Fault recording automatic identification method and related device

Abstract

The invention relates to the technical field of data processing, and provides a fault recording automatic identification method and a related device, and the method comprises the steps: obtaining fault filtering data of a fault filtering terminal; performing waveform acquisition according to the fault filtering data to obtain an initial fault waveform; performing optimization processing on the initial fault waveform to obtain a target fault waveform; and performing fault analysis processing on the fault filtering terminal by using the target fault waveform to obtain the fault type information, and automatically analyzing and processing according to the fault filtering data to obtain the corresponding fault type, thereby reducing human participation and improving the accuracy of determining the fault type.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method for automatically identifying fault recordings and related devices. Background Art

[0002] At present, the three levels of production and operation supervision at the provincial, municipal and county levels need to accurately judge the fault type and required maintenance measures when completing the actual fault analysis in real time. However, for new or less experienced repair personnel, they may lack sufficient experience and knowledge to quickly and accurately judge the fault filter data, resulting in extended fault handling time and low efficiency. In addition, relying solely on historical faults in the historical lightning location system lacks the accuracy of sudden fault analysis, which not only increases the workload of business personnel, but also affects the timeliness and accuracy of fault problem troubleshooting and problem closure. Summary of the invention

[0003] The embodiment of the present application provides a method for automatic identification of fault recordings and related devices, which can automatically analyze and process fault filtering data to obtain the corresponding fault type, reduce human intervention, and improve the accuracy of fault type determination.

[0004] A first aspect of an embodiment of the present application provides a method for automatically identifying a fault recording, the method comprising:

[0005] Obtaining fault filtering data of the fault filtering terminal;

[0006] Acquire a waveform according to the fault filtering data to obtain an initial fault waveform;

[0007] Optimizing the initial fault waveform to obtain a target fault waveform;

[0008] The target fault waveform is used to perform fault analysis processing on the fault filter terminal to obtain fault type information.

[0009] In this example, the fault filtering data of the fault filtering terminal is obtained, the waveform is obtained according to the fault filtering data to obtain the initial fault waveform, the initial fault waveform is optimized to obtain the target fault waveform, and the fault filtering terminal is subjected to fault analysis processing using the target fault waveform to obtain fault type information. Therefore, the corresponding fault type can be obtained by automatically analyzing and processing the fault filtering data, thereby reducing human participation and improving the accuracy in determining the fault type.

[0010] In a possible implementation, the fault filtering data includes a fault filtering image, and the waveform acquisition according to the fault filtering data to obtain the initial fault waveform includes:

[0011] Extracting features from the fault filter image to obtain fault feature data;

[0012] A waveform is constructed according to the fault characteristic data to obtain an initial fault waveform.

[0013] In a possible implementation, the step of constructing a waveform according to the fault characteristic data to obtain an initial fault waveform includes:

[0014] Acquire position information of characteristic points in the fault characteristic data to obtain a first position information set;

[0015] Performing outlier screening processing on the first location information in the first location information set to obtain a second location information set;

[0016] constructing a waveform profile using the second position information in the second position information set to obtain an initial profile of a reference fault waveform;

[0017] The initial fault waveform is obtained by constructing a profile according to the initial profile of the reference fault waveform.

[0018] In a possible implementation, the optimizing the initial fault waveform to obtain a target fault waveform includes:

[0019] Obtaining a waveform mutation point of the initial fault waveform to obtain a mutation point set;

[0020] Obtaining slope information of the initial fault waveform at a mutation point in the mutation point set to obtain a slope information set;

[0021] The initial fault waveform is shaped according to the slope information set to obtain the initial fault waveform.

[0022] In a possible implementation, the using the target fault waveform to perform fault analysis processing on the fault filter terminal to obtain fault type information includes:

[0023] Obtain historical fault waveform data set;

[0024] Obtaining the similarity between the target fault waveform and the historical fault waveform in the historical fault waveform data set to obtain a similarity set;

[0025] Extracting a first similarity and a second similarity from the similarity set, wherein the first similarity is a maximum value in the similarity set, and the second similarity is a similarity value in the similarity set that is only smaller than the first similarity;

[0026] Extracting a first historical fault waveform corresponding to the first similarity from a historical fault waveform data set, and extracting a second historical fault waveform corresponding to the second similarity from a historical fault waveform data set;

[0027] Acquire a target correlation between the first historical fault waveform and the second historical fault waveform;

[0028] If the target correlation is less than a preset correlation threshold, determining the fault type corresponding to the first historical fault waveform as the fault type information;

[0029] If the target correlation is greater than a preset correlation threshold, the fault type corresponding to the first historical fault waveform and the fault type corresponding to the second historical fault waveform are determined as the fault type information.

[0030] A second aspect of an embodiment of the present application provides a fault recording automatic identification device, the device comprising:

[0031] A first acquisition unit, used to acquire fault filtering data of a fault filtering terminal;

[0032] A second acquisition unit, configured to acquire a waveform according to the fault filtering data to obtain an initial fault waveform;

[0033] A processing unit, configured to optimize the initial fault waveform to obtain a target fault waveform;

[0034] The analysis unit is used to perform fault analysis processing on the fault filter terminal using the target fault waveform to obtain fault type information.

[0035] In a possible implementation manner, the fault filtering data includes a fault filtering image, and the second acquiring unit is specifically configured to:

[0036] Extracting features from the fault filter image to obtain fault feature data;

[0037] A waveform is constructed according to the fault characteristic data to obtain an initial fault waveform.

[0038] In a possible implementation, in the aspect of constructing a waveform according to the fault characteristic data to obtain an initial fault waveform, the second acquisition unit is specifically configured to:

[0039] Acquire position information of characteristic points in the fault characteristic data to obtain a first position information set;

[0040] Performing outlier screening processing on the first location information in the first location information set to obtain a second location information set;

[0041] constructing a waveform profile using the second position information in the second position information set to obtain an initial profile of a reference fault waveform;

[0042] The initial fault waveform is obtained by constructing a profile according to the initial profile of the reference fault waveform.

[0043] In a possible implementation, the processing unit is specifically configured to:

[0044] Obtaining a waveform mutation point of the initial fault waveform to obtain a mutation point set;

[0045] Obtaining slope information of the initial fault waveform at a mutation point in the mutation point set to obtain a slope information set;

[0046] The initial fault waveform is shaped according to the slope information set to obtain the initial fault waveform.

[0047] In a possible implementation, the analysis unit is specifically used to:

[0048] Obtain historical fault waveform data set;

[0049] Obtaining the similarity between the target fault waveform and the historical fault waveform in the historical fault waveform data set to obtain a similarity set;

[0050] Extracting a first similarity and a second similarity from the similarity set, wherein the first similarity is a maximum value in the similarity set, and the second similarity is a similarity value in the similarity set that is only smaller than the first similarity;

[0051] Extracting a first historical fault waveform corresponding to the first similarity from a historical fault waveform data set, and extracting a second historical fault waveform corresponding to the second similarity from a historical fault waveform data set;

[0052] Acquire a target correlation between the first historical fault waveform and the second historical fault waveform;

[0053] If the target correlation is less than a preset correlation threshold, determining the fault type corresponding to the first historical fault waveform as the fault type information;

[0054] If the target correlation is greater than a preset correlation threshold, the fault type corresponding to the first historical fault waveform and the fault type corresponding to the second historical fault waveform are determined as the fault type information.

[0055] A third aspect of an embodiment of the present application provides a terminal, comprising a processor, an input device, an output device and a memory, wherein the processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the step instructions in the first aspect of the embodiment of the present application.

[0056] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiments of the present application.

[0057] A fifth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 A schematic flow chart of a method for automatically identifying a fault recording wave is provided for an embodiment of the present application;

[0060] Figure 2 A flowchart of another method for automatically identifying fault recording waves is provided for the embodiment of the present application;

[0061] Figure 3 A schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0062] Figure 4 A structural schematic diagram of a fault recording automatic identification device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0063] 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 only 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 ordinary technicians in this field without creative work are within the scope of protection of this application.

[0064] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0065] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0066] In order to better understand the fault recording automatic identification method provided by the embodiment of the present application, the power grid fault identification method in the existing scheme is first briefly introduced below. At present, the production and operation supervision at the provincial, municipal and county levels needs to accurately judge the fault type and the required maintenance measures when completing the actual fault analysis in real time. However, for new or less experienced repair personnel, they may lack sufficient experience and knowledge to quickly make accurate judgments on the fault filtering data, resulting in prolonged fault handling time and low efficiency, and relying solely on historical faults obtained in the historical lightning location system lacks the accuracy of sudden fault analysis, which not only increases the workload of business personnel, but also affects the timeliness and accuracy of fault problem troubleshooting and problem closure.

[0067] In response to the fault data obtained from a large number of fault filtering terminals, on-duty emergency repair personnel often encounter the following problems: First, it is difficult to accurately determine the type of fault, especially in the case of complex fault filtering data; second, for some special situations, there is a lack of sufficient response experience and solutions; third, in emergency situations, decisions need to be made quickly, but there is a lack of timely guidance and support.

[0068] In order to solve the above-mentioned problems, an embodiment of the present application provides a method for automatic identification of fault recordings, which can automatically analyze and process fault filtering data to obtain the corresponding fault type, reduce human intervention, and improve the accuracy in determining the fault type.

[0069] See also Figure 1 , Figure 1 The present invention provides a flow chart of a method for automatically identifying a fault recording. Figure 1 As shown, the method includes:

[0070] 101. Obtain fault filtering data of a fault filtering terminal.

[0071] The fault filtering data includes a fault filtering image, which can be obtained from a fault filtering terminal.

[0072] 102. Acquire a waveform according to the fault filtering data to obtain an initial fault waveform.

[0073] Specifically, the fault filtering data includes a fault filtering image. The method for acquiring a waveform based on the fault filtering data to obtain an initial fault waveform may be: A1, extracting features from the fault filtering image to obtain fault feature data; A2, constructing a waveform based on the fault feature data to obtain an initial fault waveform.

[0074] Among them, a general feature extraction method can be used to extract image features to obtain fault feature data. The fault feature data can include position information of feature points, and the feature points can be pixels of waveforms, etc.

[0075] A method for constructing a waveform according to the fault characteristic data to obtain an initial fault waveform may specifically include:

[0076] B1. Obtaining position information of characteristic points in fault characteristic data to obtain a first position information set;

[0077] B2. performing outlier screening processing on the first location information in the first location information set to obtain a second location information set;

[0078] B3, using the second position information in the second position information set to construct a waveform profile to obtain an initial profile of a reference fault waveform;

[0079] B4. Construct a profile according to the initial profile of the reference fault waveform to obtain an initial fault waveform.

[0080] Points whose positions deviate far from the overall position may be screened out to improve the accuracy of the elements in the obtained second position information set.

[0081] A smooth curve can be used to sequentially connect according to the second position information to obtain the initial outline of the reference fault waveform. Specifically, a straight line connection can be used to connect to obtain the initial outline of the reference fault waveform. However, since there is usually no sudden change between adjacent positions of the feature points, a smooth curve connection can be used to replace the straight line connection to obtain the initial fault waveform. Therefore, the accuracy of the initial fault waveform construction can be improved.

[0082] 103. Optimize the initial fault waveform to obtain a target fault waveform.

[0083] Specifically, a method for optimizing the initial fault waveform to obtain a target fault waveform includes:

[0084] C1. Obtaining the waveform mutation point of the initial fault waveform to obtain a mutation point set;

[0085] C2. Obtain slope information of the initial fault waveform at a mutation point in the mutation point set to obtain a slope information set;

[0086] C3. Perform shaping processing on the initial fault waveform according to the slope information set to obtain the initial fault waveform.

[0087] The waveform mutation point can be understood as the turning point of the waveform, etc. Thus, a mutation point set can be obtained. The slope information of the mutation point can be obtained by a general slope acquisition method, thereby obtaining a slope information set.

[0088] The initial fault waveform is shaped using the slope information in the slope information set. The method for obtaining the initial fault waveform may be: obtaining the maximum slope value and the minimum slope value of the line between the sampling points, setting the slope value corresponding to the slope information exceeding the maximum slope value to the maximum slope value, and setting the slope value corresponding to the slope information less than the minimum slope value to the minimum slope value, thereby shaping the waveform to obtain the initial fault waveform.

[0089] 104. Perform fault analysis on the fault filter terminal using the target fault waveform to obtain fault type information.

[0090] Specifically, a method for performing fault analysis processing on a fault filter terminal using the target fault waveform to obtain fault type information includes:

[0091] D1. Obtain historical fault waveform data set;

[0092] D2. Obtaining the similarity between the target fault waveform and the historical fault waveform in the historical fault waveform data set to obtain a similarity set;

[0093] D3, extracting a first similarity and a second similarity from the similarity set, wherein the first similarity is a maximum value in the similarity set, and the second similarity is a similarity value in the similarity set that is only smaller than the first similarity;

[0094] D4, extracting a first historical fault waveform corresponding to the first similarity from the historical fault waveform data set, and extracting a second historical fault waveform corresponding to the second similarity from the historical fault waveform data set;

[0095] D5. Obtaining a target correlation between the first historical fault waveform and the second historical fault waveform;

[0096] D6. If the target correlation is less than a preset correlation threshold, determining the fault type corresponding to the first historical fault waveform as the fault type information;

[0097] D7. If the target correlation is greater than a preset correlation threshold, the fault type corresponding to the first historical fault waveform and the fault type corresponding to the second historical fault waveform are determined as the fault type information.

[0098] Among them, the historical fault waveform data set can be extracted from the database to obtain the historical fault waveform data set.

[0099] A general similarity comparison method may be used to obtain the similarity between the target fault waveform and each historical fault waveform in the historical fault waveform data set, thereby obtaining a similarity set.

[0100] The similarities in the similarity set may be sorted to obtain a first sequence, the first similarity in the first sequence may be determined as the first similarity, and the second similarity in the sequence may be determined as the second similarity. Specifically, the sorting may be performed in descending order, that is, the first similarity is the maximum value in the similarity set, and the similarities are sorted from large to small to obtain the first sequence.

[0101] Since there may be relatively similar historical fault waveforms in the historical waveform, the determined similarity has two similarities with very close similarity values. Therefore, the target correlation between the first historical fault waveform and the second historical fault waveform can be extracted. The target correlation can be understood as a characterization parameter of whether the first historical fault waveform and the second historical fault waveform are the same type of fault waveforms. If the first historical fault waveform and the second historical fault waveform are the same type of fault waveforms, it can be shown that the fault type corresponding to the first historical fault waveform and the fault type corresponding to the second historical fault waveform can be determined as the fault type corresponding to the target fault waveform. If the first historical fault waveform and the second historical fault waveform are not the same type of fault waveforms, the fault type corresponding to the first historical fault waveform is determined as the fault type information, thereby improving the reliability of obtaining detailed fault type information.

[0102] In this example, the fault filtering data of the fault filtering terminal is obtained, the waveform is obtained according to the fault filtering data to obtain the initial fault waveform, the initial fault waveform is optimized to obtain the target fault waveform, and the fault filtering terminal is subjected to fault analysis processing using the target fault waveform to obtain fault type information. Therefore, the corresponding fault type can be obtained by automatically analyzing and processing the fault filtering data, thereby reducing human participation and improving the accuracy in determining the fault type.

[0103] See also Figure 2 , Figure 2 The present application provides another flow chart of a method for automatically identifying fault recordings. Figure 2 As shown, the method includes:

[0104] 201. Obtain fault filtering data of a fault filtering terminal;

[0105] 202. Acquire a waveform according to the fault filtering data to obtain an initial fault waveform;

[0106] 203. Optimizing the initial fault waveform to obtain a target fault waveform;

[0107] 204. Obtain a historical fault waveform data set;

[0108] 205. Obtain similarity between the target fault waveform and a historical fault waveform in a historical fault waveform data set to obtain a similarity set;

[0109] 206. Extract a first similarity and a second similarity from the similarity set, wherein the first similarity is a maximum value in the similarity set, and the second similarity is a similarity value in the similarity set that is only smaller than the first similarity;

[0110] 207. Extracting a first historical fault waveform corresponding to the first similarity from a historical fault waveform data set, and extracting a second historical fault waveform corresponding to the second similarity from a historical fault waveform data set;

[0111] 208. Obtain a target correlation between the first historical fault waveform and the second historical fault waveform;

[0112] 209. If the target correlation is less than a preset correlation threshold, determining the fault type corresponding to the first historical fault waveform as the fault type information;

[0113] 210. If the target correlation is greater than a preset correlation threshold, determine the fault type corresponding to the first historical fault waveform and the fault type corresponding to the second historical fault waveform as the fault type information.

[0114] Among them, the historical fault waveform data set can be extracted from the database to obtain the historical fault waveform data set.

[0115] A general similarity comparison method may be used to obtain the similarity between the target fault waveform and each historical fault waveform in the historical fault waveform data set, thereby obtaining a similarity set.

[0116] The similarities in the similarity set may be sorted to obtain a first sequence, the first similarity in the first sequence may be determined as the first similarity, and the second similarity in the sequence may be determined as the second similarity. Specifically, the sorting may be performed in descending order, that is, the first similarity is the maximum value in the similarity set, and the similarities are sorted from large to small to obtain the first sequence.

[0117] Since there may be relatively similar historical fault waveforms in the historical waveform, the determined similarity has two similarities with very close similarity values. Therefore, the target correlation between the first historical fault waveform and the second historical fault waveform can be extracted. The target correlation can be understood as a characterization parameter of whether the first historical fault waveform and the second historical fault waveform are the same type of fault waveforms. If the first historical fault waveform and the second historical fault waveform are the same type of fault waveforms, it can be shown that the fault type corresponding to the first historical fault waveform and the fault type corresponding to the second historical fault waveform can be determined as the fault type corresponding to the target fault waveform. If the first historical fault waveform and the second historical fault waveform are not the same type of fault waveforms, the fault type corresponding to the first historical fault waveform is determined as the fault type information, thereby improving the reliability of obtaining detailed fault type information.

[0118] For the above embodiments, please refer to Figure 3 , Figure 3 A schematic diagram of the structure of a terminal provided in an embodiment of the present application, such as Figure 3 As shown, it includes a processor, an input device, an output device and a memory, which are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the program includes instructions for executing the following steps;

[0119] Obtaining fault filtering data of the fault filtering terminal;

[0120] Acquire a waveform according to the fault filtering data to obtain an initial fault waveform;

[0121] Optimizing the initial fault waveform to obtain a target fault waveform;

[0122] The target fault waveform is used to perform fault analysis processing on the fault filter terminal to obtain fault type information.

[0123] In this example, the fault filtering data of the fault filtering terminal is obtained, the waveform is obtained according to the fault filtering data to obtain the initial fault waveform, the initial fault waveform is optimized to obtain the target fault waveform, and the fault filtering terminal is subjected to fault analysis processing using the target fault waveform to obtain fault type information. Therefore, the corresponding fault type can be obtained by automatically analyzing and processing the fault filtering data, thereby reducing human participation and improving the accuracy in determining the fault type.

[0124] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that in order to realize the above functions, the terminal includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0125] The embodiment of the present application can divide the terminal into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0126] In line with the above, see Figure 4 , Figure 4 The present invention provides a schematic diagram of a structure of a device for determining a business product. Figure 4 As shown, the device comprises:

[0127] A first acquisition unit 301 is used to acquire fault filtering data of a fault filtering terminal;

[0128] A second acquisition unit 302 is used to acquire a waveform according to the fault filtering data to obtain an initial fault waveform;

[0129] The processing unit 303 is used to optimize the initial fault waveform to obtain a target fault waveform;

[0130] The analysis unit 304 is used to perform fault analysis processing on the fault filter terminal using the target fault waveform to obtain fault type information.

[0131] In this example, the fault filtering data of the fault filtering terminal is obtained, the waveform is obtained according to the fault filtering data to obtain the initial fault waveform, the initial fault waveform is optimized to obtain the target fault waveform, and the fault filtering terminal is subjected to fault analysis processing using the target fault waveform to obtain fault type information. Therefore, the corresponding fault type can be obtained by automatically analyzing and processing the fault filtering data, thereby reducing human participation and improving the accuracy in determining the fault type.

[0132] In a possible implementation, the fault filtering data includes a fault filtering image, and the second acquiring unit 302 is specifically configured to:

[0133] Extracting features from the fault filter image to obtain fault feature data;

[0134] A waveform is constructed according to the fault characteristic data to obtain an initial fault waveform.

[0135] In a possible implementation, in the aspect of constructing a waveform according to the fault characteristic data to obtain an initial fault waveform, the second acquisition unit 302 is specifically configured to:

[0136] Acquire position information of characteristic points in the fault characteristic data to obtain a first position information set;

[0137] Performing outlier screening processing on the first location information in the first location information set to obtain a second location information set;

[0138] constructing a waveform profile using the second position information in the second position information set to obtain an initial profile of a reference fault waveform;

[0139] The initial fault waveform is obtained by constructing a profile according to the initial profile of the reference fault waveform.

[0140] In a possible implementation, the processing unit 303 is specifically configured to:

[0141] Obtaining a waveform mutation point of the initial fault waveform to obtain a mutation point set;

[0142] Obtaining slope information of the initial fault waveform at a mutation point in the mutation point set to obtain a slope information set;

[0143] The initial fault waveform is shaped according to the slope information set to obtain the initial fault waveform.

[0144] In a possible implementation, the analysis unit 304 is specifically configured to:

[0145] Obtain historical fault waveform data set;

[0146] Obtaining the similarity between the target fault waveform and the historical fault waveform in the historical fault waveform data set to obtain a similarity set;

[0147] Extracting a first similarity and a second similarity from the similarity set, wherein the first similarity is a maximum value in the similarity set, and the second similarity is a similarity value in the similarity set that is only smaller than the first similarity;

[0148] Extracting a first historical fault waveform corresponding to the first similarity from a historical fault waveform data set, and extracting a second historical fault waveform corresponding to the second similarity from a historical fault waveform data set;

[0149] Acquire a target correlation between the first historical fault waveform and the second historical fault waveform;

[0150] If the target correlation is less than a preset correlation threshold, determining the fault type corresponding to the first historical fault waveform as the fault type information;

[0151] If the target correlation is greater than a preset correlation threshold, the fault type corresponding to the first historical fault waveform and the fault type corresponding to the second historical fault waveform are determined as the fault type information.

[0152] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the fault recording automatic identification methods recorded in the above method embodiments.

[0153] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program enables a computer to execute part or all of the steps of any one of the fault recording automatic identification methods recorded in the above method embodiments.

[0154] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0155] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0156] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0157] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, the functional units in the various embodiments of the application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software program modules.

[0159] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0160] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0161] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for automatic identification of fault recording, characterized in that: The method comprises: Obtaining fault filtering data of the fault filtering terminal; Acquire a waveform according to the fault filtering data to obtain an initial fault waveform; Optimizing the initial fault waveform to obtain a target fault waveform; The target fault waveform is used to perform fault analysis processing on the fault filter terminal to obtain fault type information.

2. The fault recording automatic identification method according to claim 1 is characterized in that: The fault filtering data includes a fault filtering image, and the waveform acquisition is performed according to the fault filtering data to obtain an initial fault waveform, including: Extracting features from the fault filter image to obtain fault feature data; A waveform is constructed according to the fault characteristic data to obtain an initial fault waveform.

3. The fault recording automatic identification method according to claim 2 is characterized in that: The step of constructing a waveform according to the fault characteristic data to obtain an initial fault waveform includes: Acquire position information of characteristic points in the fault characteristic data to obtain a first position information set; Performing outlier screening processing on the first location information in the first location information set to obtain a second location information set; constructing a waveform profile using the second position information in the second position information set to obtain an initial profile of a reference fault waveform; The initial fault waveform is obtained by constructing a profile according to the initial profile of the reference fault waveform.

4. The fault recording automatic identification method according to claim 3 is characterized in that: The optimizing the initial fault waveform to obtain a target fault waveform includes: Obtaining a waveform mutation point of the initial fault waveform to obtain a mutation point set; Obtaining slope information of the initial fault waveform at a mutation point in the mutation point set to obtain a slope information set; The initial fault waveform is shaped according to the slope information set to obtain the initial fault waveform.

5. The fault recording automatic identification method according to claim 3 is characterized in that: The method of using the target fault waveform to perform fault analysis on the fault filter terminal to obtain fault type information includes: Obtain historical fault waveform data set; Obtaining the similarity between the target fault waveform and the historical fault waveform in the historical fault waveform data set to obtain a similarity set; Extracting a first similarity and a second similarity from the similarity set, wherein the first similarity is a maximum value in the similarity set, and the second similarity is a similarity value in the similarity set that is only smaller than the first similarity; Extracting a first historical fault waveform corresponding to the first similarity from a historical fault waveform data set, and extracting a second historical fault waveform corresponding to the second similarity from a historical fault waveform data set; Acquire a target correlation between the first historical fault waveform and the second historical fault waveform; If the target correlation is less than a preset correlation threshold, determining the fault type corresponding to the first historical fault waveform as the fault type information; If the target correlation is greater than a preset correlation threshold, the fault type corresponding to the first historical fault waveform and the fault type corresponding to the second historical fault waveform are determined as the fault type information.

6. A fault recording automatic identification device, characterized in that: The device comprises: A first acquisition unit, used to acquire fault filtering data of a fault filtering terminal; A second acquisition unit, configured to acquire a waveform according to the fault filtering data to obtain an initial fault waveform; A processing unit, configured to optimize the initial fault waveform to obtain a target fault waveform; The analysis unit is used to perform fault analysis processing on the fault filter terminal using the target fault waveform to obtain fault type information.

7. The fault recording automatic identification device according to claim 6 is characterized in that: The fault filtering data includes a fault filtering image, and the second acquisition unit is specifically used for: Extracting features from the fault filter image to obtain fault feature data; A waveform is constructed according to the fault characteristic data to obtain an initial fault waveform.

8. The fault recording automatic identification device according to claim 7 is characterized in that: In the aspect of constructing the waveform according to the fault characteristic data to obtain the initial fault waveform, the second acquisition unit is specifically used for: Acquire position information of characteristic points in the fault characteristic data to obtain a first position information set; Performing outlier screening processing on the first location information in the first location information set to obtain a second location information set; constructing a waveform profile using the second position information in the second position information set to obtain an initial profile of a reference fault waveform; The initial fault waveform is obtained by constructing a profile according to the initial profile of the reference fault waveform.

9. A terminal, characterized in that: It includes a processor, an input device, an output device and a memory, which are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the fault recording automatic identification method as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor executes the fault recording automatic identification method according to any one of claims 1 to 5.

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