Bus duct fault monitoring method and system

Abstract

The invention discloses a bus duct fault monitoring method and system, and relates to the field of data processing, and the method comprises the steps: obtaining a first measurement value data set; calculating a difference data set and a difference value of the difference data set from the first measurement value data set; based on the interval length of each measurement node, calculating to obtain an original interference evaluation value of each measurement value in the first measurement value data set, and then calculating to obtain a target interference evaluation value; correcting the first measurement value data set by using the target interference evaluation value to obtain a second measurement value data set; performing dynamic segmentation on the second measurement value data set to obtain second measurement value data sub-blocks; determining the length of a sliding area according to the target interference evaluation value to obtain a third measurement value data set; uploading the third measured value data set to a server; according to the bus duct fault monitoring method and system, in the data interference removal process, the factor of the interval length of each measurement node is introduced, so that the interference removal effect of the measurement data is more accurate.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a bus duct fault monitoring method and system. Background Art

[0002] In the busbar transmission system, multiple busbars form a power transmission line. An industrial data monitoring module can be set on the power transmission line to collect data on the current, voltage and other operating parameters of the power transmission line. The existing busbar fault monitoring system only considers processing the measurement data collected by a single industrial data monitoring module, and only performs simple filtering on the measurement data. This means that the greater the degree of interference in the measurement data collected at this location, the more inaccurate the fault warning obtained by the subsequent analysis of the measurement data. Summary of the invention

[0003] In order to solve the above technical problems, the present application provides a bus duct fault monitoring method and system, which introduces the factor of the interval length of each measurement node in the data de-interference process, so that the de-interference effect of the measurement data is more accurate and the subsequent fault warning is more accurate.

[0004] The technical solution of this application is: In a first aspect, the present application provides a bus duct fault monitoring method, which is applied to an industrial data gateway, and the method includes: Obtain a first measurement value data set of the industrial data monitoring module of each measurement node of the same transmission line; the transmission lines include multiple transmission lines, the same transmission line refers to a section of the transmission line without a convergence point or a diversion point, each transmission line is composed of multiple bus ducts, and the industrial data monitoring modules of the same transmission line are defined as the same group; Calculate a differential data set of a preset range area corresponding to each measurement value and a difference value of the differential data set from the first measurement value data set; wherein the length of the preset range area is much smaller than the length of the first measurement value data set, and the length of the preset range area is a positive integer; Based on the interval length of each measurement node, the original interference evaluation value of each measurement value in the first measurement value data set is calculated; according to the difference value corresponding to each measurement value, the regularization term of each measurement value is calculated and then multiplied by the corresponding original interference evaluation value to obtain the interference impact value of each measurement value; the interference impact value of each measurement value is normalized to obtain the target interference evaluation value of each measurement value in the first measurement value data set; Correcting the first measurement data set using the target interference evaluation value of each measurement value to obtain a second measurement data set; dynamically segmenting the second measurement data set to obtain second measurement data sub-blocks of each measurement node; Determine the length of the sliding area according to the corresponding target interference assessment value, perform a measurement value cumulative average on the second measurement value data sub-block, perform a second measurement value cumulative average on the second measurement value data sub-block in the sliding area, and obtain a third measurement value data set; wherein the length of the sliding area is a positive integer; The third measurement value data set is used as the final measurement value data set, and the final measurement value data set is uploaded to the server, so that the server performs fault monitoring on the operation status of the bus duct.

[0005] The bus duct fault monitoring method of the present application groups the industrial data monitoring modules at different measurement node positions, and takes the industrial data monitoring modules of the same transmission line as the same group, that is, considering the measurement nodes with similar measurement values, and utilizing the interval length between the measurement nodes to optimize the algorithm, thereby enhancing the accuracy and rationality of the estimation of the interference assessment value; in addition, the present application designs a preset range area corresponding to each measurement value, and utilizes the difference value of the differential data set corresponding to each measurement value to dynamically adjust the interference assessment value, thereby enhancing the local adaptability of the optimization algorithm and monitoring slight changes in the measurement value; dynamic segmentation is performed based on the corrected measurement data, so that the measurement data with similar interference assessment values ​​are segmented together, which helps to improve the effect of interference removal of the measurement data and makes subsequent fault warnings more accurate.

[0006] In a preferred embodiment, calculating a differential data set of a preset range area corresponding to each measurement value and a difference value of the differential data set from the first measurement value data set includes: Taking the measurement values ​​in the first measurement value data set as key points, defining a preset number before and after the key points as a preset range area corresponding to each measurement value; The difference between every two adjacent measurement values ​​in the preset range area corresponding to each measurement value is calculated to obtain a differential data set in the preset range area corresponding to each measurement value and a difference value of the differential data set, thereby obtaining a differential data set in the preset range area corresponding to each measurement value and a difference value of the differential data set; the differential data set is a first-order data sequence.

[0007] It should be noted that the present application reveals the local change characteristics of the data through the local characteristics of the differential data set relative to the entire first measurement value data set, and more keenly captures the subtle changes in the measurement data. The outlier characteristics of the first-order difference sequence of the differential data set also help to eliminate interference in the data and improve the accuracy of monitoring the bus duct operation parameters in complex environments.

[0008] In a preferred embodiment, calculating the original interference assessment value of each measurement value in the first measurement value data set based on the interval length of each measurement node includes: An industrial data monitoring module is used as a first measurement node, and industrial data monitoring modules other than the first measurement node are used as second measurement nodes, a measurement value of the first measurement node and a measurement value of the second measurement node at the same time point are obtained, and a difference between the two measurement values ​​is calculated; Calculating a sub-interval metric value between the first measurement node and the second measurement node and a total interval metric value between the first measurement node and the second measurement node; Calculate the ratio of the sub-interval metric value to the total interval metric value, and use the ratio as the interval metric evaluation value of the measurement value; The measured value difference of each measured value is multiplied and added with the interval metric evaluation value to obtain the original interference evaluation value of each measured value.

[0009] It should be noted that the interference evaluation value is calculated based on the difference between the measurement data of each measurement node at the same time point. The greater the difference between the measurement value of the current measurement node and the measurement values ​​of other measurement nodes, the greater its interference evaluation value. For example, the measurement values ​​of two measurement nodes with closer intervals should be more similar. Therefore, the interval length of the two measurement nodes can be used as the weight of the difference, and then the interference evaluation value of the measurement value is obtained by weighted average to improve the accuracy of the interference evaluation value calculation.

[0010] In a preferred embodiment, the second measurement value data set is dynamically segmented to obtain the second measurement value data sub-blocks of each measurement node, including: Pre-segmenting the second measurement data set to obtain original second measurement data set sub-blocks; the pre-segmenting is performed based on a preset segmentation number, which is a positive integer; Calculating an estimated error value of an original second measurement value data set sub-block, and adding the estimated error values ​​of all original second measurement value data set sub-blocks to obtain an error result of the second measurement value data set under the preset number of divisions; Taking the preset number of divisions as the horizontal direction and the error result as the vertical direction, an error change graph under different preset numbers of divisions is produced, the position of the inflection point is determined according to the shape of the error change graph, and the target sub-block length is determined according to the position of the inflection point; wherein the target sub-block length is a positive integer; The second measurement data set is divided twice based on the target sub-block length to obtain a final second measurement data set sub-block, and the final second measurement data set sub-block is used as the second measurement data sub-block corresponding to the measurement node, thereby obtaining the second measurement data sub-block of each measurement node.

[0011] It should be noted that the present application combines the optimal solution method and the elbow method to minimize the loss function by continuously iterating the number of data segmentation, so that the measurement values ​​in each second measurement value data sub-block are close in size and the degree of interference is stabilized at a level, which is more adaptable to the complex changes in the measurement values ​​in different time periods in the measurement data that are subject to different interferences.

[0012] In a preferred embodiment, calculating the error estimate of the original second measurement value data set sub-block includes: Calculate the mean of the measured values ​​in the sub-block of the original second measurement value data set to obtain the predicted value of the sub-block of the original second measurement value data set; The difference between the measured value and the predicted value of the original second measurement value data set sub-block is calculated and the square sum is performed to obtain the square residual sum of the original second measurement value data set sub-block.

[0013] In a preferred embodiment, calculating the error estimate of the original second measurement value data set sub-block includes: The mean absolute error value of the original second measurement value data set sub-block is calculated and used as the error estimate value of the original second measurement value data set sub-block.

[0014] In a preferred embodiment, calculating the error estimate of the original second measurement value data set sub-block includes: A root mean square error value of the original second measurement value data set sub-block is calculated and taken as an error estimate value of the original second measurement value data set sub-block.

[0015] In a preferred embodiment, determining the sliding area length according to the corresponding target interference evaluation value includes: Calculating a second interference assessment mean value of the second measurement value data set sub-block based on the target interference assessment value of the second measurement value data set sub-block; After calculating the product of the initial sliding area length and the second interference evaluation mean, the product is added to the initial sliding area length to obtain the expected sliding area length; The maximum positive integer value is taken for the expected sliding area length to obtain the target sliding area length, and the target sliding area length is used as the final sliding area length.

[0016] It should be noted that the present application dynamically adjusts the window size according to the expectation of the interference evaluation value in the data sub-block, and filters the second measurement value data set based on the adjusted target sliding window (that is, the sliding area length), thereby completing the data preprocessing of the first measurement value data set of the industrial data monitoring module of each measurement node.

[0017] In the second aspect, the present application also provides a bus duct fault monitoring system, including a server, an industrial data gateway and an industrial data monitoring module. The industrial data monitoring module is used to send the measurement data to the industrial data gateway. The industrial data gateway executes the bus duct fault monitoring method as described in any of the above items. The server is used to perform fault monitoring on the bus duct operation status according to the final measurement value data set.

[0018] The bus duct fault monitoring system groups the industrial data monitoring modules at different measurement node positions, and takes the industrial data monitoring modules of the same transmission line as the same group, that is, considering the measurement nodes with similar measurement values, and utilizing the interval length between the measurement nodes to optimize the algorithm, thereby enhancing the accuracy and rationality of the estimation of the interference assessment value; in addition, the application designs a preset range area corresponding to each measurement value, and utilizes the difference value of the differential data set corresponding to each measurement value to dynamically adjust the interference assessment value, thereby enhancing the local adaptability of the optimization algorithm and monitoring slight changes in the measurement value; dynamic segmentation is performed based on the corrected measurement data, so that the measurement data with similar interference assessment values ​​are segmented together, which helps to improve the effect of interference removal of the measurement data and makes subsequent fault warnings more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a structural schematic diagram of the bus duct fault monitoring system of an embodiment of the present application.

[0020] Figure 2 It is a flowchart of the steps of the bus duct fault monitoring method in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0022] In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other. For ease of description, the concepts of "first", "second", etc. mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units. The modifications of "one" and "multiple" mentioned in this application are illustrative and non-restrictive, and those skilled in the art should understand that unless otherwise clearly stated in the context, it should be understood as "one or more".

[0023] The specific implementation of the present application is described in detail below with reference to the accompanying drawings.

[0024] Power transmission lines contain multiple bus ducts, such as Figure 1 As shown, in the embodiment of the present application, the bus duct fault monitoring system is provided with an industrial data monitoring module 1 at the connection of adjacent bus ducts 3. Since the length and specification of the bus ducts 3 selected for the same section of the power transmission line are basically the same, the industrial data monitoring module can be evenly distributed throughout the power transmission line, that is, the measurement nodes of the same section of the power transmission line are evenly distributed. The industrial data monitoring module 1 is connected to the industrial data gateway 2 to transmit the collected measurement values ​​to the industrial data gateway 2 in real time. After the industrial data gateway 2 pre-processes the collected measurement data, it uploads the final measurement data after pre-processing to the server (not shown in the figure), and the server performs fault analysis and early warning of the operation status of the bus duct 3 according to the final measurement data. The industrial data monitoring module can be numbered [1, 2, 3..., m] from the trunk line to the branch line in sequence according to the bus duct 3 wiring planning diagram. The same section of the transmission line refers to a section of the transmission line without a convergence point or a diversion point. The industrial data monitoring modules of the same section of the transmission line can be identified as the same group.

[0025] Reference Figure 2 The bus duct fault monitoring method includes the following steps S100 to 700: Step S100: Obtain the first measurement value data set of the industrial data monitoring module of each measurement node of the same transmission line; the transmission lines include multiple transmission lines, and the same transmission line refers to a section of the transmission line without a convergence point or a diversion point, and each transmission line is composed of multiple bus ducts. The industrial data monitoring modules of the same transmission line are defined as the same group.

[0026] In the embodiment of the present application, when the industrial data monitoring module collects current data, the measurement data collected by each industrial data monitoring module can be described as , indicating the m Industrial data monitoring modules The industrial data monitoring module can also monitor voltage data, and the number and interval of the industrial data monitoring modules can also be arranged according to the actual needs of the system, such as setting an industrial data monitoring module for every five bus ducts, etc. The embodiment of the present application does not limit the number of industrial data monitoring modules.

[0027] Step S200: Calculate a differential data set of a preset range area corresponding to each measurement value and a difference value of the differential data set from the first measurement value data set; wherein the length of the preset range area is much smaller than the length of the first measurement value data set, and the length of the preset range area is a positive integer.

[0028] In the above step S100, the collected measurement data (that is, the first measurement value data set) is time series data. Usually, the change of the interference point in the time series data often appears as an outlier, which has nothing to do with the change of the surrounding data. The measurement value data in the preset range area and the entire measurement value data set are the relationship between the local and the whole. In the embodiment of the present application, the differential data set is a first-order data sequence, which can be used to characterize the difference between two adjacent measurement values. Specifically, the measurement value in the first measurement value data set is taken as the key point, and a preset number is defined before and after the key point as the preset range area corresponding to each measurement value; if the length of the first measurement value data set is M, the preset number can be set to N, N is much smaller than M, and can also be understood as the relationship between the whole and the local, taking any measurement value in the first measurement value data set as the key point, and obtaining N measurement values ​​before and after for subsequent calculations, then the length of the preset range area is 2N+1, and the difference between every two adjacent measurement values ​​in the preset range area is calculated. , 2N differences are obtained within the preset range, and the set of 2N differences is the first-order difference data of the preset range corresponding to the measured value. The set of 2N differences is used as the differential data set W, which can be used Indicates that the mth industrial data monitoring module is The differential data set of the measured values ​​at a time point within the preset range is, in simple terms, the first-order difference sequence of the local area of ​​the measured value. The differential data sets of other measured values ​​are calculated similarly according to the above steps, thereby obtaining the differential data sets of the preset range area corresponding to each measured value. The local characteristics of the differential data set relative to the entire first measured value data set can reveal the local change characteristics of the data and more keenly capture the subtle changes in the measured data. The outlier characteristics of the first-order difference sequence of the differential data set can also help eliminate interference in the data and improve the accuracy of monitoring bus duct operating parameters in complex environments. The outlier nature of the first-order difference data can be described based on the difference value of each measured value between adjacent measured values ​​within its local range. The difference value can be the variance. The consistency of each change can be described by the variance to characterize the outlier nature. That is, the larger the variance of the first-order difference, the higher the outlier nature. In other words, the variance of the first-order difference data can be calculated as the difference value of the differential data set, which can be used D( )express .

[0029] In short, step S200 in the embodiment of the present application includes: S201: Taking the measurement values ​​in the first measurement value data set as key points, defining a preset number N before and after the key points as a preset range area 2N+1 corresponding to each measurement value.

[0030] S202: Calculate the difference between every two adjacent measurement values ​​within the preset range (2N+1) corresponding to each measurement value , obtain the differential data set W of the preset range area corresponding to each measurement value, thereby obtaining the differential data set of the preset range area corresponding to each measurement value and the difference dataset The difference value D( ) .

[0031] Step S300: Based on the interval length of each measurement node, the original interference evaluation value of each measurement value in the first measurement value data set is calculated; according to the difference value corresponding to each measurement value, the regularization term of each measurement value is calculated and then multiplied by the corresponding original interference evaluation value to obtain the interference influence value of each measurement value; the interference influence value of each measurement value is normalized to obtain the target interference evaluation value of each measurement value in the first measurement value data set.

[0032] In the embodiment of the present application, the interference evaluation value can also be called a noise level index. In a theoretical environment, the measurement data collected at the same time point at each location of the same section of the power transmission line should be basically consistent, but in reality, due to environmental changes and the structural problems of the equipment itself, different locations will be different. If the difference between the measurement value of the measurement node and the measurement value of other measurement nodes at the same time point is greater, the noise interference that may exist in the measurement node is greater. Therefore, it is necessary to calculate the interference evaluation value of the difference between the measurement value data of each measurement node at the same time point. The greater the difference between the measurement value of the current measurement node and the measurement value of other measurement nodes, the greater its interference evaluation value. For example, the measurement values ​​of two measurement nodes with closer intervals should be more similar. The measurement nodes in the embodiment of the present application consider the measurement value data of the same section of the transmission line. Therefore, the interval length of the two measurement nodes can be used as the weight of the difference, and then the interference evaluation value of the measurement value is obtained by weighted average to improve the accuracy of the interference evaluation value calculation.

[0033] Specifically, an industrial data monitoring module is used as the first measurement node, and the industrial data monitoring modules other than the first measurement node are used as the second measurement nodes. Step S300 includes: Step S301: Obtain the measurement value of the first measurement node and the measurement value of the second measurement node at the same time point, and calculate the difference between the two measurement values. That is, the difference between the measurement values ​​of the first measurement node and the other measurement nodes is used to describe the consistency of the measurement values ​​of the two measurement nodes. The greater the change, the lower the consistency, and the greater the interference assessment value. In other words, it is necessary to calculate the difference between the first measurement node and the other measurement nodes. m Industrial data monitoring module and k The difference in the measured values ​​of the industrial data monitoring modules at time point t .

[0034] Step S302: Calculate the sub-interval metric value between the first measurement node and the second measurement node and the total interval metric value between the first measurement node and the second measurement node.

[0035] The sub-interval metric value is obtained based on the interval length between the first measurement node and the single second measurement node. The interval length is used to characterize the bus duct length or the number of bus ducts from one measurement node to another measurement node. For example, D ( m,k ) indicates the m Industrial data monitoring module and k The interval length of an industrial data monitoring module, the reciprocal of the interval length As a sub-interval measure The total interval metric is obtained based on the reciprocal sum of the interval lengths between the first measurement node and all second measurement nodes, as calculated As a total interval measure , l Indicates l industrial data monitoring modules, M represents the number of industrial data monitoring modules, and the total interval measurement value represents the importance of the consistency between the first measurement node and other measurement nodes.

[0036] Step S303: Calculate the ratio of the sub-interval metric value to the total interval metric value, and use the ratio as the interval metric evaluation value of the measurement value; In the embodiment of the present application, the interval metric evaluation value can be used as a weight coefficient. According to the fact that the measurement values ​​of two measurement nodes are more similar when the interval is closer, the interval metric evaluation value is used as an estimation indicator of the consistency of the measurement values. The closer the interval length, the higher the consistency, and the greater the weight of the corresponding measurement value.

[0037] Step S304: multiply and add the measured value difference of each measured value and the interval metric evaluation value to obtain the original interference evaluation value of each measured value.

[0038] In the embodiment of the present application, the interval metric evaluation value obtained in step S303 is used as the interval weight coefficient, and the cumulative sum of the product of the measurement value difference and the weight coefficient is calculated, and the calculation result is used as the original interference evaluation value. Combining the above steps S301-S304, in the embodiment of the present application, the formula of the original interference evaluation value can be expressed as: ; in, For the m Industrial data monitoring modules The original interference evaluation value at the time point, For the m Industrial data monitoring modules The measured value at a time point, For the k Industrial data monitoring modules The measured value at a time point, For the m The interval weight coefficient of an industrial data monitoring module, M represents the number of industrial data monitoring modules in this transmission line.

[0039] In the embodiment of the present application, step 200 obtains the difference value corresponding to each measurement value. ， For the mth industrial data monitoring module The first-order difference sequence of the local area of ​​the measured values ​​at the time point, the difference value That is, the variance of the first-order difference sequence in the local area of ​​the measurement value ， Calculate the regularization term for each measurement : , exp[] is an exponential algorithm, the purpose is to make the result between 0 and 1. Regularization term The original interference evaluation value obtained in step 304 Multiply to obtain the interference impact value, perform extreme value normalization on the interference impact value, remove the maximum and minimum values, and obtain the target interference evaluation value. In the embodiment of the present application, the interference point changes in the time series data often appear as outliers, which are unrelated to the changes in the surrounding data. The interference evaluation value is adjusted by the outlier of the interference measurement value. In other words, the outlier of the measurement data of the embodiment of the present application can be expressed according to the variance of adjacent measurement values ​​in the local area of ​​the measurement value. The consistency of each change can be represented by the variance, which can characterize the outlier. That is, the larger the variance of the first-order difference, the higher the outlier, which means that the higher the possible degree of interference, the higher the interference evaluation value. In this case, a smaller adjustment is made to the interference evaluation value, and the adjusted interference evaluation value is used as the final target interference evaluation value. Target interference evaluation value The formula can be expressed as: ; in, Indicates that the mth industrial data monitoring module is Interference evaluation value of the measured value at a time point, Represents the raw interference assessment value.

[0040] Step 400: Correcting a first measurement data set using a target interference evaluation value of each measurement value to obtain a second measurement data set; dynamically segmenting the second measurement data set to obtain second measurement data sub-blocks of each measurement node.

[0041] In the embodiment of the present application, by using the target interference evaluation value of each measurement value to correct the first measurement value data set, the error introduced by the interference value can be reduced, and the stability and availability of the data can be enhanced. The correction process of the first measurement value data set is a well-known technology and will not be repeated here. In step 500, the second measurement value data set is dynamically segmented to obtain the second measurement value data sub-blocks of each measurement node, which may include the following steps 501-504: Step 401: Pre-segment the second measurement data set to obtain original second measurement data set sub-blocks. The pre-segmentation is performed based on a preset segmentation number, which is a positive integer; for example, the preset segmentation number is set to [4, 5, 6, 7, 8], and the second measurement data set is segmented into 4, 5, 6, 7, and 8 data sub-blocks, respectively.

[0042] Step 402: Calculate the error estimate of the original second measurement value data set sub-block, and add the error estimate of all the original second measurement value data set sub-blocks to obtain the error result of the second measurement value data set under the preset number of divisions.

[0043] The calculated error estimate can be calculated using a square residual sum function, a mean absolute error function, or a root mean square error value. Preferably, the embodiment of the present application uses a square residual sum function for calculation, specifically: calculating the mean of the measured values ​​in the original second measurement value data set sub-block to obtain the predicted value of the original second measurement value data set sub-block; calculating the difference between the measured value and the predicted value of the original second measurement value data set sub-block and performing the square sum to obtain the square residual sum of the original second measurement value data set sub-block, and then adding the square residual sum of the original second measurement value data set sub-block under the preset number of segmentations to obtain the error result under the preset number of segmentations, and the error result S formula can be: , Wherein, Z is the preset number of divisions, Y is the total number of measurements of the sub-blocks of the original second measurement value data set, is the observation value of the sub-block of the original second measurement data set, is the predicted value of the original second measurement value data set sub-block, which may be the mean of all measurement values ​​of the original second measurement value data set sub-block, For example, if the preset number of segments is [4,5,6,7,8], the error results for the preset number of segments [4,5,6,7,8] can be obtained by calculating with the above formula. .

[0044] Step 403: Using the preset number of divisions as the horizontal direction and the error result as the vertical direction, an error change graph under different preset numbers of divisions is produced, the position of the inflection point is determined according to the shape of the error change graph, and the target sub-block length is determined according to the position of the inflection point; wherein the target sub-block length is a positive integer.

[0045] By analyzing the shape of the curve, the inflection point, which can also be called the elbow point, is determined. The preset segmentation number value corresponding to the inflection point position is used to determine the optimal target sub-block length. For example, in the preset segmentation number [4,5,6,7,8], an inflection point appears when the preset segmentation number value is 5, then 5 is used as the target sub-block length. This method can avoid overfitting and quickly determine the target sub-block length. It should be emphasized that the embodiment of the present application minimizes the loss function by combining the optimal solution method and the elbow method by continuously iterating the number of data segmentations, so that the measurement values ​​in each second measurement value data sub-block are close in size, and combined with the above steps S401-S403, the interference degree of the measurement value of the final segmented data sub-block is stabilized at a level, that is, the noise degree is stable, and it is more adaptable to the complex changes in the measurement values ​​in different time periods in the measurement data that are subject to different interference.

[0046] Step 404: split the second measurement data set twice based on the target sub-block length to obtain a final second measurement data set sub-block, and use the final second measurement data set sub-block as the second measurement data sub-block corresponding to the measurement node, thereby obtaining the second measurement data sub-block of each measurement node.

[0047] The second measurement value data set is used for data segmentation. Since the second measurement value data set is corrected by the target interference evaluation value, that is, the data segmentation is performed based on the target interference evaluation value, the interference intensity of each sub-block of the second measurement value data set is consistent, and different interference parameters are applied in each data sub-block, thereby reducing over-smoothing and facilitating the accuracy of data monitoring.

[0048] Step 500: Determine the length of the sliding area according to the corresponding target interference assessment value, perform a cumulative average of the measurement values ​​of the second measurement value data sub-block, perform a second cumulative average of the measurement values ​​of the second measurement value data sub-block within the sliding area, and obtain a third measurement value data set; wherein the length of the sliding area is a positive integer.

[0049] The above-mentioned determining the sliding area length according to the corresponding target interference evaluation value includes the following steps 601-603: Step 501: Calculate a second interference assessment mean value of a second measurement value data set sub-block based on a target interference assessment value of a second measurement value data set sub-block; Step 502: Calculate the product of the initial sliding area length and the second interference assessment mean value, and add the product to the initial sliding area length to obtain the expected sliding area length; Step 503: taking the maximum positive integer value of the expected sliding area length to obtain the target sliding area length, and taking the target sliding area length as the final sliding area length.

[0050] In the above steps 501 to 503, the formula for the final sliding area length can be expressed as: ; in, is the preset initial sliding area length, is the mean value of the second interference assessment values ​​in the second measurement value data set sub-block.

[0051] In the embodiment of the present application, Gaussian filtering can be used for processing, wherein the filtering effect is determined by the window size. For a data sub-block, the larger the interference evaluation value, the longer the required sliding area length. The size of the window is dynamically adjusted according to the expectation of the interference evaluation value in the data sub-block, and the second measurement value data set is filtered based on the adjusted target sliding window (i.e., the sliding area length), thereby completing the data preprocessing of the first measurement value data set of the industrial data monitoring module of each measurement node.

[0052] Step 600: Using the third measurement value data set as the final measurement value data set, uploading the final measurement value data set to a server, and using the server to perform fault monitoring on the operation status of the bus duct.

[0053] The server can apply statistical analysis and machine learning methods to analyze the abnormal status of the measured values, and use historical data to build a detection model for abnormal operating conditions. When the measured values ​​are detected to deviate from the detection model, potential faults can be identified. By real-time monitoring of the voltage and current parameters of each measurement node of the bus duct, combined with the anomaly detection algorithm, abnormal fluctuations in the operation of the bus duct can be effectively captured, thereby achieving early warning and diagnosis of faults.

[0054] like Figure 1 As shown, the embodiment of the present application also provides a bus duct fault monitoring system, including a server, an industrial data gateway and an industrial data monitoring module, the industrial data monitoring module is used to send the measurement data (that is, the first measurement data set) to the industrial data gateway, the industrial data gateway executes the bus duct fault monitoring method as described above, and the server is used to perform fault monitoring on the bus duct operation status according to the final measurement value data set (that is, the third measurement data set). The method executed by the industrial data gateway can refer to the content of the above application embodiment, which will not be repeated here.

[0055] Similarly, the bus duct fault monitoring system groups the industrial data monitoring modules at different measurement node positions, and takes the industrial data monitoring modules of the same transmission line as the same group, that is, considering the measurement nodes with similar measurement values, and utilizing the interval length between the measurement nodes to optimize the algorithm, thereby enhancing the accuracy and rationality of the interference assessment value estimation; in addition, the application designs a preset range area corresponding to each measurement value, and utilizes the difference value of the differential data set corresponding to each measurement value to dynamically adjust the interference assessment value, thereby enhancing the local adaptability of the optimization algorithm and monitoring slight changes in the measurement value; dynamic segmentation is performed based on the corrected measurement data, so that the measurement data with similar interference assessment values ​​are segmented together, which helps to improve the effect of measuring data interference removal and makes subsequent fault warnings more accurate.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. A bus duct fault monitoring method, characterized in that: Applied to an industrial data gateway, the method comprises: Obtain a first measurement value data set of the industrial data monitoring module of each measurement node of the same transmission line; the transmission lines include multiple transmission lines, the same transmission line refers to a section of the transmission line without a convergence point or a diversion point, each transmission line is composed of multiple bus ducts, and the industrial data monitoring modules of the same transmission line are defined as the same group; Calculate a differential data set of a preset range area corresponding to each measurement value and a difference value of the differential data set from the first measurement value data set; wherein the length of the preset range area is much smaller than the length of the first measurement value data set, and the length of the preset range area is a positive integer; Based on the interval length of each measurement node, the original interference evaluation value of each measurement value in the first measurement value data set is calculated; according to the difference value corresponding to each measurement value, the regularization term of each measurement value is calculated and then multiplied by the corresponding original interference evaluation value to obtain the interference impact value of each measurement value; the interference impact value of each measurement value is normalized to obtain the target interference evaluation value of each measurement value in the first measurement value data set; Correcting the first measurement data set using the target interference evaluation value of each measurement value to obtain a second measurement data set; dynamically segmenting the second measurement data set to obtain second measurement data sub-blocks of each measurement node; Determine the length of the sliding area according to the corresponding target interference assessment value, perform a measurement value cumulative average on the second measurement value data sub-block, perform a second measurement value cumulative average on the second measurement value data sub-block in the sliding area, and obtain a third measurement value data set; wherein the length of the sliding area is a positive integer; The third measurement value data set is used as the final measurement value data set, and the final measurement value data set is uploaded to the server, so that the server performs fault monitoring on the operation status of the bus duct.

2. The method according to claim 1, characterized in that Calculating a differential data set of a preset range area corresponding to each measurement value and a difference value of the differential data set from the first measurement value data set includes: Taking the measurement values ​​in the first measurement value data set as key points, defining a preset number before and after the key points as a preset range area corresponding to each measurement value; The difference between every two adjacent measurement values ​​in the preset range area corresponding to each measurement value is calculated to obtain a differential data set in the preset range area corresponding to each measurement value and a difference value of the differential data set, thereby obtaining a differential data set in the preset range area corresponding to each measurement value and a difference value of the differential data set; the differential data set is a first-order data sequence.

3. The method according to claim 1, characterized in that Based on the interval length of each measurement node, the original interference assessment value of each measurement value in the first measurement value data set is calculated, including: An industrial data monitoring module is used as a first measurement node, and industrial data monitoring modules other than the first measurement node are used as second measurement nodes, a measurement value of the first measurement node and a measurement value of the second measurement node at the same time point are obtained, and a difference between the two measurement values ​​is calculated; Calculating a sub-interval metric value between the first measurement node and the second measurement node and a total interval metric value between the first measurement node and the second measurement node; Calculate the ratio of the sub-interval metric value to the total interval metric value, and use the ratio as the interval metric evaluation value of the measurement value; The measured value difference of each measured value is multiplied and added with the interval metric evaluation value to obtain the original interference evaluation value of each measured value.

4. The method according to claim 1, characterized in that: Dynamically segmenting the second measurement value data set to obtain second measurement value data sub-blocks of each measurement node includes: Pre-segmenting the second measurement data set to obtain original second measurement data set sub-blocks; the pre-segmenting is performed based on a preset segmentation number, which is a positive integer; Calculating an estimated error value of an original second measurement value data set sub-block, and adding the estimated error values ​​of all original second measurement value data set sub-blocks to obtain an error result of the second measurement value data set under the preset number of segmentations; Taking the preset number of divisions as the horizontal direction and the error result as the vertical direction, an error change graph under different preset numbers of divisions is produced, the position of the inflection point is determined according to the shape of the error change graph, and the target sub-block length is determined according to the position of the inflection point; wherein the target sub-block length is a positive integer; The second measurement data set is divided twice based on the target sub-block length to obtain a final second measurement data set sub-block, and the final second measurement data set sub-block is used as the second measurement data sub-block corresponding to the measurement node, thereby obtaining the second measurement data sub-block of each measurement node.

5. The method according to claim 4, characterized in that Calculating an error estimate of a sub-block of the original second measurement value data set includes: Calculate the mean of the measured values ​​in the sub-block of the original second measured value data set to obtain the predicted value of the sub-block of the original second measured value data set; The difference between the measured value and the predicted value of the original second measurement value data set sub-block is calculated and squared to obtain the square residual sum of the original second measurement value data set sub-block.

6. The method according to claim 4, characterized in that Calculating an error estimate of a sub-block of the original second measurement value data set includes: The mean absolute error value of the original second measurement value data set sub-block is calculated and used as the error estimate value of the original second measurement value data set sub-block.

7. The method according to claim 4, characterized in that Calculating an error estimate of a sub-block of the original second measurement value data set includes: A root mean square error value of the original second measurement value data set sub-block is calculated and taken as an error estimate value of the original second measurement value data set sub-block.

8. The method according to claim 1, characterized in that The sliding area length is determined according to the corresponding target interference evaluation value, including: Calculating a second interference assessment mean value of the second measurement value data set sub-block based on the target interference assessment value of the second measurement value data set sub-block; After calculating the product of the initial sliding area length and the second interference evaluation mean, the product is added to the initial sliding area length to obtain the expected sliding area length; The maximum positive integer value is taken for the expected sliding area length to obtain the target sliding area length, and the target sliding area length is used as the final sliding area length.

9. The bus duct fault monitoring system is characterized by: It includes a server, an industrial data gateway and an industrial data monitoring module. The industrial data monitoring module is used to send measurement data to the industrial data gateway. The industrial data gateway executes the bus duct fault monitoring method as described in any one of claims 1 to 8. The server is used to perform fault monitoring on the bus duct operation status according to the final measurement value data set.

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