Non-intrusive multi-dimensional sensing optimal arrangement method for mechanical state of circuit breaker

Abstract

The invention discloses a non-intrusive multi-dimensional perception optimal arrangement method for a mechanical state of a circuit breaker, relates to the technical field of optimal arrangement of circuit breakers, and aims to solve the problem that targeted arrangement is not carried out according to actual conditions of the circuit breakers when the circuit breakers are arranged. According to the method, the arrangement position of the circuit breaker is automatically confirmed through the constructed Bayesian network and the performance influence relation, the subjectivity and uncertainty of human decision making are reduced, the multi-dimensional characteristic data and the performance evaluation data of the circuit breaker are combined, the influence relation between the data is analyzed by using a multivariate statistical method, and the reliability of the circuit breaker is improved. The objectivity and accuracy of decision making are improved, a special feature extraction method is adopted for different types of data, and it is ensured that key information of each data type can be effectively captured. The targeted feature extraction mode is beneficial to improving the accuracy and efficiency of subsequent analysis, and early diagnosis of the mechanical state of the circuit breaker and prediction of potential faults are realized through deep analysis of the multi-dimensional feature data.

Description

Technical Field

[0001] The present invention relates to the technical field of optimal arrangement of circuit breakers, and in particular to a non-intrusive multi-dimensional sensing optimal arrangement method for mechanical states of circuit breakers. Background Art

[0002] Optimal circuit breaker layout refers to the reasonable arrangement of the location and configuration of circuit breakers in the power system to achieve the best system performance and operating efficiency.

[0003] The Chinese patent with the announcement number CN116298844B discloses a substation high-voltage circuit breaker status monitoring system and method based on semi-dynamic arrangement, which mainly realizes long-term differentiated monitoring of some circuit breakers by applying statically arranged circuit breaker online monitoring devices based on different principles; on the other hand, the dynamically arranged circuit breaker latent fault monitoring device is used to implement non-long-term (several days to several months) circuit breaker defect monitoring for circuit breakers that need to be monitored urgently (there are family defects that have not been handled and the maintenance cycle is exceeded). Although the above patent solves the problem of data monitoring, there are still the following problems in actual operation:

[0004] 1. When acquiring the mechanical status data of the circuit breaker, the data acquisition is inaccurate due to the imperfect data acquisition method adopted.

[0005] 2. The acquired mechanical state data of the circuit breaker is not subjected to targeted data feature extraction, resulting in inaccurate analysis results in the subsequent data analysis.

[0006] 3. When arranging the positions of the circuit breakers, a more optimized arrangement was not made and a model was not constructed according to the arrangement positions, which made it inconvenient to adjust the circuit breakers later. Summary of the invention

[0007] The purpose of the present invention is to provide a non-intrusive multi-dimensional perception optimal layout method for the mechanical state of a circuit breaker. By constructing a Bayesian network and performance influence relationship, the layout position of the circuit breaker is automatically confirmed, reducing the subjectivity and uncertainty of human decision-making. The multidimensional feature data of the circuit breaker and the performance evaluation data are combined, and the influence relationship between the data is analyzed by multivariate statistics, which improves the objectivity and accuracy of the decision. A special feature extraction method is used for different types of data to ensure that the key information of each data type can be effectively captured. This targeted feature extraction method helps to improve the accuracy and efficiency of subsequent analysis. Through in-depth analysis of multidimensional feature data, early diagnosis of the mechanical state of the circuit breaker and prediction of potential faults can be achieved, which can solve the problems in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A non-intrusive multi-dimensional perception optimal arrangement method for mechanical states of circuit breakers comprises the following steps:

[0010] S1: Circuit breaker data acquisition: monitor and obtain the original mechanical state data of the circuit breaker, and mark the obtained original mechanical state data of the circuit breaker as state data to be processed;

[0011] S2: Circuit breaker data analysis: preprocess the state data to be processed, extract the circuit breaker index data after the data preprocessing is completed, and obtain the circuit breaker multi-dimensional feature data after the index data is extracted;

[0012] S3: Circuit breaker optimization layout: The circuit breakers are arranged according to the data of the circuit breakers to be arranged, and a three-dimensional model is constructed to obtain standard circuit breaker position data;

[0013] S4: Circuit breaker data evaluation: collect real-time monitoring data of the circuit breakers in the standard circuit breaker position data, and evaluate the collected real-time monitoring data, and judge the effect of the circuit breaker position arrangement according to the evaluation results.

[0014] Preferably, monitoring and acquiring the original mechanical state data of the circuit breaker in S1 includes:

[0015] The mechanical status data of the circuit breaker includes temperature data, acoustic data, vibration data, electrical data, optical data, gas analysis data, environmental data and mechanical characteristic data;

[0016] Among them, temperature data includes infrared thermal imaging data and resistance temperature data; acoustic data includes acoustic emission signals and sound spectrum analysis; vibration data includes vibration waveforms and spectrum analysis; electrical data includes partial discharge signals, current and voltage waveforms; optical data includes visual images and optical sensors; gas analysis data is SF6 gas composition analysis; environmental data includes humidity and temperature; mechanical property data includes operating time and operating force.

[0017] Preferably, monitoring and acquiring the original mechanical state data of the circuit breaker in S1 further includes: monitoring the mechanical state data of the circuit breaker using a non-intrusive sensor;

[0018] Among them, temperature data is monitored by non-contact infrared thermal imagers and temperature sensors; acoustic data is monitored by acoustic emission sensors and external microphones; vibration data is monitored by MEMS accelerometers; electrical data is monitored by high-frequency current transformers and voltage transformers; optical data is monitored by high-definition cameras and laser rangefinders; gas analysis data is monitored by gas samplers; environmental data is monitored by temperature and humidity sensors; mechanical property data is monitored by displacement sensors and force sensors;

[0019] The monitored temperature data, acoustic data, vibration data, electrical data, optical data, gas analysis data, environmental data and mechanical property data are uniformly marked as status data to be processed.

[0020] Preferably, data preprocessing is performed on the state data to be processed in S2, and after the data preprocessing is completed, the indicator data of the circuit breaker is extracted, including:

[0021] Perform data preprocessing on the state data to be processed;

[0022] Among them, the data preprocessing process is to denoise, calibrate, normalize and interpolate the temperature data in the state data to be processed in turn; denoise, sound enhance and time synchronize the acoustic data in the state data to be processed in turn; denoise, calibrate and reduce the dimension of the vibration data in the state data to be processed in turn; denoise, sound enhance and time synchronize the electrical data in the state data to be processed in turn; image enhance and denoise the optical data in the state data to be processed in turn; correct and denoise the gas analysis data in the state data to be processed in turn; denoise and calibrate the environmental data in the state data to be processed in turn; denoise and calibrate the mechanical property data in the state data to be processed in turn;

[0023] Finally, the data preprocessing is completed and the data is in the state of being processed.

[0024] Preferably, the acoustic data in the state data to be processed is subjected to sound enhancement processing, including:

[0025] Extracting acoustic data from the state data to be processed;

[0026] Extracting a preset initial volume threshold from a database; wherein the initial volume threshold is not lower than a preset minimum volume value that can meet data quality requirements;

[0027] The volume threshold is set according to the acoustic data in the state data to be processed in combination with the initial volume threshold, wherein the volume threshold is obtained by the following formula:

[0028]

[0029] Wherein, F represents the volume threshold. When the volume threshold is lower than the preset minimum volume value that can meet the data quality requirements, let F = 1.08F x , and F x Indicates the preset minimum volume value that can meet the data quality requirements; F yc represents the initial volume threshold; n represents the number of frames contained in the acoustic data in the state data to be processed; m represents the number of frames contained in the acoustic data in the state data to be processed that are lower than the initial volume threshold; Fi represents the volume value corresponding to the i-th frame of acoustic data in the state data to be processed; F i Indicates the volume median value corresponding to n frames of acoustic data in the state data to be processed; F max and F min Indicates the maximum volume and minimum volume corresponding to n frames of acoustic data in the state data to be processed; F b Indicates the volume standard deviation corresponding to n frames of acoustic data in the state data to be processed;

[0030] Comparing the acoustic data in the state data to be processed with the volume threshold;

[0031] Acoustic data below the volume threshold is extracted, and volume enhancement processing is performed on the acoustic data below the volume threshold.

[0032] Preferably, extracting acoustic data below the volume threshold and performing volume enhancement processing on the acoustic data below the volume threshold includes:

[0033] extracting acoustic data below the volume threshold;

[0034] Extract the preset minimum volume value that can meet the data quality requirements;

[0035] Obtaining a first volume adjustment coefficient corresponding to the acoustic data below the volume threshold by using the volume value corresponding to the acoustic data below the volume threshold and the preset minimum volume value that can meet the data quality requirement;

[0036] The first volume adjustment coefficient is obtained by the following formula:

[0037]

[0038] Among them, f 01 represents the first volume adjustment coefficient; k represents the number of frames corresponding to the acoustic data below the volume threshold; F j represents the volume value corresponding to the acoustic data of the jth frame that is lower than the volume threshold; F represents the volume threshold; F yc Indicates the initial volume threshold; F x Indicates the preset minimum volume value that can meet the data quality requirements;

[0039] The second volume adjustment coefficient corresponding to the acoustic data below the volume threshold is set by using the quantitative relationship between the volume value corresponding to the acoustic data below the volume threshold and the volume threshold; wherein the second volume adjustment coefficient is obtained by the following formula:

[0040]

[0041] Among them, f 02 represents the second volume adjustment coefficient; k represents the number of frames corresponding to the acoustic data below the volume threshold; F j represents the volume value corresponding to the acoustic data of the jth frame that is lower than the volume threshold; F represents the volume threshold; F x represents the preset minimum volume value that can meet the data quality requirements; c represents the adjustment factor, and the adjustment factor is obtained by the following formula:

[0042]

[0043] Where c represents the adjustment factor; F min Indicates the minimum volume value corresponding to n frames of acoustic data in the state data to be processed; F m represents the maximum volume value in the acoustic data of k frames below the volume threshold; F p represents the average volume of k frames of acoustic data below the volume threshold;

[0044] The volume value corresponding to the acoustic data below the volume threshold is adjusted using the first volume adjustment coefficient and the second volume adjustment coefficient to obtain the adjusted acoustic data; wherein the volume value of the adjusted acoustic data is obtained by the following formula:

[0045]

[0046] Among them, F t represents the volume value of the adjusted acoustic data corresponding to the acoustic data of each frame below the volume threshold; f 01 represents the first volume adjustment coefficient; f 02 represents the second volume adjustment coefficient; F represents the volume threshold; F0 represents the volume value of the acoustic data before adjustment corresponding to the acoustic data of each frame below the volume threshold.

[0047] Preferably, the process of performing data preprocessing on the state data to be processed in S2, extracting the index data of the circuit breaker after the data preprocessing is completed, further includes:

[0048] Extract data features from the data in the pending state after data preprocessing;

[0049] When extracting data features, feature extraction is performed separately according to the data attributes of the state data to be processed;

[0050] Among them, the feature extraction of temperature data is to perform hot spot detection, temperature gradient calculation, temperature difference recognition and time series recognition on infrared thermal imaging data; the resistance temperature data is used for temperature change trend analysis and abnormal value recognition;

[0051] The feature extraction of acoustic data is to identify the frequency and signal energy of the acoustic emission signal; the sound spectrum analysis is to identify the spectrum characteristics and time-frequency recognition;

[0052] The feature extraction of vibration data is to identify the time domain features, frequency domain features and time-frequency features by analyzing the vibration waveform and spectrum;

[0053] The feature extraction of electrical data is to identify the number of discharges, the discharge phase and the discharge energy of the partial discharge signal; to identify the waveform characteristics and the frequency characteristics of the current and voltage waveforms;

[0054] Feature extraction of optical data is to identify the shape, texture and color features of visual images; to measure and identify the distance of optical sensors;

[0055] The feature extraction of gas analysis data is to identify the concentration features and concentration change features;

[0056] The feature extraction of environmental data is to identify statistical features and correlation features;

[0057] Feature extraction of mechanical property data for identification of statistical features and operating modes;

[0058] After feature extraction of the state data to be processed, feature selection is performed on the extracted features, wherein the feature selection uses principal component analysis to calculate the principal components of the extracted features;

[0059] After the principal component calculation, the index data in the state data to be processed is obtained and marked as multi-dimensional feature data of the circuit breaker.

[0060] Preferably, the circuit breakers are arranged according to the data of the circuit breakers to be arranged in S3, and a three-dimensional model is constructed, including:

[0061] The circuit breaker is subjected to performance evaluation according to the multi-dimensional characteristic data of the circuit breaker, and the performance evaluation includes contact performance evaluation, insulator performance evaluation, operating mechanism performance evaluation and mechanical state evaluation;

[0062] A Bayesian network is constructed according to the performance evaluation results and the position arrangement rules, wherein the position arrangement rules are obtained from a database;

[0063] After the Bayesian network is constructed, confirm each node and each edge in the Bayesian network;

[0064] Each node is an option point for the performance parameters and location layout of the circuit breaker; each edge is an associated edge between the performance parameters and location layout;

[0065] The circuit breaker multidimensional feature data and the performance evaluation data are used to determine the performance impact, wherein the multivariate statistical method is used to analyze the impact relationship between the circuit breaker multidimensional feature data and the performance evaluation data;

[0066] The layout position of the circuit breaker is confirmed according to the constructed Bayesian network and the performance impact relationship, and the circuit breaker simulation position data is obtained after confirmation.

[0067] Preferably, the step of arranging the positions of the circuit breakers according to the data of the circuit breakers to be arranged in S3 and constructing a three-dimensional model further includes:

[0068] Construct a three-dimensional model of the simulated position data of the circuit breaker;

[0069] Among them, the basic information of the circuit breaker is retrieved from the database, and the basic information includes size, shape and material; the three-dimensional coordinate data is acquired by using a three-dimensional scanner according to the basic information of the circuit breaker;

[0070] Use Geomagic tools to convert 3D coordinate data into editable 3D models;

[0071] The converted editable three-dimensional model is 3D rendered, and after the 3D rendering is completed, the standard circuit breaker position data is obtained.

[0072] Preferably, real-time monitoring data is collected for the circuit breakers in the standard circuit breaker position data in S4, and the collected real-time monitoring data is evaluated, and the effect of the circuit breaker position arrangement is judged according to the evaluation result:

[0073] Arrange the circuit breakers according to the standard circuit breaker position data, and monitor the circuit breakers in real time after the position arrangement is completed;

[0074] Obtain the historical fault records of the circuit breaker from the database and construct a fault diagnosis model based on the historical fault records;

[0075] Use the built fault diagnosis model to identify faults using the monitored real-time data;

[0076] Determine the level of location arrangement effect based on the fault identification results;

[0077] If the position arrangement effect level is within the adjustment range, the circuit breaker is rearranged; if the position arrangement effect level is not within the adjustment range, the circuit breaker does not need to be rearranged.

[0078] Compared with the prior art, the present invention has the following beneficial effects:

[0079] 1. The present invention adopts a non-intrusive multi-dimensional sensing optimal arrangement method for the mechanical state of the circuit breaker and adopts non-intrusive sensors for monitoring, thus avoiding the defect of the traditional monitoring method that requires direct contact with the internal structure of the circuit breaker, reducing the impact on the normal operation of the equipment and potential safety risks. At the same time, it also reduces the risk of equipment failure caused by monitoring operations.

[0080] 2. The present invention adopts a non-intrusive multi-dimensional perception optimal arrangement method for the mechanical state of the circuit breaker, and adopts a special feature extraction method for different types of data to ensure that the key information of each data type can be effectively captured. This targeted feature extraction method helps to improve the accuracy and efficiency of subsequent analysis. Through in-depth analysis of multi-dimensional feature data, early diagnosis of the mechanical state of the circuit breaker and prediction of potential faults can be achieved.

[0081] 3. The present invention adopts a non-intrusive multi-dimensional perception optimal layout method of the mechanical state of the circuit breaker. According to the constructed Bayesian network and performance influence relationship, the layout position of the circuit breaker is automatically confirmed, thereby reducing the subjectivity and uncertainty of human decision-making. The multi-dimensional feature data of the circuit breaker and the performance evaluation data are combined, and the influence relationship between the data is analyzed by multivariate statistical methods, thereby improving the objectivity and accuracy of the decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 A schematic diagram of the steps of the circuit breaker optimal arrangement method of the present invention;

[0083] Figure 2 It is a schematic flow chart of the optimal arrangement method of circuit breakers of the present invention. DETAILED DESCRIPTION

[0084] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In order to solve the problem of inaccurate data acquisition due to imperfect data acquisition methods when acquiring mechanical state data of circuit breakers in the prior art, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0085] A non-intrusive multi-dimensional perception optimal arrangement method for mechanical states of circuit breakers comprises the following steps:

[0086] S1: Circuit breaker data acquisition: monitor and obtain the original mechanical state data of the circuit breaker, and mark the obtained original mechanical state data of the circuit breaker as state data to be processed;

[0087] Among them, the multi-dimensional monitoring method can more comprehensively reflect the actual operating status of the circuit breaker and improve the accuracy and timeliness of fault diagnosis;

[0088] S2: Circuit breaker data analysis: preprocess the state data to be processed, extract the circuit breaker index data after the data preprocessing is completed, and obtain the circuit breaker multi-dimensional feature data after the index data is extracted;

[0089] Among them, special feature extraction methods are used for different types of data to ensure that the key information of each data type can be effectively captured;

[0090] S3: Circuit breaker optimization layout: The circuit breakers are arranged according to the data of the circuit breakers to be arranged, and a three-dimensional model is constructed to obtain standard circuit breaker position data;

[0091] Among them, through intelligent location layout, it can ensure that the circuit breaker operates in the optimal position, thereby improving the operating efficiency and reliability of the equipment;

[0092] S4: Circuit breaker data evaluation: collect real-time monitoring data of the circuit breakers in the standard circuit breaker position data, and evaluate the collected real-time monitoring data, and judge the effect of the circuit breaker position arrangement according to the evaluation results;

[0093] Among them, through real-time monitoring and fault diagnosis, potential fault problems can be discovered and dealt with in advance, avoiding system downtime or accidents caused by the expansion of faults.

[0094] The original mechanical status data of the circuit breaker in S1 is monitored and obtained, including:

[0095] The mechanical status data of the circuit breaker includes temperature data, acoustic data, vibration data, electrical data, optical data, gas analysis data, environmental data and mechanical characteristic data;

[0096] Among them, temperature data includes infrared thermal imaging data and resistance temperature data; acoustic data includes acoustic emission signals and sound spectrum analysis; vibration data includes vibration waveforms and spectrum analysis; electrical data includes partial discharge signals, current and voltage waveforms; optical data includes visual images and optical sensors; gas analysis data is SF6 gas composition analysis; environmental data includes humidity and temperature; mechanical property data includes operating time and operating force.

[0097] Use non-intrusive sensors to monitor the mechanical status data of the circuit breaker;

[0098] Among them, temperature data is monitored by non-contact infrared thermal imagers and temperature sensors; acoustic data is monitored by acoustic emission sensors and external microphones; vibration data is monitored by MEMS accelerometers; electrical data is monitored by high-frequency current transformers and voltage transformers; optical data is monitored by high-definition cameras and laser rangefinders; gas analysis data is monitored by gas samplers; environmental data is monitored by temperature and humidity sensors; mechanical property data is monitored by displacement sensors and force sensors;

[0099] The monitored temperature data, acoustic data, vibration data, electrical data, optical data, gas analysis data, environmental data and mechanical property data are uniformly marked as status data to be processed.

[0100] Specifically, combined with the monitoring of multiple physical quantities such as temperature, acoustics, and vibration, it can fully reflect the operating status of the circuit breaker and improve the accuracy and reliability of fault diagnosis. Data of different dimensions can verify each other to form a more complete fault portrait, which is helpful to discover hidden problems that are difficult to capture with a single monitoring method. The use of advanced sensors and data analysis technology improves the measurement accuracy and stability. By integrating multiple sensors such as infrared thermal imagers, temperature sensors, acoustic emission sensors, external microphones, MEMS accelerometers, high-frequency current transformers, voltage transformers, high-definition cameras, laser rangefinders, gas samplers, temperature and humidity sensors, displacement sensors, and force sensors, it realizes comprehensive monitoring of the temperature, acoustics, vibration, electrical, optical, gas analysis, environment, and mechanical characteristics of the mechanical state of the circuit breaker. This multi-dimensional monitoring method can more comprehensively reflect the actual operating status of the circuit breaker, improve the accuracy and timeliness of fault diagnosis, and use non-intrusive sensors for monitoring, avoiding the defects of traditional monitoring methods that require direct contact with the internal structure of the circuit breaker, reducing the impact on the normal operation of the equipment and potential safety risks. At the same time, it also reduces the risk of equipment failure caused by monitoring operations. Modern sensor technologies, such as infrared thermal imagers, MEMS accelerometers, high-frequency current transformers, etc., have high precision and high sensitivity, and can capture tiny state changes, thereby improving the accuracy and reliability of monitoring data. This is crucial for timely detection of potential equipment failures and abnormal conditions. In order to solve the problem in the prior art that the mechanical state data of the circuit breaker is not extracted for targeted data features, resulting in inaccurate analysis results in the later data analysis, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0101] The state data to be processed in S2 is preprocessed, and after the data preprocessing is completed, the indicator data of the circuit breaker is extracted, including:

[0102] Perform data preprocessing on the state data to be processed;

[0103] Among them, the data preprocessing process is to denoise, calibrate, normalize and interpolate the temperature data in the state data to be processed in turn; denoise, sound enhance and time synchronize the acoustic data in the state data to be processed in turn; denoise, calibrate and reduce the dimension of the vibration data in the state data to be processed in turn; denoise, sound enhance and time synchronize the electrical data in the state data to be processed in turn; image enhance and denoise the optical data in the state data to be processed in turn; correct and denoise the gas analysis data in the state data to be processed in turn; denoise and calibrate the environmental data in the state data to be processed in turn; denoise and calibrate the mechanical property data in the state data to be processed in turn;

[0104] Finally, the data preprocessing is completed and the data is in the state of being processed.

[0105] Extract data features from the data in the pending state after data preprocessing;

[0106] When extracting data features, feature extraction is performed separately according to the data attributes of the state data to be processed;

[0107] Among them, the feature extraction of temperature data is to perform hot spot detection, temperature gradient calculation, temperature difference recognition and time series recognition on infrared thermal imaging data; the resistance temperature data is used for temperature change trend analysis and abnormal value recognition;

[0108] The feature extraction of acoustic data is to identify the frequency and signal energy of the acoustic emission signal; the sound spectrum analysis is to identify the spectrum characteristics and time-frequency recognition;

[0109] The feature extraction of vibration data is to identify the time domain features, frequency domain features and time-frequency features by analyzing the vibration waveform and spectrum;

[0110] The feature extraction of electrical data is to identify the number of discharges, the discharge phase and the discharge energy of the partial discharge signal; to identify the waveform characteristics and the frequency characteristics of the current and voltage waveforms;

[0111] Feature extraction of optical data is to identify the shape, texture and color features of visual images; to measure and identify the distance of optical sensors;

[0112] The feature extraction of gas analysis data is to identify the concentration features and concentration change features;

[0113] The feature extraction of environmental data is to identify statistical features and correlation features;

[0114] Feature extraction of mechanical property data for identification of statistical features and operating modes;

[0115] After feature extraction of the state data to be processed, feature selection is performed on the extracted features, wherein the feature selection uses principal component analysis to calculate the principal components of the extracted features;

[0116] After the principal component calculation, the index data in the state data to be processed is obtained and marked as multi-dimensional feature data of the circuit breaker.

[0117] Specifically, through denoising, the noise signal in the original data can be effectively removed, interference can be reduced, and the purity and accuracy of the data can be improved. The calibration process can ensure the accuracy and consistency of the data, especially for physical quantities that need to be accurately measured (such as temperature, vibration, etc.). Calibration can eliminate measurement deviations. Normalization processing converts the data to the same dimension and range, which is helpful for subsequent data analysis and processing. Especially in machine learning and deep learning applications, normalization is a key step to improve model performance. For missing data, interpolation technology can fill in the gaps, maintain data integrity, and avoid analysis deviations caused by missing data. For high-dimensional data (such as vibration data), data dimensionality reduction technology can reduce the complexity of the data and reduce the amount of calculation while retaining key information to facilitate subsequent data analysis and processing. Through a strict data preprocessing process, errors and anomalies in the data can be discovered and corrected in a timely manner, thereby improving the reliability and stability of the entire system. Especially in the status monitoring and fault diagnosis of key equipment such as circuit breakers, accurate data is the basis for ensuring the safe operation of the system. Through the extraction of multi-dimensional data features such as infrared thermal imaging, resistance temperature, acoustics, vibration, electrical, optical, gas analysis, environment and mechanical characteristics, it is possible to achieve comprehensive and detailed monitoring of the mechanical state of the circuit breaker. This multi-dimensional data collection and analysis helps to more accurately identify the operating status and potential problems of the equipment. Special feature extraction methods are used for different types of data, such as hot spot detection, frequency identification, time-frequency feature identification, etc., to ensure that the key information of each data type can be effectively captured. This targeted feature extraction method helps to improve the accuracy and efficiency of subsequent analysis. Through in-depth analysis of multi-dimensional feature data, early diagnosis of the mechanical state of the circuit breaker and prediction of potential faults can be achieved. This helps to take measures in advance to prevent the occurrence of faults and improve the reliability and safety of equipment operation. Specifically, the acoustic data in the status data to be processed is sound enhanced, including:

[0118] Extracting acoustic data from the state data to be processed;

[0119] Extracting a preset initial volume threshold from a database; wherein the initial volume threshold is not lower than a preset minimum volume value that can meet data quality requirements;

[0120] The volume threshold is set according to the acoustic data in the state data to be processed in combination with the initial volume threshold, wherein the volume threshold is obtained by the following formula:

[0121]

[0122] Wherein, F represents the volume threshold. When the volume threshold is lower than the preset minimum volume value that can meet the data quality requirements, let F = 1.08F x , and F x Indicates the preset minimum volume value that can meet the data quality requirements; F yc represents the initial volume threshold; n represents the number of frames contained in the acoustic data in the state data to be processed; m represents the number of frames contained in the acoustic data in the state data to be processed that are lower than the initial volume threshold; F i represents the volume value corresponding to the i-th frame of acoustic data in the state data to be processed; F i Indicates the volume median value corresponding to n frames of acoustic data in the state data to be processed; F max and F min Indicates the maximum volume and minimum volume corresponding to n frames of acoustic data in the state data to be processed; F b Indicates the volume standard deviation corresponding to n frames of acoustic data in the state data to be processed;

[0123] Comparing the acoustic data in the state data to be processed with the volume threshold;

[0124] Acoustic data below the volume threshold is extracted, and volume enhancement processing is performed on the acoustic data below the volume threshold.

[0125] The technical effect of the above technical solution is: by comprehensively considering the acoustic data characteristics in the state data to be processed (such as the number of frames, volume distribution, standard deviation, etc.), as well as the preset initial volume threshold and the minimum volume value requirement, a suitable volume threshold is dynamically calculated. This adaptive method can more accurately identify the low-volume part that needs to be enhanced, avoiding the problem of over-enhancement or under-enhancement that may be caused by a single fixed threshold. By ensuring that the volume threshold is not lower than the preset minimum volume value that can meet the data quality requirements, the technical solution ensures that the processed acoustic data at least meets a higher quality standard in terms of volume, which is crucial for subsequent applications such as sound analysis and speech recognition.

[0126] Only acoustic data below the volume threshold is processed for volume enhancement. This targeted processing method not only reduces unnecessary consumption of computing resources, but also avoids damage to the sound quality of the non-low volume part, thereby effectively improving the clarity and recognizability of the low volume part while ensuring the overall sound quality. In application scenarios such as audio processing, speech recognition, and voice communication, the low volume part is often easily ignored or misunderstood. Through the volume enhancement processing of this technical solution, the auditory effect of these low volume parts can be significantly improved, enhancing the overall user experience. The formulas and parameters in this technical solution (such as the initial volume threshold, the minimum volume value, the volume standard deviation, etc.) can be adjusted according to specific application scenarios and needs. It has high flexibility and scalability, and is easy to adapt to different sound processing needs.

[0127] In summary, this technical solution effectively improves the quality and recognizability of acoustic data by adaptively setting the volume threshold and only performing volume enhancement processing on acoustic data below the threshold, while maintaining the stability of the overall sound quality. This is of great significance for improving user experience and meeting specific application requirements.

[0128] Specifically, extracting acoustic data below the volume threshold, and performing volume enhancement processing on the acoustic data below the volume threshold, includes:

[0129] extracting acoustic data below the volume threshold;

[0130] Extract the preset minimum volume value that can meet the data quality requirements;

[0131] Obtaining a first volume adjustment coefficient corresponding to the acoustic data below the volume threshold by using the volume value corresponding to the acoustic data below the volume threshold and the preset minimum volume value that can meet the data quality requirement;

[0132] The first volume adjustment coefficient is obtained by the following formula:

[0133]

[0134] Among them, f 01 represents the first volume adjustment coefficient; k represents the number of frames corresponding to the acoustic data below the volume threshold; F j represents the volume value corresponding to the acoustic data of the jth frame that is lower than the volume threshold; F represents the volume threshold; F yc Indicates the initial volume threshold; F x Indicates the preset minimum volume value that can meet the data quality requirements;

[0135] The second volume adjustment coefficient corresponding to the acoustic data below the volume threshold is set by using the quantitative relationship between the volume value corresponding to the acoustic data below the volume threshold and the volume threshold; wherein the second volume adjustment coefficient is obtained by the following formula:

[0136]

[0137] Among them, f 02 represents the second volume adjustment coefficient; k represents the number of frames corresponding to the acoustic data below the volume threshold; F j represents the volume value corresponding to the acoustic data of the jth frame that is lower than the volume threshold; F represents the volume threshold; F x represents the preset minimum volume value that can meet the data quality requirements; c represents the adjustment factor, and the adjustment factor is obtained by the following formula:

[0138]

[0139] Where c represents the adjustment factor; F min Indicates the minimum volume value corresponding to n frames of acoustic data in the state data to be processed; F m represents the maximum volume value in the acoustic data of k frames below the volume threshold; F p represents the average volume of k frames of acoustic data below the volume threshold;

[0140] The volume value corresponding to the acoustic data below the volume threshold is adjusted using the first volume adjustment coefficient and the second volume adjustment coefficient to obtain the adjusted acoustic data; wherein the volume value of the adjusted acoustic data is obtained by the following formula:

[0141]

[0142] Among them, F t represents the volume value of the adjusted acoustic data corresponding to the acoustic data of each frame below the volume threshold; f 01 represents the first volume adjustment coefficient; f 02 represents the second volume adjustment coefficient; F represents the volume threshold; F0 represents the volume value of the acoustic data before adjustment corresponding to the acoustic data of each frame below the volume threshold.

[0143] The technical effect of the above technical solution is: by introducing the first volume adjustment coefficient and the second volume adjustment coefficient, the technical solution realizes a more refined volume adjustment for acoustic data below the volume threshold. The first volume adjustment coefficient is based on the volume threshold, the initial volume threshold, the preset minimum volume value and the specific volume value of each frame, ensuring that the adjusted volume at least meets the preset minimum requirement. The second volume adjustment coefficient further considers the volume distribution characteristics of the acoustic data below the volume threshold. By introducing the adjustment factor, the volume adjustment is more in line with the characteristics of the data itself, avoiding the problem of over-adjustment or under-adjustment.

[0144] Through refined volume adjustment, the technical solution can significantly improve the sound quality of acoustic data below the volume threshold, so that its volume reaches or exceeds the preset minimum requirement, thereby improving the quality of the overall data. This is particularly important for applications in the fields of speech recognition, voice communication, audio analysis, etc., because low-volume data often affects the accuracy and efficiency of these applications. In the process of volume enhancement, the technical solution not only considers the increase in volume value, but also tries to maintain the balance of sound quality by setting the second volume adjustment coefficient and adjustment factor. That is, while increasing the volume, minimize the damage to the original sound quality characteristics, such as avoiding distortion, noise and other adverse effects. The formulas and parameters (such as the first and second volume adjustment coefficients, adjustment factors, etc.) in the technical solution are highly flexible and can be adjusted according to specific application scenarios and requirements. For example, the preset minimum volume value, initial volume threshold and other parameters can be adjusted according to different audio types, acquisition environments and other factors to meet different sound processing requirements. By improving the sound quality and recognizability of low-volume data, the technical solution can significantly improve the user experience in scenarios such as speech recognition, voice communication, and audio playback. Users no longer need to strain to hear low-volume parts, which also reduces misunderstandings or misidentifications caused by low-volume data.

[0145] In summary, this technical solution effectively improves the quality and recognizability of acoustic data below the volume threshold through refined volume adjustment and adaptive parameter setting, while maintaining the balance and stability of sound quality, which is of great significance for improving user experience and meeting specific application requirements.

[0146] In order to solve the problem in the prior art that when arranging the positions of circuit breakers, there is no more optimized arrangement and no model construction based on the arrangement positions, which leads to inconvenient adjustment of the circuit breakers in the later stage, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0147] According to the data of the circuit breakers to be arranged in S3, the circuit breakers are arranged and a three-dimensional model is constructed, including:

[0148] The circuit breaker is subjected to performance evaluation according to the multi-dimensional characteristic data of the circuit breaker, and the performance evaluation includes contact performance evaluation, insulator performance evaluation, operating mechanism performance evaluation and mechanical state evaluation;

[0149] A Bayesian network is constructed according to the performance evaluation results and the position arrangement rules, wherein the position arrangement rules are obtained from a database;

[0150] After the Bayesian network is constructed, confirm each node and each edge in the Bayesian network;

[0151] Each node is an option point for the performance parameters and location layout of the circuit breaker; each edge is an associated edge between the performance parameters and location layout;

[0152] The circuit breaker multidimensional feature data and the performance evaluation data are used to determine the performance impact, wherein the multivariate statistical method is used to analyze the impact relationship between the circuit breaker multidimensional feature data and the performance evaluation data;

[0153] The layout position of the circuit breaker is confirmed according to the constructed Bayesian network and the performance impact relationship, and the circuit breaker simulation position data is obtained after confirmation.

[0154] Construct a three-dimensional model of the simulated position data of the circuit breaker;

[0155] Among them, the basic information of the circuit breaker is retrieved from the database, and the basic information includes size, shape and material; the three-dimensional coordinate data is acquired by using a three-dimensional scanner according to the basic information of the circuit breaker;

[0156] Use Geomagic tools to convert 3D coordinate data into editable 3D models;

[0157] The converted editable three-dimensional model is 3D rendered, and after the 3D rendering is completed, the standard circuit breaker position data is obtained.

[0158] Specifically, through multi-dimensional evaluations such as contact performance, insulator performance, operating mechanism performance and mechanical status, the comprehensive performance of the circuit breaker can be fully and deeply understood. The performance evaluation is performed using the multi-dimensional feature data of the circuit breaker to ensure the scientificity and accuracy of the evaluation results. The performance parameters and position layout of the circuit breaker are modeled using the Bayesian network, which can capture the complex relationship between the performance parameters and the position layout, providing strong support for the optimization layout. According to the constructed Bayesian network and performance influence relationship, the layout position of the circuit breaker is automatically confirmed, reducing the subjectivity and uncertainty of human decision-making. The multi-dimensional feature data of the circuit breaker is combined with the performance evaluation data, and the influence relationship between the data is analyzed using multivariate statistical methods, which improves the objectivity and accuracy of the decision. Through intelligent position layout, it can ensure that the circuit breaker operates in the optimal position, thereby improving the operating efficiency and reliability of the equipment. The three-dimensional coordinate data of the circuit breaker is obtained using a three-dimensional scanner, which can ensure the high accuracy and detail of the data. The 3D scanner has the characteristics of non-contact measurement, fast scanning speed and high precision. It can accurately capture the outline and detailed features of the circuit breaker. Through professional tools such as Geomagic, the 3D coordinate data is converted into an editable 3D model. This process further improves the fineness and realism of the model. Tools such as Geomagic have rich functions and powerful capabilities in data processing and model optimization, which can ensure the quality of the model. The 3D scanner can quickly obtain the 3D coordinate data of the circuit breaker, and tools such as Geomagic can efficiently convert this data into an editable 3D model. This process greatly shortens the modeling time and improves work efficiency. The converted editable 3D model allows users to modify and adjust it as needed. This provides users with greater flexibility and creative space, and they can optimize and improve the model according to actual needs. Through 3D rendering technology, the 3D model of the circuit breaker can be presented in an intuitive and vivid way. This visual display helps users better understand the structure and layout of the circuit breaker and improve the efficiency of fault diagnosis and maintenance. The constructed standard circuit breaker position data can be applied to multiple scenarios, such as simulation analysis, fault prediction, maintenance guidance, etc. This flexibility and versatility make the solution have a wider application prospect.

[0159] In S4, real-time monitoring data of the circuit breakers in the standard circuit breaker position data is collected, and the collected real-time monitoring data is evaluated, and the effect of the circuit breaker position arrangement is judged according to the evaluation results:

[0160] Arrange the circuit breakers according to the standard circuit breaker position data, and monitor the circuit breakers in real time after the position arrangement is completed;

[0161] Obtain the historical fault records of the circuit breaker from the database and construct a fault diagnosis model based on the historical fault records;

[0162] Use the built fault diagnosis model to identify faults using the monitored real-time data;

[0163] Determine the level of location arrangement effect based on the fault identification results;

[0164] If the position arrangement effect level is within the adjustment range, the circuit breaker is rearranged; if the position arrangement effect level is not within the adjustment range, the circuit breaker does not need to be rearranged.

[0165] Specifically, by obtaining the historical fault records of the circuit breaker and constructing a fault diagnosis model, the model can learn the laws and characteristics of fault occurrence, improve the accuracy and efficiency of fault diagnosis, and use the constructed fault diagnosis model to identify faults in real-time monitoring data, which can quickly identify the possible fault types and locations of the circuit breaker, and provide strong support for subsequent maintenance and replacement. According to the fault identification results, the effect of the circuit breaker position layout is graded and the layout effect is measured by quantitative evaluation standards, making the evaluation results more objective and scientific. If the position layout effect level is within the adjustment range, the circuit breaker is rearranged in time to optimize the layout effect; if the effect level is not within the adjustment range, it is confirmed that the current layout is reasonable and does not need to be rearranged. This dynamic adjustment mechanism ensures the continuous optimization and adaptability of the circuit breaker position layout. Through real-time monitoring and fault diagnosis, potential fault problems can be discovered and handled in advance, avoiding system downtime or accidents caused by fault expansion, and improving the overall operation efficiency of the system.

[0166] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0167] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A non-intrusive multi-dimensional perception optimal arrangement method for mechanical states of circuit breakers, characterized in that: The steps include: S1: Circuit breaker data acquisition: monitor and obtain the original mechanical state data of the circuit breaker, and mark the obtained original mechanical state data of the circuit breaker as state data to be processed; S2: Circuit breaker data analysis: preprocess the state data to be processed, extract the circuit breaker index data after the data preprocessing is completed, and obtain the circuit breaker multi-dimensional feature data after the index data is extracted; S3: Circuit breaker optimization layout: The circuit breakers are arranged according to the data of the circuit breakers to be arranged, and a three-dimensional model is constructed to obtain standard circuit breaker position data; S4: Circuit breaker data evaluation: collect real-time monitoring data of the circuit breakers in the standard circuit breaker position data, and evaluate the collected real-time monitoring data, and judge the effect of the circuit breaker position arrangement according to the evaluation results.

2. A non-intrusive multi-dimensional sensing optimal arrangement method for mechanical states of circuit breakers according to claim 1, characterized in that: The original mechanical status data of the circuit breaker in S1 is monitored and obtained, including: The mechanical status data of the circuit breaker includes temperature data, acoustic data, vibration data, electrical data, optical data, gas analysis data, environmental data and mechanical characteristic data; Among them, temperature data includes infrared thermal imaging data and resistance temperature data; acoustic data includes acoustic emission signals and sound spectrum analysis; vibration data includes vibration waveforms and spectrum analysis; electrical data includes partial discharge signals, current and voltage waveforms; optical data includes visual images and optical sensors; gas analysis data is SF6 gas composition analysis; environmental data includes humidity and temperature; mechanical property data includes operating time and operating force.

3. The non-intrusive multi-dimensional sensing optimal arrangement method for mechanical state of circuit breakers according to claim 2, characterized in that: The original mechanical status data of the circuit breaker in S1 is monitored and obtained, including: Use non-intrusive sensors to monitor the mechanical status data of the circuit breaker; Among them, temperature data is monitored by non-contact infrared thermal imagers and temperature sensors; acoustic data is monitored by acoustic emission sensors and external microphones; vibration data is monitored by MEMS accelerometers; electrical data is monitored by high-frequency current transformers and voltage transformers; optical data is monitored by high-definition cameras and laser rangefinders; gas analysis data is monitored by gas samplers; environmental data is monitored by temperature and humidity sensors; mechanical property data is monitored by displacement sensors and force sensors; The monitored temperature data, acoustic data, vibration data, electrical data, optical data, gas analysis data, environmental data and mechanical property data are uniformly marked as status data to be processed.

4. The non-intrusive multi-dimensional sensing optimal arrangement method for mechanical status of circuit breakers according to claim 3, characterized in that: The state data to be processed in S2 is preprocessed, and after the data preprocessing is completed, the indicator data of the circuit breaker is extracted, including: Perform data preprocessing on the state data to be processed; Among them, the data preprocessing process is to denoise, calibrate, normalize and interpolate the temperature data in the state data to be processed in turn; denoise, sound enhance and time synchronize the acoustic data in the state data to be processed in turn; denoise, calibrate and reduce the dimension of the vibration data in the state data to be processed in turn; denoise, sound enhance and time synchronize the electrical data in the state data to be processed in turn; image enhance and denoise the optical data in the state data to be processed in turn; correct and denoise the gas analysis data in the state data to be processed in turn; denoise and calibrate the environmental data in the state data to be processed in turn; denoise and calibrate the mechanical property data in the state data to be processed in turn; Finally, the data preprocessing is completed and the data is in the state of being processed.

5. The non-intrusive multi-dimensional sensing optimal arrangement method for mechanical status of circuit breakers according to claim 4, characterized in that: Performing sound enhancement processing on the acoustic data in the state data to be processed, including: Extracting acoustic data from the state data to be processed; Extracting a preset initial volume threshold from a database; wherein the initial volume threshold is not lower than a preset minimum volume value that can meet data quality requirements; The volume threshold is set according to the acoustic data in the state data to be processed in combination with the initial volume threshold, wherein the volume threshold is obtained by the following formula: Wherein, F represents the volume threshold. When the volume threshold is lower than the preset minimum volume value that can meet the data quality requirements, let F = 1.08F x , and F x Indicates the preset minimum volume value that can meet the data quality requirements; F yc represents the initial volume threshold; n represents the number of frames contained in the acoustic data in the state data to be processed; m represents the number of frames contained in the acoustic data in the state data to be processed that are lower than the initial volume threshold; F i represents the volume value corresponding to the i-th frame of acoustic data in the state data to be processed; F i Indicates the volume median value corresponding to n frames of acoustic data in the state data to be processed; F max and F min Indicates the maximum volume and minimum volume corresponding to n frames of acoustic data in the state data to be processed; F b Indicates the volume standard deviation corresponding to n frames of acoustic data in the state data to be processed; Comparing the acoustic data in the state data to be processed with the volume threshold; Acoustic data below the volume threshold is extracted, and volume enhancement processing is performed on the acoustic data below the volume threshold.

6. A non-intrusive multi-dimensional sensing optimal arrangement method for mechanical states of circuit breakers according to claim 5, characterized in that: Extracting acoustic data below the volume threshold, and performing volume enhancement processing on the acoustic data below the volume threshold, including: extracting acoustic data below the volume threshold; Extract the preset minimum volume value that can meet the data quality requirements; Obtaining a first volume adjustment coefficient corresponding to the acoustic data below the volume threshold by using the volume value corresponding to the acoustic data below the volume threshold and the preset minimum volume value that can meet the data quality requirement; The first volume adjustment coefficient is obtained by the following formula: Among them, f 01 represents the first volume adjustment coefficient; k represents the number of frames corresponding to the acoustic data below the volume threshold; F j represents the volume value corresponding to the acoustic data of the jth frame that is lower than the volume threshold; F represents the volume threshold; F yc Indicates the initial volume threshold; F x Indicates the preset minimum volume value that can meet the data quality requirements; The second volume adjustment coefficient corresponding to the acoustic data below the volume threshold is set by using the quantitative relationship between the volume value corresponding to the acoustic data below the volume threshold and the volume threshold; wherein the second volume adjustment coefficient is obtained by the following formula: Among them, f 02 represents the second volume adjustment coefficient; k represents the number of frames corresponding to the acoustic data below the volume threshold; F j represents the volume value corresponding to the acoustic data of the jth frame that is lower than the volume threshold; F represents the volume threshold; F x represents the preset minimum volume value that can meet the data quality requirements; c represents the adjustment factor, and the adjustment factor is obtained by the following formula: Where c represents the adjustment factor; F min Indicates the minimum volume value corresponding to n frames of acoustic data in the state data to be processed; F m represents the maximum volume value in the acoustic data of k frames below the volume threshold; F p represents the average volume of k frames of acoustic data below the volume threshold; The volume value corresponding to the acoustic data below the volume threshold is adjusted using the first volume adjustment coefficient and the second volume adjustment coefficient to obtain the adjusted acoustic data; wherein the volume value of the adjusted acoustic data is obtained by the following formula: Among them, F t represents the volume value of the adjusted acoustic data corresponding to the acoustic data of each frame below the volume threshold; f 01 represents the first volume adjustment coefficient; f 02 represents the second volume adjustment coefficient; F represents the volume threshold; F0 represents the volume value of the acoustic data before adjustment corresponding to the acoustic data of each frame below the volume threshold.

7. The non-intrusive multi-dimensional sensing optimal arrangement method for mechanical status of circuit breakers according to claim 4, characterized in that: The state data to be processed in S2 is preprocessed, and after the data preprocessing is completed, the indicator data of the circuit breaker is extracted, which also includes: Extract data features from the data in the pending state after data preprocessing; When extracting data features, feature extraction is performed separately according to the data attributes of the state data to be processed; Among them, the feature extraction of temperature data is to perform hot spot detection, temperature gradient calculation, temperature difference recognition and time series recognition on infrared thermal imaging data; the resistance temperature data is used for temperature change trend analysis and abnormal value recognition; The feature extraction of acoustic data is to identify the frequency and signal energy of the acoustic emission signal; the sound spectrum analysis is to identify the spectrum characteristics and time-frequency recognition; The feature extraction of vibration data is to identify the time domain features, frequency domain features and time-frequency features by analyzing the vibration waveform and spectrum; The feature extraction of electrical data is to identify the number of discharges, the discharge phase and the discharge energy of the partial discharge signal; to identify the waveform characteristics and the frequency characteristics of the current and voltage waveforms; Feature extraction of optical data is to identify the shape, texture and color features of visual images; to measure and identify the distance of optical sensors; The feature extraction of gas analysis data is to identify the concentration features and concentration change features; The feature extraction of environmental data is to identify statistical features and correlation features; Feature extraction of mechanical property data for identification of statistical features and operating modes; After feature extraction of the state data to be processed, feature selection is performed on the extracted features, wherein the feature selection uses principal component analysis to calculate the principal components of the extracted features; After the principal component calculation, the index data in the state data to be processed is obtained and marked as multi-dimensional feature data of the circuit breaker.

8. The non-intrusive multi-dimensional sensing optimal arrangement method for mechanical states of circuit breakers according to claim 7, characterized in that: According to the data of the circuit breakers to be arranged in S3, the circuit breakers are arranged and a three-dimensional model is constructed, including: The circuit breaker is subjected to performance evaluation according to the multi-dimensional characteristic data of the circuit breaker, and the performance evaluation includes contact performance evaluation, insulator performance evaluation, operating mechanism performance evaluation and mechanical state evaluation; A Bayesian network is constructed according to the performance evaluation results and the position arrangement rules, wherein the position arrangement rules are obtained from a database; After the Bayesian network is constructed, confirm each node and each edge in the Bayesian network; Each node is an option point for the performance parameters and location layout of the circuit breaker; each edge is an associated edge between the performance parameters and location layout; The circuit breaker multidimensional feature data and the performance evaluation data are used to determine the performance impact, wherein the multivariate statistical method is used to analyze the impact relationship between the circuit breaker multidimensional feature data and the performance evaluation data; The layout position of the circuit breaker is confirmed according to the constructed Bayesian network and the performance impact relationship, and the circuit breaker simulation position data is obtained after confirmation.

9. A non-intrusive multi-dimensional sensing optimal arrangement method for mechanical states of circuit breakers according to claim 8, characterized in that: The circuit breakers are arranged according to the data of the circuit breakers to be arranged in S3, and a three-dimensional model is constructed, which also includes: Construct a three-dimensional model of the simulated position data of the circuit breaker; Among them, the basic information of the circuit breaker is retrieved from the database; According to the basic information of the circuit breaker, the three-dimensional coordinate data is acquired by using a three-dimensional scanner; Use Geomagic tools to convert 3D coordinate data into editable 3D models; The converted editable three-dimensional model is 3D rendered, and after the 3D rendering is completed, the standard circuit breaker position data is obtained.

10. A non-intrusive multi-dimensional sensing optimal arrangement method for mechanical states of circuit breakers according to claim 9, characterized in that: In S4, real-time monitoring data of the circuit breakers in the standard circuit breaker position data is collected, and the collected real-time monitoring data is evaluated, and the effect of the circuit breaker position arrangement is judged according to the evaluation results: Arrange the circuit breakers according to the standard circuit breaker position data, and monitor the circuit breakers in real time after the position arrangement is completed; Obtain the historical fault records of the circuit breaker from the database and construct a fault diagnosis model based on the historical fault records; Use the built fault diagnosis model to identify faults using the monitored real-time data; Determine the level of location arrangement effect based on the fault identification results; If the position arrangement effect level is within the adjustment range, the circuit breaker is rearranged; if the position arrangement effect level is not within the adjustment range, the circuit breaker does not need to be rearranged.

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