomous power supply to the acoustic sensing units 102, preprocessing unit 104, feature extraction unit 106, localization unit 108, communication unit 110, and controller 112. The power unit 114 may be configured to support long-term deployment in remote agricultural environments without reliance on grid-based electrical infrastructure.
[0047] The power unit 114 may be, but not limited to, photovoltaic solar panels, rechargeable battery systems, lithium-ion battery packs, energy storage modules, charge controllers, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the power unit 114, including known, related art, and/or later developed technologies.
[0048] FIG. 2 illustrates a connectivity diagram of the system 100 with the visualization unit 200, according to an embodiment of the present invention. In an embodiment of the present invention the visualization unit 200 may be adapted to present information associated with leak detection. The visualization unit 200 may present spatial information associated with pipeline layout, sensor placement, and detected leak locations. The graphical representation may include, but not limited to, two-dimensional maps, schematic layouts, geospatial overlays, and so forth. Embodiments of the present application are intended to include or otherwise cover any type of graphical representation generated by the visualization unit 200, including known, related art, and/or later developed technologies.
[0049] In an embodiment of the present invention, the visualization unit 200 may display previously recorded leak events, acoustic patterns, and system 100 performance metrics. The historical data representation may assist in trend analysis, maintenance planning, and performance evaluation of the pressurized irrigation pipeline. Embodiments of the present application are intended to include or otherwise cover any type of historical data representation, including charts, logs, timelines, and so forth.
[0050] The visualization unit 200 may be adapted to generate interactive dashboards comprising real-time pipeline status, leak location indicators, historical leak data, and system performance metrics. The visualization unit 200 may be adapted to support geospatial mapping using Global Positioning System (GPS) coordinates and Geographic Information System (GIS) overlays to represent pipeline layouts and leak positions. The visualization unit 200 may further enable predictive maintenance by analysing historical trends and providing actionable insights for system operators.
[0051] The visualization unit 200 may be, but not limited to, display systems, web-based dashboards, mobile application interfaces, graphical user interface (GUI) systems, Geographic Information System (GIS)-based visualization platforms, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the visualization unit 200, including known, related art, and/or later developed technologies.
[0052] In an embodiment of the present invention, the visualization unit 200 is installed with a user interface 202 configured to enable representation of the generated visual representation of the pressurized irrigation pipeline including the location of the leak and the alert notification. The alert notifications may include visual indicators, text-based messages, and priority levels to assist in decision-making. Embodiments of the present application are intended to include or otherwise cover any type of alert presentation mechanisms, including known, related art, and/or later developed technologies.
[0053] The alert notifications may be transmitted through multiple communication channels including Short Message Service (SMS), electronic mail (e-mail), and mobile application-based push notifications. The alert notifications may include severity classification, timestamp information, and precise leak location coordinates to facilitate rapid response and maintenance actions.
[0054] The user interaction may include selection of specific pipeline segments, zooming into regions of interest, querying leak-related data, and customization of display parameters. The user interface 202 may be, but not limited to, touchscreen interfaces, web-based interfaces, mobile application interfaces, command-line interfaces, graphical user interface (GUI) frameworks, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the user interface 202, including known, related art, and/or later developed technologies.
[0055] FIG. 3 illustrates components of the controller 112 of the system 100, according to an embodiment of the present application. The controller 112 may comprise an acoustic analysis module 300, a classification module 302, a leak localization module 304, and an alert module 306.
[0056] In an embodiment of the present invention, the acoustic analysis module 300 may be configured to receive characteristic parameters from the feature extraction unit 106. The acoustic analysis module 300 may be configured to analyse the received characteristic parameters to identify patterns associated with acoustic signals within the pressurized irrigation pipeline. The characteristic parameters may include, but not limited to, spectral entropy, root mean square energy, zero-crossing rate, and so forth. The acoustic analysis module 300 may be further configured to distinguish between normal operational conditions and anomaly conditions based on variations in the characteristic parameters. The acoustic analysis module 300 may be configured to transmit analysed data to the classification module 302.
[0057] In an embodiment of the present invention, the classification module 302 may be configured to receive analysed data from the acoustic analysis module 300. The classification module 302 may be configured to process the analysed data using the trained artificial intelligence model to classify acoustic patterns corresponding to leak conditions. The trained artificial intelligence model may be, but not limited to, a convolutional neural network, a recurrent neural network, hybrid learning models, and so forth. The classification module 302 may be configured to generate classification outputs indicative of presence or absence of leak conditions. Further, the classification module 302 may be configured to transmit classification outputs to the alert module 306 and the leak localization module 304.
[0058] In an embodiment of the present invention, the leak localization module 304 may be configured to receive time-synchronized acoustic data from the localization unit 108 and classification outputs from the classification module 302. The leak localization module 304 may be configured to determine the location of the leak within the pressurized irrigation pipeline. The leak localization module 304 may be configured to determine the location of the leak using triangulation techniques based on time differences of arrival of acoustic signals between the adjacent acoustic sensing units 102. The leak localization module 304 may be further configured to compute positional information associated with the leak location and transmit the positional information to the alert module 306.
[0059] In an embodiment of the present invention, the alert module 306 may be configured to receive classification outputs from the classification module 302 and positional information from the leak localization module 304. The alert module 306 may be configured to generate output data indicative of leak condition and corresponding location. The alert module 306 may be configured to determine severity levels based on predefined conditions and generate alert notifications. Further, the alert module 306 may be configured to transmit generated data to the communication unit 110 and control the visualization unit 200 for presentation of leak-related information. Embodiments of the present application are intended to include or otherwise cover any type of decision-making and alert generation mechanisms, including known, related art, and/or later developed technologies.
[0060] FIG. 4 depicts a flowchart of a method 400 for detecting and mapping leaks in pressurized irrigation pipeline, according to an embodiment of the present invention.
[0061] At step 402, the system 100 may receive the acoustic signals from the acoustic sensing units 102.
[0062] At step 404, the system 100 may receive the frequency-domain data from the preprocessing unit 104.
[0063] At step 406, the system 100 may receive the characteristic parameters from the feature extraction unit 106.
[0064] At step 408, the system 100 may process the characteristic parameters using the trained artificial intelligence model to identify the acoustic anomaly corresponding to the leak condition.
[0065] At step 410, the system 100 may determine the location of the leak using triangulation based on the time-synchronized acoustic data received from the localization unit 108.
[0066] At step 412, the system 100 may generate the data indicative of the leak condition and the location of the leak.
[0067] At step 414, the system 100 may transmit the generated data through the communication unit 110.
[0068] At step 416, the system 100 may control the visualization unit 200 to generate the visual representation of the pressurized irrigation pipeline including the location of the leak and the alert notification. , Claims:CLAIMS
I/We Claim:
1. A system (100) for detecting and mapping leaks in a pressurized irrigation pipeline, the system (100) comprising:
acoustic sensing units (102a-102n) adapted to capture acoustic signals generated within the pressurized irrigation pipeline;
a preprocessing unit (104) adapted to generate frequency-domain data from the acoustic signals using signal transformation and noise reduction techniques;
a feature extraction unit (106) adapted to derive characteristic parameters including spectral entropy, root mean square energy, and zero-crossing rate from the frequency-domain data;
a localization unit (108) adapted to provide time-synchronized acoustic data associated with the adjacent acoustic sensing units (102a-102n);
a communication unit (110) adapted to enable transmission of data through a wireless communication network; and
a controller (112), characterized in that controller (112) is configured to:
receive the acoustic signals from the acoustic sensing units (102a-102n);
receive the frequency-domain data from the preprocessing unit (104);
receive the characteristic parameters from the feature extraction unit (106);
process the characteristic parameters using a trained artificial intelligence model to identify an acoustic anomaly corresponding to a leak condition;
determine a location of the leak using triangulation based on the time-synchronized acoustic data received from the localization unit (108);
generate data indicative of the leak condition and the location of the leak;
transmit the generated data through the communication unit (110); and
control a visualization unit (200) to generate a visual representation of the pressurized irrigation pipeline including the location of the leak and an alert notification.
2. The system (100) as claimed in claim 1, comprising a power unit (114) adapted to supply energy using a solar energy source for continuous operation in remote agricultural environments.
3. The system (100) as claimed in claim 1, wherein the visualization unit (200) is installed with a user interface (202) configured to enable presentation of the generated visual representation of the pressurized irrigation pipeline including the location of the leak and the alert notification.
4. The system (100) as claimed in claim 1, wherein the acoustic sensing units (102a-102n) comprise hydrophones, piezoelectric microphones, or a combination thereof adapted to detect vibration and sound signals generated by fluid flow within the pressurized irrigation pipeline.
5. The system (100) as claimed in claim 1, wherein the preprocessing unit (104) is adapted to perform a fast Fourier transform and apply denoising techniques to isolate leak-related frequency components.
6. The system (100) as claimed in claim 1, wherein the controller (112) is configured to process the characteristic parameters using a convolutional neural network or a recurrent neural network to classify acoustic patterns associated with leak events.
7. The system (100) as claimed in claim 1, wherein the controller (112) is configured to determine the location of the leak by correlating time differences of arrival of acoustic signals between the adjacent acoustic sensing units (102a-102n).
8. The system (100) as claimed in claim 1, wherein the communication unit (110) comprises a low-power wide-area communication interface including a Long-Range Wide Area Network (LoRaWAN) radio.
9. The system (100) as claimed in claim 1, wherein the controller (112) is configured to perform real-time inference without reliance on cloud-based computation.
10. A method (400) for detecting and mapping leaks in a pressurized irrigation pipeline, the method (400) is characterized by steps of:
receiving acoustic signals from acoustic sensing units (102a-102n);
receiving frequency-domain data from a preprocessing unit (104);
receiving characteristic parameters from a feature extraction unit (106);
processing the characteristic parameters using a trained artificial intelligence model to identify an acoustic anomaly corresponding to a leak condition;
determining a location of the leak using triangulation based on time-synchronized acoustic data received from a localization unit (108);
generating data indicative of the leak condition and the location of the leak;
transmitting the generated data through a communication unit (110); and
controlling a visualization unit (200) to generate a visual representation of the pressurized irrigation pipeline including the location of the leak and an alert notification.
Date: April 14, 2026
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant