Circuit breaker mechanical characteristics online monitoring data processing method and device, fault diagnosis method and device, and storage medium

Abstract

The present invention discloses a method and device for processing online monitoring data of the mechanical characteristics of a circuit breaker, a fault diagnosis method and device, and a storage medium. The online monitoring data processing method sequentially performs preprocessing, slope calculation, singular point extraction, multi-order B-spline interpolation fitting, and condition identification on the current sampling data of the circuit breaker's opening and closing coils. Simultaneously, during the processing process, the singular point set is cyclically optimized so that the current-time fitting curve can retain the characteristics of the measured original data to a greater extent, thereby making the obtained characteristic matrix for judging mechanical characteristic faults of the circuit breaker more accurate. Moreover, during the processing process, data anomalies caused by the acquisition or transmission of the measured original data can be identified. Therefore, the present invention solves the problem of feature selection of the measured original data of the circuit breaker's opening and closing coil current for mechanical characteristic fault diagnosis, and improves the accuracy of subsequent diagnosis of mechanical characteristic faults of the circuit breaker.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit breaker fault monitoring, and in particular to a circuit breaker mechanical characteristic online monitoring data processing method and device, a fault diagnosis method and device, and a storage medium. Background Art

[0002] As a key protective device in power systems, the reliable operation of circuit breakers is crucial for ensuring grid security. However, circuit breakers are prone to failure during long-term operation, with operating mechanism failure being one of the most common types. The operating mechanism is the core mechanical component that performs the circuit breaker's opening and closing operations. Failures can include coil core jamming, drive linkage failure, and spring failure. These failures not only affect the circuit breaker's normal operation but can also lead to more serious equipment damage or safety accidents. Therefore, research on diagnostic methods for circuit breaker operating mechanism failures is of great engineering significance.

[0003] In recent years, with advances in sensor technology and data analysis methods, fault diagnosis technology based on signal analysis has become a research hotspot. This involves diagnosing the mechanical condition of a circuit breaker by analyzing the characteristics of the current signal collected in real time during the circuit breaker's opening and closing operations. Currently, the main approach to intelligent diagnosis of circuit breaker operating mechanism faults is to determine whether an operating mechanism fault exists by comparing the deviation between the extreme point time of the current signal collected in real time during the circuit breaker opening and closing operations and the standard operating time for the circuit breaker opening and closing operations. For example, the first peak extreme point of the current signal can represent the operating time of the starting component. If it exceeds the standard operating time, it indicates a fault such as a stuck starting component. However, due to the objective presence of many extreme points in the measured raw data during the circuit breaker opening and closing operations, the processing of the measured raw data and the selection of the extreme points used for comparison with the standard operating time directly affect the accuracy of the circuit breaker operating mechanism fault diagnosis. However, there is currently no effective solution for processing the measured raw data and selecting the extreme points used for comparison with the standard operating time to ensure accurate circuit breaker operating mechanism fault diagnosis. Summary of the Invention

[0004] In view of the deficiencies of the prior art described above, the purpose of the present invention is to provide a method for processing data of online monitoring of mechanical characteristics of circuit breakers, which, by sequentially performing preprocessing, slope calculation, singular point extraction, multi-order B-spline interpolation fitting and condition identification on the current sampling data of the circuit breaker's opening and closing coils, can not only obtain a feature matrix for judging mechanical characteristic faults of circuit breakers, but also identify the problem of inaccurate data caused by the acquisition or transmission process of the current sampling data of the opening and closing coils; thereby solving the problem of feature selection of the measured original data of the opening and closing coil current of the circuit breaker for mechanical characteristic fault diagnosis, and at the same time, improving the accuracy of subsequent diagnosis of mechanical characteristic faults of circuit breakers.

[0005] To achieve the above-mentioned object of the invention, the present invention provides a method for processing data of online monitoring of mechanical characteristics of a circuit breaker, which comprises the following steps:

[0006] S1: Acquire current sampling data of the opening and closing coils of the circuit breaker, and pre-process the acquired current sampling data of the opening and closing coils to obtain current sample data to be processed;

[0007] S2: Calculate the slope of every two adjacent sample points in the current sample data to obtain a slope data set ; Where n represents the total number of sample points;

[0008] S3: extracting singular points according to the slope data set to obtain a singular point set;

[0009] S4: performing multi-order B-spline interpolation fitting on the singular point set as an interpolation point set, and obtaining a current-time fitting curve after the fitting is completed;

[0010] S5: Generate a characteristic matrix based on the extreme points of the current-time fitting curve ; Wherein, the element of the characteristic matrix is ​​0 or 1, which is used to indicate that the extreme point is a trough point or a peak point;

[0011] S6: Perform condition identification based on the characteristic matrix and generate a condition identification result; if the condition identification result is a first result, output the characteristic matrix for circuit breaker mechanical characteristic fault judgment and jump to end; if the condition identification result is a second result, output a data abnormality prompt and jump to end; if the condition identification result is other results, update the number of condition identification executions;

[0012] S7: Determine whether the number of executions of the condition identification reaches a set number; if so, output a prompt indicating that the data processing has failed; otherwise, determine a sample point in the current sample data as a newly added singular point or delete a singular point in the singular point set based on the singular point time distribution of the singular point set, and after updating the singular point set, jump to step S4.

[0013] According to a specific embodiment, the present invention provides a method for processing data of online monitoring of mechanical characteristics of a circuit breaker. In step S1, the acquired opening and closing coil current sampling data is preprocessed by fitting the current signal sampling data corresponding to multiple opening actions or closing actions before the circuit breaker is put into use into a noisy current-time curve, and comparing the noisy current-time curve with a current-time standard curve to separate the noise-time curve; and, based on the noise-time curve, calculating the noise mean during the opening action or closing action of the circuit breaker, and denoising the opening and closing coil current sampling data based on the noise mean to obtain the current sample data; wherein, the current-time standard curve is the current-time curve provided by the manufacturer of the circuit breaker.

[0014] According to a specific embodiment, the method for processing data of online monitoring of mechanical characteristics of a circuit breaker provided by the present invention, step S3 includes the following steps:

[0015] S301: Based on the slope data set , calculate the specific value corresponding to each sample point;

[0016] S302: Determine whether the singular value of each sample point meets a set condition. If so, extract the corresponding sample point as a singular point and add it to the singular point set.

[0017] Preferably, in step S301, the method for calculating the unique value corresponding to each sample point is:

[0018] If i < 200, then ; If i ≥ 200, then ;

[0019] Among them, a i represents the unique value corresponding to the i-th sample point, k i and k j They represent the slopes of the i-th sample point and the j-th sample point respectively.

[0020] Further preferably, in step S302, the setting condition is configured as follows: ;in, is the mean of the specific values ​​corresponding to all sample points, is the variance of the specific values ​​corresponding to all sample points, and m is the extraction coefficient.

[0021] According to a specific embodiment, the method for processing data of online monitoring of mechanical characteristics of a circuit breaker provided by the present invention, step S4 includes the following steps:

[0022] S401: performing multi-order B-spline interpolation fitting on the singular point set as an interpolation point set, and obtaining a current-time fitting curve after the fitting is completed;

[0023] S402: determining in sequence whether the current value deviation of each extreme point of the current-time fitting curve and the sample point at the same time in the current sample data exceeds a set range; if so, adding the corresponding sample point in the current sample data to the singular point set;

[0024] S403: Determine whether the singular point set is updated. If so, jump to step S401; otherwise, continue to execute step S5.

[0025] According to a specific embodiment, the method for processing data of online monitoring of mechanical characteristics of a circuit breaker provided by the present invention, step S5 includes the following steps:

[0026] S501: Obtain extreme points of the current-time fitting curve, and filter out extreme points whose current values ​​are lower than a set threshold;

[0027] S502: Generate a characteristic matrix based on the remaining extreme points of the current-time fitting curve .

[0028] According to a specific embodiment, the method for processing data of online monitoring of mechanical characteristics of a circuit breaker provided by the present invention, in step S6, condition identification is performed according to the number of elements indicating peak points in the characteristic matrix;

[0029] If the number of elements indicating peak points is greater than a lower threshold and less than an upper threshold, the condition recognition result indicated as a first result is generated;

[0030] If the number of elements indicating peak points is lower than the lower threshold or greater than the upper threshold, generating the condition recognition result indicated as the second result;

[0031] If the number of elements indicating peak points is equal to the lower threshold or the upper threshold, the condition recognition result indicated as the third result or the fourth result is generated, and the number of condition recognition execution times is updated.

[0032] According to a specific embodiment, in the method for processing online monitoring data of mechanical characteristics of a circuit breaker provided by the present invention, in step S7, a method for determining a sample point in the current sample data as a newly added singular point or deleting a singular point in the singular point set based on the singular point time distribution of the singular point set is as follows:

[0033] If the condition identification result indicates the third result, a sample point at the middle moment between two adjacent singular points with the largest time interval in the singular point set in the current sample data is used as a new singular point;

[0034] If the condition recognition result indicates the fourth result, the singular point with the smallest sum of time intervals with two adjacent singular points in the singular point set is deleted.

[0035] Preferably, the lower threshold and the upper threshold are determined by an installation method of the circuit breaker.

[0036] On the other hand, the present invention also provides a method for diagnosing mechanical characteristic faults of a circuit breaker, which comprises the following steps:

[0037] Obtaining a characteristic matrix for circuit breaker mechanical characteristic fault judgment based on the circuit breaker mechanical characteristic online monitoring data processing method provided by the present invention;

[0038] Determining whether a first time interval corresponding to a first peak point and a first trough point in the characteristic matrix is ​​within a first set time range; and determining whether a second time interval corresponding to a first trough point and a last peak point in the characteristic matrix is ​​within a second set time range;

[0039] If the first set time range is exceeded, it indicates that the circuit breaker starting component has a fault; if the second set time range is exceeded, it indicates that the circuit breaker main actuator has a fault; otherwise, it indicates that the mechanical characteristics of the circuit breaker are normal.

[0040] Based on the same inventive concept, the present invention also provides a circuit breaker mechanical characteristics online monitoring data processing device, which includes:

[0041] A preprocessing module is used to obtain the current sampling data of the opening and closing coils of the circuit breaker, and preprocess the obtained current sampling data of the opening and closing coils to obtain the current sample data to be processed;

[0042] The slope data calculation module is used to calculate the slope of two adjacent sample points in the current sample data to obtain a slope data set. ; Where n represents the total number of sample points;

[0043] A singular point extraction module, configured to extract singular points from the slope data set to obtain a singular point set;

[0044] A curve fitting module is used to perform multi-order B-spline interpolation fitting using the singular point set as an interpolation point set, and obtain a current-time fitting curve after the fitting is completed;

[0045] A characteristic matrix generation module is used to generate a characteristic matrix according to the extreme points of the current-time fitting curve. ; Wherein, the element of the characteristic matrix is ​​0 or 1, which is used to indicate that the extreme point is a trough point or a peak point;

[0046] a condition identification module, configured to perform condition identification based on the characteristic matrix; wherein, if the condition identification result indicates a first result or a second result, a prompt indicating that the output data is normal or abnormal is displayed, and the characteristic matrix with the condition identification result of the first result is used for circuit breaker mechanical characteristic fault judgment; otherwise, the condition identification execution count is updated;

[0047] a singular point set updating module, configured to determine whether the condition identification has been executed a set number of times; if so, outputting a prompt indicating data processing failure; otherwise, determining a sample point in the current sample data as a newly added singular point or deleting a singular point in the singular point set based on the singular point time distribution of the singular point set; and outputting the updated singular point set to the curve fitting module for re-performing multi-order B-spline interpolation fitting.

[0048] On the other hand, the present invention further provides a circuit breaker mechanical characteristic fault diagnosis device, comprising: the circuit breaker mechanical characteristic online monitoring data processing device provided by the present invention, and a fault diagnosis module;

[0049] The fault diagnosis module is configured to obtain a characteristic matrix for circuit breaker mechanical characteristic fault diagnosis provided by the circuit breaker mechanical characteristic online monitoring data processing device, and determine, based on the characteristic matrix, whether a first time interval corresponding to a first singular point indicated as a peak point and a first singular point indicated as a trough point is within a first set time range; and determine whether a second time interval corresponding to the first singular point indicated as a trough point and a last singular point indicated as a peak point is within a second set time range;

[0050] If the first set time range is exceeded, it is prompted that the circuit breaker starting component has a fault; if the second set time range is exceeded, it is prompted that the circuit breaker main actuator has a fault.

[0051] On the other hand, the present invention also provides a readable storage medium having one or more programs stored thereon, which, when executed by one or more processors, implement the circuit breaker mechanical characteristics online monitoring data processing method or circuit breaker mechanical characteristics fault diagnosis method provided by the present invention.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The method for processing data of online monitoring of mechanical characteristics of a circuit breaker provided by the present invention sequentially performs preprocessing, slope calculation, singular point extraction, multi-order B-spline interpolation fitting, and condition identification processing operations on the sampling data of the circuit breaker's opening and closing coil current. At the same time, during the processing process, the singular point set is cyclically optimized so that the current-time fitting curve can retain the characteristics of the measured original data to a greater extent, thereby making the obtained characteristic matrix for judging the mechanical characteristic faults of the circuit breaker more accurate. Moreover, during the processing process, data anomalies caused by the measured original data during the collection or transmission process can be identified. Therefore, the present invention solves the problem of feature selection of the measured original data of the circuit breaker's opening and closing coil current for mechanical characteristic fault diagnosis, and improves the accuracy of subsequent diagnosis of mechanical characteristic faults of the circuit breaker. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Schematic diagram of the flow of the data processing method for online monitoring of mechanical characteristics of a circuit breaker according to the present invention;

[0055] Figure 2 Schematic diagram of a specific implementation flow of step S4 of the method for processing data of online monitoring of mechanical characteristics of a circuit breaker according to the present invention;

[0056] Figure 3 Schematic diagram of the current-time curve change in the specific process of step S4 of the present invention;

[0057] Figure 4 Schematic diagram of a specific implementation flow of step S7 of the method for processing data of online monitoring of mechanical characteristics of a circuit breaker according to the present invention;

[0058] Figure 5 The following is a typical current-time curve of an actual model vertical circuit breaker;

[0059] Figure 6 Schematic diagram of the flow of the circuit breaker mechanical characteristic fault diagnosis method of the present invention;

[0060] Figure 7 A modular schematic diagram of a circuit breaker mechanical characteristic online monitoring data processing device according to the present invention;

[0061] Figure 8 A modular schematic diagram of the circuit breaker mechanical characteristic fault diagnosis device of the present invention. DETAILED DESCRIPTION

[0062] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments, as all technologies implemented based on the present invention fall within the scope of the present invention.

[0063] like Figure 1As shown, in one embodiment of the present invention, the method for processing data of online monitoring of mechanical characteristics of a circuit breaker provided by the present invention includes the following steps:

[0064] S1: Acquire current sampling data of the opening and closing coils of the circuit breaker, and pre-process the acquired current sampling data of the opening and closing coils to obtain current sample data to be processed;

[0065] S2: Calculate the slope of every two adjacent sample points in the current sample data to obtain a slope data set ; Where n represents the total number of sample points;

[0066] S3: extracting singular points according to the slope data set to obtain a singular point set;

[0067] S4: performing multi-order B-spline interpolation fitting on the singular point set as an interpolation point set, and obtaining a current-time fitting curve after the fitting is completed;

[0068] S5: Generate a characteristic matrix based on the extreme points of the current-time fitting curve ; Wherein, the element of the characteristic matrix is ​​0 or 1, which is used to indicate that the extreme point is a trough point or a peak point;

[0069] S6: Perform condition identification based on the characteristic matrix and generate a condition identification result; if the condition identification result is a first result, output the characteristic matrix for circuit breaker mechanical characteristic fault judgment and jump to end; if the condition identification result is a second result, output a data abnormality prompt and jump to end; if the condition identification result is other results, update the number of condition identification executions;

[0070] S7: Determine whether the number of executions of the condition identification reaches a set number; if so, output a prompt indicating that the data processing has failed; otherwise, determine a sample point in the current sample data as a newly added singular point or delete a singular point in the singular point set based on the singular point time distribution of the singular point set, and after updating the singular point set, jump to step S4.

[0071] Specifically, in step S1, since the acquisition module is online and real-time acquiring the current signal of the circuit breaker around the clock, the recording module also performs recording synchronously with the acquisition module; therefore, once the opening and closing control circuit of the circuit breaker detects the opening action signal or the closing action signal, the recording module obtains the current signal recording data including the opening or closing action of the circuit breaker, that is, the opening and closing coil current sampling data of the circuit breaker is obtained. During implementation, the acquired opening and closing coil current sampling data is usually longer than the opening or closing action duration of the circuit breaker. For example, the action time of a 10kV-level circuit breaker is generally tens of milliseconds, and the acquired opening and closing coil current sampling data duration is about one hundred milliseconds.

[0072] At the same time, considering that the circuit breaker is affected by the power supply, load and circuit stability in a specific electrical environment, the impact of its noise on the current signal is different; at the same time, considering that a new circuit breaker generally does not have mechanical characteristic faults in the early stage of being put into use; therefore, in step S1, the acquired opening and closing coil current sampling data needs to be preprocessed, that is, the noise influence is removed; and in the denoising preparation stage, first, the current signal sampling data corresponding to multiple opening actions or closing actions in the early stage of the circuit breaker being put into use are fitted into a noisy current-time curve; then, the noisy current-time curve is compared with the current-time standard curve to separate the noise-time curve; finally, based on the separated noise-time curve, the noise mean value during the opening action or closing action of the circuit breaker is calculated; in this way, the noise mean value can be used to denoise the opening and closing coil current sampling data, that is, the current value corresponding to the opening and closing coil current sampling data is subtracted from the noise mean value to obtain the current sample data.

[0073] During implementation, the current signal sampling data corresponding to multiple opening or closing actions before the circuit breaker is put into use are fitted into a noisy current-time curve. In fact, multiple curves are fitted into one curve. Therefore, the least squares method is used to fit the noisy current-time curve. By minimizing the sum of squared residuals between all data points and the fitted curve, the optimal parameters are found, which can enable the fitting curve to retain the noisy characteristics of the current signal sampling data to the greatest extent.

[0074] Specifically, in step S2, since the current sample data is the current value corresponding to different sampling points within the sampling time, it is discrete data; therefore, with time as the horizontal axis and current value as the horizontal axis, the i-th sampling point is represented as (x i ,y i ), then the slope of every two adjacent sample points in the current sample data is calculated as: ; According to the above calculation method, the slope data set can be obtained .

[0075] Specifically, in step S3, singular points are extracted based on the slope data set, and the singular point set is obtained in the following manner:

[0076] S301: Based on the slope data set , calculate the specific value corresponding to each sample point;

[0077] S302: Determine whether the singular value of each sample point meets a set condition. If so, extract the corresponding sample point as a singular point and add it to the singular point set.

[0078] In practice, since the current signal sampling data usually increases suddenly around the 200th point, in order to eliminate the influence of the first sudden increase position, a sliding window is used for feature extraction after exceeding 200 points; and since the data after the sudden increase are basically at the same height, a sliding window with a width of 150 points is used for feature extraction. That is, in step S301, the method for calculating the specific value corresponding to each sample point is:

[0079] If i < 200, then ; If i ≥ 200, then ;

[0080] Among them, a i represents the unique value corresponding to the i-th sample point, k i and k j They represent the slopes of the i-th sample point and the j-th sample point respectively.

[0081] At the same time, in step S302, the setting conditions are configured as follows: ;in, is the mean of the specific values ​​corresponding to all sample points, is the variance of the unique values ​​corresponding to all sample points, and m is the extraction coefficient. It can be tested and configured according to the actual data processing scenario. After testing, m is 1, which has the best effect.

[0082] Specifically, such as Figure 2 As shown, the method for processing data of online monitoring of mechanical characteristics of a circuit breaker provided by the present invention, step S4 includes the following steps:

[0083] S401: performing multi-order B-spline interpolation fitting on the singular point set as an interpolation point set, and obtaining a current-time fitting curve after the fitting is completed;

[0084] S402: determining in sequence whether the current value deviation of each extreme point of the current-time fitting curve and the sample point at the same time in the current sample data exceeds a set range; if so, adding the corresponding sample point in the current sample data to the singular point set;

[0085] S403: Determine whether the singular point set is updated. If updated, jump to step S401; otherwise, continue to execute step S5.

[0086] In step S401, third-order (cubic) B-spline interpolation fitting is performed. Since the cubic spline function requires continuity of the function value, first-order derivative, and second-order derivative at each interpolation point, the interpolation points must be determined when performing a cubic B-spline fit. Therefore, after using the singular point set extracted in step S3 as the interpolation point set, the cubic B-spline basis function is calculated, and then a system of linear equations is established and solved, ultimately obtaining the cubic B-spline function that fits the data points. The key lies in leveraging the properties of the B-spline basis function and the least squares method to solve for the control points, thereby achieving a smooth fit of the current sample data and obtaining a B-spline curve, i.e., the current-time fitting curve of the current sample data in the present invention.

[0087] Among them, the B-spline curve is a segmented curve with m+n+1 vertices, which can define m+1 segments of n-degree spline curves. The mathematical expression of the i-th segment of the n-degree B-spline curve is:

[0088]

[0089] Where: 0 <t<1;i=1,2,3,...,m; is the kth nth degree B-spline basis function; n is the number of control points. All these curves are called nth degree B-spline curves, and their control points P i The polygon formed is called the characteristic polygon of the B-spline curve. The basis function is defined as:

[0090]

[0091] in, represents the number of combinations of j elements selected from n+1 elements, and j represents the summation variable; thus, n control points can form n-3 segments of cubic B-spline curves that are second-order continuous at the connection points, that is, 4 adjacent control points can define a segment of cubic B-spline curve.

[0092] According to the definition, the kth segment of the nth degree B-spline curve is only connected to n+1 control points P i Therefore, when one of the control points is changed, it will only affect the adjacent n+1 segments, and will not affect the entire curve (when m>>n). Therefore, the B-spline curve fitting is very suitable for the curve fitting of the current sample data of the present invention, which requires local adjustment of the curve.

[0093] In step S402, first, the extreme points of the current-time fitting curve obtained in step S401 are extracted, and the total number of the extracted extreme points is recorded as N, and i=1 is initialized; then, it is determined whether the deviation between the first extreme point and the current value of the sample point at the same time in the current sample data exceeds the set range. If it exceeds the set range, the corresponding sample point in the current sample data is added to the unique point set, and then it is determined whether all extreme points have been traversed by judging whether i is equal to N. If not all extreme points have been traversed, i=i+1, and it is continued to be determined whether the deviation between the first extreme point and the current value of the sample point at the same time in the current sample data exceeds the set range, and so on until all extreme points have been traversed; if it does not exceed the set range, it is directly determined whether all extreme points have been traversed by judging whether i is equal to N.

[0094] In step S403, it is determined whether to re-perform B-spline interpolation fitting by judging whether the singular point set is updated. If the singular point set is updated, the process jumps to step S401; otherwise, the process continues to step S5.

[0095] like Figure 3 As shown, the horizontal axis represents time (unit: milliseconds), and the vertical axis represents the current value (unit: millivolts). The red discrete lines represent current sample data, the blue curve represents the current-time fitting curve of the preliminary fitting, and the black curve represents the current-time fitting curve fitted after optimizing the singular point set. It can be seen that the present invention uses the current sample data in step S4 to gradually optimize the current-time fitting curve fitted by B-spline interpolation, so that the current-time fitting curve can retain the characteristics of the measured original data to a greater extent, thereby making the characteristic matrix used for circuit breaker mechanical characteristic fault judgment obtained in the subsequent steps more accurate.

[0096] Specifically, the method for processing data of online monitoring of mechanical characteristics of a circuit breaker provided by the present invention, step S5 includes the following steps:

[0097] S501: Obtain the extreme points of the current-time fitting curve and filter out the extreme points where the current value is lower than a set threshold. Since the process of judging the mechanical characteristic fault of the circuit breaker mainly focuses on the data area from the sudden increase of the current value to the sudden decrease of the current value, filtering out the extreme points where the current value is lower than the set threshold can simplify the calculation and facilitate subsequent data processing.

[0098] S502: Generate a characteristic matrix based on the remaining extreme points of the current-time fitting curve .

[0099] Specifically, in the method for processing data of online monitoring of mechanical characteristics of circuit breaker provided by the present invention, in step S6, the condition identification is performed based on the characteristic matrix by performing condition identification according to the number of elements indicating peak points in the characteristic matrix.

[0100] If the number of elements indicating peak points is greater than a lower threshold and less than an upper threshold, the condition recognition result indicated as a first result is generated;

[0101] If the number of elements indicating peak points is lower than the lower threshold or greater than the upper threshold, generating the condition recognition result indicated as the second result;

[0102] If the number of elements indicating peak points is equal to the lower threshold or the upper threshold, the condition recognition result indicated as the third result or the fourth result is generated, and the number of condition recognition execution times is updated.

[0103] During implementation, the lower and upper thresholds are determined by the circuit breaker's installation method, i.e., horizontal or vertical. For a vertical circuit breaker, for example, the number of opening peaks is 3, while the closing data may show 4 peaks; therefore, the lower threshold is set to 2 and the upper threshold is set to 5. If the number of elements in the characteristic matrix indicating peak points is exactly 3 or 4, the conditional identification result is the first result, indicating that the circuit breaker is performing an opening or closing action. If the number of elements in the characteristic matrix indicating peak points is exactly 1 or 6, the conditional identification result is the second result, indicating that the current sample data (current signal sampling data) is missing or has excessive noise, representing a data anomaly. If the number of elements in the characteristic matrix indicating peak points is exactly 2 or 5, the conditional identification result is the third or fourth result, indicating that there is a deviation in the B-spline curve fitting, which may be caused by an insufficient or excessive number of singular points selected from the singular point set, and can be corrected through subsequent processing.

[0104] Specifically, such as Figure 4 As shown, the method for processing data for online monitoring of mechanical characteristics of circuit breakers provided by the present invention, in step S7, first determines whether the condition identification has been executed a set number of times. If so, a data processing failure prompt is output and the process jumps to the end. Otherwise, a determination is then made as to whether the condition identification result is a third result (other results, in addition to the first and second results, include the third and fourth results). If the condition identification result is the third result, the sample point in the current sample data corresponding to the middle moment between two adjacent singular points with the largest time interval in the singular point set is added as a new singular point. If the condition identification result is not the third result, then the condition identification result must be the fourth result, and the singular point with the smallest sum of time intervals with two adjacent singular points in the singular point set is deleted. This approach can avoid the impact of insufficient or excessive selection of singular points in the singular point set on the extraction of the feature matrix used for circuit breaker mechanical characteristic fault diagnosis.

[0105] Another aspect of the present invention provides a method for diagnosing mechanical characteristic faults of circuit breakers. Since the principle of opening and closing operation of circuit breakers on the market is basically completed by the starting part and the main actuator, although circuit breakers are divided into horizontal circuit breakers and vertical circuit breakers according to the installation method, as long as it is a horizontal circuit breaker, regardless of the model of the circuit breaker, the peak and trough characteristics of the current waveform of the opening and closing control circuit are consistent; similarly, the same is true for vertical circuit breakers; the current waveform characteristics of a typical opening and closing control circuit of a vertical circuit breaker are: starting current index increase-peak-trough-peak-trough-peak-current index decrease-end; it’s just that the current waveform graphs presented by different models of vertical circuit breakers are different, but the peak and trough characteristics are consistent. The characteristic points for distinguishing the mechanical characteristic faults of the starting part and the main actuator are mainly the first peak area and the first trough area; specifically, taking the current waveform of a typical opening and closing control circuit of a vertical circuit breaker of an actual model as an example, such as Figure 5 As shown, the horizontal axis represents time (unit: milliseconds), and the vertical axis represents current value (unit: millivolts). If the duration from the time point t1 corresponding to the extreme point (ideally the only extreme point) in the first peak area to the time point t2 corresponding to the extreme point (ideally the only extreme point) in the first trough area is greater than 10% of the corresponding reference value, it means that the switchgear opening / closing starting component has coil aging, jamming and other fault defects; and if the duration from the time point t2 corresponding to the extreme point (ideally the only extreme point) in the first trough area to the time point when the circuit breaker opening action or closing action ends (the time point when the opening / closing control circuit is cut off) is greater than 10% of the corresponding reference value, it means that the switchgear opening / closing main actuator spring is fatigued, the mechanism is jammed, and the transmission components are not lubricated enough.

[0106] Specifically, such as Figure 6 As shown, the present invention provides a circuit breaker mechanical characteristic fault diagnosis method, which includes the following steps:

[0107] Obtaining a characteristic matrix for circuit breaker mechanical characteristic fault judgment based on the circuit breaker mechanical characteristic online monitoring data processing method provided by the present invention;

[0108] Determining whether a first time interval corresponding to a first peak point and a first trough point in the characteristic matrix is ​​within a first set time range; and determining whether a second time interval corresponding to a first trough point and a last peak point in the characteristic matrix is ​​within a second set time range;

[0109] If the first set time range is exceeded, it indicates that the circuit breaker starting component has a fault; if the second set time range is exceeded, it indicates that the circuit breaker main actuator has a fault; otherwise, it indicates that the mechanical characteristics of the circuit breaker are normal.

[0110] During implementation, the first set time range and the second set time range are 10%; different value ranges can be set according to the importance of the circuit breaker in the line.

[0111] like Figure 7 As shown, the present invention also provides a circuit breaker mechanical characteristics online monitoring data processing device, which includes:

[0112] A preprocessing module is used to obtain the current sampling data of the opening and closing coils of the circuit breaker, and preprocess the obtained current sampling data of the opening and closing coils to obtain the current sample data to be processed;

[0113] The slope data calculation module is used to calculate the slope of two adjacent sample points in the current sample data to obtain a slope data set. ; Where n represents the total number of sample points;

[0114] A singular point extraction module, configured to extract singular points from the slope data set to obtain a singular point set;

[0115] A curve fitting module is used to perform multi-order B-spline interpolation fitting using the singular point set as an interpolation point set, and obtain a current-time fitting curve after the fitting is completed;

[0116] A characteristic matrix generation module is used to generate a characteristic matrix according to the extreme points of the current-time fitting curve. ; Wherein, the element of the characteristic matrix is ​​0 or 1, which is used to indicate that the extreme point is a trough point or a peak point;

[0117] a condition identification module, configured to perform condition identification based on the characteristic matrix; wherein, if the condition identification result indicates a first result or a second result, a prompt indicating that the output data is normal or abnormal is displayed, and the characteristic matrix with the condition identification result of the first result is used for circuit breaker mechanical characteristic fault judgment; otherwise, the condition identification execution count is updated;

[0118] a singular point set updating module, configured to determine whether the condition identification has been executed a set number of times; if so, outputting a prompt indicating data processing failure; otherwise, determining a sample point in the current sample data as a newly added singular point or deleting a singular point in the singular point set based on the singular point time distribution of the singular point set; and outputting the updated singular point set to the curve fitting module for re-performing multi-order B-spline interpolation fitting.

[0119] Specifically, considering that the circuit breaker is affected by the power supply, load and circuit stability in a specific electrical environment, the impact of its noise on the current signal is different; at the same time, considering that a new circuit breaker generally does not have mechanical characteristic failures in the early stage of being put into use; therefore, it is necessary to remove the noise influence on the acquired opening and closing coil current sampling data in the preprocessing module; and in the denoising preparation stage, first, the current signal sampling data corresponding to multiple opening actions or closing actions in the early stage of the circuit breaker being put into use are fitted into a noisy current-time curve; then, the noisy current-time curve is compared with the current-time standard curve to separate the noise-time curve; finally, based on the separated noise-time curve, the noise mean during the opening action or closing action of the circuit breaker is calculated; in this way, the noise mean can be used to denoise the opening and closing coil current sampling data, that is, the current value corresponding to the opening and closing coil current sampling data is subtracted from the noise mean, thereby obtaining the current sample data.

[0120] During implementation, the current signal sampling data corresponding to multiple opening or closing actions before the circuit breaker is put into use are fitted into a noisy current-time curve. In fact, multiple curves are fitted into one curve. Therefore, the least squares fitting method is used in the preprocessing module to fit the noisy current-time curve. By minimizing the sum of squared residuals between all data points and the fitted curve, the optimal parameters are found, which can enable the fitting curve to retain the noisy characteristics of the current signal sampling data to the greatest extent.

[0121] Specifically, in the slope data calculation module, since the current sample data is the current value corresponding to different sampling points within the sampling time, it is discrete data; therefore, with time as the horizontal axis and current value as the horizontal axis, the i-th sampling point is expressed as (x i ,y i ), then the slope of every two adjacent sample points in the current sample data is calculated as: ; According to the above calculation method, the slope data set can be obtained .

[0122] Specifically, in the singular point extraction module, the singular point is extracted according to the slope data set, and the singular point set is obtained in the following manner: first, according to the slope data set , calculate the specific value corresponding to each sample point; then, determine whether the specific value of each sample point meets the set conditions one by one. If so, extract the corresponding sample point as a specific point and add it to the specific point set.

[0123] In implementation, since the current signal sampling data usually spikes around the 200th point, a sliding window is used for feature extraction after 200 points to eliminate the influence of the first spike position. Moreover, since the data after the spike is basically at the same height, a sliding window with a width of 150 points is used for feature extraction. That is, in the singular point extraction module, the method for calculating the singular value corresponding to each sample point is:

[0124] If i < 200, then ; If i ≥ 200, then ;

[0125] Among them, a i represents the unique value corresponding to the i-th sample point, k i and k j They represent the slopes of the i-th sample point and the j-th sample point respectively.

[0126] At the same time, in the singular point extraction module, the setting conditions are configured as follows: ;in, is the mean of the specific values ​​corresponding to all sample points, is the variance of the unique values ​​corresponding to all sample points, and m is the extraction coefficient. It can be tested and configured according to the actual data processing scenario. After testing, m is 1, which has the best effect.

[0127] Specifically, after the curve fitting module uses the singular point set as an interpolation point set to perform multi-order B-spline interpolation fitting, and after the fitting is completed to obtain a current-time fitting curve, it further compares the current value deviation of each extreme point of the current-time fitting curve with the sample point at the same time in the current sample data one by one. If the current value deviation exceeds a set range, the corresponding sample point in the current sample data is added to the singular point set; then, it is determined whether the singular point set is updated. If updated, the multi-order B-spline interpolation fitting is re-performed based on the updated singular point set. Otherwise, the relevant data of the fitted current-time fitting curve is transmitted to the characteristic matrix generation module to generate a characteristic matrix.

[0128] During implementation, the curve fitting module uses third-order (cubic) B-spline interpolation fitting. Since the cubic spline function requires continuity of the function value, first-order derivative, and second-order derivative at each interpolation point, the interpolation points must be determined before performing a cubic B-spline fit. Therefore, after using the singular point set extracted by the singular point extraction module as the interpolation point set, the cubic B-spline basis function is calculated, and then a system of linear equations is established and solved, ultimately obtaining the cubic B-spline function that fits the data points. The key lies in leveraging the properties of the B-spline basis function and the least squares method to solve for the control points, thereby achieving a smooth fit of the current sample data and obtaining a B-spline curve, i.e., the current-time fitting curve of the current sample data in the present invention.

[0129] Furthermore, during its operation, the curve fitting module first extracts the extreme points of the current-time fitting curve obtained by the first fitting, records the total number of extreme points extracted as N, and initializes i=1. Next, it determines whether the deviation between the first extreme point and the current value of the sample point at the same time in the current sample data exceeds a set range. If so, the corresponding sample point in the current sample data is added to the singular point set. It then determines whether all extreme points have been traversed by determining whether i is equal to N. If not, i=i+1, and continues to determine whether the deviation between the first extreme point and the current value of the sample point at the same time in the current sample data exceeds the set range. This continues until all extreme points have been traversed. If not, it directly determines whether all extreme points have been traversed by determining whether i is equal to N. After traversing all extreme points, it determines whether the singular point set has been updated to determine whether to re-perform B-spline interpolation fitting.

[0130] Specifically, after the characteristic matrix generation module obtains the extreme points of the current-time fitting curve, it will filter out the extreme points whose current values ​​are lower than the set threshold. Since the main focus in the circuit breaker mechanical characteristic fault judgment process is on the data area from the sudden increase of the current value to the sudden drop of the current value, filtering out the extreme points whose current values ​​are lower than the set threshold can simplify the calculation and facilitate subsequent data processing. Then, the characteristic matrix generation module generates the characteristic matrix according to the remaining extreme points of the current-time fitting curve. .

[0131] Specifically, the condition identification module performs condition identification based on the feature matrix in the following manner: performing condition identification according to the number of elements indicating peak points in the feature matrix.

[0132] If the number of elements indicating peak points is greater than a lower threshold and less than an upper threshold, the condition recognition result indicated as a first result is generated;

[0133] If the number of elements indicating peak points is lower than the lower threshold or greater than the upper threshold, generating the condition recognition result indicated as the second result;

[0134] If the number of elements indicating peak points is equal to the lower threshold or the upper threshold, the condition recognition result indicated as the third result or the fourth result is generated, and the number of condition recognition execution times is updated.

[0135] During implementation, the lower and upper thresholds in the conditional identification module are determined by the circuit breaker's installation method, i.e., horizontal or vertical. For a vertical circuit breaker, for example, the number of opening peaks is 3, while the closing data may show 4 peaks; therefore, the lower threshold is set to 2 and the upper threshold is set to 5. If the number of elements in the characteristic matrix indicating peak points is exactly 3 or 4, the conditional identification result is the first result, indicating that the circuit breaker is performing an opening or closing action. If the number of elements in the characteristic matrix indicating peak points is exactly 1 or 6, the conditional identification result is the second result, indicating that the current sample data (current signal sampling data) is missing or has excessive noise, representing a data anomaly. If the number of elements in the characteristic matrix indicating peak points is exactly 2 or 5, the conditional identification result is the third or fourth result, indicating a deviation in the B-spline curve fitting. This may be caused by an insufficient or excessive number of singular points in the singular point set, which can be corrected through subsequent processing.

[0136] Specifically, when the singular point set update module is operating, it first determines whether the condition identification has been executed a set number of times. If so, it outputs a message indicating data processing failure and jumps to the end. Otherwise, it then determines whether the condition identification result is the third result (other results, excluding the first and second results, include the third and fourth results). If the condition identification result is the third result, the sample point in the current sample data corresponding to the middle moment between two adjacent singular points with the largest time interval in the singular point set is added as a new singular point. If the condition identification result is not the third result, it must be the fourth result, and the singular point with the smallest sum of time intervals with two adjacent singular points in the singular point set is deleted. This approach can prevent the impact of insufficient or excessive number of singular points in the singular point set on the extraction of the feature matrix used for circuit breaker mechanical characteristic fault diagnosis.

[0137] like Figure 8 As shown, the present invention also provides a circuit breaker mechanical characteristic fault diagnosis device, comprising: the circuit breaker mechanical characteristic online monitoring data processing device provided by the present invention, and a fault diagnosis module;

[0138] The fault diagnosis module is configured to obtain a characteristic matrix for circuit breaker mechanical characteristic fault diagnosis provided by the circuit breaker mechanical characteristic online monitoring data processing device, and determine whether a first time interval corresponding to a first peak point and a first trough point in the characteristic matrix is ​​within a first set time range; and determine whether a second time interval corresponding to a first trough point and a last peak point in the characteristic matrix is ​​within a second set time range;

[0139] If the first set time range is exceeded, it indicates that the circuit breaker starting component has a fault; if the second set time range is exceeded, it indicates that the circuit breaker main actuator has a fault; otherwise, it indicates that the mechanical characteristics of the circuit breaker are normal.

[0140] The present invention also provides a readable storage medium having one or more programs stored thereon, which, when executed by one or more processors, implement the circuit breaker mechanical characteristic online monitoring data processing method or circuit breaker mechanical characteristic fault diagnosis method provided by the present invention.

[0141] It should be understood that the devices disclosed herein are designed to be compatible with the method steps provided herein and can be implemented in other ways. For example, the module division described is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, the communication connections between modules may be through interfaces, indirect coupling of devices or units, or communication connections, which may be electrical or otherwise.

[0142] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each module may exist physically separately, or two or more modules may be integrated into a single processing unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0143] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for processing data of online monitoring of mechanical characteristics of a circuit breaker, characterized in that: The following steps are involved: S1: Acquire current sampling data of the opening and closing coils of the circuit breaker, and pre-process the acquired current sampling data of the opening and closing coils to obtain current sample data to be processed; S2: Calculate the slope of every two adjacent sample points in the current sample data to obtain a slope data set ; Where n represents the total number of sample points; S3: extracting singular points according to the slope data set to obtain a singular point set; S4: performing multi-order B-spline interpolation fitting on the singular point set as an interpolation point set, and obtaining a current-time fitting curve after the fitting is completed; S5: Generate a characteristic matrix based on the extreme points of the current-time fitting curve ; Wherein, the element of the characteristic matrix is ​​0 or 1, which is used to indicate that the extreme point is a trough point or a peak point; S6: Perform condition identification based on the characteristic matrix and generate a condition identification result; if the condition identification result is a first result, output the characteristic matrix for circuit breaker mechanical characteristic fault judgment and jump to end; if the condition identification result is a second result, output a data abnormality prompt and jump to end; if the condition identification result is other results, update the number of condition identification executions; S7: Determine whether the number of executions of the condition identification reaches a set number; if so, output a prompt indicating that the data processing has failed and jump to the end; otherwise, determine a sample point in the current sample data as a new singular point or delete a singular point in the singular point set based on the singular point time distribution of the singular point set, and after updating the singular point set, jump to step S4; In step S6, conditional identification is performed based on the number of elements indicating peak points in the feature matrix; if the number of elements indicating peak points is greater than a lower threshold and less than an upper threshold, a conditional identification result indicating a first result is generated; if the number of elements indicating peak points is less than the lower threshold or greater than the upper threshold, a conditional identification result indicating a second result is generated; if the number of elements indicating peak points is equal to the lower threshold or the upper threshold, a conditional identification result indicating a third result or a fourth result is generated, and the number of conditional identification executions is updated; In step S7, based on the time distribution of the singular points in the singular point set, a sample point in the current sample data is determined as a newly added singular point or a singular point in the singular point set is deleted in the following manner: if the conditional identification result is the third result, a sample point in the current sample data corresponding to the middle moment between two adjacent singular points with the largest time interval in the singular point set is used as a newly added singular point; if the conditional identification result is the fourth result, a singular point in the singular point set with the smallest sum of time intervals with the two adjacent singular points is deleted.

2. The method for processing data of online monitoring of mechanical characteristics of a circuit breaker according to claim 1, characterized in that: In step S1, the acquired opening and closing coil current sampling data is preprocessed as follows: current signal sampling data corresponding to multiple opening actions or closing actions before the circuit breaker is put into use are fitted into a noisy current-time curve, and the noisy current-time curve is compared with a current-time standard curve to separate the noise-time curve; and, based on the noise-time curve, the noise mean during the opening action or closing action of the circuit breaker is calculated, and based on the noise mean, the opening and closing coil current sampling data is denoised to obtain the current sample data; wherein, the current-time standard curve is the current-time curve provided by the manufacturer of the circuit breaker.

3. The method for processing data of online monitoring of mechanical characteristics of a circuit breaker according to claim 1, wherein: Step S3 includes the following steps: S301: Based on the slope data set , calculate the specific value corresponding to each sample point; S302: Determine whether the singular value of each sample point meets a set condition. If so, take the corresponding sample point as a singular point and add it to the singular point set.

4. The method for processing data of online monitoring of mechanical characteristics of a circuit breaker according to claim 3, wherein: In step S301, the method for calculating the unique value corresponding to each sample point is: If i < 200, then ; If i ≥ 200, then ; Among them, a i represents the unique value corresponding to the i-th sample point, k i and k j They represent the slopes of the i-th sample point and the j-th sample point respectively.

5. The method for processing data of online monitoring of mechanical characteristics of a circuit breaker according to claim 4, characterized in that: In step S302, the setting conditions are configured as follows: ;in, is the mean of the specific values ​​corresponding to all sample points, is the variance of the specific values ​​corresponding to all sample points, and m is the extraction coefficient.

6. The method for processing data of online monitoring of mechanical characteristics of a circuit breaker according to claim 1, wherein: Step S4 includes the following steps: S401: performing multi-order B-spline interpolation fitting on the singular point set as an interpolation point set, and obtaining a current-time fitting curve after the fitting is completed; S402: determining in sequence whether the current value deviation of each extreme point of the current-time fitting curve and the sample point at the same time in the current sample data exceeds a set range; if so, adding the corresponding sample point in the current sample data to the singular point set; S403: Determine whether the singular point set is updated. If so, jump to step S401; otherwise, continue to execute step S5.

7. The method for processing data of online monitoring of mechanical characteristics of a circuit breaker according to claim 1, wherein: Step S5 includes the following steps: S501: Obtain extreme points of the current-time fitting curve, and filter out extreme points whose current values ​​are lower than a set threshold; S502: Generate a characteristic matrix based on the remaining extreme points of the current-time fitting curve .

8. The method for processing data of online monitoring of mechanical characteristics of a circuit breaker according to claim 1, wherein: The lower threshold and the upper threshold are determined by the installation method of the circuit breaker.

9. A circuit breaker mechanical characteristic fault diagnosis method, characterized in that: The following steps are involved: Obtaining a characteristic matrix for circuit breaker mechanical characteristic fault judgment obtained based on the circuit breaker mechanical characteristic online monitoring data processing method according to any one of claims 1 to 8; Determining whether a first time interval corresponding to a first peak point and a first trough point in the characteristic matrix is ​​within a first set time range; and determining whether a second time interval corresponding to a first trough point and a last peak point in the characteristic matrix is ​​within a second set time range; If the first set time range is exceeded, it indicates that the circuit breaker starting component has a fault; if the second set time range is exceeded, it indicates that the circuit breaker main actuator has a fault; otherwise, it indicates that the mechanical characteristics of the circuit breaker are normal.

10. A circuit breaker mechanical characteristics online monitoring data processing device, characterized in that: include: A preprocessing module is used to obtain the current sampling data of the opening and closing coils of the circuit breaker, and preprocess the obtained current sampling data of the opening and closing coils to obtain the current sample data to be processed; The slope data calculation module is used to calculate the slope of two adjacent sample points in the current sample data to obtain a slope data set. ; Where n represents the total number of sample points; A singular point extraction module, configured to extract singular points from the slope data set to obtain a singular point set; A curve fitting module is used to perform multi-order B-spline interpolation fitting using the singular point set as an interpolation point set, and obtain a current-time fitting curve after the fitting is completed; A characteristic matrix generation module is used to generate a characteristic matrix according to the extreme points of the current-time fitting curve. ; Wherein, the element of the characteristic matrix is ​​0 or 1, which is used to indicate that the extreme point is a trough point or a peak point; a condition identification module, configured to perform condition identification based on the characteristic matrix; wherein, if the condition identification result indicates a first result or a second result, a prompt indicating that the output data is normal or abnormal is displayed, and the characteristic matrix with the condition identification result of the first result is used for circuit breaker mechanical characteristic fault judgment; otherwise, the condition identification execution count is updated; a singular point set updating module, configured to determine whether the condition identification has been executed a set number of times; if so, outputting a prompt indicating data processing failure; otherwise, determining a sample point in the current sample data as a new singular point or deleting a singular point in the singular point set based on the singular point time distribution of the singular point set; and outputting the updated singular point set to the curve fitting module for re-performing multi-order B-spline interpolation fitting; The condition identification module performs condition identification based on the characteristic matrix in the following manner: condition identification is performed according to the number of elements indicating peak points in the characteristic matrix; if the number of elements indicating peak points is greater than a lower threshold and less than an upper threshold, a condition identification result indicating a first result is generated; if the number of elements indicating peak points is less than the lower threshold or greater than the upper threshold, a condition identification result indicating a second result is generated; if the number of elements indicating peak points is equal to the lower threshold or the upper threshold, a condition identification result indicating a third result or a fourth result is generated, and the number of condition identification executions is updated; when the singular point set updating module is in operation, if the condition identification result is the third result, a sample point in the current sample data corresponding to the middle moment between two adjacent singular points with the largest time interval in the singular point set is added as a new singular point; if the condition identification result is the fourth result, a singular point in the singular point set with the smallest sum of time intervals with two adjacent singular points is deleted.

11. A circuit breaker mechanical characteristic fault diagnosis device, characterized in that: include: The circuit breaker mechanical characteristic online monitoring data processing device according to claim 10, and a fault diagnosis module; The fault diagnosis module is configured to obtain a characteristic matrix for circuit breaker mechanical characteristic fault diagnosis provided by the circuit breaker mechanical characteristic online monitoring data processing device, and determine, based on the characteristic matrix, whether a first time interval corresponding to a first singular point indicated as a peak point and a first singular point indicated as a trough point is within a first set time range; and determine whether a second time interval corresponding to the first singular point indicated as a trough point and a last singular point indicated as a peak point is within a second set time range; If the first set time range is exceeded, it is prompted that the circuit breaker starting component has a fault; if the second set time range is exceeded, it is prompted that the circuit breaker main actuator has a fault.

12. A readable storage medium having one or more programs stored thereon, characterized in that: When the one or more programs are executed by one or more processors, the method for processing online monitoring data of mechanical characteristics of a circuit breaker according to any one of claims 1 to 8 or the method for diagnosing mechanical characteristics faults of a circuit breaker according to claim 9 is implemented.

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