Intelligent online monitoring device for press plate

Abstract

The application relates to the field of pressing plate monitoring, and particularly discloses a pressing plate intelligent online monitoring device which comprises a pressing plate monitoring sensor, a convergence node, a management unit and network equipment. In order to solve the problems of single information dimension and inability to identify the sub-health state of the existing pressing plate monitoring technology, the key of the application lies in that the pressing plate monitoring sensor capable of collecting continuous analog reading is adopted, the analog data collected and containing rich features such as position and stability are completely transmitted to the management unit through the network equipment and the convergence node. The management unit no longer performs simple threshold judgment, but performs deep analysis and intelligent diagnosis on the analog reading. In this way, the device can break through the limitation of binary logic, realize quantitative monitoring and accurate early warning of the sub-health state of the pressing plate, and fundamentally improve the depth and reliability of the monitoring.

Description

Technical Field

[0001] This application relates to the field of pressure plate monitoring, and more specifically, to a smart online monitoring device for pressure plates. Background Technology

[0002] As a core manual component used for switching control circuits in critical fields such as power systems and industrial control, the accuracy and reliability of the control plate's operating status directly affect the safe and stable operation of the entire system. A single incorrect control plate status judgment may lead to protection devices failing to operate or malfunctioning, or even causing serious accidents such as equipment damage or large-scale power outages. Therefore, real-time and accurate online monitoring of the control plate status is an indispensable technical link to ensure the safe operation of the system.

[0003] However, existing monitoring solutions for the pressure plate status generally have significant limitations. Most of these solutions use non-invasive sensors such as magnetic switches and Hall effect switches for status sensing, but these sensors are essentially digital switching devices, only outputting binary switching signals representing "on" or "off". While this monitoring method can determine the final position of the pressure plate, it cannot effectively identify and warn of sub-optimal states such as the pressure plate being almost in place or poor contact, partial on / off, or partial off due to mechanical wear, vibration, etc., leaving significant safety hazards.

[0004] The root cause of this technological predicament lies in the inherent defects of existing monitoring technologies in terms of information dimension and judgment logic. Firstly, at the perception level, mainstream sensors, in their hardware design, directly convert continuously changing physical quantities (such as magnetic field strength) into high / low level signals. This results in the loss of rich intermediate process information that could characterize the state at the source of data acquisition, leading to a singular information dimension. Secondly, because the data source is binary, the data model and analysis logic of the entire monitoring system are correspondingly fixed into simple binary judgments, lacking the ability to define and quantify non-ideal states. Furthermore, traditional low-frequency polling or event-triggered reporting mechanisms make it difficult for the system to capture key dynamic characteristics such as millisecond-level state jitter reflecting contact stability, thus missing crucial opportunities for early warning of sub-healthy states. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. According to this application, a pressure plate intelligent online monitoring device includes: a pressure plate monitoring sensor, a aggregation node, a management unit, and a network device; the pressure plate monitoring sensor is used to collect analog sensor readings and transmit them to the aggregation node via the network device; after receiving the analog sensor readings, the aggregation node transmits them to the management unit via the network device; the management unit is used to monitor and issue early warnings for the sub-health status of the pressure plate based on the analog sensor readings.

[0006] In the aforementioned intelligent online monitoring device for pressure plates, the pressure plate monitoring sensor is a linear Hall element.

[0007] In the aforementioned intelligent online monitoring device for pressure plates, the analog sensor readings include timestamps, average voltage values, and standard deviations.

[0008] In the aforementioned intelligent online monitoring device for pressure plates, the management unit includes: an analog sensor data acquisition subunit, used to acquire multiple sets of analog sensor readings when the pressure plate is in the ideal engagement and ideal disengagement positions; a pressure plate health baseline calibration subunit, used to perform pressure plate health baseline self-learning and calibration on the multiple sets of analog sensor readings to obtain pressure plate health baseline characteristics; a health quantification subunit, used to perform real-time health quantification on the analog sensor readings based on the pressure plate health baseline characteristics to obtain health quantification indicators; and a sub-health state classification subunit, used to perform intelligent classification and diagnosis of sub-health states based on health quantification indicators to obtain sub-health state classification results.

[0009] In the aforementioned intelligent online monitoring device for pressure plates, the baseline health characteristics of the pressure plates include the mean of the ideal engagement voltage, the mean of the ideal engagement standard deviation, the mean of the ideal disengagement voltage, and the mean of the ideal disengagement standard deviation.

[0010] In the aforementioned intelligent online monitoring device for pressure plates, the health quantification subunit includes: a discrete state determination subunit, used to perform discrete state determination on analog sensor readings and pressure plate health baseline characteristics to obtain a determination state; a position determination quantification subunit, used to perform position determination quantification on analog sensor readings and pressure plate health baseline characteristics to obtain position determination; a stability quantification subunit, used to perform stability quantification on analog sensor readings and pressure plate health baseline characteristics to obtain stability; and an aggregation and encapsulation subunit, used to aggregate and encapsulate the determination state, position determination, and stability to obtain a health quantification index.

[0011] In the aforementioned intelligent online monitoring device for pressure plates, the discrete state determination secondary subunit includes: an absolute distance calculation tertiary subunit, used to calculate the absolute distance between the current average voltage value in the analog sensor readings and the average ideal engagement voltage value and the average ideal disengagement voltage value to obtain the engagement absolute distance and disengagement absolute distance; a first determination state setting tertiary subunit, used to set the determination state to "engage" in response to the engagement absolute distance being less than the disengagement absolute distance; and a second determination state setting tertiary subunit, used to set the determination state to "disengage" in response to the disengagement absolute distance being less than the engagement absolute distance.

[0012] In the aforementioned intelligent online monitoring device for pressure plates, the secondary subunit for position determination and quantification is used to: quantify the position of the analog sensor readings and the health baseline characteristics of the pressure plate using the following formula: ;in, This represents the average current voltage reading from the analog sensor. The reference voltage is the average value of the ideal on-state voltage when the judgment state is "on" and the average value of the ideal off-state voltage when the judgment state is "off". The average value of the ideal switching voltage. This represents the average value of the ideal retraction voltage. For adjustment coefficients, For positional certainty.

[0013] In the aforementioned intelligent online monitoring device for pressure plates, the stability quantification subunit is used to: quantify the stability of analog sensor readings and pressure plate health baseline characteristics using the following formula: ;in, This represents the current standard deviation of the analog sensor readings. denoted as the benchmark standard deviation, where, when the judgment status is "throw", the benchmark standard deviation is the mean of the ideal throwing position standard deviation, and when the judgment status is "retreat", the benchmark standard deviation is the mean of the ideal retreat position standard deviation. For adjustment coefficients, To find the maximum value function, For stability.

[0014] Compared with existing technologies, this application provides a smart online monitoring device for pressure plates, which includes a pressure plate monitoring sensor, a aggregation node, a management unit, and network equipment. This device aims to address the technical shortcomings of existing pressure plate monitoring technologies, which use switch-type sensors that can only output binary on / off signals, resulting in limited information dimensions and an inability to effectively identify and warn of sub-health conditions such as poor contact, false on / off states, etc. To achieve this, the key to this application is the use of a pressure plate monitoring sensor capable of acquiring continuous analog readings. The acquired analog data, containing rich features such as position and stability, is completely transmitted to the management unit through the network equipment and aggregation node. The management unit no longer performs simple threshold judgments but instead performs in-depth analysis and intelligent diagnosis of these analog readings. In this way, the device can overcome the limitations of binary logic, achieving quantitative monitoring and accurate early warning of sub-health conditions of the pressure plate, fundamentally improving the depth and reliability of monitoring. Attached Figure Description

[0015] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 This is a schematic block diagram of a pressure plate intelligent online monitoring device according to an embodiment of this application.

[0017] Figure 2 This is a schematic block diagram of the management unit in the intelligent online monitoring device for pressure plates according to an embodiment of this application.

[0018] Figure 3 This is a schematic diagram of the data flow in the management unit of the intelligent online monitoring device for pressure plates according to an embodiment of this application.

[0019] Figure 4 This is a schematic block diagram of the health measurement subunit in the intelligent online monitoring device for pressure plates according to an embodiment of this application.

[0020] Figure 5 This is a schematic diagram of the logic flow of the discrete state determination secondary subunit in the intelligent online monitoring device for pressure plates according to an embodiment of this application. Detailed Implementation

[0021] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0022] It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0023] Figure 1 This is a schematic block diagram of a pressure plate intelligent online monitoring device according to an embodiment of this application. Specifically, as Figure 1 As shown, the intelligent online monitoring device 100 for pressure plates according to an embodiment of this application includes: a pressure plate monitoring sensor 110, a convergence node 120, a management unit 130, and a network device 140.

[0024] In the intelligent online monitoring device for pressure plates, all components work collaboratively to form a closed-loop intelligent monitoring architecture from front-end sensing, mid-level transmission to back-end analysis, enabling accurate monitoring of the sub-health state of the pressure plates. Specifically, the pressure plate monitoring sensor 110, acting as the data acquisition front-end, is responsible for acquiring analog sensor readings reflecting the pressure plate's state in real time. These readings are then transmitted to the aggregation node 120 via network device 140. Network device 140 serves as the data transmission channel here. After receiving data from one or more sensors, aggregation node 120 aggregates and forwards the data, and then uses network device 140 again to send the data to management unit 130. Management unit 130, as the analysis and decision-making core of the entire device, receives and processes the analog readings from the aggregation node, performs a series of calculations such as health baseline calibration, health quantification, and sub-health state classification and diagnosis, and finally outputs the monitoring and early warning results for the pressure plate's state. The entire process forms a complete workflow from data acquisition, transmission and aggregation to intelligent analysis. Each part will be described in detail below.

[0025] Specifically, the pressure plate monitoring sensor 110 is used to collect analog sensor readings and transmit them to the aggregation node via network equipment. The pressure plate monitoring sensor 110 is the core front-end sensing component of the intelligent online pressure plate monitoring device. Its fundamental function is to replace traditional binary switch sensors to solve the problem of their limited information dimensions. This sensor is responsible for directly sensing the physical state of the pressure plate and converting it into an analyzable electrical signal. Its specific function is to collect analog sensor readings that can precisely characterize the position and stability of the pressure plate, thereby providing a rich data foundation for subsequent sub-health condition diagnosis.

[0026] In detail, the analog sensor reading acquisition process is as follows: First, hardware selection and configuration are performed. Specifically, the pressure plate monitoring sensor here is a linear Hall element, which can output a continuous voltage signal linearly related to the magnetic field strength, thereby accurately capturing all intermediate states of the pressure plate from the engaged position to the disengaged position. To achieve a comprehensive evaluation of the pressure plate state, the sensor integrates a microprocessor unit, which can perform high-speed sampling of the signal output by the linear Hall element within a preset time window to obtain the analog sensor reading. In a specific example of this application, the analog sensor reading includes a timestamp, average voltage, and standard deviation. The timestamp provides a time-series reference for tracing state changes and analyzing dynamic processes; the average voltage is used to characterize the average value of all sampling points within the time window, which mainly reflects the macroscopic average position of the pressure plate during that time period; and the standard deviation is used to represent the standard deviation of all sampling points within the time window, which mainly reflects the microscopic stability of the pressure plate position. The standard deviation of a stable pressure plate should be very small; while the standard deviation of a vibrating or poorly contacted pressure plate will increase significantly.

[0027] In its workflow, the sensor's built-in microprocessor first continuously samples the analog voltage output of the linear Hall element at a preset high sampling frequency. This sampling frequency must be set to ensure the capture of millisecond-level fluctuations; for example, it can be set to 1000 Hz, meaning one voltage sample is acquired every millisecond. Subsequently, the microprocessor processes the acquired raw sample sequence within a preset time window. The width of this time window is a key parameter, balancing real-time monitoring with data smoothness, and can be set according to the response requirements of the application scenario, for example, to 100 milliseconds. This means the microprocessor processes every 100 consecutive voltage samples as a group.

[0028] Next, the microprocessor performs statistical calculations within each time window. The first calculation is the voltage mean, which is obtained by summing 100 voltage samples within the time window and dividing by 100 to obtain a voltage mean that represents the macroscopic average position of the pressure plate during that time period. For example, if the pressure plate is in a sub-optimal state with poor contact, although its macroscopic position is close to the operating position, it exhibits moderate instability. If the ideal operating voltage is 3.0 volts, then the 100 sample points collected within these 100 milliseconds may fluctuate significantly between 2.6 volts and 3.1 volts, and the calculated voltage mean may deviate from the ideal value, for example, to 2.85 volts. The second calculation is the standard deviation, which calculates the dispersion of these 100 voltage samples relative to their mean. In the example of poor contact mentioned above, due to the fluctuation of sample values, the calculated standard deviation will increase significantly, for example, reaching 0.008 volts.

[0029] Finally, after completing the calculation, the microprocessor encapsulates the current timestamp, the calculated average voltage value, and the standard deviation into a structured analog sensor reading data packet. For example, a complete data packet reflecting a sub-healthy state might be {timestamp: "T0+100ms", average voltage value: 2.85, standard deviation: 0.008}. This data packet is then transmitted via the sensor's built-in wireless communication module to the designated aggregation node 120 through network device 140.

[0030] Specifically, after receiving the analog sensor readings, the aggregation node 120 transmits them to the management unit via network equipment. The aggregation node 120 is the network communication unit responsible for data relay and aggregation in the intelligent online monitoring device for pressure plates. Its specific function is to receive analog sensor readings transmitted by one or more pressure plate monitoring sensors 110 through network equipment 140, and after receiving these readings, to perform necessary data aggregation and processing. After aggregation, the aggregation node 120 then forwards these data to the management unit 130 via network equipment 140 for centralized analysis and processing. This node plays a bridging role in the overall device architecture, enabling efficient and orderly data transmission between front-end distributed data acquisition and back-end centralized intelligent diagnostics.

[0031] Specifically, the management unit 130 is used to monitor and issue early warnings about the sub-health status of the pressure plate based on analog sensor readings. The management unit 130 is the core unit in the intelligent online monitoring device for the pressure plate, enabling intelligent analysis and decision-making. Its fundamental function is to receive analog sensor readings forwarded by the aggregation node 120 and, based on this data containing rich status information, to monitor and issue early warnings about the sub-health status of the pressure plate. The specific functions of this unit encompass a complete intelligent diagnostic process. First, it acquires multiple sets of analog sensor readings of the pressure plate under ideal engagement and disengagement conditions, and performs self-learning and calibration on these data to establish a precise baseline characteristic of the pressure plate's health. During real-time monitoring, the management unit 130 compares newly acquired readings with the calibrated health baseline characteristics, performs health quantification calculations, and obtains health metric indicators that can quantitatively characterize the current state's quality. Finally, based on these health metric indicators, the management unit 130 completes the intelligent classification and diagnosis of sub-health states such as poor contact, false engagement / disengagement, etc., and outputs corresponding monitoring or early warning results.

[0032] Specifically, network device 140. Network device 140 is a communication component in the intelligent online monitoring device for pressure plates that provides a data transmission channel. It can be a wired network such as industrial Ethernet or fieldbus, or a wireless communication network such as LoRa or Wi-Fi. Its specific function is to act as a medium connecting various independent units, responsible for the reliable transfer of analog sensor readings between different units. Specifically, it first supports the pressure plate monitoring sensor 110 in sending the collected readings to the aggregation node 120, and then it also supports the aggregation node 120 in further sending the aggregated data to the management unit 130. Therefore, network device 140 constitutes the necessary path for the information flow of the entire device, and its real-time performance and reliability are the key foundation for ensuring the response speed and data integrity of the entire monitoring device.

[0033] As mentioned in the background technology, existing monitoring schemes, due to their limited information dimensions and binary judgment logic, cannot effectively identify sub-health conditions such as poor contact, false insertion, and false withdrawal of pressure plates. Simply collecting analog data containing rich details only solves the problem of information acquisition at its source; without corresponding analysis methods, this data itself cannot be directly converted into an effective judgment of sub-health conditions. The key lies in how to perform in-depth, non-binary intelligent analysis of this continuously changing data containing weak features. Therefore, this application introduces a management unit 130 based on the problems in the background technology, as the core for realizing intelligent diagnosis and early warning.

[0034] In a specific example of this application, Figure 2 This is a schematic block diagram of the management unit in the intelligent online monitoring device for pressure plates according to an embodiment of this application. Figure 3 This is a schematic diagram of the data flow in the management unit of the intelligent online monitoring device for pressure plates according to an embodiment of this application. Figure 2 and Figure 3 As shown, the management unit 130 includes: an analog sensor data acquisition subunit 131, used to acquire multiple sets of analog sensor readings when the pressure plate is in the ideal engagement and ideal disengagement positions; a pressure plate health baseline calibration subunit 132, used to perform pressure plate health baseline self-learning and calibration on the multiple sets of analog sensor readings to obtain pressure plate health baseline characteristics; a health quantification subunit 133, used to perform real-time health quantification on the analog sensor readings based on the pressure plate health baseline characteristics to obtain health quantification indicators; and a sub-health state classification subunit 134, used to perform intelligent classification and diagnosis of sub-health states based on health quantification indicators to obtain sub-health state classification results.

[0035] Specifically, the analog sensor data acquisition subunit 131 is used to acquire multiple sets of analog sensor readings when the pressure plate is in its ideal engaged and ideal disengaged positions. It should be understood that due to manufacturing tolerances, subtle differences in installation location, and the influence of the complex electromagnetic environment at the site, even if each pressure plate is of the exact same model, the electrical signal characteristics output by its corresponding monitoring sensor under ideal conditions are not a fixed, universal value. Using a uniform, pre-set threshold to assess the health status of all pressure plates will inevitably lead to insufficient accuracy and even misjudgments. Therefore, before performing any real-time health quantification and sub-health diagnosis, it is necessary to establish a unique, personalized health status benchmark for each monitored pressure plate. Acquiring multiple sets of analog sensor readings when the pressure plate is confirmed to be in its ideal engaged and ideal disengaged positions is precisely to construct such a high-precision, personalized health baseline, which will serve as the gold standard or reference system for all subsequent state comparisons and quantitative analyses.

[0036] In detail, the analog sensor data acquisition subunit 131 processes the following: The management unit 130 is switched to a specific debug / learning mode. This mode is activated by maintenance personnel through a local maintenance terminal or remote monitoring interface connected to the management unit 130. Once this mode is entered, the analog sensor data acquisition subunit 131 is awakened and ready to perform the baseline data acquisition task.

[0037] The entire implementation process is divided into two main stages. The first stage is the acquisition of ideal engagement data. Maintenance personnel first operate the handle of the target pressure plate to the "engaged" position and, through visual inspection and manual confirmation, ensure that its position is secure, without any play, and in a physically ideal engagement state. After confirmation, the maintenance personnel issue the command "Start collecting ideal engagement data" through the operation interface. At this time, the analog sensor data acquisition subunit 131 begins to receive and process the analog sensor reading data packets continuously sent by the pressure plate monitoring sensor 110 corresponding to the pressure plate via the aggregation node 120. To ensure the statistical robustness of the baseline data, this subunit does not collect only a single data point, but rather collects a preset number of continuous data packets; for example, the collection quantity N can be set to 20 sets. This means that it will continuously buffer data for 20 time windows. For example, it will sequentially receive 20 sets of readings, including {timestamp: "T1+100ms", average voltage: 3.001, standard deviation: 0.005}, {timestamp: "T1+200ms", average voltage: 2.998, standard deviation: 0.006}, up to {timestamp: "T1+2000ms", average voltage: 3.002, standard deviation: 0.004}. These 20 sets of readings together constitute a sample set that can fully reflect the small normal fluctuations under ideal positioning conditions. After the acquisition is completed, the analog signal sensor data acquisition subunit 131 treats these 20 sets of readings as a whole, marks them as the ideal positioning raw dataset, and temporarily stores them in the internal storage area.

[0038] The second stage involves acquiring the ideal retraction data, and its process is similar to the first stage. The maintenance personnel operate the pressure plate handle to the "retracted" position, ensuring its accuracy, and then issue the command "Start acquiring ideal retraction data." The analog sensor data acquisition subunit 131 restarts the acquisition program, continuously acquiring and buffering another 20 sets of analog sensor readings. Because the pressure plate is in the retracted position, the voltage values ​​output by the sensors will be significantly different. For example, the acquired data packets might be {timestamp: "T2+100ms", voltage mean: 0.502, standard deviation: 0.004}, {timestamp: "T2+200ms", voltage mean: 0.505, standard deviation: 0.005}, up to {timestamp: "T2+2000ms", voltage mean: 0.499, standard deviation: 0.006}. These 20 sets of data are also stored as a whole, labeled as the ideal retraction raw dataset.

[0039] Finally, once the data acquisition in both phases is complete, the final output consists of two structured datasets: the ideal positioning raw dataset and the ideal retraction raw dataset. These two datasets contain the most original health status information tailored to this specific pressure plate, comprising multiple sets of analog sensor readings.

[0040] Specifically, the pressure plate health baseline calibration subunit 132 is used to perform pressure plate health baseline self-learning and calibration on multiple sets of analog sensor readings to obtain pressure plate health baseline characteristics. In particular, the pressure plate health baseline characteristics here include the average ideal engagement voltage, the average ideal engagement standard deviation, the average ideal disengagement voltage, and the average ideal disengagement standard deviation. Correspondingly, the raw dataset received from the analog sensor data acquisition subunit 131, while accurately reflecting the pressure plate's performance under ideal conditions, is essentially a discrete set of samples containing inherent minor fluctuations. These discrete, multi-set readings cannot be directly used as a stable reference for real-time diagnosis because directly using them for point-by-point comparison would introduce unnecessary computational complexity and is easily affected by the random bias of a single sample. Therefore, this application further performs statistical refinement and aggregation on these raw data to transform these dynamic sample sets into a set of static, accurate, and representative pressure plate health baseline characteristics through self-learning and calibration.

[0041] In detail, the platen health baseline calibration subunit 132 processes the following: First, the platen health baseline calibration subunit 132 processes the ideal switching raw dataset. This dataset contains 20 sets of analog sensor readings. The subunit iterates through these 20 sets of data and extracts all voltage mean and standard deviation terms. Taking the data collected in the previous steps as an example, it will obtain a list containing 20 voltage means, such as [3.001, 2.998, ..., 3.002], and a list containing 20 standard deviations, such as [0.005, 0.006, ..., 0.004]. Next, the subunit performs an arithmetic mean operation on these two lists respectively. For the voltage mean list, it adds all 20 values ​​and divides by 20 to calculate a final average. For example, (3.001 + 2.998 + ... + 3.002) / 20 might result in 3.000 volts. This value is officially calibrated as the ideal switching voltage mean. Similarly, it performs the same operation on the standard deviation list, for example (0.005+0.006+...+0.004) / 20, and the result may be 0.005 volts, which is calibrated as the mean of the ideal position standard deviation.

[0042] Subsequently, the platen health baseline calibration subunit 132 processes the ideal retraction raw dataset in exactly the same way. It also extracts all voltage mean and standard deviation terms from the 20 readings contained in the dataset. Taking the data acquired in the previous step as an example, it obtains a list of voltage means [0.502, 0.505, ..., 0.499] and a list of standard deviations [0.004, 0.005, ..., 0.006]. The subunit performs an arithmetic mean operation on these two lists again. For example, (0.502 + 0.505 + ... + 0.499) / 20 might result in 0.501 volts, which is calibrated as the ideal retraction voltage mean. And (0.004 + 0.005 + ... + 0.006) / 20 might result in 0.005 volts, which is calibrated as the ideal retraction standard deviation mean.

[0043] After completing all the above calculations, the pressure plate health baseline calibration subunit 132 encapsulates the four calibrated core parameters—namely, the average ideal engagement voltage, the average ideal engagement standard deviation, the average ideal disengagement voltage, and the average ideal disengagement standard deviation—into a structured pressure plate health baseline feature data object. Taking the aforementioned example calculation result, the content of this data object is {average ideal engagement voltage: 3.000, average ideal engagement standard deviation: 0.005, average ideal disengagement voltage: 0.501, average ideal disengagement standard deviation: 0.005}.

[0044] Specifically, the health measurement subunit 133 is used to perform real-time health measurement of analog sensor readings based on the pressure plate health baseline characteristics to obtain health measurement indicators. It is understandable that after obtaining accurate pressure plate health baseline characteristics and real-time analog sensor readings, the device cannot immediately make a detailed quantitative assessment of the pressure plate's health status. This is because the original sensor readings are essentially continuous, unlabeled values, while health assessment requires a clear context, namely, determining whether the pressure plate is currently in an "on" or "off" state. Without this basic determination, subsequent calculations of indicators such as position deviation and stability are impossible, as they all require comparison with a correct benchmark. Therefore, before performing any refined health measurement, a macroscopic, binary state attribution determination is needed for the current analog readings to assign a discrete initial state label of "on" or "off" to the continuous analog readings, providing a clear benchmark selection basis for all subsequent targeted and more refined health measurement calculations.

[0045] In a specific example of this application, Figure 4 This is a schematic block diagram of the health measurement subunit in the intelligent online monitoring device for pressure plates according to an embodiment of this application. Figure 4As shown, the health measurement subunit 133 includes: a discrete state determination subunit 1331, used to perform discrete state determination on analog sensor readings and pressure plate health baseline characteristics to obtain a determination state; a position determination measurement subunit 1332, used to perform position determination measurement on analog sensor readings and pressure plate health baseline characteristics to obtain position determination; a stability measurement subunit 1333, used to perform stability quantification on analog sensor readings and pressure plate health baseline characteristics to obtain stability; and an aggregation and encapsulation subunit 1334, used to aggregate and encapsulate the determination state, position determination, and stability to obtain a health measurement index.

[0046] In detail, the health measurement subunit 133 processes the following: First, a discrete state determination secondary subunit 1331 is performed. In a specific example of this application, Figure 5 This is a schematic diagram of the logic flow of the discrete state determination secondary subunit in the intelligent online monitoring device for pressure plates according to an embodiment of this application. Figure 5 As shown, the discrete state determination secondary subunit 1331 includes: an absolute distance calculation tertiary subunit 1331-1, used to calculate the absolute distance between the current average voltage value in the analog sensor readings and the ideal average voltage value and the ideal retraction voltage value to obtain the absolute distance between the voltage and the voltage; a first determination state setting tertiary subunit 1331-2, used to set the determination state to "voltage" in response to the absolute distance between the voltage and the voltage being less than the absolute distance between the voltage and the voltage; and a second determination state setting tertiary subunit 1331-3, used to set the determination state to "retraction" in response to the absolute distance between the voltage and the voltage being less than the absolute distance between the voltage and the voltage.

[0047] Specifically, firstly, the third-level subunit 1331-1 for absolute distance calculation is activated. This subunit extracts the average voltage value (2.85 volts) from the current reading of the analog sensor and calculates the absolute distance between it and the two ideal average voltage values ​​in the health baseline characteristics. The first calculation is the absolute distance of the input position, calculated as: |current average voltage - ideal input average voltage|. Substituting the values, i.e., |2.85 - 3.000|, we get an input absolute distance of 0.15. The second calculation is the absolute distance of the output position, calculated as: |current average voltage - ideal output average voltage|. Substituting the values, i.e., |2.85 - 0.501|, we get an output absolute distance of 2.349. Next, to avoid misjudgment in the intermediate region, a transition zone determination is performed, i.e., a transition zone threshold is set (for example, 10% of the ideal voltage difference, i.e., 0.1 * |3.000 - 0.501| = 0.2499). If the calculated result of |pick absolute distance - backslip absolute distance| is less than the threshold, the judgment state is set to "conversion in progress". In this example, |0.15 - 2.349| = 2.199, which is not less than 0.2499, so the judgment process continues to make a clear attribution judgment. Next, the output of the third-level sub-unit 1331-1 for absolute distance calculation is two values: the pick absolute distance of 0.15 and the backslip absolute distance of 2.349, which are passed to the first judgment state setting third-level sub-unit 1331-2 and the second judgment state setting third-level sub-unit 1331-3. These two sub-units respond based on a simple comparison logic. They compare the size of the pick absolute distance and the backslip absolute distance. In this example, 0.15 is less than 2.349, that is, the pick absolute distance is less than the backslip absolute distance. This comparison result satisfies the trigger condition of the first judgment state setting third-level sub-unit 1331-2. Therefore, this sub-unit is activated and performs its preset function: setting an internal variable judgment state to the string "pick". At the same time, since the comparison result does not meet the condition that the absolute distance of the retraction is less than the absolute distance of the throw, the second judgment state setting third-level sub-unit 1331-3 remains inactive.

[0048] Next, the position determination quantization secondary subunit 1332 is used. In a specific example of this application, the position determination quantization secondary subunit 1332 is used to: perform position determination quantization on the analog sensor readings and the pressure plate health baseline characteristics using the following formula: ;in, This represents the average current voltage reading from the analog sensor. The reference voltage is the average value of the ideal on-state voltage when the judgment state is "on" and the average value of the ideal off-state voltage when the judgment state is "off". The average value of the ideal switching voltage. This represents the average value of the ideal retraction voltage. For adjustment coefficients, To ensure positional accuracy, this subunit first selects the correct reference voltage based on the input indicating a "start" state. According to the preset logic, when the determination state is "on", the reference voltage... This represents the ideal average switching voltage, and is therefore set to 3,000 volts. Next, the sub-unit substitutes all necessary parameters into the core quantization formula. (Exponential function) The use of this technology improves the positional certainty. The value range is restricted to (0,1], and the response to deviation is non-linear; that is, small deviations have little impact on the results, while large deviations lead to a sharp decline in the results, which aligns with the intuitive understanding of health assessment. (The remaining text appears to be incomplete and requires further context.) The absolute deviation between the current voltage and the ideal reference voltage was calculated, which directly reflects the degree of positional deviation. The denominator contains... The total range between the ideal engagement and disengagement voltages was calculated. This range serves as a normalization factor, eliminating potential voltage range differences between different pressure plates or sensors, thus ensuring the universality and comparability of the calculation results. It is a preset adjustment coefficient that controls the sensitivity of the quantization model. The higher the value, the more severe the penalty for voltage deviation in positional accuracy. This value can be set according to the severity level of the monitoring; for example, a higher value can be set for the pressure plate of a critical circuit. For a typical circuit, a suitable value can be set, such as... In this example, set Substitute all values ​​into the formula for calculation: First, calculate the absolute deviation in the numerator: Then calculate the normalized range in the denominator: Substitute these values ​​into: The calculated result, 0.741, represents the positional certainty in the current state. This intuitively shows that although the pressure plate is determined to be in the "throw" position, its positional certainty is only about 74.1%, significantly lower than the ideal level of nearly 1.0. This numerically and accurately characterizes its sub-optimal positional deviation. Ultimately, the output of the positional certainty quantification sub-unit 1332 is a single floating-point value: 0.741. This "positional certainty" metric will be passed to the aggregated encapsulation sub-unit 1334 as one of the key dimensions constituting the final health metric.

[0049] The stabilization quantification secondary subunit 1333 is then used. In a specific example of this application, the stabilization quantification secondary subunit 1333 is used to: quantify the stability of the analog sensor readings and the platen health baseline characteristics using the following formula: ;in, This represents the current standard deviation of the analog sensor readings. denoted as the benchmark standard deviation, where, when the judgment status is "throw", the benchmark standard deviation is the mean of the ideal throwing position standard deviation, and when the judgment status is "retreat", the benchmark standard deviation is the mean of the ideal retreat position standard deviation. For adjustment coefficients, To find the maximum value function, For stability, this sub-unit first selects the correct baseline standard deviation based on the input that the decision state is "cast". According to the preset logic, when the judgment status is "cast", the benchmark standard deviation is... That is, the mean of the standard deviation of the ideal position, therefore It was set to 0.005 volts. Then, the sub-cell substituted all the necessary parameters into the core quantization formula. The relative increment of the current standard deviation relative to the baseline standard deviation was calculated. This normalized ratio, rather than the absolute difference, eliminates the effect of small differences in the standard deviations of different pressure plates. It is a preset adjustment coefficient used to adjust the stability's sensitivity to changes in standard deviation. A higher value means that even a slight increase in the standard deviation will result in a sharp drop in the stability score. This value can be set according to the vibration environment and monitoring requirements at the site. For example, for control loops requiring extremely high stability, it can be set to... =2, while in general cases, it can be set to 2. =1. In this example, set =1. Expression The structure transforms a growing, unbounded relative bias into a decreasing score with an ideal upper limit of 1. Finally, The function ensures that the final stability result will not be lower than 0, meaning the worst-case stability is quantified as 0. Substituting all values ​​into the formula for calculation: First, calculate the relative increment of the standard deviation: =(0.008-0.005) / 0.005=0.6. This result indicates that the jitter in the current state is 0.6 times that in the ideal state. Then, substituting this result into the complete expression: =max(0,1-1×0.6)=0.4. This calculated result of 0.4 represents the stability under the current condition. It clearly indicates that the dynamic stability of the pressure plate has decreased to 40% of the ideal state, with obvious signs of vibration or poor contact, even though its macroscopic position may still be acceptable.

[0050] Finally, the aggregation and encapsulation of the secondary subunit 1334 is performed. The inputs to this subunit are: the determination status result from the discrete status determination secondary subunit 1331, which is the string "cast"; the position determination degree result from the position determination degree quantification secondary subunit 1332, which is the floating-point number 0.741; and the stability result from the stability quantification secondary subunit 1333, which is the floating-point number 0.4. After receiving these three inputs, this subunit will create a new data object, which is defined as a health metric. Subsequently, it assigns the three input values received to three fields preset inside this data object. Specifically, it assigns the string "cast" to the determination status field, the value 0.741 to the position determination degree field, and the value 0.4 to the stability field. Finally, the specific content of the output health metric is: {determination status: "cast", position determination degree: 0.741, stability: 0.4}.

[0051] In a preferred specific example of this application, the health degree quantification subunit 133 includes: a determination status generation secondary subunit 133-1, which is used to perform discrete status determination on the analog sensor reading and the clamp health baseline feature to obtain the determination status; a position determination degree calculation secondary subunit 133-2, which is used to perform quantification based on the adaptive exponential decay model on the analog sensor reading and the clamp health baseline feature to obtain the position determination degree; a stability calculation secondary subunit 133-3, which is used to perform quantification based on the adaptive linear penalty model on the analog sensor reading and the clamp health baseline feature to obtain the stability; and a health metric generation secondary subunit 133-4, which is used to perform aggregation and encapsulation on the determination status, the position determination degree, and the stability to obtain the health metric. In particular, the specific implementation processes of the determination status generation secondary subunit 133-1 and the health metric generation secondary subunit 133-4 are the same as those of the discrete status determination secondary subunit 1331 and the aggregation and encapsulation secondary subunit 1334 in the above specific example, so no elaboration is made. In particular, the implementation processes of the position determination degree calculation secondary subunit 133-2 and the stability calculation secondary subunit 133-3 are described in detail here.

[0052] It is understandable that when quantitatively assessing the health status of pressure plates, using a fixed, universal mathematical model often requires engineers to repeatedly adjust its internal parameters (such as adjustment coefficients) based on their experience. This process is not only inefficient but also lacks intuitive physical meaning, making it difficult to guarantee consistent and optimal diagnostic results across different models and operating conditions of pressure plates. Therefore, this application introduces a mechanism that allows model parameters to self-optimize. This step transforms the key adjustment coefficients kp and ks in the quantitative model from static parameters that require manual setting into dynamic dependent variables automatically calculated from more intuitive and physically meaningful diagnostic thresholds. By directly defining business requirements (e.g., when the voltage deviates from the ideal position by more than 5%, the position certainty should drop to exactly 95%), and then back-calculating the optimal adjustment coefficients, the entire quantitative model becomes adaptive, greatly improving the interpretability, configurability, and accuracy of the diagnostic logic.

[0053] Specifically, the second-level subunit 133-2 for position determination calculation is used to quantify the analog sensor readings and the health baseline characteristics of the pressure plate based on an adaptive exponential decay model to obtain position determination. The exponential decay model can well simulate the nonlinear characteristics of health: for small deviations from the ideal position, the health should decrease relatively slowly; while for larger deviations, the health should decrease rapidly to reflect a sharp increase in risk. The result is a standardized index between 0 and 1 that accurately reflects position accuracy.

[0054] The specific implementation process of this step is as follows: First, in order to make the adjustment coefficient... The settings are directly linked to business needs, introducing two core business indicators: the position determination health threshold (T_p_healthy), which represents the lowest position determination score allowed for a healthy state, for example, it can be set to 0.95; and the critical relative deviation (P_crit), which represents the maximum voltage relative deviation that a healthy state can tolerate, for example, it can be set to 0.05, meaning that as long as the voltage deviation exceeds 5% of the total stroke, it is considered no longer fully healthy.

[0055] Subsequently, these two business indicators and adjustment coefficients were established. The objective relationship equation between them is based on the following logic: when the actual relative voltage deviation (dv_rel) is exactly equal to the preset critical relative deviation (P_crit), the calculated position certainty (Cp) should be exactly equal to the preset position certainty health threshold (T_p_healthy), where, Substitute this target into the original position determination equation. The objective equation is obtained as follows: By performing a mathematical transformation on the objective equation, the adjustment coefficient can be solved inversely. The calculation formula is as follows: .so, The parameters, which previously required experience-based settings, are now automatically calculated by two explicit business metrics, T_p_healthy and P_crit. This improves interpretability and intuitiveness; business rules such as "health threshold of 0.95 and maximum tolerance for deviation of 5%" become clear and straightforward. Furthermore, because... The value is mathematically bound to the classification threshold T_p_healthy. If the sensitivity of the classification needs to be adjusted (e.g., changing the health threshold from 0.95 to 0.98), the value can be adjusted accordingly. It can also automatically update to match new rules, ensuring the consistency of model behavior. Furthermore, the device configuration is more robust; that is, for pressure plates of different models and strokes, simply setting the same business percentage (e.g., P_crit=5%) will automatically calculate the most suitable value for each pressure plate. value.

[0056] Finally, in real-time monitoring, the sub-unit first calculates the relative deviation of the current voltage. Then, the adaptive formula is calculated using the above formula. Value, substitute into the original equation Finally, the current positional certainty is obtained.

[0057] Specifically, the stability calculation subunit 133-3 is used to quantify the analog sensor readings and the health baseline characteristics of the pressure plate based on an adaptive linear penalty model to obtain the stability. The linear penalty model can intuitively reflect the inverse relationship between stability and signal jitter: the greater the relative increment of signal jitter, the more the stability score should decrease linearly. The effect is to obtain a standardized index between 0 and 1 that can quantify the dynamic stability of the pressure plate.

[0058] Specifically: Similarly, in order to achieve the adjustment coefficient The adaptive calculation introduces two core business metrics related to stability: the stability health threshold (T_s_healthy), which represents the minimum stability score allowed for a healthy state, for example, it can be set to 0.9; and the critical relative increment (F_crit), which represents the maximum standard deviation relative increment that a healthy state can tolerate, for example, it can be set to 0.2, meaning that if the signal jitter increases by 20% compared to the baseline, it is considered that it is no longer completely stable.

[0059] Next, establish these business indicators and adjustment coefficients. The objective relationship equation is based on the following logic: when the actual relative increment of standard deviation (ds_rel) is exactly equal to the preset critical relative increment (F_crit), the calculated stability (Cs) should be exactly equal to the preset stability health threshold (T_s_healthy). Substituting this objective into the original stability calculation equation... ,in, The objective equation is obtained as follows: .

[0060] The adjustment coefficient can be obtained by performing a simple algebraic transformation on the objective equation. The calculation formula is as follows: This formula also makes The parameters, which previously required experience-based settings, are now automatically calculated by two explicit business metrics, T_s_healthy and F_crit, thus possessing clear physical meaning. Specifically, by configuring rules such as "stability health threshold of 0.9 and maximum tolerable jitter increase of 20%", the calculation is more efficient than adjusting dimensionless parameters. The coefficients need to be much more clearly defined. Furthermore, The value of F_crit is coordinated with the downstream stability classification threshold T_s_healthy. When business needs change and a stricter or more lenient stability judgment is required, only T_s_healthy or F_crit needs to be modified. This automatically adapts, ensuring the consistency and uniformity of the entire diagnostic logic. Furthermore, by setting a uniform tolerance increment (F_crit) for different types of devices (which may have different inherent noise levels), the diagnostic criteria can be kept consistent throughout the device. That is, the device will automatically calculate a personalized but standardized diagnostic model based on the unique Sbaseline of each device.

[0061] Finally, in real-time monitoring, the sub-unit first calculates the relative increment of the current standard deviation. Then, the adaptive formula is calculated using the above formula. Value, substitute into the original equation This process ultimately yields the current stability level. In this way, the consistency and uniformity of the entire diagnostic logic are ensured; when business requirements change, only the business metrics need to be modified, and the adjustment coefficients will automatically adapt.

[0062] Specifically, the preferred portion described above will be explained using a specific example from this application. In the calculation of location certainty, two core business indicators are first set: the location certainty health threshold (T_p_healthy) is set to 0.95, and the critical relative deviation (P_crit) is set to 0.05. Based on these business requirements, the adaptive adjustment coefficient is first calculated. : =-ln(T_p_healthy) / P_crit=-ln(0.95) / 0.05≈1.026, which The value is automatically generated according to clearly defined business rules. Subsequently, in real-time monitoring, the received current average voltage value... The voltage is 2.85 volts, and the average ideal switching voltage is within the calibrated healthy baseline characteristics. The average ideal disengagement voltage is 3,000 volts. The value is 0.501 volts. First, calculate the relative deviation of the current voltage, dv_rel: =|2.85-3.000| / |3.000-0.501|≈0.06. Finally, adaptive... Calculate the current positional certainty. : The positional certainty calculated through this optimization process is 0.940. This value is significantly higher than the 0.741 calculated using a fixed adjustment coefficient (kp=5). This is because the adaptive model generates a more reasonable adjustment coefficient of 1.026 based on the mild business rule that "the score drops to 95% when the deviation is 5%". Since the current actual deviation (6%) is only slightly above the critical value (5%), its score is also only slightly below the healthy threshold (95%). This result more accurately reflects the sub-healthy state of this slight positional shift and avoids the excessive penalty that might be caused by fixed parameters.

[0063] In the stability calculation, business indicators are first set: the stability health threshold (T_s_healthy) is set to 0.9, and the critical relative increment (F_crit) is set to 0.2. Based on these, the adaptive adjustment coefficient is calculated. : In real-time monitoring, the current standard deviation is received. It is 0.008 volts, and the calibrated reference standard deviation is... It is 0.005 volts. First, calculate the relative increment of the current standard deviation. : =(0.008-0.005) / 0.005=0.6. Finally, use adaptive... The value is used to calculate the current stability. : The stability calculated through this optimization process is 0.7. This value is significantly higher than the 0.4 calculated using a fixed adjustment coefficient (ks=1). This is because the adaptive adjustment coefficient of 0.5 is generated based on the rule that "the score drops to 90% when jitter increases by 20%". The current actual jitter increase is 60%, which is serious, but the penalty term (0.3) calculated under this rule is more reasonable than the original penalty term (0.6). The final score of 0.7 accurately defines it as a moderately unstable state, rather than the previously near-completely unstable 0.4, making the diagnostic results more granular and reliable.

[0064] Specifically, the sub-health state classification subunit 134 is used to intelligently classify and diagnose sub-health states based on health measurement indicators to obtain sub-health state classification results. In other words, although the health measurement indicators output by the health measurement subunit are highly condensed and information-rich structured data, their essence is still a quantitative description geared towards machine analysis. For the final maintenance personnel or automated control logic, a data object such as {judgment status: "investment", location certainty: 0.741, stability: 0.4} cannot directly trigger a clear operation or judgment. To transform this low-level, multi-dimensional numerical assessment into a high-level, directly understandable and usable diagnostic conclusion, this application performs a final step of intelligent decision-making and semantic mapping. Based on a set of preset expert rules or diagnostic models, it performs a final logical judgment on the input health measurement indicators, intelligently classifying and mapping them to a specific sub-health state classification result with clear semantics.

[0065] In detail, the sub-health state classification subunit 134 processes the following: This subunit has a fixed set of rules. The thresholds for these rules are not arbitrarily set, but are determined based on a large amount of experimental data, long-term field operation experience, and a deep understanding of the failure mechanism of the pressure plate. The threshold setting takes into account the safety margin of different application scenarios. For example, in the main protection circuit with extremely high safety requirements, the threshold for the "healthy" state will be set more stringently. In a specific example of this application, the rule set may include the following core rules, which are matched in order of priority from high to low: Rule 1 (Healthy State): If the position determination is greater than 0.95 and the stability is greater than 0.95, the sub-health state classification result is judged as "healthy". This rule defines the pressure plate as being in a near-perfect working state. Rule 2 (Severe Sub-health): If the position determination is less than or equal to 0.7 or the stability is less than or equal to 0.5, the sub-health state classification result is judged as "severe sub-health". This rule is used to capture dangerous situations that have significantly deviated from the normal state and may cause functional failure at any time, and has a high judgment priority. Rule 3 (Vibration Instability): If the stability is greater than 0.5 and less than or equal to 0.7, the sub-health state classification result is determined as "Vibration Instability". This rule is specifically for situations where dynamic stability has decreased but has not yet reached a severe level. Rule 4 (Position Deviation): If the positional certainty is greater than 0.7 and less than or equal to 0.95, the sub-health state classification result is determined as "Position Deviation". This rule is used to identify early symptoms of false landing or false retreat with slight deviations in static position. Rule 5 (Transitioning State): If the determined state is equal to "Transitioning", the sub-health state classification result is determined as "Transitioning".

[0066] Taking the output of the previous step as an example, the input received by this sub-unit is a health metric: {Judgment State: “Voice”, Location Determinism: 0.741, Stability: 0.4}. The classification and diagnosis process begins, and the sub-unit matches rules according to priority. First, rule one is applied: the location determination of the input is 0.741, which does not meet the condition of being greater than 0.95, therefore rule one does not match, and the process continues. Next, rule two, which has the second highest priority, is applied: the stability of the input is 0.4, which meets the condition of being less than or equal to 0.5. The triggering condition of rule two is met. Therefore, the sub-unit immediately classifies the sub-health state as “severe sub-health” and terminates the matching of all subsequent rules. This result accurately reflects that although its macroscopic location is acceptable, i.e., location determination is 0.741, its severe dynamic instability, i.e., stability of only 0.4, already constitutes a major risk.

[0067] Ultimately, the output of the sub-health status classification subunit 134 is a single, semantically clear string: "Severe sub-health". This result is a high-level, concise final judgment on the current health status of the pressure plate, which can be directly displayed on the monitoring interface or used as a direct basis for triggering alarms and prompting maintenance personnel to carry out maintenance.

[0068] Specifically, the health measurement index obtained from the position certainty calculation sub-unit 133-2 and the stability calculation sub-unit 133-3 in the preferred health measurement sub-unit 133 is {judgment state: "cast", position certainty: 0.94, stability: 0.7}. The management unit matches according to the same preset rule priority: Matching rule 1 (healthy state): Condition: position certainty greater than 0.95 and stability greater than 0.95. Judgment: 0.940>0.95 is false. Condition not met. Matching rule 2 (severe sub-health): Condition: position certainty less than or equal to 0.7 or stability less than or equal to 0.5. Judgment: 0.940≤0.7 is false; 0.7≤0.5 is false. Condition not met. Matching rule 3 (vibration instability): Condition: stability greater than 0.5 and less than or equal to 0.7. Judgment: 0.7>0.5 is true; 0.7≤0.7 is true. Condition met. Since the condition of Rule 3 was met, the classification process terminated, and the matching result was output. The final sub-health state classification result is "vibration instability." Compared with the original "severe sub-health" result calculated based on fixed parameters, this new classification result is more accurate and specific. It clearly points out that the main problem of the current pressure plate is insufficient dynamic stability (the stability score of 0.7 falls exactly within the "vibration instability" range), rather than a general, critical, and severe state. This fully demonstrates the advantages of the adaptive model in improving diagnostic accuracy and problem localization capabilities.

[0069] In summary, the intelligent online monitoring device 100 for pressure plates based on the embodiments of this application is explained, comprising a pressure plate monitoring sensor, a convergence node, a management unit, and network equipment. This device aims to address the technical shortcomings of existing pressure plate monitoring technologies, which use switch-type sensors that can only output binary signals for engagement / disengagement, resulting in limited information dimensions and an inability to effectively identify and warn of sub-health conditions such as poor contact or incomplete engagement / disengagement. To achieve this objective, the key to this application lies in employing a pressure plate monitoring sensor capable of acquiring continuous analog readings. The acquired analog data, containing rich features such as position and stability, is completely transmitted to the management unit through the network equipment and convergence node. The management unit no longer performs simple threshold judgments but instead conducts in-depth analysis and intelligent diagnosis of these analog readings. In this way, the device can overcome the limitations of binary logic, achieving quantitative monitoring and accurate early warning of sub-health conditions of the pressure plates, fundamentally improving the depth and reliability of monitoring.

[0070] Various implementations of this disclosure have been described above. The foregoing description is exemplary and not exhaustive. Furthermore, it is not limited to the disclosed implementations, and many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations.

Claims

1. A smart online monitoring device for pressure plates, characterized in that, include: The system includes a pressure plate monitoring sensor, a convergence node, a management unit, and network equipment. The pressure plate monitoring sensor is used to collect analog sensor readings and send them to the convergence node via the network equipment. After receiving the analog sensor readings, the convergence node sends them to the management unit via the network equipment. A management unit is used to monitor and issue early warnings for the sub-health status of the pressure plate based on analog sensor readings. The management unit includes: an analog sensor data acquisition subunit, used to acquire multiple sets of analog sensor readings when the pressure plate is in the ideal engagement and retraction positions; a pressure plate health baseline calibration subunit, used to perform self-learning and calibration of the pressure plate health baseline from multiple sets of analog sensor readings to obtain pressure plate health baseline characteristics; a health quantification subunit, used to perform real-time health quantification of the analog sensor readings based on the pressure plate health baseline characteristics to obtain health quantification indicators; and a sub-health status classification subunit, used to perform intelligent classification and diagnosis of sub-health status based on health quantification indicators to obtain sub-health status classification results. The health quantification subunit includes: a discrete state determination subunit, used to perform discrete state determination on the analog sensor readings and pressure plate health baseline characteristics to obtain a determination state; and a position determination quantification subunit, used to perform position determination quantification on the analog sensor readings and pressure plate health baseline characteristics using the following formula to obtain position determination degree: ;in, This represents the average current voltage reading from the analog sensor. The reference voltage is the average value of the ideal on-state voltage when the judgment state is "on" and the average value of the ideal off-state voltage when the judgment state is "off". The average value of the ideal switching voltage. This represents the average value of the ideal retraction voltage. For adjustment coefficients, The system comprises: a position determination unit; a stability quantification sub-unit, used to quantify the stability of analog sensor readings and pressure plate health baseline characteristics to obtain stability; and an aggregation and encapsulation sub-unit, used to aggregate and encapsulate the determination state, position determination, and stability to obtain health metrics.

2. The intelligent online monitoring device for pressure plates according to claim 1, characterized in that, The pressure plate monitoring sensor is a linear Hall element.

3. The intelligent online monitoring device for pressure plates according to claim 2, characterized in that, The analog sensor readings include timestamps, average voltage, and standard deviation.

4. The intelligent online monitoring device for pressure plates according to claim 1, characterized in that, The baseline characteristics of the pressure plate health include the mean of the ideal engagement voltage, the mean of the ideal engagement standard deviation, the mean of the ideal disengagement voltage, and the mean of the ideal disengagement standard deviation.

5. The intelligent online monitoring device for pressure plates according to claim 4, characterized in that, The discrete state determination secondary subunit includes: an absolute distance calculation tertiary subunit, used to calculate the absolute distance between the current average voltage value in the analog sensor readings and the average ideal engagement voltage value and the average ideal disengagement voltage value to obtain the engagement absolute distance and disengagement absolute distance; a first determination state setting tertiary subunit, used to set the determination state to "engage" in response to the engagement absolute distance being less than the disengagement absolute distance; and a second determination state setting tertiary subunit, used to set the determination state to "disengage" in response to the disengagement absolute distance being less than the engagement absolute distance.

6. The intelligent online monitoring device for pressure plates according to claim 5, characterized in that, The stability quantification subunit is used to: quantify the stability of analog sensor readings and pressure plate health baseline characteristics using the following formula: ;in, This represents the current standard deviation of the analog sensor readings. denoted as the benchmark standard deviation, where, when the judgment status is "throw", the benchmark standard deviation is the mean of the ideal throwing position standard deviation, and when the judgment status is "retreat", the benchmark standard deviation is the mean of the ideal retreat position standard deviation. For adjustment coefficients, To find the maximum value function, For stability.

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