Active detection-based power automatic adaptation control method and system, computer device and storage medium

Abstract

The application relates to the technical field of power supply adaptation and regulation, and specifically discloses a power supply automatic adaptation control method and system based on active detection, computer equipment and a storage medium, which are applied to power supply driving control of a safety clamp coil. By collecting a coil loop electrical parameter set and temperature data, a multi-dimensional feature vector is extracted after filtering processing, the coil specification and working state are dynamically identified, optimal initial excitation power configuration parameters are matched from a pre-stored mapping table according to the identification result, a reference is determined when the matching is not accurate, and fine tuning is combined with the working state, a pulse width modulation excitation signal is generated based on the initial parameters, the duty cycle and frequency are closed-loop optimized, through an in-place detection signal, algorithm parameter optimization and a fault prediction model are executed to perform early warning. The application can realize accurate adaptation of the power supply and the coil specification and dynamic working conditions, improve braking reliability and coil service life, reduce fault downtime loss, adapt to complex installation scenes, and guarantee the operation safety of special equipment.

Description

Technical Field

[0001] This application relates to the field of power adaptation and regulation technology, and in particular to automatic power adaptation control methods, systems, computer equipment and storage media based on active detection. Background Technology

[0002] As a core braking component of special equipment such as elevators and lifting equipment, the reliability of the safety gear's operation is directly related to the safety of the equipment. The safety gear's drive relies on the electromagnetic attraction of the coil. However, safety gear coils produced by different manufacturers have various specifications and models, with significant differences in electrical parameters such as impedance, inductance, and rated current. At the same time, the electrical characteristics of the same coil will dynamically change due to factors such as aging and temperature rise during long-term use.

[0003] In existing technologies, the power supply configuration of safety clamp coils mainly adopts fixed parameter control, that is, by manually preset excitation parameters such as pulse width modulation duty cycle and frequency. This method cannot be adapted to coils of different specifications and models, and is prone to problems such as insufficient excitation leading to delayed safety clamp action, or excessive excitation causing coil overheating and damage. In addition, there is also control based on simple parameter matching, which selects the power supply configuration only by a single electrical parameter, ignoring the transient response characteristics and real-time working status of the coil, and does not consider the influence of external factors such as power grid fluctuations and mechanical jamming, resulting in low matching accuracy between power supply and coil.

[0004] Furthermore, existing technologies lack comprehensive monitoring and fault warning mechanisms for coil operating conditions, relying solely on temperature sensors or current thresholds for simple protection. This fails to identify potential faults such as inter-turn short circuits and mechanical jamming in advance, often leading to sudden equipment shutdowns. These issues make existing power control methods ill-suited for the high-precision, high-reliability requirements of safety clamps under complex operating conditions, thus hindering the improvement of safety protection levels for special equipment.

[0005] Therefore, there is an urgent need for power supply automatic adaptation control methods, systems, computer equipment, and storage media based on active detection to solve the above problems. Summary of the Invention

[0006] The purpose of this application is to provide a power supply automatic adaptation control method based on active detection, comprising the following steps:

[0007] Real-time acquisition and monitoring of electrical parameter sets of the circuit electrically connected to the safety clamp coil, as well as coil temperature data;

[0008] Based on the electrical parameter set, the specifications and working status of the currently connected safety clamp coil are dynamically identified through a pre-built coil feature recognition model.

[0009] Based on the identified coil specifications and operating status, the optimal initial excitation power supply configuration parameters are automatically matched and loaded from the pre-stored power supply configuration mapping table.

[0010] A pulse width modulation excitation signal is generated based on the initial excitation power supply configuration parameters. The duty cycle and frequency of the pulse width modulation excitation signal are then adjusted in a closed loop using a dynamic adjustment algorithm based on the real-time monitored electrical parameter set and temperature data.

[0011] The system receives status signals from the positioning detection sensors on the safety clamp via power line carrier communication, and optimizes the parameters of the dynamic adjustment algorithm based on these status signals and real-time electrical parameters, and performs fault diagnosis and early warning.

[0012] Furthermore, the step of real-time acquisition and monitoring of the electrical parameter set of the circuit electrically connected to the safety clamp coil and the coil's temperature data includes:

[0013] At a preset high-frequency sampling rate, the instantaneous voltage and instantaneous current values ​​flowing through the safety clamp coil are simultaneously acquired, and based on the instantaneous voltage and instantaneous current values, the rate of change of current and the real-time impedance of the coil within a time window are calculated.

[0014] By integrating a temperature sensor into or near the coil circuit, the real-time temperature value of the coil is obtained, and the rate of temperature rise per unit time is calculated.

[0015] The acquired raw instantaneous voltage and current values ​​are digitally filtered to eliminate high-frequency noise interference. The digital filtering process uses a finite impulse response filter based on a window function, and the signal is smoothed by convolving the sampled sequence with predefined filter coefficients.

[0016] The filtered instantaneous voltage value, the filtered instantaneous current value, the calculated current change rate, the real-time impedance, the real-time temperature value, and the temperature rise rate are collectively used to form the electrical parameter set and temperature data.

[0017] Furthermore, the step of dynamically identifying the specifications and operating status of the currently connected safety clamp coil based on the electrical parameter set and through a pre-built coil feature recognition model includes:

[0018] Feature vectors are extracted from the electrical parameter set. The feature vectors include steady-state impedance value, peak value of transient inrush current, time constant for current to reach peak value, and phase difference under specific frequency excitation.

[0019] The feature vector is input into a pre-trained coil feature recognition model. The Euclidean distance between the input feature vector and the center vector of the hidden layer node is calculated. A nonlinear transformation is performed by applying the radial basis function. The model is then weighted and summed using the connection weights from the hidden layer to the output layer to obtain the probability output of each specification model.

[0020] The coil feature recognition model uses the model with the highest probability as the dynamic recognition result based on the calculated probabilities of each specification and model.

[0021] Based on the deviation between the real-time impedance and the steady-state impedance, and the comparison between the real-time temperature and the preset safe temperature threshold, the working state of the coil is comprehensively judged as normal, overheated, or has a potential fault. Specifically, if the impedance deviation is less than the preset impedance deviation threshold and the real-time temperature is lower than the safe temperature threshold, the state is normal. If the real-time temperature reaches or exceeds the safe temperature threshold, the state is overheated. If the impedance deviation reaches or exceeds the preset impedance deviation threshold and the real-time temperature is lower than the safe temperature threshold, the state is a potential fault.

[0022] Furthermore, the step of automatically matching and loading the optimal initial excitation power supply configuration parameters from the pre-stored power supply configuration mapping table based on the identified coil specifications and operating status includes:

[0023] The pre-stored power configuration mapping table is stored in the form of a data dictionary. Its key is the coil specification model and the value is the corresponding optimal initial excitation power configuration parameter group. The parameter group includes the initial pulse width modulation duty cycle, the initial pulse width modulation frequency, the excitation voltage upper limit, and the current protection threshold.

[0024] Based on the dynamically identified coil specifications and model, a search is performed in the power configuration mapping table. If a match is found, the corresponding optimal initial excitation power configuration parameter group is read.

[0025] If an exact match is not found, a fuzzy matching algorithm is started to calculate the Euclidean distance between the currently identified coil feature vector and the typical feature vectors corresponding to each model in the mapping table, and the configuration parameter group corresponding to the model with the smallest distance is selected as the loading reference.

[0026] Based on the determined coil operating state, the initial configuration parameters of the load are fine-tuned. If the state is overheating, the initial pulse width modulation duty cycle is reduced proportionally and the initial pulse width modulation frequency is increased. If the state is a potential fault, a more stringent current protection threshold is set.

[0027] The finalized initial excitation power supply configuration parameter set is loaded into the pulse width modulation signal generator and power drive circuit to complete the initial power supply adaptation.

[0028] Furthermore, the step of generating a pulse width modulation excitation signal based on the initial excitation power supply configuration parameters and, based on the real-time monitored electrical parameter set and temperature data, performing closed-loop adjustment of the duty cycle and frequency of the pulse width modulation excitation signal through a dynamic adjustment algorithm includes:

[0029] A pulse width modulation excitation signal is generated based on the initial configuration parameters to drive the safety clamp coil;

[0030] Real-time monitoring of coil impedance and current change rate, and calculation of their deviation from the ideal characteristic curve of the corresponding model;

[0031] An incremental PID controller is used to calculate the pulse width modulation duty cycle adjustment in real time based on the deviation.

[0032] The pulse width modulation frequency is dynamically adjusted according to the real-time temperature and temperature rise rate. When the temperature continues to rise and the temperature rise rate exceeds the limit, the operating frequency is automatically increased to reduce losses.

[0033] The calculated adjustment amount is applied to the current pulse width modulation signal to achieve closed-loop control.

[0034] Furthermore, the step of receiving the status signal sent by the positioning detection sensor on the safety clamp via power line carrier communication, and optimizing the parameters of the dynamic adjustment algorithm based on the status signal and real-time electrical parameters, and performing fault diagnosis and early warning includes:

[0035] The bit detection signal is continuously received via power line carrier communication;

[0036] When a signal indicating that the coil is in position is received, the coil's operating status is determined and the pulse width modulation parameters are maintained or finely adjusted accordingly.

[0037] If no arrival signal is received within a preset time and an abnormal electrical parameter is detected, a high-voltage pulse sequence is output to overcome the jamming.

[0038] A fault prediction model is established based on historical anomaly data, and the probability of faults is predicted through real-time parameters.

[0039] When the probability of failure exceeds the limit, an early warning is generated and a safety policy is implemented.

[0040] Furthermore, this application also discloses a power automatic adaptation control system based on active detection, including:

[0041] The acquisition module is used to acquire and monitor in real time the electrical parameter set of the circuit electrically connected to the safety clamp coil and the coil temperature data;

[0042] The identification module is used to dynamically identify the specifications and working status of the currently connected safety clamp coil based on the electrical parameter set and a pre-built coil feature identification model.

[0043] The matching module is used to automatically match and load the optimal initial excitation power supply configuration parameters from the pre-stored power supply configuration mapping table based on the identified coil specifications and operating status.

[0044] The adjustment module is used to generate a pulse width modulation excitation signal based on the initial excitation power supply configuration parameters, and to perform closed-loop adjustment of the duty cycle and frequency of the pulse width modulation excitation signal through a dynamic adjustment algorithm based on the real-time monitored electrical parameter set and temperature data.

[0045] The diagnostic module is used to receive status signals sent by the positioning detection sensors on the safety clamp via power line carrier communication, and optimize the parameters of the dynamic adjustment algorithm based on the status signals and real-time electrical parameters, and perform fault diagnosis and early warning.

[0046] Furthermore, the matching module includes:

[0047] The storage unit is used to store the pre-stored power configuration mapping table in the form of a data dictionary. Its key is the coil specification model and the value is the corresponding optimal initial excitation power configuration parameter group. The parameter group includes the initial pulse width modulation duty cycle, the initial pulse width modulation frequency, the excitation voltage upper limit and the current protection threshold.

[0048] The identification unit is used to search the power configuration mapping table according to the dynamically identified coil specifications and model. If a match is found, the corresponding optimal initial excitation power configuration parameter group is read.

[0049] The calculation unit is used to start the fuzzy matching algorithm if a precise match is not possible, calculate the Euclidean distance between the currently identified coil feature vector and the typical feature vectors corresponding to each model in the mapping table, and select the configuration parameter group corresponding to the model with the smallest distance as the loading reference.

[0050] The fine-tuning unit is used to fine-tune the initial configuration parameters of the load according to the determined coil operating state. If the state is overheating, the initial pulse width modulation duty cycle is reduced proportionally and the initial pulse width modulation frequency is increased. If the state is potential fault, a stricter current protection threshold is set.

[0051] The loading unit is used to load the final determined initial excitation power supply configuration parameter set into the pulse width modulation signal generator and power drive circuit to complete the initial power supply adaptation.

[0052] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described power automatic adaptation control method based on active detection.

[0053] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described power automatic adaptation control method based on active detection.

[0054] The beneficial effects of this application are as follows:

[0055] Firstly, this application has high adaptation accuracy and covers multiple coil specifications. It can accurately identify coil specifications and models by extracting multi-dimensional feature vectors such as coil steady-state impedance and transient inrush current peak, combined with a radial basis function neural network multi-classifier. With the help of a fuzzy matching algorithm, it covers new coil models outside the mapping table, which solves the adaptation deviation problem caused by traditional fixed parameter or single parameter matching, thereby improving the accuracy of model identification and ensuring the accurate correspondence between power supply configuration and coil specifications.

[0056] Secondly, this application can dynamically adapt to operating conditions and ensure operational safety. Based on real-time electrical parameters and temperature data, it uses an incremental PID controller to adjust the pulse width modulation duty cycle in a closed loop and dynamically optimizes the frequency by combining the temperature rise rate. This achieves real-time matching between the power output and the dynamic operating conditions of the coil, avoiding the problems of insufficient excitation or overheating caused by coil aging and temperature rise in traditional fixed control. It keeps the coil temperature rise rate within 2℃ / s and keeps the operating temperature stable below the safety threshold.

[0057] Third, the fault warning of this application is timely, reducing downtime losses. By receiving the position detection signal through power line carrier communication and combining it with real-time electrical parameters to build a fault prediction model, the risk of coil faults and mechanical jamming can be predicted in advance, improving the accuracy of fault probability prediction. At the same time, for minor jamming, a high-voltage pulse sequence is output for proactive processing, which reduces downtime caused by faults and lowers equipment maintenance costs and operational risks. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of a method flow proposed in an embodiment of this application.

[0059] Figure 2 This is a schematic diagram of the system structure proposed in one embodiment of this application.

[0060] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of this application.

[0061] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0062] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0063] like Figure 1 As shown, this application provides a power supply automatic adaptation control method based on active detection, including the following steps:

[0064] S1, real-time acquisition and monitoring of electrical parameter sets of the circuit electrically connected to the safety clamp coil and coil temperature data;

[0065] S2, Based on the electrical parameter set, the specifications and working status of the currently connected safety clamp coil are dynamically identified through a pre-built coil feature recognition model;

[0066] S3, based on the identified coil specifications and operating status, automatically matches and loads the optimal initial excitation power supply configuration parameters from the pre-stored power supply configuration mapping table;

[0067] S4. A pulse width modulation excitation signal is generated based on the initial excitation power supply configuration parameters. The duty cycle and frequency of the pulse width modulation excitation signal are then adjusted in a closed loop according to the real-time monitored electrical parameter set and temperature data through a dynamic adjustment algorithm.

[0068] S5 receives the status signal sent by the positioning detection sensor on the safety clamp via power line carrier communication, and optimizes the parameters of the dynamic adjustment algorithm based on the status signal and real-time electrical parameters, and performs fault diagnosis and early warning.

[0069] As described in steps S1-S5 above, the safety clamp is a critical safety component. Its coils come in various specifications and models, with significant differences in electrical characteristics between different specifications. Furthermore, factors such as coil temperature changes and wear during operation can cause continuous fluctuations in the operating state. If the power supply configuration is mismatched with the coil, insufficient excitation can lead to safety clamp failure, or excessive excitation can cause coil overheating and damage, increasing energy consumption. Therefore, dynamic matching between the power supply and the coil is necessary to ensure reliable safety clamp response. Existing technologies often use fixed power supply configuration parameters or rely on manual configuration switching, which cannot adapt to the connection requirements of different coil specifications and cannot respond to changes in the coil's operating state in real time. Fixed parameter solutions are prone to adaptation deviations when connecting different coil specifications, and manual switching methods suffer from lag and cannot promptly handle real-time anomalies during coil operation, resulting in poor safety clamp operation stability and a high failure rate.

[0070] This application achieves automatic and precise matching between the power supply and the safety clamp coil by real-time acquisition and monitoring of relevant electrical parameters and temperature data of the safety clamp coil, dynamic identification of coil specifications and working status, automatic matching of optimal initial power supply configuration parameters, closed-loop adjustment of pulse width modulation excitation signal, and combination of position detection signal optimization algorithm and fault early warning, thus ensuring stable and reliable operation of the safety clamp under different working conditions.

[0071] This application achieves dynamic adaptation between the power supply and the safety clamp coil through a progressive process of data acquisition, state identification, parameter matching, closed-loop adjustment, and optimization early warning. First, it collects the electrical parameter set and temperature data of the coil circuit in real time. Based on this electrical parameter set, a coil feature recognition model constructed using a radial basis function neural network extracts feature vectors such as steady-state impedance and transient inrush current peak value to dynamically identify the coil specifications and operating status, clarifying the target object attributes for power supply adaptation. Then, based on the identification results, it automatically matches the optimal initial excitation power supply configuration parameters from a pre-stored power supply configuration mapping table. If an exact match is not found, a baseline is determined through fuzzy matching, and fine-tuning is performed in conjunction with the operating status to complete the initial power supply adaptation. The system adapts to the initial parameters and then generates a PWM excitation signal. Based on real-time monitored electrical parameters and temperature data, it uses an incremental PID controller to adjust the PWM duty cycle and frequency in a closed loop to ensure that the power output matches the real-time requirements of the coil. Finally, it receives the position detection sensor signal through power line carrier communication and optimizes the dynamic adjustment algorithm parameters in combination with real-time electrical parameters. At the same time, it builds a fault prediction model based on historical abnormal data and executes early warning and safety strategies when the fault probability exceeds the limit. The overall principle is to solve the problems of poor adaptability and lag response of traditional fixed power supply configurations by actively detecting real-time data, dynamically identifying the status, and adaptively matching and adjusting parameters, thus ensuring the stable and reliable operation of the safety clamp.

[0072] In one embodiment, the step of real-time acquisition and monitoring of the electrical parameter set of the circuit electrically connected to the safety clamp coil and the coil's temperature data includes:

[0073] S11, at a preset high-frequency sampling rate, synchronously acquire the instantaneous voltage and instantaneous current values ​​flowing through the safety clamp coil, and based on the instantaneous voltage and instantaneous current values, calculate the rate of change of current and the real-time impedance of the coil within a time window;

[0074] S12 obtains the real-time temperature value of the coil by means of a temperature sensor integrated in or near the coil circuit, and calculates the rate of temperature rise per unit time.

[0075] S13, the acquired raw instantaneous voltage and instantaneous current values ​​are digitally filtered to eliminate high-frequency noise interference; the digital filtering process uses a finite impulse response filter based on a window function, and the signal is smoothed by convolving the sampled sequence with predefined filter coefficients;

[0076] S14. The filtered instantaneous voltage value, the filtered instantaneous current value, the calculated current change rate, the real-time impedance, the real-time temperature value, and the temperature rise rate are combined to form the electrical parameter set and temperature data, and stored in a circular buffer for subsequent analysis.

[0077] As described in steps S11-S14 above, by acquiring the electrical parameters of the coil circuit at high frequency, obtaining coil temperature data, performing digital filtering on the raw data, and constructing and storing a complete dataset, accurate and real-time monitoring of the basic operating data of the safety clamp coil can be achieved.

[0078] The electrical parameters of a safety clamp coil directly reflect its electrical characteristics. Coils of different specifications and models will exhibit different rates of current change and impedance values ​​under the same excitation. Coil temperature data, such as real-time temperature and temperature rise rate, are related to its heat loss and insulation status. If this basic data is not collected in a timely manner, is inaccurate, or is subject to noise interference, it will lead to deviations in subsequent coil feature identification, resulting in a mismatch between power supply configuration parameters and the actual needs of the coil, causing problems such as insufficient coil excitation or overheating damage. At the same time, the coil is affected by power grid harmonics and electromagnetic interference during operation, and the raw data is easily mixed with high-frequency noise, which will further amplify the identification error if not processed.

[0079] The instantaneous voltage and current values ​​flowing through the safety clamp coil are synchronously acquired using a preset high-frequency sampling rate of 1kHz. This sampling rate can completely capture the transient inrush current changes within 0-10ms of the coil being energized. Synchronous acquisition is achieved by setting a unified clock trigger signal in the acquisition circuit to ensure that the voltage and current data at the same moment correspond and match. Based on the acquired instantaneous voltage and current values, a 10ms time window is selected. The current change rate is obtained by calculating the ratio of the current difference between adjacent sampling points within this window to the time interval. The real-time impedance of the coil is obtained by the ratio of the instantaneous voltage and current values ​​at the same moment. The transient response characteristics and steady-state electrical parameters of the coil can be obtained simultaneously. For example, when a coil of a certain specification is connected, the 1kHz sampling rate can accurately record the entire process of the current rising from 0 to the peak value at the moment of energization. The current change rate calculated in the 10ms time window can distinguish the response differences of this coil from other coils of different specifications, and the real-time impedance provides a basis for subsequent state judgment.

[0080] The real-time temperature value of the coil is obtained by an NTC temperature sensor integrated into the coil circuit. The measurement accuracy of the temperature sensor is controlled within ±0.5℃ to ensure the accuracy of the temperature data. Based on the ratio of the difference between the real-time temperature values ​​of two consecutive sampling periods to the sampling period, the temperature rise rate per unit time is calculated. For example, when the coil temperature rises from 40℃ to 45℃ due to excessive excitation, and this process takes 200ms, the temperature rise rate is 25℃ / s. This allows for timely detection of abnormal coil heat loss and avoids insulation aging due to excessive temperature.

[0081] The acquired raw instantaneous voltage and current values ​​are digitally filtered using a finite impulse response filter based on a Hanning window. The filter order is set to 16, and the predefined filter coefficients are obtained through offline calculation. Specifically, the sampling sequence and the predefined coefficients are calculated point by point according to the convolution operation rules to suppress high-frequency noise in the raw data, such as the 200Hz interference signal derived from the 50Hz harmonic of the power grid. The fluctuation amplitude of the filtered data is controlled within 5% of the raw data, ensuring that the subsequently extracted steady-state impedance value, transient current peak value and other characteristic parameters can truly reflect the physical characteristics of the coil and avoid characteristic distortion caused by noise.

[0082] The filtered instantaneous voltage value, filtered instantaneous current value, calculated current change rate, real-time impedance, real-time temperature value, and temperature rise rate together constitute the electrical parameter set and temperature data. These data are stored in a circular buffer in time sequence. The circular buffer adopts a first-in-first-out storage rule to ensure that the latest acquired data can cover the oldest expired data, while meeting the needs of the subsequent coil feature recognition module for rapid retrieval of data within the most recent 1 second. This avoids the impact of data storage delay on recognition efficiency. The final complete data set can provide comprehensive and reliable basic support for subsequent steps, ensuring the accuracy of the entire power adaptation process.

[0083] In one embodiment, the step of dynamically identifying the specifications and operating status of the currently connected safety clamp coil based on the electrical parameter set and a pre-built coil feature identification model includes:

[0084] S21, extract feature vectors from the electrical parameter set. The feature vectors include steady-state impedance value, peak value of transient inrush current, time constant when current reaches peak value, and phase difference under specific frequency excitation.

[0085] S22, The feature vector is input into the pre-trained coil feature recognition model. The coil feature recognition model adopts a multi-classifier based on radial basis function neural network. It calculates the Euclidean distance between the input feature vector and the center vector of the hidden layer node, applies the radial basis function for nonlinear transformation, and performs weighted summation through the connection weights from the hidden layer to the output layer to obtain the probability output of each specification model.

[0086] S23, the coil feature recognition model uses the model corresponding to the maximum probability as the dynamic recognition result based on the calculated probabilities of each specification and model.

[0087] S24. Based on the deviation between the real-time impedance and the steady-state impedance value, and the comparison between the real-time temperature value and the preset safe temperature threshold, the coil's operating state is comprehensively judged as normal, overheated, or has a potential fault. If the impedance deviation is less than the preset impedance deviation threshold and the real-time temperature value is lower than the safe temperature threshold, the state is normal. If the real-time temperature value reaches or exceeds the safe temperature threshold, the state is overheated. If the impedance deviation reaches or exceeds the preset impedance deviation threshold and the real-time temperature value is lower than the safe temperature threshold, the state is a potential fault.

[0088] As described in steps S21-S24 above, by extracting multi-dimensional feature vectors from the electrical parameter set and inputting them into a pre-trained radial basis function neural network multi-classifier, the specifications and models of the safety clamp coil are dynamically identified. The coil's working status is then comprehensively judged by combining real-time impedance deviation and temperature data, thereby achieving accurate identification of the coil's attributes and operating status.

[0089] The specifications and model of a safety clamp coil directly determine its electrical characteristics, such as impedance, current carrying capacity, and response speed. Different specifications of coils require different power supply excitation parameters, such as pulse width modulation duty cycle and frequency. If the model cannot be accurately identified, subsequent power supply configuration will be mismatched, such as configuring a low-impedance coil with high current excitation, leading to overheating. Simultaneously, the coil's operating state can be categorized as normal, overheated, and potentially faulty, reflecting its current ability to withstand specific excitation intensities. Overheating easily leads to aging of the coil insulation layer, while potential faults cause abnormal electrical characteristics. Configuring a power supply solely based on the model while ignoring the state will exacerbate coil losses or create safety risks. The coil's electrical parameter set, such as steady-state impedance and transient current, are the physical identifiers of its specifications, while real-time impedance deviation and temperature are the physical characteristics of its state. Therefore, accurate identification must be achieved through the analysis of these physical quantities to solve the problem of blind matching caused by unclear model and unknown state.

[0090] Feature vectors are extracted from the electrical parameter set. These feature vectors include the steady-state impedance value, the peak value of the transient inrush current, the time constant for the current to reach its peak value, and the phase difference under specific frequency excitation. The electrical parameter set comes from data stored in a circular buffer. The steady-state impedance value is taken as the arithmetic mean of the real-time impedances over one second after filtering. For example, if the real-time impedances of a coil over one second are 10.1Ω, 9.9Ω, and 10.0Ω, the steady-state impedance value will be 10.0Ω, ensuring that the electrical characteristics of the coil under stable operation are reflected. The formula for calculating the steady-state impedance value is:

[0091] ;

[0092] in, This represents the steady-state impedance value, where N represents the total number of periods used to calculate the steady-state value, and the value is the number of sampling periods corresponding to the preset time window. This represents the real-time impedance over N consecutive cycles, derived from the previously calculated real-time impedance. The value of k starts at 1 and ends at the preset total number of cycles N. The peak transient inrush current is the maximum value of the filtered current within the initial 0-10 milliseconds after coil energization. For example, if the current reaches 5A within 3 milliseconds after energization, and this is the maximum value during that period, the peak value is 5A, reflecting the coil's transient response capability. The time constant for the current to reach its peak value is determined by fitting the current rise curve from 0 to its peak value, reflecting the differences in the coil's inductance characteristics. The phase difference under specific frequency excitation is determined by applying a 50Hz standard sinusoidal excitation signal to the coil and measuring the phase difference between the filtered voltage and current. If the measured voltage leads the current by 5°, the phase difference is 5°, supplementing the coil's frequency response characteristics. These characteristics characterize the coil's physical properties from three dimensions: steady-state, transient, and frequency. The characteristic vectors of different coil models differ significantly.

[0093] The feature vectors are input into a pre-trained radial basis function neural network (RBF) multi-classifier. This network has 4 input layer nodes, corresponding to 4 feature dimensions. The number of hidden layer nodes is set according to the number of coil models; for example, if the system supports 5 models, 10 hidden layer nodes are used. The number of output layer nodes equals the number of models. During pre-training, 100 typical feature vectors for each coil model are collected as samples. The center vectors of the hidden layer nodes are optimized using gradient descent. Each hidden layer node corresponds to a center vector; for example, the center vector of a hidden layer node is [10Ω, 5A, 3ms, 5°], which is connected to the output layer weights. During operation, the Euclidean distance between the input feature vector and the center vector of each hidden layer node is first calculated. For example, the distance between the input vector [10.2Ω, 4.9A, 3.1ms, 4.8°] and the aforementioned center vector is 0.3. Then, the distance is substituted into a Gaussian radial basis function for nonlinear transformation to obtain the hidden layer output value. Finally, the hidden layer outputs and connection weights are weighted and summed. For example, if a hidden layer output is 0.9 and the corresponding connection weight is 0.8, the contribution value is 0.72. After normalization using the softmax function, the probability outputs for each model are obtained, such as model A with a probability of 0.93 and model B with a probability of 0.04. By using nonlinear transformation to handle the small differences in feature vectors, the ability to distinguish similar features is improved compared to traditional linear classifiers, ensuring that the model identification accuracy is also improved simultaneously.

[0094] The coil feature recognition model uses the model number corresponding to the highest probability as the identification result. For example, if the probability of model A in the output probability is 0.93, which is the highest probability, then the currently connected coil is determined to be model A. By ranking the probabilities, the identification result is clearly defined, avoiding the ambiguity of no matching model or multiple matching models that may occur with traditional threshold judgment.

[0095] The operating status is determined by comparing the deviation between real-time impedance and steady-state impedance, and the real-time temperature value with a preset safe temperature threshold. The real-time impedance is derived from an electrical parameter set, and the real-time temperature value is derived from data collected by a temperature sensor. The deviation is calculated as follows:

[0096] ;

[0097] in, Indicates the percentage of impedance deviation. Indicates real-time impedance. This indicates the steady-state impedance value, with a preset impedance deviation threshold of ±10% and a preset safe temperature threshold of 85℃. If the real-time impedance is 10.5Ω, the steady-state impedance is 10.0Ω, the deviation is 5% (less than 10%), and the real-time temperature is 75℃ (less than 85℃), the status is normal. If the real-time temperature is 86℃ (more than 85℃), regardless of the impedance deviation, the status is overheating. If the real-time impedance is 8.8Ω, the steady-state impedance is 10.0Ω, the deviation is -12% (less than -10%), and the real-time temperature is 70℃ (less than 85℃), the status is a potential fault. This dual-judgment system covers both abnormal thermal load and abnormal electrical characteristics of the coil, identifying potential faults that traditional temperature-based judgments cannot detect, increasing the fault detection rate to over 95%, and proactively avoiding the risk of coil damage.

[0098] In one embodiment, the step of automatically matching and loading the optimal initial excitation power supply configuration parameters from a pre-stored power supply configuration mapping table based on the identified coil specifications and operating status includes:

[0099] S31, the pre-stored power configuration mapping table is stored in the form of a data dictionary. Its key is the coil specification model and the value is the corresponding optimal initial excitation power configuration parameter group. The parameter group includes the initial pulse width modulation duty cycle, the initial pulse width modulation frequency, the excitation voltage upper limit and the current protection threshold.

[0100] S32, based on the dynamically identified coil specifications and model, search in the power configuration mapping table. If a match is found, read the corresponding optimal initial excitation power configuration parameter group.

[0101] S33. If a precise match is not achieved, the fuzzy matching algorithm is activated to calculate the Euclidean distance between the currently identified coil feature vector and the typical feature vectors corresponding to each model in the mapping table. The configuration parameter group corresponding to the model with the smallest distance is selected as the loading reference.

[0102] S34, based on the determined coil operating state, fine-tune the initial configuration parameters of the load. If the state is overheating, the initial pulse width modulation duty cycle is reduced proportionally and the initial pulse width modulation frequency is increased. If the state is a potential fault, a stricter current protection threshold is set.

[0103] S35 loads the final determined initial excitation power supply configuration parameter set into the pulse width modulation signal generator and power drive circuit to complete the initial power supply adaptation.

[0104] As described in steps S31-S35 above, by pre-storing a power configuration mapping table constructed in the form of a data dictionary, and combining the precise matching and fuzzy matching mechanism of coil specifications and models, the initial configuration parameters are then finely adjusted according to the coil's working state. Finally, the adapted initial excitation power configuration parameters are loaded into the execution unit, realizing the initial precise adaptation between the power supply and the safety clamp coil, laying a reliable parameter foundation for the subsequent generation and closed-loop adjustment of the pulse width modulation excitation signal.

[0105] The specifications of the safety clamp coil directly determine its inherent electrical characteristics. Different coil models have significant differences in impedance and inductance values. For example, model A coil has an impedance of 10Ω and an inductance of 5mH, while model B coil has an impedance of 15Ω and an inductance of 8mH. These differences require different initial excitation power supply parameters. If a low-impedance model A coil is configured with a high duty cycle parameter, the current will exceed the carrying capacity, causing overheating. If a high-impedance model B coil is configured with a low duty cycle parameter, insufficient excitation will prevent the safety clamp from operating normally. At the same time, the coil's operating state reflects its current load limit. Under overheating conditions, the coil's heat dissipation capacity decreases, requiring a reduction in power input to reduce heat generation. Under potential fault conditions, the coil's electrical characteristics are unstable, requiring a tightening of the current protection threshold to prevent the fault from escalating. Therefore, it is necessary to match the initial parameters by combining both the specifications and the operating state to solve the problem of functional failure or equipment damage caused by the mismatch between parameters and coil characteristics.

[0106] The pre-stored power configuration mapping table is stored in the system's non-volatile memory in the form of a data dictionary. The keys are the coil specifications (e.g., Model X, Model Y, Model Z), and the values ​​are the corresponding optimal initial excitation power configuration parameter sets. The initial pulse width modulation (PWM) duty cycle, initial PWM frequency, excitation voltage upper limit, and current protection threshold in these parameter sets are all obtained through offline experimental calibration. For each coil specification, the response time and temperature rise under different parameter combinations are tested, and parameters with a response time ≤ 50ms and a temperature rise ≤ 10℃ within 1 hour are selected as the optimal values. For example, the optimal parameter set for Model X coil is an initial PWM duty cycle of 50%, an initial PWM frequency of 1kHz, an excitation voltage upper limit of 12V, and a current protection threshold of 5A. The optimal parameter set for Model Y coil is an initial PWM duty cycle of 40%, an initial PWM frequency of 1.2kHz, an excitation voltage upper limit of 12V, and a current protection threshold of 4A. These parameter sets are pre-written into the data dictionary to ensure a clear basis for subsequent matching.

[0107] Based on the dynamically identified coil specifications (e.g., model X), a keyword search is performed in the power configuration mapping table. If a key (model X) that perfectly matches the identified model is found, the corresponding value is directly read, such as the 50% duty cycle and 1kHz frequency parameter set. This process requires no manual intervention, reducing configuration time and avoiding the error rate of manual input, ensuring accurate correspondence between parameters and model. For example, when identified as model X, the read 50% duty cycle enables the coil to output 4.8A current at 12V, which perfectly meets the safety clamp operation requirements and allows for controllable temperature rise.

[0108] If the dynamically identified coil specification model does not find a completely matching key in the power configuration mapping table, such as the new model M, a fuzzy matching algorithm is activated. This algorithm calls the extracted current coil feature vector, such as steady-state impedance 12Ω, transient surge current peak 4.5A, time constant for current to reach peak value 3.2ms, and phase difference under specific frequency excitation 4.5°, and calculates the Euclidean distance between it and the corresponding typical feature vectors of each model in the mapping table, such as the typical feature vector of model X [10Ω, 5A, 3ms, 5°] and the typical feature vector of model Y [15Ω, 4A, 3.5ms, 4°]. During the calculation, each feature dimension is first normalized, and then the distance is calculated. For example, if the distance between the feature vector of model M and the typical vector of model X is 0.3, and the distance between the feature vector of model M and the typical vector of model Y is 0.8, then the configuration parameter group corresponding to model X with the smallest distance, such as 50% duty cycle and 1kHz frequency, is selected as the loading reference. This mechanism allows the system to adapt to new coil models outside the mapping table without requiring system downtime to update the mapping table, improving system compatibility and continuous operation capability.

[0109] Based on the determined coil operating state, the initial configuration parameters are fine-tuned: If the operating state is overheating (real-time temperature ≥ 85℃ safety temperature threshold), the initial pulse width modulation duty cycle is reduced by 10%, for example, from 50% to 45% for model X. Simultaneously, the initial pulse width modulation frequency is increased by 200Hz, for example, from 1kHz to 1.2kHz. Reducing the duty cycle decreases the power input per unit time, lowering the coil heating rate, while increasing the frequency reduces the coil's switching losses. This dual adjustment reduces the coil temperature rise rate from 2℃ / s during overheating to below 1℃ / s. If the operating state is a potential fault (impedance deviation ≥ 10% of the preset impedance deviation threshold and temperature is normal), the current protection threshold is reduced by 10%, for example, from 5A to 4.5A for model X. A stricter threshold prevents abnormal current increases during fault conditions, which could exacerbate inter-turn short circuits. Through state fine-tuning, the initial parameters are adapted to the coil's current load capacity, avoiding the risk of damage caused by parameter mismatch with the state.

[0110] The finalized initial excitation power supply configuration parameters, such as the overheat-adjusted parameters for model X (45% duty cycle, 1.2kHz frequency, 12V upper voltage limit, 4.5A current threshold), are loaded into the pulse width modulation signal generator and power drive circuit via the system's internal bus. The pulse width modulation signal generator generates corresponding pulse signals based on the parameters, and the power drive circuit adjusts its output capability according to the upper voltage limit and current threshold, completing the initial power supply adaptation. This ensures that the initial excitation signal output by the power supply accurately matches the coil's specifications and condition, providing a stable starting point for subsequent closed-loop regulation and ensuring that the safety clamp operates normally and the coil operates within a safe range.

[0111] In one embodiment, the step of generating a pulse width modulation excitation signal based on the initial excitation power supply configuration parameters and, based on real-time monitored electrical parameter sets and temperature data, performing closed-loop adjustment of the duty cycle and frequency of the pulse width modulation excitation signal using a dynamic adjustment algorithm includes:

[0112] S41, generates a pulse width modulation excitation signal based on the initial configuration parameters of the load to drive the safety clamp coil;

[0113] S42 monitors the real-time impedance and current change rate of the coil in real time and calculates the deviation from the ideal characteristic curve of the corresponding model.

[0114] S43, adopts an incremental PID controller, calculates the pulse width modulation duty cycle adjustment amount in real time according to the deviation, and performs weighted calculation based on the comprehensive deviation value of the current and historical moments using preset control coefficients when calculating the pulse width modulation duty cycle adjustment amount;

[0115] S44 dynamically adjusts the pulse width modulation frequency according to the real-time temperature and temperature rise rate. When the temperature continues to rise and the temperature rise rate exceeds the limit, it automatically increases the operating frequency to reduce losses.

[0116] S45 applies the calculated adjustment amount to the current pulse width modulation signal to achieve closed-loop control.

[0117] As described in steps S41-S45 above, a pulse width modulation excitation signal is generated based on the initial excitation power supply configuration parameters. Then, relying on the real-time monitored electrical parameter set and temperature data, a dynamic adjustment algorithm is used to perform closed-loop adjustment of the duty cycle and frequency of the pulse width modulation excitation signal. This achieves precise matching between the power supply output and the real-time operating conditions of the safety clamp coil, ensuring that the coil can stably drive the safety clamp action under different operating conditions, while avoiding overheating or insufficient excitation.

[0118] During operation, the operating conditions of the safety clamp coil change dynamically over time. On the one hand, increased coil temperature leads to increased internal resistance. If the initial duty cycle is maintained, the actual output current will decrease, potentially causing a delay in the safety clamp's response. On the other hand, fluctuations in mains voltage or coil aging can cause the current change rate to deviate from the ideal range. If not corrected in real time, excessive current may cause overheating, or insufficient current may lead to operational failure. Furthermore, the coil temperature rise rate directly reflects the degree of heat loss. When the temperature rise rate exceeds the limit, sustained high losses will accelerate insulation aging and shorten coil life. Therefore, it is necessary to optimize the duty cycle and frequency in real time through closed-loop regulation to address the functional deviations or equipment losses caused by fixed initial parameters failing to adapt to dynamic operating conditions.

[0119] Based on the finalized initial excitation power supply configuration parameters, a pulse width modulation (PWM) excitation signal is generated. Initial parameters include a 50% initial PWM duty cycle for coil model X, an initial PWM frequency of 1kHz, and an upper limit excitation voltage of 12V. The PWM signal generator generates a periodic square wave signal based on these parameters. At 1kHz, the signal period is 1ms. The 50% duty cycle means that each cycle has a 0.5ms high level and a 0.5ms low level. This signal is amplified by the power drive circuit and output to the safety clamp coil, driving the coil to generate electromagnetic attraction and actuate the safety clamp. This step provides the initial driving force for the coil, and its parameters are based on the aforementioned adaptation results, ensuring that the initial output conforms to the coil's specifications.

[0120] The system monitors the real-time impedance and current change rate of the coil. Data is sourced from an electrical parameter set stored in a circular buffer. The real-time impedance is the arithmetic mean over 100ms after filtering. The current change rate is calculated using the current difference between adjacent sampling points and the sampling interval. Ideal characteristic curves for each model are calibrated offline. For example, the ideal characteristic curve for model X coil is set as follows: real-time impedance stable at 10Ω ± 0.5Ω, and current change rate stable at 1.6A / ms ± 0.2A / ms. When calculating deviations, an absolute deviation formula is used. For instance, when the real-time impedance is 10.4Ω, the deviation from the ideal value of 10Ω is 0.4Ω. When the current change rate is 1.3A / ms, the deviation from the ideal value of 1.6A / ms is -0.3A / ms. These deviation values ​​directly reflect the difference between the current power output and the ideal requirements of the coil.

[0121] An incremental PID controller is used to calculate the pulse width modulation duty cycle adjustment. The input to this controller is the combined deviation between the real-time impedance and the rate of change of current. The deviations are weighted and summed with impedance deviation accounting for 60% and current rate of change deviation accounting for 40%. The output is the duty cycle adjustment. The calculation process follows the incremental PID algorithm, and the specific formula is as follows:

[0122] Δu=Kp(Δe-Δep)+Ki×Δe+Kd(Δe-2Δep+Δep2);

[0123] Where Δu represents the pulse width modulation duty cycle adjustment, Kp represents the proportional coefficient, Ki represents the integral coefficient, Kd represents the derivative coefficient, Δe represents the current overall deviation value, Δep represents the deviation value of the previous cycle, and Δep2 represents the deviation value of the previous two cycles. The preset control coefficients of the controller are determined through offline optimization, for example, proportional coefficient Kp=0.2, integral coefficient Ki=0.05, and derivative coefficient Kd=0.02. For example, if the current overall deviation value is 0.3, the deviation value of the previous cycle is 0.2, and the deviation value of the previous two cycles is 0.1, substituting into the formula, we get Δu=0.035, that is, the pulse width modulation duty cycle adjustment is 3.5%. The incremental PID algorithm avoids the integral accumulation problem of positional PID, controls the single adjustment amount within 5%, and the current fluctuation amplitude is less than 8%, ensuring a smooth adjustment process without overshoot.

[0124] The pulse width modulation (PWM) frequency is dynamically adjusted based on real-time temperature and temperature rise rate. The real-time temperature and temperature rise rate are obtained from data collected by a temperature sensor, with a preset temperature rise rate threshold of 2℃ / s. When the real-time temperature rises from 75℃ to 77℃, the system automatically increases the PWM frequency from the initial 1kHz to 1.2kHz. This frequency increase shortens the switching cycle of the PWM signal, reducing coil switching losses by approximately 17%, thus reducing heat generation. If the temperature rise rate drops below 1℃ / s, the frequency is lowered back to 1kHz to avoid increased electromagnetic interference at high frequencies. This adjustment mechanism directly relates to heat loss and frequency characteristics, suppressing temperature rise at its source and keeping the coil temperature rise rate below 2℃ / s, thereby extending the insulation life.

[0125] The calculated duty cycle adjustment and frequency adjustment are synchronously applied to the current pulse width modulation signal. For example, the duty cycle is increased by 3.5% from the initial 50% to 53.5%, and the frequency is increased from 1kHz to 1.2kHz. After the new pulse width modulation signal is output by the power supply drive circuit, the coil's real-time impedance decreases from 10.4Ω to 10.1Ω, and the current change rate decreases from 1.3A / ms to 1.5A / ms, both within the ideal characteristic curve range. At the same time, the temperature stabilizes at 76℃ and no longer rises. Through real-time closed-loop feedback, parameter correction is completed every 100ms, ensuring that the power supply output continuously adapts to the coil's dynamic operating conditions, achieving the goals of stable safety clamp operation and coil thermal safety.

[0126] In one embodiment, the step of receiving a status signal from a positioning detection sensor on the safety clamp via power line carrier communication, optimizing the parameters of the dynamic adjustment algorithm based on the status signal and real-time electrical parameters, and performing fault diagnosis and early warning includes:

[0127] S51 continuously receives bit detection signals via power line carrier communication;

[0128] S52, when a signal indicating that the coil has arrived is received, determines the working state of the coil and maintains or finely adjusts the pulse width modulation parameters accordingly;

[0129] S53, if no arrival signal is received within a preset time and an abnormal electrical parameter is detected, outputs a high-voltage pulse sequence to overcome the jamming;

[0130] S54, establish a fault prediction model based on historical abnormal data, and predict the probability of faults through real-time parameters;

[0131] S55 generates an early warning and executes a safety policy when the probability of failure exceeds the limit.

[0132] As described in steps S51-S55 above, the status signal of the safety clamp positioning detection sensor is continuously received through power line carrier communication. Combined with the real-time monitored electrical parameters, the parameters of the dynamic adjustment algorithm are optimized in a targeted manner. At the same time, a fault prediction model is built based on historical abnormal data to perform fault diagnosis and early warning, so as to realize the coordinated control of the mechanical action status and electrical parameters of the safety clamp, ensure that the safety clamp moves in place and system faults can be avoided in advance, and ensure the overall operational safety and reliability.

[0133] The core function of a safety clamp is to provide braking protection through mechanical action. Its positioning status is the indicator that the protection function is effective. Even if the pulse width modulation parameters are adjusted to ensure the coil's electrical characteristics are normal, if the safety clamp fails to position due to mechanical obstruction, it will still fail to provide protection. Conversely, if only the positioning signal is relied upon while ignoring electrical parameters, coil overheating or abnormal current may cause operational failure. Furthermore, abnormalities in the safety clamp's operation will first manifest as fluctuations in electrical parameters; without early warning, these fluctuations can develop into irreversible faults. Power line carrier communication can utilize the existing power supply lines of the safety clamp to transmit signals, eliminating the need for additional wiring and adapting to complex installation environments. Therefore, it is essential to combine the positioning signal with real-time electrical parameters to achieve closed-loop management of mechanical status confirmation, electrical parameter optimization, and early fault warning. This addresses the problem of focusing solely on electrical characteristics while neglecting mechanical positioning, and only addressing faults after they occur without warning, leading to protection failures or equipment damage.

[0134] The system continuously receives position detection signals via power line carrier communication. The power line carrier communication module is integrated into both the power control unit and the position detection sensor. Utilizing the power supply line of the safety clamp coil as the transmission medium, no additional signal cabling is required. The communication rate is set to 9600bps, ensuring a signal transmission delay of ≤100ms to avoid misjudgments due to latency. The position detection sensor is a Hall effect position sensor installed at the end of the safety clamp's mechanical movement. When the safety clamp's mechanical structure moves to the braking position (positioned), the sensor outputs a high-level signal. When not in position, it outputs a low-level signal. The system receives a signal every 100ms, continuously monitoring to ensure no changes in the safety clamp's movement status are missed. This communication method reduces installation steps by 40% and lowers the signal loss rate to below 3%, making it suitable for complex installation environments.

[0135] Upon receiving a signal indicating arrival, the coil's operating status is determined based on dynamic identification results. If the status is normal, the current pulse width modulation parameters are maintained to prevent current fluctuations caused by parameter adjustments from affecting the stability of mechanical braking. If the status is overheating, the pulse width modulation parameters are fine-tuned, reducing the duty cycle from 50% to 45% and increasing the frequency from 1kHz to 1.1kHz. This reduces coil heating by lowering the power input, ensuring the coil remains within a safe temperature range after arrival, below the 85℃ safety threshold. If the status indicates a potential fault, the current protection threshold is tightened from 5A to 4.5A to prevent abnormal current increases that could exacerbate damage during a fault.

[0136] The preset time is set to 2 seconds, calibrated according to the standard action time of the safety clamp to ensure a reasonable duration for the mechanical action to complete. If no positioning signal is received within 2 seconds, and an abnormality in real-time electrical parameters is detected (the electrical parameters are from the cyclic buffer of weight 2), the abnormality judgment criteria are a real-time impedance deviation ≥10% or a current change rate deviation ≥20%), the system outputs a high-voltage pulse sequence. The upper limit of the high-voltage pulse is 1.2 times the upper limit of the initial excitation voltage. For example, if the initial voltage upper limit is 12V, the high voltage is 14.4V. The pulse width is 50ms, and it is output continuously 3 times. The short-term high voltage enhances the electromagnetic attraction of the coil. The attraction force is proportional to the square of the voltage. At 14.4V, the attraction force is 1.44 times that at 12V, overcoming slight mechanical jamming, such as jamming caused by foreign objects on the guide rail surface, without requiring machine stoppage and manual intervention, thus improving the system's continuous operation capability.

[0137] A fault prediction model is built based on historical anomaly data, including real-time impedance deviation, temperature rise rate, and current change rate deviation of the coil over the past year, along with corresponding fault records such as inter-turn short circuits and mechanical jamming. A total of 1000 data sets were collected. The model employs a logistic regression algorithm. The input layer consists of three real-time parameters: impedance deviation, temperature rise rate, and current change rate deviation. The output layer is the fault probability (0-100%). The model regression coefficients are optimized using gradient descent, with coefficients such as 0.3 for impedance deviation, 0.4 for temperature rise rate, and 0.3 for current change rate deviation, thus improving the model's prediction accuracy. During real-time operation, real-time parameters are read from the circular buffer every 500ms and input into the model to calculate the current fault probability. This model shifts fault judgment from reactive detection to proactive prediction, providing a quantitative basis for early warning.

[0138] The preset fault probability threshold is 85%, determined through offline testing. When the probability exceeds this threshold, the actual fault occurrence probability is ≥90%. When the fault probability exceeds 85%, the system generates an early warning, issuing a buzzer and red light alarm via an audible and visual alarm integrated into the power control unit. Simultaneously, the warning information, including the fault probability and real-time parameters, is uploaded to the upper-level monitoring system. When executing the safety strategy, the fault type is first determined based on real-time parameters: if the impedance deviation is ≥15% and the temperature is normal, it is determined to be an electrical fault, and the pulse width modulation excitation signal is immediately cut off to prevent coil burnout. If the current change rate is abnormal and the position is not reached, it is determined to be mechanical jamming, the high-voltage pulse output is stopped, and the system is locked to prevent further jamming and deformation of mechanical components. This reduces the economic losses caused by the fault and ensures the safe operation of the system.

[0139] like Figure 2 As shown, this application also discloses an active detection-based automatic power adaptation control system, including:

[0140] Acquisition module 1 is used to acquire and monitor in real time the electrical parameter set of the circuit electrically connected to the safety clamp coil and the coil temperature data;

[0141] The identification module 2 is used to dynamically identify the specifications and working status of the currently connected safety clamp coil based on the electrical parameter set and through a pre-built coil feature identification model.

[0142] Matching module 3 is used to automatically match and load the optimal initial excitation power supply configuration parameters from the pre-stored power supply configuration mapping table based on the identified coil specifications and operating status.

[0143] The adjustment module 4 is used to generate a pulse width modulation excitation signal based on the initial excitation power supply configuration parameters, and to perform closed-loop adjustment of the duty cycle and frequency of the pulse width modulation excitation signal through a dynamic adjustment algorithm based on the real-time monitored electrical parameter set and temperature data.

[0144] The diagnostic module 5 is used to receive the status signal sent by the position detection sensor on the safety clamp via power line carrier communication, and optimize the parameters of the dynamic adjustment algorithm based on the status signal and real-time electrical parameters, and perform fault diagnosis and early warning.

[0145] In one embodiment, the matching module includes:

[0146] The storage unit is used to store the pre-stored power configuration mapping table in the form of a data dictionary. Its key is the coil specification model and the value is the corresponding optimal initial excitation power configuration parameter group. The parameter group includes the initial pulse width modulation duty cycle, the initial pulse width modulation frequency, the excitation voltage upper limit and the current protection threshold.

[0147] The identification unit is used to search the power configuration mapping table according to the dynamically identified coil specifications and model. If a match is found, the corresponding optimal initial excitation power configuration parameter group is read.

[0148] The calculation unit is used to start the fuzzy matching algorithm if a precise match is not possible, calculate the Euclidean distance between the currently identified coil feature vector and the typical feature vectors corresponding to each model in the mapping table, and select the configuration parameter group corresponding to the model with the smallest distance as the loading reference.

[0149] The fine-tuning unit is used to fine-tune the initial configuration parameters of the load according to the determined coil operating state. If the state is overheating, the initial pulse width modulation duty cycle is reduced proportionally and the initial pulse width modulation frequency is increased. If the state is potential fault, a stricter current protection threshold is set.

[0150] The loading unit is used to load the final determined initial excitation power supply configuration parameter set into the pulse width modulation signal generator and power drive circuit to complete the initial power supply adaptation.

[0151] like Figure 3 As shown, this application also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 3As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores all data required for the process of the active detection-based automatic power adaptation control method. The network interface is used for communication with external terminals via a network connection. The computer program is executed by the processor to implement the active detection-based automatic power adaptation control method.

[0152] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.

[0153] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described power automatic adaptation control method based on active detection.

[0154] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0155] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0156] The above description is merely a preferred embodiment of this application and does not limit the scope of this application. Any equivalent results or equivalent process transformations made based on the content of this application specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of protection of this application.

Claims

1. A power supply automatic adaptation control method based on active detection, characterized in that, Includes the following steps: Real-time acquisition and monitoring of electrical parameter sets of the circuit electrically connected to the safety clamp coil, as well as coil temperature data; Based on the electrical parameter set, the specifications and working status of the currently connected safety clamp coil are dynamically identified through a pre-built coil feature recognition model. Based on the identified coil specifications and operating status, the optimal initial excitation power supply configuration parameters are automatically matched and loaded from the pre-stored power supply configuration mapping table. Specific steps include: The pre-stored power configuration mapping table is stored in the form of a data dictionary. The key of the data dictionary is the coil specification model, and the value of the data dictionary is the corresponding optimal initial excitation power configuration parameter set. The parameter set includes the initial pulse width modulation duty cycle, the initial pulse width modulation frequency, the excitation voltage upper limit, and the current protection threshold. Based on the dynamically identified coil specifications and model, a search is performed in the power configuration mapping table. If a match is found, the corresponding optimal initial excitation power configuration parameter group is read. If an exact match is not found, a fuzzy matching algorithm is started to calculate the Euclidean distance between the currently identified coil feature vector and the typical feature vectors corresponding to each model in the mapping table, and the configuration parameter group corresponding to the model with the smallest distance is selected as the loading reference. Based on the determined coil operating state, the initial configuration parameters of the load are fine-tuned. If the state is overheating, the initial pulse width modulation duty cycle is reduced proportionally and the initial pulse width modulation frequency is increased. If the state is a potential fault, a more stringent current protection threshold is set. The finalized initial excitation power supply configuration parameter set is loaded into the pulse width modulation signal generator and power drive circuit to complete the initial power supply adaptation. A pulse width modulation excitation signal is generated based on the initial excitation power supply configuration parameters. The duty cycle and frequency of the pulse width modulation excitation signal are then adjusted in a closed loop using a dynamic adjustment algorithm based on the real-time monitored electrical parameter set and temperature data. The system receives status signals from the positioning detection sensors on the safety clamp via power line carrier communication, and optimizes the parameters of the dynamic adjustment algorithm based on these status signals and real-time electrical parameters, and performs fault diagnosis and early warning.

2. The active detection based power auto-adaptation control method of claim 1, wherein, The steps of real-time acquisition and monitoring of electrical parameter sets of the circuit electrically connected to the safety clamp coil and the coil temperature data include: At a preset high-frequency sampling rate, the instantaneous voltage and instantaneous current values ​​flowing through the safety clamp coil are simultaneously acquired, and based on the instantaneous voltage and instantaneous current values, the rate of change of current and the real-time impedance of the coil within a time window are calculated. By integrating a temperature sensor into or near the coil circuit, the real-time temperature value of the coil is obtained, and the rate of temperature rise per unit time is calculated. The acquired raw instantaneous voltage and current values ​​are digitally filtered to eliminate high-frequency noise interference. The digital filtering process uses a finite impulse response filter based on a window function, and the signal is smoothed by convolving the sampled sequence with predefined filter coefficients. The filtered instantaneous voltage value, the filtered instantaneous current value, the calculated current change rate, the real-time impedance, the real-time temperature value, and the temperature rise rate are collectively used to form the electrical parameter set and temperature data.

3. The active detection based power auto-adaptation control method of claim 1, wherein, The step of dynamically identifying the specifications and operating status of the currently connected safety clamp coil based on the electrical parameter set and through a pre-built coil feature recognition model includes: Feature vectors are extracted from the electrical parameter set. The feature vectors include steady-state impedance value, peak value of transient inrush current, time constant for current to reach peak value, and phase difference under specific frequency excitation. The feature vector is input into a pre-trained coil feature recognition model. The Euclidean distance between the input feature vector and the center vector of the hidden layer node is calculated. A nonlinear transformation is performed by applying the radial basis function. The model is then weighted and summed using the connection weights from the hidden layer to the output layer to obtain the probability output of each specification model. The coil feature recognition model uses the model with the highest probability as the dynamic recognition result based on the calculated probabilities of each specification and model. Based on the deviation between the real-time impedance and the steady-state impedance, and the comparison between the real-time temperature and the preset safe temperature threshold, the working state of the coil is comprehensively judged as normal, overheated, or potentially faulty. Specifically, if the impedance deviation is less than the preset impedance deviation threshold and the real-time temperature is lower than the safe temperature threshold, the state is normal. If the real-time temperature reaches or exceeds the safe temperature threshold, the state is overheated. If the impedance deviation reaches or exceeds the preset impedance deviation threshold and the real-time temperature is lower than the safe temperature threshold, the state is potentially faulty.

4. The active detection based power auto-adaptation control method of claim 1, wherein, The steps of generating a pulse width modulation excitation signal based on the initial excitation power supply configuration parameters and, based on real-time monitored electrical parameter sets and temperature data, performing closed-loop adjustment of the duty cycle and frequency of the pulse width modulation excitation signal using a dynamic adjustment algorithm include: A pulse width modulation excitation signal is generated based on the initial configuration parameters to drive the safety clamp coil; Real-time monitoring of coil impedance and current change rate, and calculation of the deviation between real-time impedance and current change rate and the ideal characteristic curve of the corresponding model; An incremental PID controller is used to calculate the pulse width modulation duty cycle adjustment in real time based on the deviation. The pulse width modulation frequency is dynamically adjusted according to the real-time temperature and temperature rise rate. When the temperature continues to rise and the temperature rise rate exceeds the limit, the operating frequency is automatically increased to reduce losses. The calculated adjustment amount is applied to the current pulse width modulation signal to achieve closed-loop control.

5. The active detection based power-adaptation control method of claim 1, wherein, The steps of receiving the status signal sent by the positioning detection sensor on the safety clamp via power line carrier communication, optimizing the parameters of the dynamic adjustment algorithm based on the status signal and real-time electrical parameters, and performing fault diagnosis and early warning include: The bit detection signal is continuously received via power line carrier communication; When a signal indicating that the coil is in position is received, the coil's operating status is determined and the pulse width modulation parameters are maintained or finely adjusted accordingly. If no arrival signal is received within a preset time and an abnormal electrical parameter is detected, a high-voltage pulse sequence is output to overcome the jamming. A fault prediction model is established based on historical anomaly data, and the probability of faults is predicted through real-time parameters. When the probability of failure exceeds the limit, an early warning is generated and a safety policy is implemented.

6. An active detection based automatic power adaptation control system, characterized in that, include: The acquisition module is used to acquire and monitor in real time the electrical parameter set of the circuit electrically connected to the safety clamp coil and the coil temperature data; The identification module is used to dynamically identify the specifications and working status of the currently connected safety clamp coil based on the electrical parameter set and a pre-built coil feature identification model. The matching module is used to automatically match and load the optimal initial excitation power supply configuration parameters from a pre-stored power supply configuration mapping table based on the identified coil specifications and operating status. Specific steps include: The pre-stored power configuration mapping table is stored in the form of a data dictionary. The key of the data dictionary is the coil specification model, and the value of the data dictionary is the corresponding optimal initial excitation power configuration parameter set. The parameter set includes the initial pulse width modulation duty cycle, the initial pulse width modulation frequency, the excitation voltage upper limit, and the current protection threshold. Based on the dynamically identified coil specifications and model, a search is performed in the power configuration mapping table. If a match is found, the corresponding optimal initial excitation power configuration parameter group is read. If an exact match is not found, a fuzzy matching algorithm is started to calculate the Euclidean distance between the currently identified coil feature vector and the typical feature vectors corresponding to each model in the mapping table, and the configuration parameter group corresponding to the model with the smallest distance is selected as the loading reference. Based on the determined coil operating state, the initial configuration parameters of the load are fine-tuned. If the state is overheating, the initial pulse width modulation duty cycle is reduced proportionally and the initial pulse width modulation frequency is increased. If the state is a potential fault, a more stringent current protection threshold is set. The finalized initial excitation power supply configuration parameter set is loaded into the pulse width modulation signal generator and power drive circuit to complete the initial power supply adaptation. The adjustment module is used to generate a pulse width modulation excitation signal based on the initial excitation power supply configuration parameters, and to perform closed-loop adjustment of the duty cycle and frequency of the pulse width modulation excitation signal through a dynamic adjustment algorithm based on the real-time monitored electrical parameter set and temperature data. The diagnostic module is used to receive status signals sent by the positioning detection sensors on the safety clamp via power line carrier communication, and optimize the parameters of the dynamic adjustment algorithm based on the status signals and real-time electrical parameters, and perform fault diagnosis and early warning.

7. The active detection based automatic power adaptation control system of claim 6, wherein, The matching module includes: The storage unit is used to store the pre-stored power configuration mapping table in the form of a data dictionary. The key of the data dictionary is the coil specification model, and the value of the data dictionary is the corresponding optimal initial excitation power supply configuration parameter set. The parameter set includes the initial pulse width modulation duty cycle, the initial pulse width modulation frequency, the excitation voltage upper limit, and the current protection threshold. The identification unit is used to search the power configuration mapping table according to the dynamically identified coil specifications and model. If a match is found, the corresponding optimal initial excitation power configuration parameter group is read. The calculation unit is used to start the fuzzy matching algorithm if a precise match is not possible, calculate the Euclidean distance between the currently identified coil feature vector and the typical feature vectors corresponding to each model in the mapping table, and select the configuration parameter group corresponding to the model with the smallest distance as the loading reference. The fine-tuning unit is used to fine-tune the initial configuration parameters of the load according to the determined coil operating state. If the state is overheating, the initial pulse width modulation duty cycle is reduced proportionally and the initial pulse width modulation frequency is increased. If the state is potential fault, a more stringent current protection threshold is set. The loading unit is used to load the final determined initial excitation power supply configuration parameter set into the pulse width modulation signal generator and power drive circuit to complete the initial power supply adaptation.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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