Cable fault identification system and method based on parameter jump information

Abstract

The invention discloses a cable fault identification system and method based on parameter jump information, and the method comprises the steps: obtaining fault parameter data at a cable fault moment under the condition that a cable fault is detected; comparing the fault parameter data with historical fault parameter data to determine an initial fault reason; acquiring other parameter data except the fault parameter data in the preset range of the fault moment, and detecting whether a jump point exists in the other parameter data or not; under the condition that the jump point exists, whether the jump point meets the adjustment condition of the cable fault reason or not is judged; if the jump point meets the adjustment condition of the cable fault reason, determining the cable fault reason based on the jump point and the initial fault reason, and if the jump point does not meet the adjustment condition of the cable fault reason, determining that the initial fault reason is the cable fault reason; and under the condition that the jump point does not exist, determining that the initial fault reason is the cable fault reason. According to the technical scheme, the accuracy of fault diagnosis is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of cable fault identification, and specifically relates to a cable fault identification system and method based on parameter jump information. Background Art

[0002] Electricity is the foundation of modern society. Cables are the main medium for power transmission. Failures in cables will directly lead to power outages. They may even cause fires or electric shocks, posing a serious threat to personal and property safety. Timely cable fault detection can timely discover and eliminate potential safety hazards and protect people's lives and property.

[0003] With the popularization of intelligent cables, cable monitoring platforms play an increasingly important role in real-time monitoring and display of cable parameters. Most existing technologies determine the cause of cable faults through various detection values. However, there is a certain risk of misjudgment when only relying on various detection values ​​to determine the cause of cable faults, and there is a lack of an auxiliary identification method to improve the accuracy of cable fault identification. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a cable fault identification system and method based on parameter jump information, with the aim of solving the problem that the prior art relies solely on various detection values ​​to determine the cause of the cable fault, which has a certain risk of misjudgment. The present solution can use additional jump parameters for further analysis and adjustment based on the preliminary determination of the cause of the fault. This revision mechanism improves the accuracy of fault diagnosis, reduces fault handling errors caused by misjudgment, and thus improves the overall reliability and stability of the cable.

[0005] In a first aspect, an embodiment of the present application provides a cable fault identification method based on parameter jump information, the method comprising:

[0006] When a cable fault is detected, obtaining fault parameter data at the time of the cable fault;

[0007] Comparing the fault parameter data with historical fault parameter data to determine the initial fault cause;

[0008] Acquire other parameter data except the fault parameter data within a preset range at the fault moment, and detect whether there is a jump point in the other parameter data;

[0009] In the case where there is a jump point, judging whether the jump point satisfies the adjustment condition of the cable fault cause; in the case where the jump point satisfies the adjustment condition of the cable fault cause, determining the cable fault cause based on the jump point and the initial fault cause, and in the case where the jump point does not satisfy the adjustment condition of the cable fault cause, determining the initial fault cause as the cable fault cause;

[0010] In the absence of a trip point, the initial fault cause is determined to be the cable fault cause.

[0011] Optionally, the fault parameter data includes a fault parameter value, a fault location, and a fault time, the historical fault parameter data includes a historical fault value, a historical fault location, a historical fault time, and a historical fault cause, and comparing the fault parameter data with the historical fault parameter data to determine the initial fault cause includes:

[0012] If the difference between the fault parameter value and the historical fault value is less than a preset difference, the distance between the fault location and the historical fault location is less than a preset distance, and the interval period between the fault time and the historical fault time is less than a preset period, the historical fault cause is determined as the initial fault cause.

[0013] Optionally, the detecting whether there is a jump point in the other parameter data includes:

[0014] Record the parameter values ​​of each parameter data in chronological order to form time series data;

[0015] Draw a change curve of each parameter data based on the time series data;

[0016] Acquire the fluctuation range of each parameter data based on the change curve;

[0017] The fluctuation amplitude of each parameter data is compared with a preset amplitude threshold, and if the fluctuation amplitude exceeds the amplitude threshold, it is determined that a jump point exists in the parameter data.

[0018] Optionally, the process of determining the jump point includes:

[0019] Calculate an average value based on the parameter values ​​of each data point in the parameter data;

[0020] Calculate a difference cumulative value for each data point in the parameter data, the difference cumulative value being the sum of the difference values ​​of all current data points and the average value;

[0021] The difference cumulative value is compared with a preset threshold value, and when the difference cumulative value exceeds the preset threshold value, the current data point is determined to be a jump point.

[0022] Optionally, after determining that the current data point is a transition point, the method further includes:

[0023] Determine the data collection time corresponding to the jump point based on the time series data;

[0024] Acquire the target signal of the cable under test at the data acquisition time;

[0025] Acquire a wavelet component of the target signal by wavelet transform, wherein the wavelet component includes an approximate coefficient and a detail coefficient;

[0026] The detail coefficient is compared with a preset threshold. If the detail coefficient meets the preset threshold, it is determined that the jump point is correct. If the detail coefficient does not meet the preset threshold, the jump point is re-determined.

[0027] Optionally, the trip point satisfies an adjustment condition of a cable fault cause, including:

[0028] Determine a candidate fault cause according to the trip point;

[0029] Determining the degree of correlation between the fault parameter data and the trip point;

[0030] When the candidate fault causes do not include the initial fault cause and the correlation degree exceeds a preset correlation threshold, it is determined that the trip point meets the adjustment condition of the cable fault cause.

[0031] Optionally, determining the cable fault cause based on the trip point and the initial fault cause includes:

[0032] Extracting data features corresponding to each candidate fault cause from the historical fault data based on the jump point and the candidate fault cause;

[0033] Extracting data features of the initial fault cause;

[0034] Inputting the data features of each candidate fault cause and the data features of the initial fault cause into a preset correlation analysis model to calculate the correlation degree between each candidate fault cause and the initial fault cause;

[0035] The candidate fault cause with the highest correlation with the initial fault cause is determined as the cable fault cause.

[0036] In a second aspect, an embodiment of the present application provides a cable fault identification system based on parameter jump information, the system comprising:

[0037] A first data acquisition module is used to acquire fault parameter data of the cable at the time of the fault when a cable fault is detected;

[0038] A first fault determination module, used to compare the fault parameter data with historical fault parameter data to determine the initial fault cause;

[0039] A second data acquisition module, used to acquire other parameter data except the fault parameter data within a preset range at the fault moment;

[0040] A jump point detection module, used to detect whether there is a jump point in the other parameter data;

[0041] A fault adjustment judgment module is used to judge whether the jump point meets the adjustment condition of the cable fault cause when there is a jump point;

[0042] The second fault determination module is used to determine the cable fault cause based on the jump point and the initial fault cause when the jump point meets the adjustment condition of the cable fault cause; and to determine the initial fault cause as the cable fault cause when the jump point does not meet the adjustment condition of the cable fault cause; and to determine the initial fault cause as the cable fault cause when there is no jump point.

[0043] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the first aspect.

[0044] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0045] In an embodiment of the present application, when a cable fault is detected, the fault parameter data at the time of the cable fault is obtained; the fault parameter data is compared with the historical fault parameter data to determine the initial fault cause; other parameter data other than the fault parameter data within the preset range at the time of the fault is obtained, and a jump point is detected in the other parameter data; if a jump point exists, it is determined whether the jump point meets the adjustment condition of the cable fault cause; if the jump point meets the adjustment condition of the cable fault cause, the cable fault cause is determined based on the jump point and the initial fault cause, and if the jump point does not meet the adjustment condition of the cable fault cause, the initial fault cause is determined to be the cable fault cause; if there is no jump point, the initial fault cause is determined to be the cable fault cause. The above-mentioned cable fault identification method based on parameter jump information can solve the problem that the prior art only relies on various detection values ​​to determine the cause of the cable fault, and there is a certain risk of misjudgment. This solution can use additional jump parameters for further analysis and adjustment based on the preliminary determination of the fault cause. This revision mechanism improves the accuracy of fault diagnosis and reduces fault handling errors caused by misjudgment, thereby improving the overall reliability and stability of the cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a cable fault identification method based on parameter jump information provided by an embodiment of the present application;

[0047] Figure 2 It is a schematic diagram of a process for detecting whether there is a jump point in the other parameter data provided by an embodiment of the present application;

[0048] Figure 3 It is a flow chart of determining the cause of the cable fault based on the trip point and the initial fault cause provided by an embodiment of the present application;

[0049] Figure 4 It is a structural schematic diagram of a cable fault identification system based on parameter jump information provided by an embodiment of the present application;

[0050] Figure 5 It is a structural schematic diagram of a cable fault identification device based on parameter jump information provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical scheme and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for the convenience of description, only the part related to the present application but not all the contents are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow chart describes each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0053] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0054] In conjunction with the accompanying drawings, the cable fault identification device, method and equipment based on parameter jump information provided by the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0055] The cable fault identification method based on parameter jump information provided in the embodiment of the present application can be applied to fault identification scenarios where the cable fault cause cannot be accurately identified by the cable parameter value alone. The execution subject of each step of the cable fault identification method based on parameter jump information provided in the embodiment of the present application can be a server.

[0056] Figure 1 FIG. 1 is a flow chart of a cable fault identification method based on parameter jump information provided by an embodiment of the present application. Figure 1As shown, the method includes:

[0057] S101. When a cable fault is detected, obtain fault parameter data at the time of the cable fault.

[0058] Among them, cable failure refers to the phenomenon that the cable cannot continue to work normally or its performance is significantly reduced due to various internal and external factors during normal operation. The cable failure time refers to the specific time point when the cable failure is identified. Fault parameter data refers to various measurement and recording data related to the fault collected when a cable or other equipment fails.

[0059] In one embodiment, when the server detects that the cable cannot continue to work normally or its performance is significantly degraded, it immediately triggers the monitoring node to collect and record various parameter data at the time when the cable fault occurs, such as voltage, current, temperature, etc.

[0060] Optionally, detailed information such as the time, location and manifestation of the fault may also be recorded.

[0061] S102: Compare the fault parameter data with historical fault parameter data to determine the initial fault cause.

[0062] Among them, historical fault parameter data refers to the relevant parameter information recorded when the cable fails in the past period of time. These data usually include key parameters such as the time, location, fault type, fault current, fault voltage, temperature, and equipment operating status and environmental conditions before and after the fault occurs. The initial fault cause refers to the cause of the cable fault obtained after preliminary analysis based on the parameter data and historical fault information when the fault occurs during the fault diagnosis process.

[0063] In one embodiment, the currently collected fault parameter data is matched with the records in the historical fault database to find similar parameter features. In addition, the change trend of the current fault parameter data is analyzed and compared with the change trend of the historical fault parameter data to find historical fault data with the same change trend. The cable fault cause corresponding to the most matching historical fault data is identified as the initial fault cause.

[0064] Optionally, the fault parameter data includes a fault parameter value, a fault location, and a fault time, the historical fault parameter data includes a historical fault value, a historical fault location, a historical fault time, and a historical fault cause, and comparing the fault parameter data with the historical fault parameter data to determine the initial fault cause includes:

[0065] If the difference between the fault parameter value and the historical fault value is less than a preset difference, the distance between the fault location and the historical fault location is less than a preset distance, and the interval period between the fault time and the historical fault time is less than a preset period, the historical fault cause is determined as the initial fault cause.

[0066] Among them, the fault parameter value refers to the specific value of each parameter related to the fault when the cable fails. The fault location of the cable refers to the specific location where the cable fails. The fault time of the cable refers to the time point when the cable fault is identified. The historical fault value refers to the fault-related cable parameter value recorded when the cable fails in the past period of time. The historical fault location of the cable refers to the specific location when the cable failed in the past. The historical fault time of the cable refers to the specific time when the cable failed in the past, including detailed information such as year, month, day, hour, and minute. The historical fault cause of the cable refers to the root cause of the cable failure in the past. The preset difference refers to the maximum difference range allowed between the preset current fault parameter value and the historical fault parameter value. The preset distance refers to the maximum spatial distance allowed between the preset current fault location and the historical fault location. The preset time period refers to the maximum time interval allowed between the preset current fault time and the historical fault time.

[0067] In one embodiment, first, the current fault parameter value is compared with the historical fault parameter value stored in the database. If the difference between the two is less than the preset difference, it is considered that the current fault and a certain historical fault have similar fault values. Secondly, the location of the current fault cable is compared with the above historical fault location. If the distance between the two is less than the preset distance, it is considered that the current fault and the above historical fault are similar in spatial distribution. Then, the fault time of the current cable is compared with the above historical fault time. If the time interval between the two is less than the preset period, it is considered that the current fault and the above historical fault are similar in fault period. Then, the fault cause corresponding to the above historical fault is determined as the initial fault cause.

[0068] S103: Acquire other parameter data except the fault parameter data within a preset range at the fault moment, and detect whether there is a jump point in the other parameter data.

[0069] The preset range at the time of fault refers to a preset range with the time of fault as the center and extending forward and backward for a certain period of time, such as 5 minutes before the fault to 5 minutes after the fault. Other parameter data other than the fault parameter data refers to other cable parameter data other than the fault parameter data that is similar to the historical fault data. The jump point refers to the point where the data value changes significantly in a short period of time.

[0070] In one embodiment, the monitoring node obtains other cable parameter values, such as specific values ​​of ambient temperature, humidity, and cable load changes, in addition to the fault parameter data similar to the historical fault data, within a preset range extending forward and backward for a certain period of time with the fault moment as the center. Then, the machine learning model is used to identify whether there are abnormal points (i.e., jump points) in other cable parameter data that do not conform to the characteristics of normal data by learning the characteristics of normal data.

[0071] Figure 2 is a flow chart of detecting whether there is a jump point in the other parameter data provided by an embodiment of the present application, such as Figure 2 The specific steps include:

[0072] S1031. Record the parameter values ​​of each parameter data in chronological order to form time series data.

[0073] The parameter value of each parameter data refers to the data value of each other parameter data except the fault parameter data. Recording in time series means arranging and recording the collected cable parameter values ​​in chronological order. Time series is a data representation method that emphasizes the time sequence relationship between data, that is, data changes over time. Recording in time series usually requires following a certain time interval, such as every second, every minute, or every hour. Time series data refers to cable parameter data recorded in time series.

[0074] In one embodiment, the parameter values ​​of each parameter data collected are sorted in chronological order to ensure that each data point has an accurate timestamp and the timestamps are evenly distributed according to the set intervals. The data is sorted into a format suitable for analysis, such as CSV, Excel or database table. The sorted data is arranged in chronological order to form time series data.

[0075] S1032: Draw a change curve of each parameter data based on the time series data.

[0076] The variation curves of various parameter data are graphical representations of how different types of parameter data of the cable change over time under specific conditions.

[0077] In one embodiment, a mapping relationship is established between the timestamp column and the parameter value column, and a line chart is selected as the chart type. Ensure that the timestamp is correctly interpreted as the X-axis (horizontal axis) and the parameter value is interpreted as the Y-axis (vertical axis). Import the processed time series data into a drawing tool, and draw a parameter change curve in chronological order. It is understandable that different types of parameter data correspond to different change curves.

[0078] Optionally, after generating the change curve, you can also preview the chart to check whether the data is displayed as expected and adjust the time range, zoom ratio, or add data point labels as needed.

[0079] S1033. Obtain the fluctuation range of each parameter data based on the change curve.

[0080] The fluctuation range of cable parameters refers to the range of fluctuations of cable parameters relative to a certain reference value or average value during the operation of the cable. This range is usually used to describe the stability and dynamic characteristics of cable parameters as they change over time.

[0081] In one embodiment, for each parameter data, according to the change curve, find the highest point and the lowest point of the parameter value in the specified time period. The extreme value usually represents the extreme situation of the parameter value and is the key to calculating the fluctuation range. Specifically, the built-in function or tool can be used to automatically find the highest and lowest values, or it can be checked manually. The difference between the highest and lowest values ​​can be understood as the maximum amplitude value of the fluctuation, thereby determining the fluctuation range, that is, the range of change of the parameter value in the specified time period.

[0082] S1034: Compare the fluctuation amplitude of each parameter data with a preset amplitude threshold. If the fluctuation amplitude exceeds the amplitude threshold, it is determined that a jump point exists in the parameter data.

[0083] The preset amplitude threshold refers to the maximum fluctuation amplitude value allowed for the cable usage parameters during safe operation, which is determined based on historical cable fault data.

[0084] In one embodiment, the maximum amplitude value of each parameter data is compared with a preset amplitude threshold value. If the maximum amplitude value of any parameter data is greater than or equal to the preset amplitude threshold value, it is determined that a jump point exists in the parameter data of the current type.

[0085] Optionally, the process of determining the jump point includes:

[0086] Calculate an average value based on the parameter values ​​of each data point in the parameter data;

[0087] Calculate a difference cumulative value for each data point in the parameter data, the difference cumulative value being the sum of the difference values ​​of all current data points and the average value;

[0088] The difference cumulative value is compared with a preset threshold value, and when the difference cumulative value exceeds the preset threshold value, the current data point is determined to be a jump point.

[0089] The average value is used to represent the central tendency or average level of a set of data. It is obtained by adding all the values ​​in a set of data and then dividing by the number of these values ​​(i.e. the number of data points). The difference between all current data points and the average value refers to the absolute value of the difference between the data value of each data point and the average value. The preset threshold refers to the maximum value of the cumulative difference corresponding to each data point under the condition of safe operation of the cable based on historical fault data.

[0090] In one embodiment, for each type of parameter data, the entire data set is traversed to calculate the average value of all data points, and this average value is used as the basis for subsequent difference calculations. For each data point in the data set, the difference value between the current data point and the average value is calculated, that is, the absolute value of the difference between the data value of each data point and the average value. This absolute value is added to the previously calculated difference cumulative value to update the difference cumulative value. After each update of the difference cumulative value, it is compared with a preset threshold. If the difference cumulative value is greater than or equal to the preset threshold, the current data point is regarded as a jump point.

[0091] Optionally, after determining that the current data point is a transition point, the method further includes:

[0092] Determine the data collection time corresponding to the jump point based on the time series data;

[0093] Acquire the target signal of the cable under test at the data acquisition time;

[0094] Acquire a wavelet component of the target signal by wavelet transform, wherein the wavelet component includes an approximate coefficient and a detail coefficient;

[0095] The detail coefficient is compared with a preset threshold. If the detail coefficient meets the preset threshold, it is determined that the jump point is correct. If the detail coefficient does not meet the preset threshold, the jump point is re-determined.

[0096] Among them, the data acquisition moment corresponding to the jump point usually refers to the specific time point when a parameter value changes significantly (i.e., jumps) in the data sequence, and the change is captured and recorded by the data acquisition system. The target signal of the cable to be tested refers to the electrical signal and physical signal of the current detection cable. Wavelet transform is a transform analysis method widely used in many fields such as signal processing, mathematical analysis, and image processing. It inherits and develops the localization idea of ​​short-time Fourier transform, while overcoming the shortcomings such as the window size not changing with frequency, thus providing a more flexible and powerful signal analysis tool. The basic idea of ​​wavelet transform is to decompose a signal or function into a superposition of a series of wavelet functions, which are obtained by translation and scaling of the mother wavelet. By calculating the inner product of the signal and the wavelet function at different scales and positions, the coefficients of the wavelet transform can be obtained, which reflect the characteristics of the signal at different scales and positions. The wavelet component of the target signal refers to the part obtained by decomposing the target signal through wavelet transform. Each wavelet component represents the characteristics of the signal at different scales and frequencies, which helps to analyze the details and local changes of the signal. The approximation coefficient and detail coefficient of the wavelet component are two important parameters generated in the wavelet transform process, which are used to describe the characteristics of the signal at different frequencies or scales. Among them, the approximation coefficient represents the overall trend or slow change of the signal in the low-frequency range. In the wavelet transform, the approximation coefficient is obtained through a low-pass filter, which retains most of the energy in the signal but removes the detail information of the high-frequency part. The detail coefficient represents the detail information of the signal in the high-frequency range. In the wavelet transform, the detail coefficient is obtained through a high-pass filter, which captures the fast-changing parts of the signal, such as spikes, edges and other detail features. The preset threshold is the minimum value corresponding to the detail coefficient when the data point has a significant change point.

[0097] In one embodiment, when a jump point is detected in the cable parameter data, the acquisition time of the data point corresponding to the jump point is determined by querying the time series data. At the determined data acquisition time, the target signal value of the cable to be tested is collected through the detection node. This signal value may be a measurement value of a physical quantity such as voltage, current, temperature, etc. The collected target signal is subjected to wavelet transformation and the approximate coefficient and detail coefficient are extracted from the result of the wavelet transformation. The approximate coefficient represents the low-frequency part of the signal, while the detail coefficient represents the high-frequency part of the signal, that is, the sudden change or detail information of the signal. The extracted detail coefficient is compared with a preset threshold. If the detail coefficient meets the preset threshold (that is, the value of the detail coefficient exceeds or equals the preset threshold), the jump point is determined to be correct. If the detail coefficient does not meet the preset threshold (that is, the value of the detail coefficient does not exceed the threshold), it is necessary to review the detection process of the jump point, and it may be necessary to adjust the parameters or methods of the jump point detection algorithm, and then redetermine the jump point.

[0098] S104. When a jump point exists, determine whether the jump point satisfies the adjustment condition of the cable fault cause; when the jump point satisfies the adjustment condition of the cable fault cause, determine the cable fault cause based on the jump point and the initial fault cause; when the jump point does not satisfy the adjustment condition of the cable fault cause, determine the initial fault cause as the cable fault cause.

[0099] The adjustment conditions of the cable fault cause refer to the standards or requirements used to correct or optimize the fault cause judgment during the fault diagnosis process.

[0100] In one embodiment, the adjustment conditions of the cable fault cause are first defined, for example: whether the signal characteristics near the jump point meet the specific fault mode. If the jump point meets the adjustment conditions, the jump point is combined with the initial fault cause to analyze the fault mode of the cable. Using the fault diagnosis model or rule, the characteristics of the jump point are compared with the known fault causes to confirm or correct the fault cause. If the jump point does not meet the adjustment conditions, the initial fault cause is considered to be the final cable fault cause. Because the jump point fails to meet the adjustment conditions, it may mean that the jump point itself does not meet the actual cause of the cable fault. Therefore, the initial fault cause is maintained as the final judgment of the cable fault.

[0101] Optionally, the trip point satisfies an adjustment condition of a cable fault cause, including:

[0102] Determine a candidate fault cause according to the trip point;

[0103] Determining the degree of correlation between the fault parameter data and the trip point;

[0104] When the candidate fault causes do not include the initial fault cause and the correlation degree exceeds a preset correlation threshold, it is determined that the trip point meets the adjustment condition of the cable fault cause.

[0105] Among them, the candidate fault cause refers to the cause that may cause the cable fault based on the jump point. The degree of association is a metric that characterizes the closeness of the association between two things. The fault parameter data refers to the cable parameter data used to determine the initial fault cause. The correlation threshold is the minimum correlation value that determines the correlation between the fault parameter data and the jump point.

[0106] In one embodiment, first, the characteristics of the detected jump point are analyzed in detail, such as the occurrence time, amplitude, duration, frequency component, etc. These characteristics are matched with the known cable fault mode or the fault characteristics in the database, wherein the fault mode library contains various cable fault types and their corresponding signal characteristics. Based on the matching results, one or more possible candidate fault causes are generated. In addition, the fault parameter data and the jump point are associated with each other, which can be achieved by calculating the correlation, similarity or distance between the two. The result of the association analysis is quantified into a specific degree of association value, which can be a percentage, correlation coefficient or other appropriate measurement unit. If the initial fault cause is not included in the candidate fault cause list, and the degree of association of the jump point used to determine the initial fault cause is greater than or equal to the preset association threshold, then the jump point is considered to meet the adjustment condition of the cable fault cause. It should be noted that the initial fault cause is also determined based on the fault parameter data, although there may be inaccuracies, but it is impossible to be completely wrong. That is to say, if the fault parameter data used to determine the initial fault is completely unrelated to the jump point, it means that the initial fault cause is completely wrong. In this case, the fault cause adjustment condition is not met. In addition, if the candidate fault causes include the initial fault cause, the initial fault cause can be directly determined as the final cable fault cause, and the adjustment condition of the cable fault cause is not satisfied.

[0107] Figure 3 1 is a flow chart of determining the cause of the cable fault based on the jump point and the initial fault cause provided by an embodiment of the present application. Figure 3 As shown, the method includes:

[0108] S1041. Extract data features corresponding to each candidate fault cause from the historical fault data based on the jump point and the candidate fault cause.

[0109] The data features of the candidate fault causes refer to a series of quantifiable and representative data or attributes associated with the candidate causes suspected to be the possible cause of the fault.

[0110] In one embodiment, first, data records related to each candidate fault cause are screened out from the historical fault data set. For each candidate fault cause, data features closely related to it are selected. These features may include signal waveforms, parameter values ​​(such as current, voltage, temperature, etc.), fault duration, environmental factors, etc. when the fault occurs. Then, signal processing technology, statistical analysis, or machine learning algorithms are used to extract key data features from the screened data.

[0111] S1042: Extract data features of the initial fault cause.

[0112] In one embodiment, corresponding data features are extracted based on the fault parameter data that determines the cause of the initial fault.

[0113] S1043: Input the data features of each candidate fault cause and the data features of the initial fault cause into a preset correlation analysis model to calculate the correlation degree between each candidate fault cause and the initial fault cause.

[0114] Among them, the correlation analysis model is a statistical or machine learning model used to reveal the relationship between variables. The main purpose of this model is to find the correlation or dependency between different variables or features in the data.

[0115] In one embodiment, the data features of each candidate fault cause and the data features of the initial fault cause are input into a preset correlation analysis model. The model calculates the degree of correlation between each candidate fault cause and the initial fault cause based on the input data features. The degree of correlation can be a specific numerical value indicating the closeness between the two. Specifically, the method for constructing the correlation analysis model can be to use a machine learning algorithm, and use historical data to train the model so that it can learn the correlation between different fault causes.

[0116] S1044: Determine the candidate fault cause that is most highly correlated with the initial fault cause as the cable fault cause.

[0117] Among them, the candidate fault cause with the highest correlation with the initial fault cause is the cable fault cause that is different from the initial fault cause and most correlated with it. It can be understood that the candidate fault cause is a correction of the initial fault cause, that is, the two are different and closely related. If the candidate fault cause has a very low correlation with the initial fault cause or is irrelevant, it can be understood that the current candidate fault cause is irrelevant to the current cable fault.

[0118] In one embodiment, the calculated correlation degrees are sorted, and the candidate fault cause with the highest correlation degree to the initial fault cause is selected, and the candidate fault cause with the highest correlation degree is determined as the final cable fault cause.

[0119] S105: When there is no jump point, determine that the initial fault cause is the cable fault cause.

[0120] In one embodiment, when no obvious jump point is found during the detection process, it means that no other more reasonable candidate causes appear, and the initial fault cause is directly regarded as the current cable fault cause.

[0121] In the example of the present application, in the embodiment of the present application, when a cable fault is detected, fault parameter data at the time of the cable fault is obtained; the fault parameter data is compared with historical fault parameter data to determine the initial fault cause; other parameter data other than the fault parameter data within a preset range at the time of the fault is obtained, and it is detected whether there is a jump point in the other parameter data; if there is a jump point, it is determined whether the jump point meets the adjustment condition of the cable fault cause; if the jump point meets the adjustment condition of the cable fault cause, the cable fault cause is determined based on the jump point and the initial fault cause, and if the jump point does not meet the adjustment condition of the cable fault cause, the initial fault cause is determined to be the cable fault cause; if there is no jump point, the initial fault cause is determined to be the cable fault cause. The above-mentioned cable fault identification method based on parameter jump information can solve the problem that the existing technology only relies on various detection values ​​to determine the cause of the cable fault, which has a certain risk of misjudgment. This solution can use additional jump parameters for further analysis and adjustment based on the preliminary determination of the cause of the fault. This revision mechanism improves the accuracy of fault diagnosis and reduces fault handling errors caused by misjudgment, thereby improving the overall reliability and stability of the cable.

[0122] Figure 4 Schematic diagram of a cable fault identification system based on parameter jump information provided by an embodiment of the present application. Figure 4 As shown, it specifically includes the following modules:

[0123] The first data acquisition module 401 is used to acquire the fault parameter data of the cable at the time of the fault when a cable fault is detected;

[0124] A first fault determination module 402, configured to compare the fault parameter data with historical fault parameter data to determine the cause of the initial fault;

[0125] The second data acquisition module 403 is used to acquire other parameter data except the fault parameter data within a preset range at the fault moment;

[0126] A jump point detection module 404 is used to detect whether there is a jump point in the other parameter data;

[0127] A fault adjustment judgment module 405 is used to judge whether the trip point meets the adjustment condition of the cable fault cause when there is a trip point;

[0128] The second fault determination module 406 is used to determine the cable fault cause based on the jump point and the initial fault cause when the jump point meets the adjustment condition of the cable fault cause; and to determine the initial fault cause as the cable fault cause when the jump point does not meet the adjustment condition of the cable fault cause; and to determine the initial fault cause as the cable fault cause when there is no jump point.

[0129] The cable fault identification system based on parameter jump information provided in the embodiment of the present application corresponds to the cable fault identification method based on parameter jump information provided in the above embodiment, has the same functional modules and beneficial effects, and will not be repeated here to avoid repetition.

[0130] Figure 5 Schematic diagram of a cable fault identification device based on parameter jump information provided by an embodiment of the present application. Figure 5 As shown, the device includes a processor 501, a memory 502, an input device 503 and an output device 504; the number of processors 501 in the device can be one or more. Figure 5 A processor 501 is taken as an example; the processor 501, the memory 502, the input device 503 and the output device 504 in the device can be connected by a bus or other means. Figure 5 The example of connecting through a bus is taken. The memory 502, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions or modules corresponding to the cable defect detection method based on image recognition in the embodiment of the present invention. The processor 501 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 502, that is, realizes the above-mentioned cable defect detection method based on image recognition. The input device 503 can be used to receive input digital or character information, and generate key signal input related to user settings and function control of the device. The output device 504 may include a display device such as a display screen.

[0131] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0132] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned cable fault identification device embodiment based on parameter jump information are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0133] The processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0134] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0135] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0136] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0137] The above are only preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions that can be made by those skilled in the art will not deviate from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A cable fault identification method based on parameter jump information, characterized in that: The method comprises: When a cable fault is detected, obtaining fault parameter data at the time of the cable fault; Comparing the fault parameter data with historical fault parameter data to determine the initial fault cause; Acquire other parameter data except the fault parameter data within a preset range at the fault moment, and detect whether there is a jump point in the other parameter data; In the case where there is a jump point, judging whether the jump point satisfies the adjustment condition of the cable fault cause; in the case where the jump point satisfies the adjustment condition of the cable fault cause, determining the cable fault cause based on the jump point and the initial fault cause, and in the case where the jump point does not satisfy the adjustment condition of the cable fault cause, determining the initial fault cause as the cable fault cause; In the absence of a trip point, the initial fault cause is determined to be the cable fault cause.

2. The cable fault identification method based on parameter jump information according to claim 1 is characterized in that: The fault parameter data includes a fault parameter value, a fault location, and a fault time, the historical fault parameter data includes a historical fault value, a historical fault location, a historical fault time, and a historical fault cause, and comparing the fault parameter data with the historical fault parameter data to determine the initial fault cause includes: If the difference between the fault parameter value and the historical fault value is less than a preset difference, the distance between the fault location and the historical fault location is less than a preset distance, and the interval period between the fault time and the historical fault time is less than a preset period, the historical fault cause is determined as the initial fault cause.

3. The cable fault identification method based on parameter jump information according to claim 1 is characterized in that: The detecting whether there is a jump point in the other parameter data includes: Record the parameter values ​​of each parameter data in chronological order to form time series data; Draw a change curve of each parameter data based on the time series data; Acquire the fluctuation range of each parameter data based on the change curve; The fluctuation amplitude of each parameter data is compared with a preset amplitude threshold, and if the fluctuation amplitude exceeds the amplitude threshold, it is determined that a jump point exists in the parameter data.

4. The cable fault identification method based on parameter jump information according to claim 3 is characterized in that: The process of determining the jump point includes: Calculate an average value based on the parameter values ​​of each data point in the parameter data; Calculate a difference cumulative value for each data point in the parameter data, the difference cumulative value being the sum of the difference values ​​of all current data points and the average value; The difference cumulative value is compared with a preset threshold value, and when the difference cumulative value exceeds the preset threshold value, the current data point is determined to be a jump point.

5. The cable fault identification method based on parameter jump information according to claim 4 is characterized in that: After determining that the current data point is a jump point, it also includes: Determine the data collection time corresponding to the jump point based on the time series data; Acquire the target signal of the cable under test at the data acquisition time; Acquire a wavelet component of the target signal by wavelet transform, wherein the wavelet component includes an approximate coefficient and a detail coefficient; The detail coefficient is compared with a preset threshold. If the detail coefficient meets the preset threshold, it is determined that the jump point is correct. If the detail coefficient does not meet the preset threshold, the jump point is re-determined.

6. The cable fault identification method based on parameter jump information according to claim 1, characterized in that: The trip point meets the adjustment conditions of the cable fault cause, including: Determine a candidate fault cause according to the trip point; Determining the degree of correlation between the fault parameter data and the trip point; When the candidate fault causes do not include the initial fault cause and the correlation degree exceeds a preset correlation threshold, it is determined that the trip point meets the adjustment condition of the cable fault cause.

7. The cable fault identification method based on parameter jump information according to claim 6 is characterized in that: The determining the cable fault cause based on the trip point and the initial fault cause includes: Extracting data features corresponding to each candidate fault cause from the historical fault data based on the jump point and the candidate fault cause; Extracting data features of the initial fault cause; Inputting the data features of each candidate fault cause and the data features of the initial fault cause into a preset correlation analysis model to calculate the correlation degree between each candidate fault cause and the initial fault cause; The candidate fault cause with the highest correlation with the initial fault cause is determined as the cable fault cause.

8. A cable fault identification system based on parameter jump information, characterized in that: The system comprises: A first data acquisition module is used to acquire fault parameter data of the cable at the time of the fault when a cable fault is detected; A first fault determination module, used to compare the fault parameter data with historical fault parameter data to determine the initial fault cause; A second data acquisition module, used to acquire other parameter data except the fault parameter data within a preset range at the fault moment; A jump point detection module, used to detect whether there is a jump point in the other parameter data; A fault adjustment judgment module is used to judge whether the jump point meets the adjustment condition of the cable fault cause when there is a jump point; The second fault determination module is used to determine the cable fault cause based on the jump point and the initial fault cause when the jump point meets the adjustment condition of the cable fault cause; and to determine the initial fault cause as the cable fault cause when the jump point does not meet the adjustment condition of the cable fault cause; and to determine the initial fault cause as the cable fault cause when there is no jump point.

9. A cable fault identification device based on parameter jump information, the device comprising: one or more processors; A storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the cable fault identification method based on parameter jump information as described in any one of claims 1-7.

10. A storage medium storing computer executable instructions, wherein the computer executable instructions are used to execute the cable fault identification method based on parameter jump information as described in any one of claims 1 to 7 when executed by a computer processor.

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