Description:TECHNICAL FIELD
[0001] The present disclosure relates generally to the field of Internet of Things (IoT)-enabled liquid quality analysis systems. More particularly, the present disclosure relates to an IoT-based liquid analyser and monitoring system for real-time multi-parameter estimation and cost evaluation.

BACKGROUND
[0002] In the field of liquid quality analysis, conventional quality assessment methodologies, such as lactometry, Gerber fat quantification method, and reagent-based chemical tests, are predominantly manual and procedurally complex. Conventional portable diagnostic devices typically measure single parameters individually using separate instruments. Conventional approaches present challenges as the separate instruments require repeated handling of liquid samples. Consequently, operators cannot concurrently determine mass and determine an associated monetary value at the point of collection. The lack of integrated multi-parameter estimation represents a key technical problem, as utilizing isolated single-parameter sensing modules directly results in increased operational handling time and delays in raw material procurement workflows.
[0003] Further, conventional field-deployed liquid analysers often operate without strict calibration controls. Field devices lacking embedded calibration lock mechanisms are susceptible to unverified manual field adjustments. The unverified manual field adjustments frequently lead to physical sensor drift and inconsistent quality readings across a deployed fleet. Additionally, liquid collection data is routinely recorded utilizing paper ledgers or standalone systems lacking centralized visibility. The standalone systems lack integrated wireless communication interfaces for immediate data transmission to centralized quality monitoring repositories. Without integrated cloud connectivity, decentralized procurement centers remain isolated, which prevents end-to-end data traceability and delays automated alerting for quality threshold breaches.
[0004] Therefore, there is a need to address at least the above-mentioned drawbacks and any other shortcomings, or at the very least, provide a valuable alternative to the existing methods and systems.

OBJECTS OF THE PRESENT DISCLOSURE
[0005] A general object of the present disclosure relates to an Internet of Things (IoT)-based liquid analyser and monitoring system configured with an integrated sensor array and edge-based processing to provide real-time multi-parameter estimation, automated cost evaluation, and cloud-connected data traceability directly at decentralized collection environments.
[0006] An object of the present disclosure is to eliminate the need for sequential handling of liquid samples across separate instruments by structurally integrating a sensor module and a weight estimation module to simultaneously measure multiple parameters and sample weight for immediate edge-level cost computation.
[0007] Another object of the present disclosure is to establish end-to-end data traceability by incorporating a wireless communication module configured to transmit calibrated parameters and evaluated costs to a cloud server for centralized quality monitoring and threshold-based alerting.
[0008] Another object of the present disclosure is to maintain standardized measurement accuracy across field-deployed devices by embedding a calibration lock mechanism configured to restrict unauthorized access to stored references, thereby preventing unverified manual field adjustments.
[0009] Yet another object of the present disclosure is to provide a portable, cost effective energy-efficient apparatus that maximizes off-grid operational uptime.

SUMMARY
[0010] The present disclosure relates to an Internet of Things (IoT)-based liquid analyser and monitoring system for real-time multi-parameter estimation and cost evaluation. The system includes a portable liquid analyser that includes a liquid sampling and conditioning module, a sensor module, a weight estimation module, and a microcontroller. The microcontroller acquires and calibrates liquid parameters, such as fat percentage and solids-not-fat values, and receives weight data to determine a cost of the liquid sample locally. A communication module transmits the calibrated parameters and the determined cost to a cloud server. The cloud server authenticates the analyser, evaluates the liquid quality against predefined threshold values, and generates alerts for a user interface. By integrating multi-sensor analysis, automatic cost computation, and an embedded calibration lock mechanism, the system enables rapid non-destructive testing, prevents manual computation errors, and provides end-to-end data traceability in decentralized liquid collection environments.
[0011] Aspects of the present disclosure relate to an IoT-based liquid analyser and monitoring system. The system may include a liquid analyser. The liquid analyser may include a liquid sampling and conditioning module to receive liquid sample and a sensor module operatively connected to the liquid sampling and conditioning module, where the sensor module is configured to measure one or more parameters of the liquid sample. The liquid analyser may include a microcontroller operatively connected to the sensor module, where the microcontroller may be configured to receive the one or more parameters of the liquid sample from the sensor module. The microcontroller may be configured to calibrate the received one or more parameters based on stored references of the liquid sample and receive weight data of the liquid sample from a weight estimation module. Further, the microcontroller may determine a cost of the liquid sample based on the calibrated one or more parameters and the weight data, and transmit the determined cost of the liquid sample and the calibrated one or more parameters to a cloud server associated with the IoT-based liquid analyser and monitoring system through a communication module.
[0012] In an embodiment, the cloud server may be configured to authenticate registration of the liquid analyser and upon successful authentication, receive the determined cost of the liquid sample and the calibrated one or more parameters from the authenticated liquid analyser through the communication module. The cloud server may be configured to evaluate a quality of the liquid sample by comparing the received calibrated one or more parameters with corresponding predefined threshold values. Further, the cloud server may transmit an alert to a user interface, when the evaluated quality of the liquid sample exceeds at least one of the corresponding predefined threshold values and display status of the evaluation on the user interface.
[0013] In an embodiment, to calibrate the received one or more parameters based on the stored references of the liquid sample, the microcontroller may be configured to receive measured parameters of a reference liquid sample from the sensor module and compare the measured parameters of the reference liquid sample with predefined baseline parameters associated with the reference liquid sample. Further, the microcontroller may determine a physical sensor drift in the sensor module based on a deviation identified between the measured parameters and the predefined baseline parameters and update, based on the determined sensor drift, the stored references in a memory associated with the system to compensate for the physical sensor drift.
[0014] In an embodiment, the microcontroller may be configured to restrict unauthorized access to the stored references to prevent unverified manual field adjustments.
[0015] In an embodiment, the one or more parameters of the liquid sample comprise any one or a combination of: fat percentage, solids-not-fat (SNF) value, electrical conductivity, temperature, protein content, lactose content, density, and an adulterant concentration.
[0016] In an embodiment, the sensor module may include an analog signal conditioning circuit having at least one operational amplifier and filtering component to amplify and reduce noise in analog sensor signals of one or more sensors in the sensor module prior to transmission of the one or more parameters to the microcontroller.
[0017] In an embodiment, the system may include a power management module communicatively coupled to the microcontroller. The power management module may include a rechargeable power storage device and a solar charging interface. The microcontroller may be configured to trigger a power-efficient sleep mode via the power management module for the liquid analyser upon confirming a successful data transmission via the communication module.
[0018] In an aspect, the present disclosure relates to a method for an IoT-based liquid analysis and monitoring. The method may include receiving, by an IoT based liquid analyser system, liquid sample. The method may include measuring, by the system, one or more parameters of the liquid sample via a sensor module and receiving, by the system, the one or more parameters of the liquid sample from the sensor module. The method may include calibrating, by the system, the received one or more parameters based on stored references of the liquid sample. The method may include receiving, by the system, weight data of the liquid sample from a weight estimation module and determining, by the system, a cost of the liquid sample based on the calibrated one or more parameters and the weight data. Further, the method may include transmitting, by the system, the determined cost of the liquid sample and the calibrated one or more parameters to a cloud server associated with the IoT-based liquid analyser and monitoring system through a communication module.
[0019] In an embodiment, the method may include authenticating, by the cloud server associated with the system, a registration of the liquid analyser. Upon successful authentication, the method may include receiving, by the cloud server, the determined cost of the liquid sample and the calibrated one or more parameters from the authenticated liquid analyser through the communication module. The method may include evaluating, by the cloud server, a quality of the liquid sample by comparing the received calibrated one or more parameters with corresponding predefined threshold values. Further, the method may include transmitting, by the cloud server, an alert to a user interface when the evaluated quality of the liquid sample exceeds at least one of the corresponding predefined threshold values and displaying, by the cloud server, a status of the evaluation on the user interface.
[0020] In an embodiment, the method may include restricting, by the system, unauthorized access to the stored references via a calibration lock mechanism to prevent unverified manual field adjustments.
[0021] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0023] FIG. 1 illustrates an example architecture of an Internet of Things (IoT)-based liquid analyser and monitoring system, in accordance with embodiments of the present disclosure.
[0024] FIG. 2A and 2B illustrates an example flowchart of a method for IoT based liquid analysis and monitoring, in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION
[0025] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0026] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the scope of the disclosure as set forth.
[0027] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in a block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0028] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0029] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.
[0030] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0031] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0032] The present disclosure relates to an Internet of Things (IoT)-based liquid analyser and monitoring system for real-time multi-parameter estimation and cost evaluation. The system includes a portable liquid analyser that includes a liquid sampling and conditioning module, a sensor module, a weight estimation module, and a microcontroller. The microcontroller acquires and calibrates liquid parameters, such as fat percentage and solids-not-fat values, and receives weight data to determine a cost of the liquid sample locally. A communication module transmits the calibrated parameters and the determined cost to a cloud server. The cloud server authenticates the analyser, evaluates the liquid quality against predefined threshold values, and generates and transmits alerts to a user interface. By integrating multi-sensor analysis, automatic cost computation, and an embedded calibration lock mechanism, the system enables rapid non-destructive testing, prevents manual computation errors, and provides end-to-end data traceability in decentralized liquid collection environments.
[0033] Aspects of the present disclosure relate to an IoT-based liquid analyser and monitoring system. The system may include a liquid analyser. The liquid analyser may include a liquid sampling and conditioning module to receive liquid sample and a sensor module operatively connected to the liquid sampling and conditioning module, where the sensor module is configured to measure one or more parameters of the liquid sample. The liquid analyser may include a microcontroller operatively connected to the sensor module, where the microcontroller may be configured to receive the one or more parameters of the liquid sample from the sensor module. The microcontroller may be configured to calibrate the received one or more parameters based on stored references of the liquid sample and receive weight data of the liquid sample from a weight estimation module. Further, the microcontroller may determine a cost of the liquid sample based on the calibrated one or more parameters and the weight data and transmit the determined cost of the liquid sample and the calibrated one or more parameters to a cloud server associated with the IoT-based liquid analyser and monitoring system through a communication module.
[0034] In an aspect, the present disclosure relates to a method for an IoT-based liquid analysis and monitoring. The method may include receiving, by an IoT based liquid analyser system, liquid sample. The method may include measuring, by the system, one or more parameters of the liquid sample via a sensor module and receiving, by the system, the one or more parameters of the liquid sample from the sensor module. The method may include calibrating, by the system, the received one or more parameters based on stored references of the liquid sample. The method may include receiving, by the system, weight data of the liquid sample from a weight estimation module and determining, by the system, a cost of the liquid sample based on the calibrated one or more parameters and the weight data. Further, the method may include transmitting, by the system, the determined cost of the liquid sample and the calibrated one or more parameters to a cloud server associated with the IoT-based liquid analyser and monitoring system through a communication module.
[0035] Various embodiments with respect to the present disclosure will be explained in detail with reference to FIGs. 1- 2B.
[0036] FIG. 1 illustrates an example architecture of an Internet of Things (IoT)-based liquid analyser and monitoring system 100, in accordance with embodiments of the present disclosure.
[0037] Referring to FIG. 1, in an embodiment, the IoT-based liquid analyser and monitoring system 100 may include a liquid analyzer 102 communicatively coupled to a cloud server 118. The cloud server 118 may be further communicatively coupled to a user interface 120 of a computing device, such as a smartphone, a tablet computer, a personal digital assistant (PDA), a laptop computer, or a dedicated handheld industrial terminal, but not limited thereto. The liquid analyzer 102 may include a liquid sampling and conditioning module 104, a sensor module 106, a microcontroller 108, a weight estimation module 110, a communication module 112, a power management module 114, and a display unit 116. The system 100 may be deployed in various environments, such as village-level milk collection centers, bulk milk chilling centers, dairy cooperative societies, private dairy processing plants, or mobile milk collection vehicles, but not limited thereto.
[0038] In an embodiment, the liquid analzser 102 may be structured as a portable, integrated hardware platform. The liquid analyzer 102 may house the internal electronic components and fluidic pathways within a single enclosure, with external connections provided for power supply and structural mounts for the display unit 116. The compact integration of sensing and processing components within the liquid analyzer 102 minimizes power consumption and reduces operational overheads, thereby providing an energy-efficient and field-deployable device suitable for harsh rural environments.
[0039] In an embodiment, the liquid sampling and conditioning module 104 may be configured to physically receive and prepare a liquid sample, such as a milk sample, for analysis, but not limited thereto. The liquid sampling and conditioning module 104 may be constructed from materials such as food-grade stainless steel (for example, SS304) or a bisphenol A (BPA)-free polymer, but not limited thereto. Structurally, the liquid sampling and conditioning module 104 may incorporate a peristaltic pump and a temperature stabilization jacket having a Peltier element. The liquid sampling and conditioning module 104 may further include fluidic routing components, such as a drain valve and a cleaning port. The peristaltic pump may be configured to draw a predefined volume of the liquid sample, such as, for example, approximately 10 to 20 milliliters. The Peltier element may be configured to actively maintain a continuous testing temperature, such as, for example, 25 degrees Celsius, within the liquid sampling and conditioning module 104. The drain valve and the cleaning port may be configured to execute a self-cleaning mode by flushing warm water through the module post-analysis. Maintaining a standardized testing temperature normalizes the physical state of the liquid sample, thereby preventing temperature-induced variations during subsequent parameter measurements. Furthermore, executing the self-cleaning mode ensures rapid sample sterilization, thereby eliminating manual operator dependency for hygiene maintenance.
[0040] In an embodiment, the sensor module 106 may be operatively connected to the liquid sampling and conditioning module 104 and configured to measure one or more parameters of the liquid sample. The sensor module 106 may include an array of localized physical sensors, such as an ultrasonic transducer pair configured to operate at a predefined frequency of 40 kilohertz (kHz), but not limited thereto. In other embodiments, the sensor module 106 may incorporate optical sensors, thermal probes, or electrochemical electrodes to identify specific adulterants or nutritional components. The sensor module 106 may further include an analog signal conditioning circuit that may include at least one operational amplifier and at least one filtering component. The ultrasonic transducer pair may be configured to generate a time-of-flight difference measurement, which is subsequently processed to infer a fat percentage. The analog signal conditioning circuit may be configured to amplify and reduce electrical noise in analog sensor signals generated by the localized physical sensors prior to transmission. Amplifying and filtering the raw analog signals via the analog signal conditioning circuit isolates true parameter measurements from background electrical noise, thereby ensuring reliable data acquisition.
[0041] In an embodiment, the weight estimation module 110 may be physically adjacent to the liquid sampling and conditioning module 104 and communicatively coupled to the microcontroller 108. The weight estimation module 110 may include a weighing balance or a load cell assembly mechanically configured to support a collection container holding the liquid sample. The weight estimation module 110 may be configured to generate weight data corresponding to the received liquid sample and transmit the weight data to the microcontroller 108. Structurally integrating the weight estimation module 110 within the liquid analyser 102 eliminates the requirement to physically transfer the collection container to a separate weighing scale, thereby reducing overall handling time and preventing data association errors between distinct measurement instruments.
[0042] In an embodiment, the microcontroller 108 may be operatively coupled to the sensor module 106, the weight estimation module 110, the communication module 112, and the power management module 114. In an embodiment, the microcontroller 108 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the microcontroller 108 may be configured to fetch and execute computer-readable instructions stored in the memory associated with the microcontroller 108. The memory may store one or more computer-readable instructions or routines, which may be fetched and executed. The memory may include any non-transitory storage device including, for example, volatile memory such as Random-Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.
[0043] In an embodiment, the microcontroller 108 may include a processing unit, such as an ESP32, STM32, or Raspberry Pi Pico, but not limited thereto, and an associated memory element, such as a 4-megabyte (MB) Flash memory and a 520-kilobyte (KB) static random-access memory (SRAM), but not limited thereto. The microcontroller 108 may further include an analog-to-digital converter (ADC) configured to acquire the amplified sensor data at a 12-bit to 16-bit resolution, and a non-volatile memory, such as an electrically erasable programmable read-only memory (EEPROM), storing calibration references. The microcontroller 108 may be configured to receive the one or more parameters from the sensor module 106 and the weight data from the weight estimation module 110. The microcontroller 108 may be configured to calibrate the received one or more parameters based on stored references, where the sored references are stored in a memory associated with the system 100. Subsequently, the microcontroller 108 may be configured to determine a cost of the liquid sample based on an algorithmic combination of the calibrated one or more parameters and the weight data. Determining the cost locally at the microcontroller 108 provides an immediate, localized economic valuation for the liquid sample, thereby preventing manual computation errors and subsequent pricing disputes.
[0044] To maintain metrological integrity, the microcontroller 108 may be further configured to execute an embedded calibration lock mechanism. The calibration lock mechanism may include cryptographic authentication or protocol-based access restrictions configured to protect the stored references within the non-volatile memory. The calibration lock mechanism restricts unauthorized access to the stored references, thereby preventing unverified manual field adjustments and ensuring standardized measurement accuracy across all deployed units.
[0045] In an embodiment, the communication module 112 may be operatively coupled to the microcontroller 108 and configured to establish wireless data transmission paths. The communication module 112 may include hardware transceivers supporting protocols such as Wi-Fi (IEEE 802.11 b/g/n), Global System for Mobile Communications (GSM) via a SIM800L module, or Long Range (LoRa) communication at 868 megahertz (MHz), but not limited thereto. The communication module 112 may be configured to format the calibrated parameters and the determined cost into lightweight data packets, such as JavaScript Object Notation (JSON) formats, using Message Queuing Telemetry Transport (MQTT) or Hypertext Transfer Protocol (HTTP) application programming interfaces (APIs), but not limited thereto. The communication module 112 may be configured to transmit the formatted data packets to the cloud server 118. Establishing wireless connectivity directly from the liquid analyser 102 enables real-time synchronization with external enterprise resource planning (ERP) systems, thereby establishing end-to-end data traceability without requiring intermediate manual data entry.
[0046] In an embodiment, the power management module 114 may be electrically coupled to the microcontroller 108 and configured to distribute operational electrical power to the active components of the liquid analyser 102. In an example, the power management module 114 may include a rechargeable power storage device, such as, for example, a 7.4-volt 2200-milliampere-hour (mAh) Lithium-ion battery, combined with a charging module, such as a TP4056 module, but not limited thereto. The power management module 114 may further include a solar charging interface coupled to an external 5-watt solar panel. The microcontroller 108 may be configured to trigger a power-efficient sleep mode for the power management module 114 upon confirming a successful data transmission via the communication module 112. Triggering the power-efficient sleep mode reduces parasitic energy drain during inactive periods, thereby extending battery backup duration for off-grid usage.
[0047] In an embodiment, the system 100 may include the display unit 116 that may be communicatively coupled to the microcontroller 108 and mechanically mounted to an exterior surface of the liquid analyser 102. The display unit 116 may include an electronic visual display, such as a liquid crystal display (LCD) or a light-emitting diode (LED) screen, but not limited thereto. The display unit 116 may be configured to visually output the measured parameters, the weight data, and the determined cost. Presenting the evaluated metrics locally via the display unit 116 provides immediate, transparent feedback to operators at collection site.
[0048] In an embodiment, the cloud server 118 may be communicatively coupled to the communication module 112 over a wide area network. The cloud server 118 may be implemented on a distributed computing infrastructure and may include databases such as, for example, InfluxDB or MySQL, but not limited thereto. The cloud server 118 may be configured to authenticate registration identifiers associated with the liquid analyser 102. Upon successful authentication, the cloud server 118 may be configured to receive and store the determined cost and the calibrated one or more parameters. The cloud server 118 may be further configured to evaluate a quality of the liquid sample by comparing the received calibrated one or more parameters with corresponding predefined threshold values. The cloud server 118 may be configured to generate and transmit an alert, such as an SMS or email, to the user interface 120 when the evaluated quality exceeds the corresponding predefined threshold values. Processing the data at the cloud server 118 enables predictive analytics, quality forecasting, and automated anomaly detection across a decentralized supply chain.
[0049] In an embodiment, the user interface 120 may be communicatively coupled to the cloud server 118. The user interface 120 may include a mobile application developed in frameworks such as, for example, Flutter or React Native, or a web-based electronic dashboard, but not limited thereto. The user interface 120 may be configured to display real-time parameter data, quality ratings, and historical trend logs. The user interface 120 may incorporate offline caching capabilities and multi-language support. Providing the user interface 120 enables remote stakeholders to visually monitor centralized procurement data and respond promptly to threshold breach alerts.
[0050] In an embodiment, the liquid analyser 102 functions as the localized edge-computing hardware, while the cloud server 118 provides centralized processing and data storage capabilities. In some embodiments, the operation of the IoT-based liquid analyser and monitoring system 100 may be defined by a sequence of actions executed by the structurally configured components to evaluate milk properties. The operation may initiate at a collection point when the liquid analyser 102 physically receives a fluidic input. For instance, the liquid sampling and conditioning module 104 may be configured to receive the milk sample and mechanically stabilize the fluid for subsequent assessment. Stabilizing the milk sample may include utilizing a peristaltic pump to draw a controlled volume and the Peltier element within a temperature stabilization jacket to maintain a testing temperature (for example, 25 degrees Celsius). Once the milk sample is received and conditioned, the sensor module 106, which is operatively connected to the liquid sampling and conditioning module 104, may be configured to measure one or more parameters of the milk sample. The measured one or more parameters may include any one or a combination of fat percentage, solids-not-fat (SNF) value, electrical conductivity, temperature, protein content, lactose content, density, and an adulterant concentration. During the measurement process, an analog signal conditioning circuit within the sensor module 106 may be configured to amplify and reduce noise in analog sensor signals generated by one or more sensors (such as an ultrasonic transducer pair) prior to transmission of the one or more parameters to the microcontroller 108. Amplifying and reducing the noise in the analog sensor signals isolates true physical measurement values from localized electromagnetic interference, thereby ensuring high-fidelity data acquisition from the milk sample.
[0051] Following the signal acquisition operation, the microcontroller 108, which is operatively connected to the sensor module 106, may be configured to receive the one or more parameters of the milk sample from the sensor module 106. Upon receiving the raw data, the microcontroller 108 may be configured to calibrate the received one or more parameters based on stored references of the milk sample. Concurrently or sequentially, the microcontroller 108 may be configured to receive weight data of the milk sample from the operatively connected weight estimation module 110. Utilizing the fused data sets, the microcontroller 108 may be configured to determine a cost of the milk sample based on the calibrated one or more parameters and the weight data. Determining the cost locally at the microcontroller 108 generates an immediate economic valuation at the edge, thereby eliminating the operational delays associated with manual pricing lookups or reliance on continuous internet connectivity during farmer milk submissions.
[0052] Upon determining the localized valuation, the microcontroller 108 may be configured to initiate a data communication operation. The microcontroller 108 may be configured to transmit the determined cost of the milk sample and the calibrated one or more parameters to the cloud server 118 associated with the system 100 through the communication module 112. Furthermore, a power management module 114, which includes a rechargeable power storage device and a solar charging interface, may be communicatively coupled to the microcontroller 108. The microcontroller 108 may be configured to trigger a power-efficient sleep mode for the liquid analyser 102 upon confirming a successful data transmission via the communication module 112. Triggering the power-efficient sleep mode deactivates non-essential sensing and communication hardware, thereby minimizing energy consumption and extending the operational uptime of the liquid analyser 102 in off-grid rural environments.
[0053] The operation of the system 100 continues at the centralized infrastructure level upon receiving the incoming transmission. The cloud server 118 may be configured to authenticate registration of the liquid analyser 102 to verify the physical identity of the liquid analyser 102. Upon successful authentication, the cloud server 118 may be configured to receive the determined cost of the milk sample and the calibrated one or more parameters from the authenticated liquid analyser 102 through the communication module 112. The cloud server 118 may be subsequently configured to evaluate a quality of the milk sample by comparing the received calibrated one or more parameters with corresponding predefined threshold values. The cloud server 118 may be configured to transmit an alert to the user interface 120 when the evaluated quality of the milk sample exceeds at least one of the corresponding predefined threshold values. The cloud server 118 may be further configured to display a status of the evaluation on the user interface 120. Executing the evaluation and alerting operations at the cloud server 118 establishes automated, end-to-end data traceability and enables supervisory systems to instantly detect supply chain anomalies in the collected milk batches.
[0054] During periodic maintenance operations, the system 100 may execute a distinct physical sensor drift compensation sequence to maintain long-term test integrity. To calibrate the received one or more parameters based on the stored references, the microcontroller 108 may be configured to receive measured parameters of a reference milk sample from the sensor module 106. The microcontroller 108 may be configured to compare the measured parameters of the reference milk sample with predefined baseline parameters associated with the reference milk sample. The microcontroller 108 may be configured to determine a physical sensor drift in the sensor module 106 based on a deviation identified between the measured parameters and the predefined baseline parameters. The microcontroller 108 may then be configured to update, based on the determined sensor drift, the stored references in a memory associated with the system 100 to compensate for the physical sensor drift. To protect the integrity of the updated references, the microcontroller 108 may be configured to restrict unauthorized access to the stored references via a calibration lock mechanism. Restricting access via the calibration lock mechanism prevents unverified manual field adjustments, thereby ensuring that the operation of the system 100 continuously aligns with regulatory metrological standards for milk testing.
[0055] In an embodiment, the calibration lock mechanism may be a software-based, hardware-based, or hybrid security layer configured to restrict access to the stored references maintained in the memory. In some embodiments, the calibration lock mechanism may include a cryptographic authentication module, a password-protected digital interface, or a physical interlock (such as a jumper, a secure boot key, or a biometric sensor), but not limited thereto. The calibration lock mechanism may be integrated within the firmware of the microcontroller 108 or may reside in a dedicated secure element or Trusted Platform Module (TPM) communicatively coupled to the microcontroller 108.
[0056] FIG. 2A and 2B illustrates an example flowchart of a method 200 for IoT-based liquid analysis and monitoring, in accordance with embodiments of the present disclosure. The method 200 may include one or more steps for executing real-time multi-parameter estimation and automated cost evaluation.
[0057] Referring to FIG. 2A, at step 202, the method 200 may include receiving a liquid sample from a user. The liquid sampling and conditioning module 104 may be configured to draw a predefined volume of the liquid sample, for instance, a milk sample, and condition the liquid sample to a target temperature (such as, for example, 25 degrees Celsius). Conditioning the liquid sample provides a normalized fluidic state for consistent measurement across consecutive samples.
[0058] At step 204, the method 200 may include determining one or more parameters of the liquid sample via a sensor module. The sensor module 106 may be configured to measure parameters, such as, for example, fat percentage and solids-not-fat (SNF) value, using localized sensors, such as an ultrasonic transducer pair. Measuring the parameters at the sensor module 106 converts physical properties into analog signals, which are subsequently amplified and filtered by an analog signal conditioning circuit to ensure signal integrity.
[0059] At step 206, the method 200 may include calibrating the one or more parameters based on stored references of the liquid sample. The microcontroller 108 may be configured to apply calibration constants retrieved from a memory (such as an EEPROM) to the acquired signals. Calibrating the received signals ensures that the output values remain standardized against certified reference samples. In some embodiments, the microcontroller 108 may further restrict access to these stored references via a calibration lock mechanism to prevent unverified manual field adjustments.
[0060] At step 208, the method 200 may include receiving weight data of the liquid sample from a weight estimation module. The weight estimation module 110 may be configured to generate the weight data by measuring a mass of a collection container housing the liquid sample. Integrating the weight measurement within the same analyser 102 eliminates the need for separate weighing instruments, thereby increasing operational efficiency.
[0061] At step 210, the method 200 may include determining a cost of the liquid sample based on the calibrated one or more parameters and the weight data. The microcontroller 108 may be configured to execute a valuation algorithm that combines the fat percentage, the SNF value, and the mass to determine an immediate economic valuation. Determining the cost at the edge level provides transparent pricing for the user at the point of collection, thereby preventing manual computation errors.
[0062] At step 212, the method 200 may include transmitting the determined cost of the liquid sample and the calibrated one or more parameters to a cloud server through a communication module. The microcontroller 108 may be configured to package the data into JSON packets and transmit the data via a wireless connection (such as Wi-Fi, GSM, or LoRa, but not limited thereto) provided by the communication module 112. Transmitting the processed data to the cloud server 118 establishes end-to-end data traceability from the collection site.
[0063] Further, referring to FIG. 2B, at step 214, the method 200 may include authenticating, by a cloud server associated with the system, a registered liquid analyser. The cloud server 118 may be configured to verify a registration identifier of the liquid analyser 102 to ensure secure data ingestion. Authenticating the liquid analyser 102 ensures that only authorized field devices can contribute data to the centralized repository.
[0064] At step 216, the method 200 may include receiving the determined cost of the liquid sample and the calibrated parameters from the authenticated liquid analyser through the communication module. Upon successful authentication, the cloud server 118 may be configured to ingest the JSON data packets and store the associated parameters and valuation data in a database (such as InfluxDB or MySQL, but not limited thereto).
[0065] At step 218, the method 200 may include evaluating a quality of the liquid sample by comparing the received calibrated one or more parameters with corresponding predefined threshold values. The cloud server 118 may be configured to analyze the parameters, such as detecting an adulterant concentration or a sub-standard fat level, but not limited thereto, against historical benchmarks or regulatory standards.
[0066] At step 220, the method 200 may include transmitting an alert to a user interface, when the evaluated quality of the liquid sample exceeds at least one of the corresponding predefined threshold values. The cloud server 118 may be configured to trigger a notification to the user interface 120 upon identifying a quality breach.
[0067] At step 222, the method 200 may include displaying, by the cloud server, a status of the evaluation on the user interface. The user interface 120 (such as a mobile application or web dashboard, but not limited thereto) may be configured to visually present real-time parameter data, quality ratings (such as, for example, grade A, B, or C), and history logs. Displaying the status on the user interface 120 provides centralized visibility and transparency across the entire liquid procurement and monitoring supply chain.
[0068] Further, the method 200 may include restricting unauthorized access to the stored references via a calibration lock mechanism. The calibration lock mechanism may be configured to maintain the stored references within a non-volatile memory of the microcontroller 108 in a default locked state. The microcontroller 108 may be configured to grant write access to the stored references only responsive to receiving and validating an authorization credential (such as an encrypted digital token, a supervisor-level password, or a hardware-bound security key, but not limited thereto). Restricting the access to the stored references via the calibration lock mechanism may prevent unverified manual field adjustments by on-site operators. Preventing the unverified manual field adjustments may eliminate the technical risk of intentional or unintentional manipulation of parameters (such as, for example, the fat percentage or the solids-not-fat (SNF) value), which may otherwise lead to inaccurate economic valuations and subsequent financial disputes. Therefore, the calibration lock mechanism ensures standardized measurement accuracy and metrological integrity across decentralized collection environments, providing a tamper-proof valuation process that maintains regulatory compliance throughout the liquid supply chain.
[0069] Therefore, the present disclosure provides the system 100 for IoT-based liquid analysis and monitoring, where the system 100 minimizes operational handling time in decentralized procurement centers by simultaneously measuring multi-sensor parameters and weight data to determine an immediate economic valuation at the edge. The system 100 ensures continuous metrological accuracy and prevents unauthorized field adjustments by utilizing an embedded calibration lock mechanism that restricts access to stored references maintained within a non-volatile memory. The system 100 eliminates data traceability gaps and manual entry errors by employing a wireless communication module that automatically synchronizes calibrated parameters and determined costs with a cloud server for real-time monitoring. Furthermore, the system 100 enhances off-grid operational uptime in rural environments through a power management module 114 that triggers a power-efficient sleep mode and supports a solar charging interface to minimize parasitic energy drain. Therefore, the present disclosure establishes a portable, tamper-proof, and connected analytical solution that significantly reduces procedural complexity, ensures fair and transparent valuation for dairy stakeholders, and streamlines quality monitoring across the entire liquid supply chain.
[0070] While the foregoing describes various embodiments of the disclosure, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the disclosure is determined by the claims that follow. The disclosure is not limited to the described embodiments, versions, or examples, which are included to enable a person having ordinary skill in the art to make and use the disclosure when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0071] The present disclosure provides an integrated Internet of Things (IoT)-based hardware platform that enables real-time multi-parameter analysis by simultaneously measuring fat percentage, solids-not-fat (SNF) value, and sample weight using a combined multi-sensor array and weight estimation module, thereby eliminating the requirement for separate instruments and reducing operational handling time at decentralized collection points.
[0072] The present disclosure provides a rapid and non-destructive testing environment by utilizing an ultrasonic transducer pair and an analog signal conditioning circuit to evaluate liquid properties without compromising sample integrity, which enables quality assessment in under sixty seconds.
[0073] The present disclosure ensures a standardized calibration procedure by implementing an embedded calibration lock mechanism and a physical sensor drift compensation algorithm that restricts unauthorized access to stored references, thereby preventing unverified manual field adjustments and ensuring metrological integrity across all field-deployed devices.
[0074] The present disclosure integrates a wireless data transmission system by utilizing a communication module configured to transmit JSON-formatted data packets to a cloud server, which enables centralized monitoring and establishes end-to-end traceability of milk batches from the point of collection to the processing facility.
[0075] The present disclosure offers cloud-based analytics and reporting tools by employing a centralized monitoring platform and a user interface, thereby providing dairy stakeholders with real-time visibility into procurement trends and automated alerting for quality threshold breaches.
[0076] The present disclosure provides an energy-efficient and field-deployable device by incorporating a power management module that supports solar charging and triggers a power-efficient sleep mode upon confirming data transmission, which maximizes operational uptime and facilitates deployment in rural environments with limited grid connectivity. , Claims:1. An Internet of Things (IoT)-based liquid analyser and monitoring system (100) comprising:
a liquid analyser (102) comprising:
a liquid sampling and conditioning module (104) to receive liquid sample;
a sensor module (106) operatively connected to the liquid sampling and conditioning module (104), wherein the sensor module (106) is configured to measure one or more parameters of the liquid sample;
a microcontroller (108) operatively connected to the sensor module, wherein the microcontroller (108) is configured to:
receive the one or more parameters of the liquid sample from the sensor module (106);
calibrate the received one or more parameters based on stored references of the liquid sample;
receive weight data of the liquid sample from a weight estimation module (110);
determine a cost of the liquid sample based on the calibrated one or more parameters and the weight data; and
transmit the determined cost of the liquid sample and the calibrated one or more parameters to a cloud server (118) associated with the IoT-based liquid analyser and monitoring system (100) through a communication module (112).
2. The system (100) as claimed in claim 1, wherein the cloud server (118) is configured to:
authenticate registration of the liquid analyser (102);
upon successful authentication, receive the determined cost of the liquid sample and the calibrated one or more parameters from the authenticated liquid analyser (102) through the communication module (112);
evaluate a quality of the liquid sample by comparing the received calibrated one or more parameters with corresponding predefined threshold values;
transmit an alert to a user interface (120), when the evaluated quality of the liquid sample exceeds at least one of the corresponding predefined threshold values; and
display status of the evaluation on the user interface (120).
3. The system (100) as claimed in claim 1, wherein to calibrate the received one or more parameters based on the stored references of the liquid sample, the microcontroller (108) is configured to:
receive measured parameters of a reference liquid sample from the sensor module;
compare the measured parameters of the reference liquid sample with predefined baseline parameters associated with the reference liquid sample;
determine a physical sensor drift in the sensor module (106) based on a deviation identified between the measured parameters and the predefined baseline parameters; and
update, based on the determined sensor drift, the stored references in a memory associated with the system (100) to compensate for the physical sensor drift.
4. The system (100) as claimed in claim 3, wherein the microcontroller (108) is configured to restrict unauthorized access to the stored references to prevent unverified manual field adjustments.
5. The system (100) as claimed in claim 1, wherein the one or more parameters of the liquid sample comprise any one or a combination of: fat percentage, solids-not-fat (SNF) value, electrical conductivity, temperature, protein content, lactose content, density, and an adulterant concentration.
6. The system (100) as claimed in claim 1, wherein the sensor module (106) comprises an analog signal conditioning circuit having at least one operational amplifier and filtering component to amplify and reduce noise in analog sensor signals of one or more sensors in the sensor module (106) prior to transmission of the one or more parameters to the microcontroller (108).
7. The system (100) as claimed in claim 1, further comprising a power management module (114) communicatively coupled to the microcontroller (108), wherein the power management module (114) comprises a rechargeable power storage device and a solar charging interface, and wherein the microcontroller (108) is configured to trigger a power-efficient sleep mode via the power management module (114) for the liquid analyser (102) upon confirming a successful data transmission via the communication module (112).
8. A method for Internet of Things (IoT)-based liquid analysis and monitoring, the method comprising:
receiving, by an IoT based liquid analyser system (100), liquid sample;
measuring, by the system (100), one or more parameters of the liquid sample via a sensor module (106);
receiving, by the system (100), the one or more parameters of the liquid sample from the sensor module (106);
calibrating, by the system (100), the received one or more parameters based on stored references of the liquid sample;
receiving, by the system (100), weight data of the liquid sample from a weight estimation module (110);
determining, by the system (100), a cost of the liquid sample based on the calibrated one or more parameters and the weight data; and
transmitting, by the system (100), the determined cost of the liquid sample and the calibrated one or more parameters to a cloud server (118) associated with the IoT-based liquid analyser and monitoring system (100) through a communication module (112).
9. The method as claimed in claim 8, further comprising:
authenticating, by a cloud server (118) associated with the system (100), a registration of the liquid analyser;
upon successful authentication, receiving, by the cloud server (118), the determined cost of the liquid sample and the calibrated one or more parameters from the authenticated liquid analyser (102) through the communication module (112);
evaluating, by the cloud server (118), a quality of the liquid sample by comparing the received calibrated one or more parameters with corresponding predefined threshold values;
transmitting, by the cloud server (118), an alert to a user interface (120), when the evaluated quality of the liquid sample exceeds at least one of the corresponding predefined threshold values; and
displaying, by the cloud server (118), a status of the evaluation on the user interface (120).
10. The method as claimed in claim 8, further comprising restricting, by the system (100), unauthorized access to the stored references to prevent unverified manual field adjustments.