General circuit breaker electric operating mechanism and compensation method and health state prediction method

Abstract

The application relates to the technical field of circuit breaker electric operating mechanisms, in particular to a universal circuit breaker electric operating mechanism and a compensation method and a health state prediction method. The device comprises a main control board, a first sliding block, a second sliding block, a first spring set, a first baffle, a second spring set, a second baffle, a trapezoidal groove, a cam connecting rod mechanism and a controller. The first sliding block is fixedly connected with the main control board, the second sliding block is fixedly connected with the main control board, one end of the first spring set is fixedly connected with the main control board, the first baffle is fixedly connected with the other end of the first spring set, the first baffle is movably connected with the main control board, one end of the second spring set is fixedly connected with the main control board, the second baffle is movably connected with the main control board, the trapezoidal groove is arranged on the main control board close to the center position, and the trapezoidal groove is used for clamping the handle of a circuit breaker. The application can be adapted to mainstream brands, is compatible with various specifications, has health diagnosis to improve reliability, can automatically dynamically compensate and can accurately match stroke requirements.

Description

Technical Field

[0001] This application relates to the technical field of circuit breaker operating mechanisms, and in particular to a general-purpose circuit breaker operating mechanism, as well as a compensation method and a health status prediction method. Background Technology

[0002] In power systems, circuit breakers are critical equipment for ensuring the safe operation of circuits, and their performance directly affects the stability and reliability of the power grid. With the increasingly urgent need for intelligent and efficient power equipment, circuit breakers have revealed several technical bottlenecks in mechanical transmission and control. Existing circuit breakers lack a dynamic compensation mechanism for handle travel differences. Under long-term, high-frequency operation, the handle travel deviates due to mechanical wear, leading to decreased accuracy in closing and opening actions. Furthermore, the lack of automatic calibration capabilities necessitates manual maintenance. Simultaneously, diagnostic methods for mechanical jamming coupling faults are scarce. The complex internal components of circuit breakers mean that jamming faults can easily affect each other, and existing detection methods struggle to quickly locate fault points and related factors, resulting in low maintenance efficiency and high costs. In addition, the coordinated control strategy for the cam transmission mechanism and spring compensation system is not yet perfect. Deviations in the timing and force matching between the two cause instability phenomena such as incomplete closing and contact jitter during circuit breaker operation, affecting switching performance and service life. These technical shortcomings hinder the intelligent upgrading of circuit breakers, necessitating research into optimization strategies such as dynamic compensation, fault diagnosis, and coordinated control to improve circuit breaker reliability and maintenance efficiency.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a universal circuit breaker operating mechanism, compensation method, and health status prediction method, which can be adapted to mainstream brands, compatible with multiple specifications, has health diagnosis to improve reliability, automatic dynamic compensation, and accurately matches travel requirements.

[0005] To achieve the above objectives, one aspect of this application provides a general-purpose circuit breaker operating mechanism, comprising:

[0006] Main control board;

[0007] A first slider, which is fixedly connected to the main control board;

[0008] The second slider is fixedly connected to the main control board;

[0009] A first spring assembly, one end of which is fixedly connected to the main control board;

[0010] The first baffle is fixedly connected to the other end of the first spring assembly, and the first baffle is movably connected to the main control board.

[0011] The second spring assembly, one end of which is fixedly connected to the main control board;

[0012] The second baffle is fixedly connected to the other end of the second spring assembly, and the second baffle is movably connected to the main control board;

[0013] A trapezoidal groove is provided near the center of the main control board, and the trapezoidal groove is used to hold the handle of the circuit breaker;

[0014] A cam linkage mechanism is provided above the main control board, and the cam linkage mechanism is used to control the first slider or the second slider to drive the main control board to move.

[0015] A controller, which is communicatively connected to the cam linkage mechanism.

[0016] In some embodiments, a first thin-film pressure sensor and a second thin-film pressure sensor are also included, wherein the first thin-film pressure sensor and the second thin-film pressure sensor are communicatively connected to the controller;

[0017] The first thin-film pressure sensor is disposed between the connection between the first spring assembly and the main control board, or between the connection between the first spring assembly and the first baffle.

[0018] The second thin-film pressure sensor is disposed between the connection between the second spring assembly and the main control board, or between the connection between the second spring assembly and the second baffle.

[0019] In some embodiments, a first magnetostrictive displacement sensor and a second magnetostrictive displacement sensor are also included, wherein the first magnetostrictive displacement sensor and the second magnetostrictive displacement sensor are communicatively connected to the controller;

[0020] The first magnetostrictive displacement sensor is disposed between the first spring groups and is connected to the first baffle.

[0021] The second magnetostrictive displacement sensor is disposed between the second spring groups and is connected to the second baffle.

[0022] In some embodiments, a temperature and humidity sensor is also included, which is disposed near the main control board and is communicatively connected to the controller.

[0023] In some embodiments, a mechanical limiting pin is further included, which is disposed inside the springs of the first spring group and the second spring group, and is fixedly connected to the first baffle and the second baffle respectively.

[0024] To achieve the above objectives, another aspect of this application proposes a compensation method for a universal circuit breaker operating mechanism. This universal circuit breaker operating mechanism compensation method is applied to the aforementioned universal circuit breaker operating mechanism, and the method includes the following steps:

[0025] When the cam linkage mechanism controls the first slider or the second slider to move the main control board, spring pressure distribution data is obtained through the first thin-film pressure sensor or the second thin-film pressure sensor, displacement data is obtained through the first magnetostrictive displacement sensor or the second magnetostrictive displacement sensor, and temperature and humidity data of the working environment are obtained through the temperature and humidity sensor.

[0026] The controller compares the spring pressure distribution data, the displacement data, and the temperature and humidity data with the pressure-displacement curve to obtain the real-time motor torque;

[0027] The cam linkage mechanism is controlled according to the real-time motor torque to push the first slider or the second slider to move.

[0028] To achieve the above objectives, another aspect of this application proposes a method for predicting the health status of a general-purpose circuit breaker operating mechanism. This method is applied to the aforementioned general-purpose circuit breaker operating mechanism and includes the following steps:

[0029] Spring pressure distribution data is obtained through the first thin-film pressure sensor or the second thin-film pressure sensor; displacement data is obtained through the first magnetostrictive displacement sensor or the second magnetostrictive displacement sensor; temperature and humidity data of the working environment are obtained through the temperature and humidity sensor; and current data passing through the motor in the cam linkage mechanism is obtained in real time through the current sensor to obtain current harmonic data.

[0030] The preprocessed spring pressure distribution data, displacement data, temperature and humidity data, and current harmonic data are input into the electric operating mechanism prediction model to obtain the electric operating mechanism prediction result.

[0031] When the prediction result of the electric operating mechanism shows a fault state, fault handling and fault warning are performed.

[0032] When the prediction result of the electric operating mechanism shows a normal state, the spring pressure distribution data, displacement data, temperature and humidity data, and current harmonic data are continuously collected and input into the electric operating mechanism prediction model for real-time prediction.

[0033] In some embodiments, the construction step of the electronic operating mechanism prediction model includes:

[0034] The preprocessed spring pressure distribution data, displacement data, temperature and humidity data, and current harmonic data are summarized using historical data to obtain an initial data set.

[0035] The optimal network parameters are obtained by optimizing the parameters of the long short-term memory neural network by combining the improved gray wolf optimization algorithm with dynamic weight factors.

[0036] The confidence level of the fault type is obtained by fusing the characteristics of the pressure-displacement curve with the characteristics of the current harmonic data using Dempster-Shafer theory.

[0037] A correlation mapping is established between the fault confidence threshold and the rated current of the circuit breaker. The threshold is then adjusted based on historical data to obtain the confidence threshold.

[0038] The initial dataset is input into the optimized long short-term memory neural network, and the prediction result of the electric operating mechanism is obtained by combining the confidence level of the fault type and the confidence level threshold.

[0039] In some embodiments, the method further includes the following steps:

[0040] By summarizing the spring pressure distribution data from multiple tests, the actual operating force data is obtained.

[0041] Calculate the mean and standard deviation of the actual operating force data, and obtain the coefficient of variation of operating force by calculating the ratio of the standard deviation of the actual operating force data to the mean of the actual operating force data;

[0042] When the coefficient of variation of the operating force is greater than the threshold of the coefficient of variation of the operating force, the protection mechanism is triggered and an alarm signal is sent.

[0043] In some embodiments, the method further includes the following steps:

[0044] The actual travel time is obtained by acquiring the time it takes for the first or second slider to travel from the starting position to the target position using the first or second magnetostrictive displacement sensor.

[0045] The reference travel time is obtained by acquiring the time from the starting position to the target position of the first slider or the second slider through the new general-purpose circuit breaker electric operating mechanism;

[0046] The difference between the actual travel time and the reference travel time is divided by the reference travel time to obtain the travel time deviation rate;

[0047] When the travel time deviation rate is greater than the travel time deviation rate threshold, the protection mechanism is triggered and an alarm signal is sent.

[0048] The embodiments of this application include at least the following beneficial effects: This application provides a universal circuit breaker operating mechanism, a compensation method, and a health status prediction method. This scheme controls the rotation of a cam linkage mechanism via a controller, thereby pushing a first or second slider. The first or second slider drives the main control board to compress a first spring frame group or a second spring group. A first or second baffle limits the first or second spring group. During the movement of the main control board, a trapezoidal slot in the main control board drives the circuit breaker handle to open or close. This application addresses the existing technical shortcomings of circuit breakers, demonstrating significant advantages in compatibility, reliability, and dynamic compensation. In terms of compatibility, it can be adapted to mainstream brand circuit breakers, fully compatible with the stroke difference and mounting hole spacing of most circuit breakers, effectively reducing the adaptation cost and time cost of replacing equipment, facilitating rapid installation and deployment, and avoiding secondary modifications due to specification mismatch. In terms of reliability, the health status prediction system for the electric operating mechanism can monitor the device's operating status in real time, intelligently diagnose and scientifically evaluate potential faults such as mechanical jamming and component wear, provide early warnings of risks, transform passive maintenance into proactive maintenance, reduce downtime due to sudden failures, ensure stable operation of the power system, and extend equipment lifespan. Regarding dynamic compensation capabilities, the overtravel spring force can automatically match different travel requirements, compensating for differences in handle travel and ensuring precise and stable closing and opening actions. Combined with the stable clamping design of the cam linkage mechanism and trapezoidal groove, it can accurately position the handle while buffering operational impact, reducing mechanical wear, further enhancing the dynamic compensation effect, improving the circuit breaker's operating performance and reliability, and providing strong support for the intelligent upgrading of power equipment. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the structure of the general-purpose circuit breaker operating mechanism provided in the embodiments of this application;

[0050] Figure 2 This is a top view of the cross-section of the electrical operating mechanism of a general-purpose circuit breaker;

[0051] Figure 3 This is a schematic diagram of the structure of a general-purpose circuit breaker's electric operating mechanism equipped with a circuit breaker;

[0052] Figure 4 It is a front view of the structural schematic diagram of the electrical operating mechanism of a general-purpose circuit breaker equipped with a circuit breaker;

[0053] Figure 5 This is a flowchart of a general-purpose circuit breaker electrical operating mechanism compensation method;

[0054] Figure 6 This is a flowchart of a general-purpose circuit breaker electrical operating mechanism health status prediction method;

[0055] Figure 7 This is a flowchart for constructing a predictive model for an electric control mechanism. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0057] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0058] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0060] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0061] LSTM Neural Network: A special type of recurrent neural network that can process long sequences of data. It mitigates gradient vanishing through a gating mechanism and is suitable for time series prediction.

[0062] Dempster-Shafer: An evidence theory that deals with uncertain reasoning, using a trust function to fuse multi-source evidence and support partially unknown information.

[0063] Gray Wolf Optimization Algorithm: An optimization algorithm that simulates the hunting behavior of gray wolves. It searches for the optimal solution through encirclement, pursuit, and attack, and has few parameters and is easy to implement.

[0064] Please see Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown in the figure, this application embodiment provides a general-purpose circuit breaker electric operating mechanism, which includes: a main control board 100, a first slider 110, a second slider 120, a first spring group 210, a first baffle 310, a second spring group 220, a second baffle 320, a trapezoidal groove 400, a cam linkage mechanism 600, and a controller (not shown in the figure). The first slider 110 is fixedly connected to the main control board 100, the second slider 120 is fixedly connected to the main control board 100, one end of the first spring assembly 210 is fixedly connected to the main control board 100, the first baffle 310 is fixedly connected to the other end of the first spring assembly 210, and the first baffle 310 is movably connected to the main control board 100. One end of the second spring assembly 220 is fixedly connected to the main control board 100, the second baffle 320 is fixedly connected to the other end of the second spring assembly 220, and the second baffle 320 is movably connected to the main control board 100. A trapezoidal groove 400 is located near the center of the main control board 100 and is used to hold the handle 700 of the circuit breaker. A cam linkage mechanism 600 is located above the main control board 100 and is used to control the first slider 110 or the second slider 120 to move the main control board 100. The controller is communicatively connected to the cam linkage mechanism 600.

[0065] Specifically, when the circuit breaker needs to be tripped, the controller sends a tripping control command to the cam linkage mechanism 600, causing the cam linkage mechanism 600 to rotate counterclockwise. The cam linkage mechanism 600 drives the fixedly connected contact below, causing the contact to rotate with the cam linkage mechanism 600. When the contact reaches the first slider 110, it begins to compress the first slider 110. Since the first slider 110 is fixedly connected to the main control board 100, the contact provides a rightward push to the main control board 100 through the first slider 110. Since the main control board 100 is connected to the first spring group 210, and the first spring group 210 and the first baffle 310 are fixedly connected and the first baffle 310 is movably connected to the main control board 100, the spring begins to compress when the main control board 100 is pushed to the right. At the same time, the circuit breaker handle 700, which is clamped in the trapezoidal groove 400 near the center of the main control board 100, moves to the right until the movement of the handle 700 causes the circuit breaker to complete the tripping operation.

[0066] When the circuit breaker needs to be closed, the controller sends a closing control command to the cam linkage mechanism 600, causing the cam linkage mechanism 600 to rotate clockwise. The cam linkage mechanism 600 drives the fixedly connected contact below, causing the contact to rotate with the cam linkage mechanism 600. When the contact reaches the second slider 120, it begins to compress the second slider 120. Since the second slider 120 is fixedly connected to the main control board 100, the contact drives the main control board 100 to move to the left through the second slider 120. The main control board 100 is connected to the second spring group 220, and the other end of the second spring group 220 is fixedly connected to the second baffle 320. During the limiting process, the second baffle 320 begins to compress the second spring group 220 due to the movement of the main control board 100. At the same time, the circuit breaker handle 700, which is clamped in the trapezoidal slot 400 of the main control board 100, moves to the right until the movement of the handle 700 causes the circuit breaker to complete the closing operation. During the closing and opening operations, the first spring group 210 and the second spring group 220 mainly play the role of storing and releasing energy, while reducing the impact of direct motor drive and improving operational stability.

[0067] In some embodiments, the first slider 110 and the second slider 120 are connected by a first spring group 210 and a second spring group 220 to move the main control board 100. The allowable stroke is 10-15mm, and the mathematical formula for the movement of the main control board 100 is:

[0068]

[0069] Where S represents the stroke compensation amount, k represents the spring stiffness coefficient, and ΔL represents the tolerance compensation amount.

[0070] The first spring group 210 and the second spring group 220 can be disc springs, compression springs, or tension springs. Taking disc springs as an example, using DIN2093 standard springs, with a pre-compression of 5mm, the compensation force calculated according to the formula F=k·x is 100N, matching the operating torque range of mainstream 250A circuit breakers, which is 80-120N·m. The main control board 100 adapts to the spring force to avoid rigid impact. The trapezoidal groove 400 is made of PA66-GF30 nylon material, which can better hold the handle 700. The included angle of the trapezoidal groove 400 is designed to be 120°, suitable for round or square handles 700, with a contact stress ≤8MPa to avoid plastic deformation of the handle 700. The linear guide rail and ball guide rod ensure the movement stability of the main control board 100.

[0071] In some embodiments, the system further includes a first thin-film pressure sensor and a second thin-film pressure sensor communicatively connected to the controller. The first thin-film pressure sensor is disposed between the connection point of the first spring group 210 and the main control board 100, or between the connection point of the first spring group 210 and the first baffle 310. The second thin-film pressure sensor is disposed between the connection point of the second spring group 220 and the main control board 100, or between the connection point of the second spring group 220 and the second baffle 320. Specifically, the first and second thin-film pressure sensors are used to monitor the pressure changes of the springs in the circuit breaker's electrical operating mechanism during energy storage, closing, and opening processes in real time, converting mechanical signals into electrical signals and outputting operating force data. A preferred sensor is the Fibaro FGR-222 thin-film pressure sensor with a contact area of ​​15mm × 20mm and a pressure distribution uniformity deviation ≤ 5%. The first and second thin-film pressure sensors can be disposed at both ends or one end of the first spring group 210 and the second spring group 220, mainly used to collect spring pressure distribution data generated by the first spring group 210 or the second spring group 220 during spring compression, providing basic data for subsequent compensation and health status prediction.

[0072] In some embodiments, the system further includes a first magnetostrictive displacement sensor 510 and a second magnetostrictive displacement sensor 520 communicatively connected to the controller. The first magnetostrictive displacement sensor 510 is disposed between the first spring groups 210 and connected to the first baffle 310; the second magnetostrictive displacement sensor 520 is disposed between the second spring groups 220 and connected to the second baffle 320. The first magnetostrictive displacement sensor 510 and the second magnetostrictive displacement sensor 520 are used to monitor the extension and retraction of the first spring groups 210 and the second spring groups 220 and the displacement change of the handle 700 in real time, converting the mechanical displacement signal into an electrical signal, and then calculating the stroke time for closing and opening the circuit breaker. This provides key data for evaluating the mechanism's action speed, synchronization, and calculating the stroke time deviation rate, and assists in judging abnormal states such as spring fatigue and jamming.

[0073] In some embodiments, a temperature and humidity sensor communicatively connected to the controller is also included, positioned near the main control board 100. The temperature and humidity sensor monitors the temperature and humidity data of the operating environment of the general-purpose circuit breaker's electrical operating mechanism in real time, providing environmental parameter support for evaluating the mechanism's operating status. Monitoring data can provide early warnings of environmental anomalies, preventing the mechanism's reliability from being affected by excessive temperature and humidity. Simultaneously, the temperature and humidity sensor is used for environmental compensation to correct spring stiffness. Experiments have shown that for every 10°C increase in temperature, the spring force decreases by 1.2%, thereby ensuring compensation accuracy over a wide temperature range of -40°C to 70°C. Positioning the temperature and humidity sensor within the housing of the general-purpose circuit breaker's electrical operating mechanism and close to the spring allows for more accurate sensing data.

[0074] In some embodiments, a thin-film pressure sensor, a magnetostrictive displacement sensor, and a temperature and humidity sensor are used to monitor the operating status and environmental conditions of the general-purpose circuit breaker's electrical operating mechanism in real time. The thin-film pressure sensor is mounted on the bottom surfaces of the first slider 110 and the second slider 120 to detect the pressure distribution during operation; the magnetostrictive displacement sensor measures the displacement of the main control board 100 caused by the first slider 110 or the second slider 120; and the temperature and humidity sensor monitors changes in the working environment. This data provides rich sensing information for the intelligent control unit. The thin-film pressure sensor integrated on the bottom surfaces of the first slider 110 and the second slider 120 collects spring pressure distribution data in real time; the magnetostrictive displacement sensor monitors the relative displacement of the main control board 100 and constructs a pressure-displacement curve, such as the point of sudden force change during closing corresponding to the sticking position of the handle 700. The temperature and humidity sensor is used to correct the spring stiffness.

[0075] In some embodiments, a mechanical limiting pin is also included. The mechanical limiting pin is disposed inside the springs of the first spring group 210 and the second spring group 220, and is fixedly connected to the first baffle 310 and the second baffle 320, respectively. When the spring is compressed by the main control board 100, the mechanical limiting pin can reduce the degree of compression of the spring by the main control board 100 to provide overload protection, limit the maximum displacement of the main control board 100, and prevent the spring from being damaged due to excessive compression. A sensor is provided at the bottom of the limiting pin, and the sensor is communicatively connected to the controller. When the main control board 100 compresses the spring, causing the mechanical limiting pin to be triggered, the sensor sends the trigger status to the controller for further operation control. The mechanical limiting pin limits the maximum displacement of the main control board 100 driven by the first slider 110 and the second slider 120. When the spring is compressed by more than 15mm, a physical limit is triggered to prevent the spring from failing due to overload. For example, it can be set to trigger when the operating torque of a 250A circuit breaker exceeds 120 N·m.

[0076] like Figure 5As shown, to achieve the above objective, another aspect of this application proposes a universal circuit breaker operating mechanism compensation method. This universal circuit breaker operating mechanism compensation method is applied to the aforementioned universal circuit breaker operating mechanism, and the method includes, but is not limited to, steps S510 to S530:

[0077] Step S510: When the cam linkage mechanism 600 controls the first slider 110 or the second slider 120 to move the main control board 100, the spring pressure distribution data is obtained through the first thin film pressure sensor or the second thin film pressure sensor, the displacement data is obtained through the first magnetostrictive displacement sensor 510 or the second magnetostrictive displacement sensor 520, and the temperature and humidity data of the working environment are obtained through the temperature and humidity sensor.

[0078] Step S520: The controller compares the spring pressure distribution data, displacement data, and temperature and humidity data with the pressure-displacement curve to obtain the real-time motor torque;

[0079] Step S530: Control the cam linkage mechanism 600 to push the first slider 110 or the second slider 120 to move according to the real-time motor torque.

[0080] In some embodiments, during steps S510 to S530, when the cam linkage mechanism 600 rotates under the drive of the motor, if the first slider 110 is moved by the synapse, the first thin-film pressure sensor senses the pressure distribution of the spring during the extension and contraction process in real time and generates a pressure signal; simultaneously, the first magnetostrictive displacement sensor 510 outputs displacement data by monitoring the position change of the magnetic ring on the slider. If the second slider 120 is moved, the second thin-film pressure sensor and the second magnetostrictive displacement sensor 520 respectively collect the pressure distribution data and displacement data of the corresponding spring. Simultaneously, the temperature and humidity sensor continuously records the temperature and humidity parameters of the working environment. All data are transmitted to the controller after analog-to-digital conversion to form a multi-dimensional raw dataset. At the same time, the cam linkage mechanism 600 is flexibly connected to the circuit breaker handle 700 through the nylon trapezoidal groove 400. The trapezoidal groove 400 is adaptable to different brands of circuit breakers. For circuit breakers with a stroke difference of up to 10mm, structural compensation ensures the stability of pressure and displacement data acquisition, providing reliable input for subsequent control.

[0081] The controller accesses a pre-set pressure-displacement curve database and compares the real-time collected spring pressure distribution data, displacement data, and temperature and humidity data with the standard curves in the database. Specifically, the controller inputs the collected spring pressure distribution data, displacement data, and temperature and humidity data into the pre-set pressure-displacement curve database and performs dynamic analysis using a fuzzy PID controller. The optimal control cycle is 10ms. More specifically, based on the current stroke stage, such as when the brake is closed to 80% of its stroke, the actual pressure value, for example, exceeding 110N, is compared with the standard value on the curve. The fuzzy algorithm quickly determines the adjustment direction, and the PID controller calculates a precise motor torque correction value, such as outputting a 50% speed reduction command. This ensures that the operating force fluctuation is less than 20% after compensation and avoids mechanical damage such as handle breakage due to overload.

[0082] The pressure-displacement curve was formed through a systematic calibration experiment of a general-purpose circuit breaker operating mechanism under standard operating conditions. First, a trapezoidal slot 400 connection structure adapted to mainstream brand circuit breakers was selected. When the mechanism was in normal operation, the control cam linkage mechanism 600 drove the slider to perform multiple closing and opening actions. During this process, spring pressure distribution data at different stroke positions were collected by the first and second thin-film pressure sensors. Simultaneously, the slider displacement at corresponding moments was recorded using the first and second magnetostrictive displacement sensors 510 and 520, and environmental parameters were recorded by temperature and humidity sensors to eliminate interference. Subsequently, the pressure and displacement data for each action were matched according to stroke stages to form multiple sets of discrete data points. Combined with the real-time torque adjustment records of the fuzzy PID controller within a 10ms control cycle, a base curve was generated using a least-squares fitting algorithm. Finally, compensation tests were conducted on different circuit breakers with a stroke difference of 10mm. The curve parameters were optimized based on measured data with operating force fluctuations <20%, establishing a database of standard pressure-displacement curves covering different operating conditions as a benchmark for subsequent real-time control.

[0083] In some embodiments, in step S530, the controller sends a pulse signal to the motor driving the cam linkage mechanism 600 based on the calculated real-time motor torque to adjust the motor output power. If the real-time torque is less than the standard value, the motor driving force is increased to accelerate the rotation of the cam and push the slider to move quickly; if the real-time torque is greater than the standard value, the motor driving force is reduced to avoid overload and component wear. Simultaneously, through a closed-loop feedback mechanism, the real-time displacement data of the first magnetostrictive displacement sensor 510 and the second magnetostrictive displacement sensor 520 are transmitted back to the controller to dynamically correct the motor output, ensuring that the first slider 110 and the second slider 120 move accurately along a preset trajectory, achieving stable control of the closing or opening action. Specifically, based on the real-time motor torque calculated by fuzzy PID, a pulse signal is sent to the motor driving the cam linkage mechanism 600 to adjust the cam rotation speed and force, thereby pushing the first slider 110 or the second slider 120 to move along a preset trajectory. During this process, the magnetostrictive displacement sensor transmits the slider position data back to the controller in real time, forming a closed-loop feedback. If the displacement deviation exceeds the threshold, the motor torque output is immediately corrected to ensure the accuracy of the slider movement. At the same time, the health management module analyzes the pressure, displacement and torque data. When abnormal fluctuations are detected, such as a sudden increase in torque caused by jamming, the protection mechanism is triggered to suspend operation, thereby achieving precise and safe control of the electric operating mechanism.

[0084] like Figure 6 As shown, to achieve the above objective, another aspect of this application proposes a general-purpose circuit breaker electrical operating mechanism health status prediction method. This method is applied to the general-purpose circuit breaker electrical operating mechanism described above, and includes, but is not limited to, steps S610 to S640:

[0085] Step S610: Obtain spring pressure distribution data through the first thin-film pressure sensor or the second thin-film pressure sensor; obtain displacement data through the first magnetostrictive displacement sensor 510 or the second magnetostrictive displacement sensor 520; obtain temperature and humidity data of the working environment through the temperature and humidity sensor; obtain current data of the motor passing through the cam linkage mechanism 600 in real time through the current sensor, and obtain current harmonic data.

[0086] Step S620: Input the preprocessed spring pressure distribution data, displacement data, temperature and humidity data and current harmonic data into the electric operating mechanism prediction model to obtain the electric operating mechanism prediction results;

[0087] Step S630: When the prediction result of the electric operating mechanism shows a fault state, perform fault handling and fault warning;

[0088] Step S640: When the prediction result of the electric operating mechanism shows a normal state, continuously collect spring pressure distribution data, displacement data, temperature and humidity data, and current harmonic data and input them into the electric operating mechanism prediction model for real-time prediction.

[0089] In some embodiments, in steps S610 to S620, spring pressure distribution data is acquired using a first thin-film pressure sensor or a second thin-film pressure sensor; displacement data is acquired using a first magnetostrictive displacement sensor 510 or a second magnetostrictive displacement sensor 520; temperature and humidity data of the working environment are acquired using a temperature and humidity sensor; the current signal of the drive motor in the cam linkage mechanism 600 is monitored in real time using a current sensor, and the original current data is subjected to spectrum analysis using Fourier transform to extract characteristic parameters such as amplitude and phase of each harmonic to obtain current harmonic data; at the same time, the data is filtered and preprocessed to remove high-frequency noise and baseline drift, ensuring the accuracy of harmonic features and providing reliable current dimension information for subsequent model input and feature fusion. The preprocessed spring pressure distribution data, displacement data, temperature and humidity data, and current harmonic data are integrated to construct a four-dimensional feature space including operating force, displacement, current, and temperature and humidity. The four-dimensional feature space data is input into the prediction model of the electric operating mechanism. The model optimizes the parameters of the long short-term memory neural network (LSTM) by introducing an improved gray wolf optimization algorithm with dynamic weight factors to improve the convergence speed of parameter optimization. Then, the Dempster-Shafer theory is used to fuse the pressure displacement curve features and current harmonic features to output the fault confidence degree associated with the rated current of the circuit breaker. Finally, the prediction result of the electric operating mechanism, i.e., the normal or fault state, is obtained.

[0090] The four-dimensional feature space can be understood as including four dimensions: operating force (F), displacement (S), current (I), and temperature / humidity (T / H). These four dimensions cover the main parameters of the operating mechanism of a general-purpose circuit breaker, comprehensively reflecting the working status of the equipment. Operating force reflects mechanical wear, displacement identifies jamming positions (e.g., a 2mm shortening of the closing stroke warns of spring fatigue), current captures sudden changes in motor load (e.g., a harmonic distortion rate >15% indicates bearing failure), and temperature / humidity (T / H) compensates for environmental influences (e.g., accelerated contact oxidation when humidity >80% RH). During prediction, the four-dimensional feature space provides rich input information for the LSTM neural network, enabling it to more accurately predict the health status of the equipment. By fusing these four dimensions of features, the LSTM neural network can learn the complex relationship between equipment faults and features, thereby improving the accuracy and reliability of predictions. For example, by constructing a 4×10 feature vector (such as the mean / variance of F, the rising slope of S, the amplitude of the 5th harmonic of I, and the daily variation of T), LSTM can distinguish 7 types of faults, including jamming, spring fatigue, contact oxidation and bearing wear, and the accuracy is significantly improved compared to the single feature model.

[0091] In some embodiments, during steps S630 to S640, when the fault confidence level output by the electric operating mechanism prediction model exceeds a threshold associated with the circuit breaker's rated current (e.g., the threshold decreases accordingly when the rated current is large), a fault state is determined. At this time, the system immediately activates a fault handling mechanism, such as pausing the current operation to avoid component damage, and sends a fault warning signal through a preset communication interface, simultaneously recording the four-dimensional feature data and confidence level value at the time of the fault, providing a basis for subsequent fault diagnosis and maintenance. When the fault confidence level output by the model does not exceed the corresponding threshold, a normal state is determined. Real-time data is continuously collected through a thin-film pressure sensor, a magnetostrictive displacement sensor, a temperature and humidity sensor, and a current sensor. The data preprocessing and four-dimensional feature space construction process are repeated, and the updated feature data is continuously input into the electric operating mechanism prediction model for dynamic prediction, forming a closed loop of real-time monitoring to ensure continuous tracking and timely response to changes in the electric operating mechanism's state.

[0092] In some embodiments, the prediction process using the electric operating mechanism prediction model specifically includes: First, data acquisition is performed, with sensors uploading raw data on force, displacement, current, temperature, and humidity every 10ms. For example, 20 sets of data can be collected within 200ms during the closing process to form a time series. Next, feature engineering is performed, preprocessing the collected sensing data and extracting time-domain features such as mean or peak values, frequency-domain features obtained by analyzing current harmonics through FFT, and time-frequency-domain features such as wavelet entropy to construct a feature matrix. Subsequently, LSTM prediction is performed, inputting the data in the four-dimensional feature space into an LSTM neural network optimized by the improved Grey Wolf optimization algorithm. The optimized model predicts the health of the current state, with values ​​ranging from 0 to 1, and outputs the probability of anomaly in the pressure-displacement curve P1 and the probability of anomaly in the current harmonics P2. Finally, Dempster-Shafer evidence fusion is performed, comparing the fault confidence output by the LSTM neural network with the threshold of the rated current of the associated circuit breaker. If the fault confidence exceeds the threshold, the equipment is judged to be in a fault state, triggering the protection mechanism to perform fault handling or early warning. If the threshold is not exceeded, the equipment is judged to be in normal condition.

[0093] like Figure 7 As shown, Figure 7 This is a flowchart of constructing a prediction model for an electric control mechanism. The process of constructing a prediction model for an electric control mechanism includes, but is not limited to, steps S621 to S625:

[0094] Step S621: Summarize the preprocessed spring pressure distribution data, displacement data, temperature and humidity data, and current harmonic data using historical data to obtain an initial data set;

[0095] Step S622: Optimize the parameters of the long short-term memory neural network by combining the improved gray wolf optimization algorithm with dynamic weight factors to obtain the optimal network parameters;

[0096] Step S623: The characteristics of the pressure-displacement curve and the characteristics of the current harmonic data are fused using Dempster-Shafer theory to obtain the confidence level of the fault type;

[0097] Step S624: Establish a correlation mapping between the fault confidence threshold and the rated current of the circuit breaker, and verify and adjust the threshold based on historical data to obtain the confidence threshold;

[0098] Step S625: Input the initial data set into the optimized long short-term memory neural network, and combine the confidence level of the fault type and the confidence threshold to obtain the prediction result of the electric operating mechanism.

[0099] In some embodiments, in steps S621 to S622, spring pressure distribution data, displacement data, temperature and humidity data, and current harmonic data from the historical operation of the general-purpose circuit breaker's electric operating mechanism are collected and preprocessed, including outlier removal, missing value imputation, and standardization. The preprocessed data are then categorized and summarized by time series or operating conditions to form an initial data set covering normal states and various fault states, such as spring fatigue or mechanical jamming, providing a complete sample basis for model training and parameter optimization.

[0100] First, the initial parameters of the LSTM neural network are determined, including the number of input layer nodes matching the four-dimensional feature dimension, the number of hidden layers and neurons, the number of output layer nodes corresponding to the fault type, and the learning rate. Then, an improved gray wolf optimization algorithm is introduced, encoding the LSTM parameters, including weights and biases, as individuals in a gray wolf population. The network prediction error is used as the objective function, and a dynamic weighting factor α = 0.5·e is employed during the iteration process. -t / T Instead of the traditional linearly decreasing α, where T represents the total number of iterations, t represents the current iteration number, and α represents the control parameter in the Grey Wolf optimization algorithm, a non-linear decay is achieved where α = 0.5 at t = 0 and α → 0 as t → T, improving the algorithm's convergence speed by 40% compared to the standard algorithm. The optimal LSTM network parameters are obtained by iteratively updating the Grey Wolf position until the objective function is minimized.

[0101] In some embodiments, in step S623, pressure-displacement curve features and current harmonic features are extracted from the initial dataset. The pressure-displacement curve features include characteristics such as slope, peak value, and fluctuation range, while the current harmonic features include characteristics such as harmonic amplitude and distortion rate. These two types of features are used as independent sources of evidence in the Dempster-Shafer theory. A basic probability allocation function is defined for each fault type, including spring failure and motor malfunction. The evidence sources are fused using Dempster-Shafer synthesis rules to eliminate redundancy and conflicts between features, and the confidence level for each fault type is calculated, forming a quantitative basis for fault judgment.

[0102] In some embodiments, in step S624, a mapping relationship between the fault confidence threshold and the rated current of the circuit breaker is established. For example, the higher the rated current, the lower the threshold is appropriately reduced to improve sensitivity. This is verified by historical fault samples in the initial dataset: the confidence distribution when a fault occurs under different rated currents is statistically analyzed, and the optimal threshold corresponding to each current level is determined by ROC curve analysis; then, it is fine-tuned by combining the confidence fluctuation range of normal samples to ensure that the threshold can effectively identify faults and reduce false alarm rate, and finally obtain a dynamic threshold system associated with the rated current.

[0103] In some embodiments, in step S625, the initial dataset is divided into a training set and a test set proportionally. The training set is input into an LSTM neural network optimized by the improved Grey Wolf optimization algorithm for training, so that the network learns the correlation between features and faults. During testing, the test set data is input into the optimized network to obtain preliminary prediction results. Then, the fault confidence obtained in step S623 and the dynamic threshold determined in step S624 are combined for fusion judgment: if the confidence of a certain fault type exceeds the corresponding threshold, the fault prediction result is output; if all confidences are below the threshold, it is determined to be a normal state, and finally the comprehensive prediction result of the electric operating mechanism is obtained.

[0104] In some embodiments, the general-purpose circuit breaker electric operating mechanism health status prediction method further includes: summarizing multiple spring pressure distribution data to obtain actual operating force data; calculating the mean and standard deviation of the actual operating force data, and calculating the operating force variation coefficient by comparing the standard deviation of the actual operating force data with the mean of the actual operating force data; when the operating force variation coefficient is greater than the operating force variation coefficient threshold, triggering the protection mechanism and sending an alarm signal.

[0105] Specifically, multiple spring pressure distribution data are collected and summarized using a thin-film pressure sensor to obtain the actual operating force data. The mean μ of the actual operating force data is calculated using the formula: the sum of all actual operating force data divided by the number of data points. The standard deviation σ of the actual operating force data is then calculated: the sum of the squares of the differences between each data point and the mean divided by the square root of the number of data points minus one. The standard deviation σ is then compared to the mean μ to obtain the coefficient of variation C of the operating force. VOF =σ(F) / μ(F); The calculated coefficient of variation of operating force is compared with the preset threshold for the coefficient of variation of operating force. When the coefficient of variation of operating force is greater than the threshold, the protection mechanism is triggered and an alarm signal is sent. For example, if the threshold for the coefficient of variation of operating force is set to 0.2, and the closing force data of a circuit breaker for 10 times is: [98,102,105,100,95,101,103,99,104,100]N, the calculated mean μ = 100.7N, standard deviation σ = 3.2N, and coefficient of variation of operating force CVOF = 3.2%, which is within the normal state because it is less than the threshold. If jamming occurs, the force data is [80,120,90,110]N, and the calculated σ = 18.7 and CVOF = 18.7%, which is close to the threshold and needs attention. When it is greater than the threshold for the coefficient of variation of operating force, the protection mechanism is triggered, the fault information is recorded, and an alarm signal is sent.

[0106] In some embodiments, the general-purpose circuit breaker operating mechanism health status prediction method further includes: obtaining the time from the starting position to the target position of the first slider 110 or the second slider 120 through the first magnetostrictive displacement sensor 510 or the second magnetostrictive displacement sensor 520 to obtain the actual travel time; obtaining the time from the starting position to the target position of the first slider 110 or the second slider 120 through the new general-purpose circuit breaker operating mechanism to obtain the reference travel time; dividing the difference between the actual travel time and the reference travel time by the reference travel time to obtain the travel time deviation rate; and triggering a protection mechanism and sending an alarm signal when the travel time deviation rate is greater than the travel time deviation rate threshold.

[0107] Specifically, the first magnetostrictive displacement sensor 510 or the second magnetostrictive displacement sensor 520 monitors the entire process of the first slider 110 or the second slider 120 moving from the starting position to the target position in real time, and records the time taken for this process to obtain the actual travel time Tcurrent. When the new general-purpose circuit breaker operating mechanism is in normal operation, the time for the first slider 110 or the second slider 120 to move from the starting position to the target position is also recorded and set as the reference travel time Tbase. For example, the reference closing time of the new mechanism is set to 120ms; according to formula D... TRT =(T current -T base ) / T base×100%, substitute the actual travel time and the reference travel time into the calculation to obtain the travel time deviation rate; compare the calculated travel time deviation rate with the preset travel time deviation rate threshold. When the travel time deviation rate is greater than the threshold, the protection mechanism is immediately triggered and an alarm signal is sent. For example, if the threshold is set to 15%, when the spring fatigues, the closing time is extended to 140ms. At this time, the calculated DTRT = (140-120) / 120 × 100% = 16.7%. Since 16.7% > 15% threshold, the protection mechanism is triggered.

[0108] In some embodiments, when the protection mechanism is triggered, the protection mechanism includes: immediately stopping the motor drive, issuing a fault warning signal, and cutting off the power supply, to ensure the safe operation of the equipment. Simultaneously, fault information is recorded to provide a basis for subsequent fault analysis and handling.

[0109] In some embodiments, a three-level protection system is set based on the coefficient of variation of operating force and the travel time deviation rate. Specifically, when the threshold for the coefficient of variation of operating force is 0.2 and the threshold for the travel time deviation rate is 15%, a first-level warning is triggered when the coefficient of variation of operating force is greater than 0.15 or the travel time deviation rate is greater than 10%. At this time, the operating speed is reduced by 50%, and the data is recorded to the local FLASH. A second-level warning is triggered when the coefficient of variation of operating force is greater than 0.2 or the travel time deviation rate is greater than 15%. The operation is immediately stopped and the motor is powered off. A fault code is output, such as E01 = mechanical jamming, and the fault code is reported to the cloud via 4G. If the mechanical limit pin triggers the third-level physical protection, it indicates that the displacement of the main control board 100 is greater than 15mm. At this time, the first slider 110 and the second slider 120 are mechanically locked to prevent the spring from breaking and splashing.

[0110] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0111] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0112] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0113] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0114] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0115] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0116] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A universal circuit breaker operating mechanism, characterized in that, include: Main control board; A first slider, which is fixedly connected to the main control board; The second slider is fixedly connected to the main control board; A first spring assembly, one end of which is fixedly connected to the main control board; The first baffle is fixedly connected to the other end of the first spring assembly, and the first baffle is movably connected to the main control board. The second spring assembly, one end of which is fixedly connected to the main control board; The second baffle is fixedly connected to the other end of the second spring assembly, and the second baffle is movably connected to the main control board; A trapezoidal groove is provided near the center of the main control board, and the trapezoidal groove is used to hold the handle of the circuit breaker; A cam linkage mechanism is provided above the main control board, and the cam linkage mechanism is used to control the first slider or the second slider to drive the main control board to move. A first thin-film pressure sensor and a second thin-film pressure sensor, wherein the first thin-film pressure sensor is disposed between the connection between the first spring assembly and the main control board, or between the connection between the first spring assembly and the first baffle; the second thin-film pressure sensor is disposed between the connection between the second spring assembly and the main control board, or between the connection between the second spring assembly and the second baffle. A first magnetostrictive displacement sensor and a second magnetostrictive displacement sensor are present. The first magnetostrictive displacement sensor is disposed between the first spring groups and is connected to the first baffle. The second magnetostrictive displacement sensor is disposed between the second spring groups and is connected to the second baffle. A temperature and humidity sensor is located near the main control board; The controller is communicatively connected to the cam linkage mechanism, the first thin-film pressure sensor, the second thin-film pressure sensor, the first magnetostrictive displacement sensor, the second magnetostrictive displacement sensor, and the temperature and humidity sensor. The controller has a built-in electric operating mechanism prediction model, which is constructed based on a long short-term memory neural network optimized by a modified gray wolf optimization algorithm and dynamic weight factor optimization. The fault type confidence is obtained by fusing pressure displacement curve features and current harmonic data features through Dempster-Shafer theory. The controller is also equipped with a three-level protection mechanism, which performs protective actions such as early warning, shutdown, and mechanical locking according to the coefficient of variation of operating force, travel time deviation rate, and triggering state of mechanical limit pin.

2. The universal circuit breaker operating mechanism according to claim 1, characterized in that, The mechanical limiting pin is disposed inside the springs of the first spring group and the second spring group, and the mechanical limiting pin is fixedly connected to the first baffle and the second baffle respectively.

3. A universal compensation method for the electrical operating mechanism of a circuit breaker, characterized in that, The universal circuit breaker operating mechanism compensation method is applied to the universal circuit breaker operating mechanism as described in any one of claims 1-2, and the method includes the following steps: When the cam linkage mechanism controls the first slider or the second slider to drive the main control board to move, spring pressure distribution data is obtained through the first thin-film pressure sensor or the second thin-film pressure sensor, displacement data is obtained through the first magnetostrictive displacement sensor or the second magnetostrictive displacement sensor, temperature and humidity data of the working environment are obtained through the temperature and humidity sensor, and current data of the motor in the cam linkage mechanism is obtained through the current sensor and current harmonic data is obtained. The preprocessed spring pressure distribution data, displacement data, temperature and humidity data, and current harmonic data are input into the electric operating mechanism prediction model to obtain the electric operating mechanism prediction result. The electric operating mechanism prediction model is constructed based on a long short-term memory neural network with improved gray wolf optimization algorithm combined with dynamic weight factor optimization. Furthermore, the fault type confidence is obtained by fusing pressure displacement curve features and current harmonic data features through Dempster-Shafer theory. The fault confidence threshold is correlated with the rated current of the circuit breaker. The controller compares the spring pressure distribution data, the displacement data, and the temperature and humidity data with the pressure-displacement curve to obtain the real-time motor torque. Based on the prediction results of the electric operating mechanism, the controller controls the cam linkage mechanism to push the first slider or the second slider to move according to the real-time motor torque. During the compensation process, the coefficient of variation of the operating force and the deviation rate of the stroke time are calculated in real time. Combined with the triggering state of the mechanical limit pin, the corresponding protection action is executed according to the three-level protection mechanism. If the protection mechanism is triggered, the pushing action of the cam linkage mechanism is paused or adjusted and an alarm signal is issued.

4. A general method for predicting the health status of circuit breaker electrical operating mechanisms, characterized in that, The general-purpose circuit breaker electrical operating mechanism health status prediction method is applied to the general-purpose circuit breaker electrical operating mechanism as described in any one of claims 1-2, and the method includes the following steps: Spring pressure distribution data is obtained through the first thin-film pressure sensor or the second thin-film pressure sensor; displacement data is obtained through the first magnetostrictive displacement sensor or the second magnetostrictive displacement sensor; temperature and humidity data of the working environment are obtained through the temperature and humidity sensor; and current data passing through the motor in the cam linkage mechanism is obtained in real time through the current sensor to obtain current harmonic data. The preprocessed spring pressure distribution data, displacement data, temperature and humidity data, and current harmonic data are input into the electric operating mechanism prediction model to obtain the electric operating mechanism prediction result. When the prediction result of the electric operating mechanism shows a fault state, fault handling and fault warning are performed. When the prediction result of the electric operating mechanism shows a normal state, the spring pressure distribution data, displacement data, temperature and humidity data, and current harmonic data are continuously collected and input into the electric operating mechanism prediction model for real-time prediction.

5. The method for predicting the health status of a general-purpose circuit breaker operating mechanism according to claim 4, characterized in that, The steps for constructing the prediction model for the electronic operating mechanism include: The preprocessed spring pressure distribution data, displacement data, temperature and humidity data, and current harmonic data are summarized using historical data to obtain an initial data set. The optimal network parameters are obtained by optimizing the parameters of the long short-term memory neural network by combining the improved gray wolf optimization algorithm with dynamic weight factors. The confidence level of the fault type is obtained by fusing the characteristics of the pressure-displacement curve with the characteristics of the current harmonic data using Dempster-Shafer theory. A correlation mapping is established between the fault confidence threshold and the rated current of the circuit breaker. The threshold is then adjusted based on historical data to obtain the confidence threshold. The initial dataset is input into the optimized long short-term memory neural network, and the prediction result of the electric operating mechanism is obtained by combining the confidence level of the fault type and the confidence level threshold.

6. The method for predicting the health status of a general-purpose circuit breaker operating mechanism according to claim 4, characterized in that, The method further includes the following steps: By summarizing the spring pressure distribution data from multiple tests, the actual operating force data is obtained. Calculate the mean and standard deviation of the actual operating force data, and obtain the coefficient of variation of operating force by calculating the ratio of the standard deviation of the actual operating force data to the mean of the actual operating force data; When the coefficient of variation of the operating force is greater than the threshold of the coefficient of variation of the operating force, the protection mechanism is triggered and an alarm signal is sent.

7. The method for predicting the health status of a general-purpose circuit breaker operating mechanism according to claim 4, characterized in that, The method further includes the following steps: The actual travel time is obtained by acquiring the time it takes for the first or second slider to travel from the starting position to the target position using the first or second magnetostrictive displacement sensor. The reference travel time is obtained by acquiring the time from the starting position to the target position of the first slider or the second slider through the new general-purpose circuit breaker electric operating mechanism; The difference between the actual travel time and the reference travel time is divided by the reference travel time to obtain the travel time deviation rate; When the travel time deviation rate is greater than the travel time deviation rate threshold, the protection mechanism is triggered and an alarm signal is sent.

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