Cable fault prediction method, device and equipment and computer readable storage medium

Abstract

The embodiment of the invention provides a cable fault prediction method and device, equipment and a computer readable storage medium. The method comprises the following steps: acquiring historical operation data of a target cable, wherein the historical operation data comprises temperature, current, humidity, vibration frequency, vibration amplitude and partial discharge capacity; based on the Pearson's correlation coefficient, determining the correlation degree between the historical operation data, obtaining a correlation degree feature matrix, and determining a first feature value of the correlation degree feature matrix; determining a covariance between the historical operation data to obtain a covariance matrix, and determining a second feature value of the covariance matrix; and determining whether the target cable has a potential fault or not according to the historical operation data, the first characteristic value and the second characteristic value. According to the method, the abnormal condition of the cable can be found in time, and the accuracy of a fault prediction result is improved.

Description

Technical Field

[0001] The present application relates to the technical field of cable fault detection, and in particular to a cable fault prediction method, device, equipment and computer-readable storage medium. Background Art

[0002] As an important carrier for transmitting information, cables play an important role in industry and life. If a cable fails and cannot be monitored and repaired in time, it may cause huge losses and add a lot of inconvenience to people's lives.

[0003] At present, the operation monitoring of cables mainly relies on manual inspections and regular checks, and lacks corresponding monitoring means, which makes it impossible to detect abnormal conditions of cables in time, thereby delaying maintenance. Summary of the invention

[0004] The embodiments of the present application provide a cable fault prediction method, apparatus, device and computer-readable storage medium, which can promptly detect abnormal conditions of the cable and improve the accuracy of cable fault prediction.

[0005] In a first aspect, an embodiment of the present application provides a method for predicting cable faults, comprising:

[0006] Acquire historical operation data of the target cable, wherein the historical operation data includes temperature, current, humidity, vibration frequency, vibration amplitude, and partial discharge amount;

[0007] Determine the correlation between each historical operation data based on the Pearson correlation coefficient, obtain a correlation characteristic matrix, and determine the first eigenvalue of the correlation characteristic matrix;

[0008] Determine the covariance between each historical operation data, obtain a covariance matrix, and determine the second eigenvalue of the covariance matrix;

[0009] It is determined whether the target cable has a potential fault according to the historical operation data, the first characteristic value, and the second characteristic value.

[0010] In a second aspect, an embodiment of the present application provides a cable fault prediction device, comprising:

[0011] An acquisition module, used to acquire historical operation data of a target cable, wherein the historical operation data includes temperature, current, humidity, vibration frequency, vibration amplitude, and partial discharge amount;

[0012] A processing module is used to determine the correlation between each historical operation data based on the Pearson correlation coefficient, obtain a correlation characteristic matrix, and determine the first eigenvalue of the correlation characteristic matrix; determine the covariance between each historical operation data, obtain a covariance matrix, and determine the second eigenvalue of the covariance matrix; determine whether the target cable has a potential fault based on the historical operation data, the first eigenvalue and the second eigenvalue.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the cable fault prediction method provided in the first aspect of the embodiment of the present application are implemented.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the cable fault prediction method provided in the first aspect of the embodiment of the present application are implemented.

[0015] The technical solution provided in the embodiment of the present application obtains the historical operation data of the target cable, determines the correlation between the historical operation data based on the Pearson correlation coefficient, obtains the correlation characteristic matrix, and determines the first eigenvalue of the correlation characteristic matrix, determines the covariance between the historical operation data, obtains the covariance matrix, and determines the second eigenvalue of the covariance matrix, and determines whether the target cable has a potential fault based on the historical operation data, the first eigenvalue and the second eigenvalue. That is, when predicting the cable fault, the historical operation data of the cable in different dimensions and the correlation information between the historical operation data in different dimensions (that is, the first eigenvalue and the second eigenvalue) are comprehensively considered, and the analysis data used is more comprehensive, thereby improving the accuracy of the fault prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a cable fault prediction method provided in an embodiment of the present application;

[0017] Figure 2 Another schematic diagram of a flow chart of a cable fault prediction method provided in an embodiment of the present application;

[0018] Figure 3 A schematic diagram of the structure of a cable fault prediction device provided in an embodiment of the present application;

[0019] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application are further described in detail through the following embodiments and in combination with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. Those skilled in the art can make adjustments to them as needed to suit specific application scenarios. It should also be noted that, for ease of description, only the parts related to the present application are shown in the accompanying drawings, rather than all structures.

[0021] The cable fault prediction method provided in the embodiment of the present application is applicable to the fault prediction scenario of the power cable, and realizes the cable fault prediction by comprehensively analyzing the historical operation data of different dimensions of the cable. The execution subject of the embodiment of the present application can be a cable fault prediction device, which can be implemented by hardware and / or software, and can generally be integrated into an electronic device. Optionally, the electronic device can be a terminal with data processing capabilities and capable of information exchange with other devices, such as a laptop computer, a tablet computer, and a personal digital assistant, or an independent server or a server cluster composed of multiple servers. The embodiment of the present application does not limit the specific form of the electronic device, and the following embodiment is introduced by taking the execution subject as an electronic device as an example.

[0022] Figure 1 A schematic diagram of a flow chart of a cable fault prediction method provided in an embodiment of the present application. Figure 1 As shown, the method may include:

[0023] S101. Acquire historical operation data of a target cable, where the historical operation data includes temperature, current, humidity, vibration frequency, vibration amplitude, and partial discharge amount.

[0024] Specifically, the target cable is a cable to be detected. In one example, a human-computer interaction interface can be provided, and the maintenance personnel can input the target cable to be detected in the human-computer interaction interface, and the electronic device obtains the target cable input by the maintenance personnel. In another example, the maintenance personnel can also input the inspection plan in the human-computer interaction interface, and the electronic device processes the inspection plan using a semantic recognition algorithm to identify the cables included in the inspection plan, and uses the identified cables as the target cables to be detected.

[0025] Intelligent sensors are arranged along each cable or at key nodes to collect the operation data of the cable. For example, temperature sensors, humidity sensors, current sensors, vibration sensors, and partial discharge sensors. The temperature data of the cable is collected by the temperature sensor, the humidity data of the cable is collected by the humidity sensor, the current data of the cable is collected by the current sensor, the vibration signal of the cable (including vibration frequency and vibration amplitude) is collected by the vibration sensor, and the partial discharge amount at the output side of the cable is collected by the partial discharge sensor. The electronic device periodically obtains the corresponding data reported by each sensor and stores it. Optionally, the temperature sensor can be a thermocouple sensor, the humidity sensor can be a capacitive sensor, the current sensor can be a mutual inductor sensor, and the vibration sensor can be a micro-electro-mechanical system (MEMS) accelerometer.

[0026] In this way, when testing the target cable, the electronic device can obtain the historical operation data of the target cable from the storage device, including temperature, current, humidity, vibration frequency, vibration amplitude and partial discharge, and determine whether the target cable has a potential fault by analyzing the historical operation data of the target cable. The above historical operation data can be the operation data of the cable within a preset time before the current time.

[0027] After obtaining the historical operation data, data preprocessing can also be performed on the historical operation data, including missing value processing, noise processing, and outlier processing.

[0028] Regarding missing value processing, in actual applications, missing values ​​may appear in historical operation data due to various reasons, such as sensor failure or data transmission interruption. Specific ways to process missing values ​​may include:

[0029] 1. Delete missing records.

[0030] If there are few missing values ​​and deleting them will not have a significant impact on the integrity of the data set, you can choose to delete records with missing values.

[0031] 2. Data interpolation.

[0032] For cases with many missing values, the following methods can be used for data interpolation:

[0033] a. Mean / median / mode filling: use the mean, median or mode of the entire data set or the same group of data to fill in missing values.

[0034] b. Nearest neighbor interpolation, using the data closest to the missing value point to fill in.

[0035] c. Regression method: establish a regression model and use the established regression model to predict missing values.

[0036] d. Interpolation method: use Lagrange interpolation or Newton interpolation and other algorithms to interpolate missing values.

[0037] Regarding noise processing, noise data is usually caused by measurement errors, environmental interference or other random factors. Noise processing methods can include:

[0038] 1. Smoothing.

[0039] The impact of random noise is reduced through methods such as moving average and exponential smoothing.

[0040] 2. Median filtering.

[0041] The median value of each point in the neighborhood of the data point is used to replace the data point value, thereby smoothing out abnormal noise.

[0042] 3. Frequency domain filtering.

[0043] Convert historical operation data into the frequency domain, remove high-frequency noise components in the frequency domain, and retain low-frequency effective signals.

[0044] Regarding outlier processing, outliers are data points in the data set that are significantly different from most other samples. They may be caused by incorrect data entry or real abnormal events. Specific ways to handle outliers may include:

[0045] 1. Delete outliers. If the outliers are wrong, they can be deleted directly.

[0046] 2. Do not process. If the outlier has little impact on the overall analysis, you can choose not to process it.

[0047] 3. Mean value correction: replace the outliers with the mean value of the entire data set or the same group of data.

[0048] 4. Treat outliers as missing values ​​and handle them according to the missing value processing method.

[0049] 5. Box plot method: Use box plot to determine the range of outliers and perform processing based on the range of outliers.

[0050] 6. Normal distribution method: identify and process outliers based on the 3σ principle of normal distribution.

[0051] In addition to the above preprocessing operations, the following preprocessing operations can also be performed on historical operation data:

[0052] 1. Data standardization / normalization, adjust the data to the same dimension to facilitate analysis and modeling.

[0053] 2. Feature encoding, converting non-numeric features into numerical features, such as one-hot encoding, label encoding, etc.

[0054] 3. Data integration, combining data from different sources to obtain a more comprehensive data set.

[0055] 4. Data transformation: perform mathematical transformation on the data, such as logarithmic transformation, Box-Cox transformation, etc., to improve the performance of the model.

[0056] S102. Determine the correlation between each historical operation data based on the Pearson correlation coefficient, obtain a correlation characteristic matrix, and determine the first eigenvalue of the correlation characteristic matrix.

[0057] Specifically, after acquiring the historical operation data, the correlation between the historical operation data of different dimensions can be calculated based on the Pearson correlation coefficient, thereby obtaining a correlation feature matrix. Optionally, the electronic device can determine the correlation R between the historical operation data of different dimensions by the following formula: ij :

[0058]

[0059] Among them, x ik represents the value of the historical running data of the i-th dimension at the k-th data point, x jk represents the value of the historical operation data of the jth dimension at the kth data point, N is the number of data points collected within time t (that is, the number of data points contained in the historical operation data of a certain dimension), It represents the mean value of the historical running data of the i-th dimension in time t. Represents the mean value of the historical operation data of the jth dimension within time t. The above i dimension and j dimension are any two of the following dimensions: temperature, current, humidity, vibration frequency, vibration amplitude, and partial discharge. For example, the i-th dimension is the value of the current sensor, and the j-th dimension is the value of the partial discharge sensor.

[0060] After obtaining the correlation characteristic matrix, the determinant value of the correlation characteristic matrix is ​​solved, and the obtained determinant value is used as the first eigenvalue.

[0061] S103: Determine the covariance between each historical operation data, obtain a covariance matrix, and determine the second eigenvalue of the covariance matrix.

[0062] Specifically, after obtaining the historical operation data, the covariance between the historical operation data of different dimensions may be calculated to obtain a covariance matrix, and the determinant value of the covariance matrix may be solved, and the obtained determinant value may be used as the second eigenvalue.

[0063] Optionally, the electronic device can determine the covariance C between the historical operation data of different dimensions by the following formula: ij :

[0064]

[0065] S104: Determine whether the target cable has a potential fault according to the historical operation data, the first characteristic value, and the second characteristic value.

[0066] After obtaining the historical operation data, the first characteristic value and the second characteristic value, a comprehensive analysis is performed on the historical operation data, the first characteristic value and the second characteristic value to determine whether the target cable has a potential fault.

[0067] Optionally, the process of S104 may include:

[0068] S1041, determining the operating state value δ of the target cable according to the following formula;

[0069]

[0070] Where, t represents the monitoring time, α represents the average temperature of the target cable within time t, β represents the average humidity of the target cable within time t, I represents the average current of the target cable within time t, P represents the average vibration frequency of the target cable within time t, F represents the average vibration amplitude of the target cable within time t, and J max represents the maximum partial discharge of the target cable within time t, A represents the first characteristic value, and B represents the second characteristic value.

[0071] S1042. Determine whether the target cable has a potential fault according to the operating status value.

[0072] After the operating status value of the target cable is obtained, the operating status value can be compared with a preset threshold. If the operating status value is greater than or equal to the preset threshold, it is determined that a potential fault exists in the target cable, and an alarm can be given as needed.

[0073] In practical applications, the preset threshold may be set based on actual needs. Optionally, the preset threshold may be 6.41 or 6.411 or 6.4112 or 6.41122.

[0074] The cable fault prediction method provided in the embodiment of the present application obtains the historical operation data of the target cable, determines the correlation between the historical operation data based on the Pearson correlation coefficient, obtains the correlation characteristic matrix, and determines the first eigenvalue of the correlation characteristic matrix, determines the covariance between the historical operation data, obtains the covariance matrix, and determines the second eigenvalue of the covariance matrix, and determines whether the target cable has a potential fault based on the historical operation data, the first eigenvalue and the second eigenvalue. That is, when predicting the cable fault, the historical operation data of the cable in different dimensions and the correlation information between the historical operation data in different dimensions (that is, the first eigenvalue and the second eigenvalue) are comprehensively considered, and the data used is more comprehensive, thereby improving the accuracy of the fault prediction result.

[0075] In one embodiment, optionally, Figure 2 As shown, the method may also include:

[0076] S201: If it is determined that a potential fault exists in the target cable, the importance of the target cable is obtained.

[0077] When a target cable is determined to have a potential fault, different warning levels and response strategies can be triggered based on the importance of the target cable. The higher the importance of the target cable, the higher the level of warning that can be triggered and the faster the fault response speed required. This priority processing mechanism ensures that critical cables can be handled promptly and effectively when potential faults occur, thereby minimizing business interruptions and losses.

[0078] As an optional implementation manner, obtaining the importance of the target cable in the above S201 may include the following steps:

[0079] Step A: query the importance of the target cable according to a preset first mapping relationship.

[0080] The first mapping relationship includes the correspondence between each cable and the importance of each cable. In practical applications, each cable segment can be numbered, and the correspondence between the cable number and the cable importance can be stored in the first mapping relationship. In this way, when determining the importance of the target cable, the importance of the target cable can be directly queried in the first mapping relationship based on the number of the target cable.

[0081] Step B: If found, the found importance is used as the importance of the target cable.

[0082] Step C: If the target cable is not found, determine the scope affected by the target cable and the number of users and associated devices affected within the corresponding scope.

[0083] If the importance of the target cable is not found in the first mapping relationship, the range area affected by the target cable can be determined based on the location information of the target cable, and the number of users and associated devices that may be affected in the range area can be determined. The number of users here can include the number of users directly affected and the number of users indirectly affected, and the associated devices can be the devices connected to the target cable, or its upstream and downstream devices, etc.

[0084] Step D: Determine the importance of the target cable according to the number of users and associated devices, and update the first mapping relationship according to the importance of the target cable.

[0085] Specifically, the electronic device can determine the importance of the target cable based on the number of affected users and the degree of dependence of the associated devices on the target cable. For example, the more users are affected, the higher the importance of the target cable. If the associated devices are some key devices, for example, the associated devices are emergency equipment in hospitals, servers in data centers, trading systems in financial institutions, etc., and the normal operation of these devices is crucial to social operations and people's lives, then the importance of the target cable is higher; if the associated devices have no backup equipment to maintain production or provide services, then the importance of the target cable is also higher.

[0086] In addition, after obtaining the importance of the target cable, the first mapping relationship can be updated based on the importance of the target cable, that is, the importance of the target cable is stored in the first mapping relationship, so that the importance of the target cable can be directly obtained from the first mapping relationship later, thereby improving the speed of obtaining the cable importance.

[0087] S202: If the importance of the target cable exceeds a preset importance, a first warning message is sent to a terminal held by a target maintenance personnel, where the first warning message is used to remind the target maintenance personnel to inspect the target cable within a first preset time.

[0088] S203: If the importance of the target cable is less than the preset importance, a second warning message is sent to the terminal held by the target maintenance personnel, and the second warning message reminds the target maintenance personnel to inspect the target cable within a second preset time.

[0089] After obtaining the importance of the target cable, the importance of the target cable is compared with the preset importance. If the importance of the target cable exceeds the preset importance, it indicates that the importance of the target cable is high and has a greater impact on the business. In this case, a high-level warning can be triggered, and the target maintenance personnel are required to respond and handle the problem in the shortest possible time. Specifically, a first warning message can be sent to the terminal held by the target maintenance personnel to remind the target maintenance personnel to inspect the target cable within the first preset time. Furthermore, the first warning message can be sent according to the first sending frequency to improve the warning effect.

[0090] If the importance of the target cable is less than the preset importance, indicating that the importance of the target cable is relatively low and the impact on the business is relatively small, a low-level warning can be triggered, and the maintenance time of the target cable can be appropriately increased. In this way, under the condition of limited resources, the target maintenance personnel can give priority to those cable problems that have a greater impact on business continuity, thereby reducing the potential risk of business interruption. Specifically, a second warning message can be sent to the terminal held by the target maintenance personnel to remind the target maintenance personnel to inspect the target cable within the second preset time. Furthermore, the second warning message can also be sent according to the second sending frequency, and the second sending frequency is less than the first sending frequency, so as to reduce information interference to the maintenance personnel under the premise of early warning.

[0091] Among them, the terminal held by the target maintenance personnel can be a mobile phone, a computer, or other interactive devices. The above-mentioned target maintenance personnel can be the maintenance personnel responsible for the area where the target cable is located. Optionally, the above-mentioned target maintenance personnel can also be obtained through the following process: obtaining the work planning time of all maintenance personnel within the preset range, and selecting the maintenance personnel whose work planning time meets the first preset time and / or the second preset time requirements as the target maintenance personnel. By sending early warning information to the maintenance personnel who meet the fault repair time requirements, the response time of the early warning can be accelerated and the fault recovery time can be shortened.

[0092] The first preset time and the second preset time can be set based on the fault level, and the first preset time is less than the second preset time. For example, the first preset time can be 1 day, and the second preset time can be 2 days.

[0093] By triggering the corresponding level of warning information through the importance of the cable, more important cables can be assigned a higher maintenance priority, allowing maintenance personnel to give priority to those cable problems that have a greater impact on business continuity and reduce the potential risk of business interruption.

[0094] In one embodiment, optionally, the method also includes: constructing a digital twin model based on cable basic data; obtaining operating status data of each cable collected by sensors, and inputting the operating status data into the digital twin model for visual display, and displaying the target cable in a differentiated manner in the digital twin model after the target cable fails.

[0095] Specifically, the basic cable data in this embodiment may include parameters such as the length, cross-sectional area, material, resistance, and inductance of the cable. Using the acquired cable data, combined with information such as the size, shape, and material of the cable, and using computer graphics, data modeling, and other technologies, a digital twin model corresponding to the physical cable is constructed. This model can display the three-dimensional structure of the cable. In addition, the operating status data of each cable collected by the on-site field sensor can be synchronized to the digital twin model, and the operating status of each cable can be displayed in real time through the digital twin model, so that maintenance personnel can intuitively understand the operating status of each cable. For example, the temperature distribution of the cable and the current flow are known.

[0096] Furthermore, after a target cable fails, the target cable can be displayed in a differentiated manner in the digital twin model, for example, by changing the color of the fault area, highlighting it, flashing it, or using other visual effects to distinguish it, so that maintenance personnel can intuitively know the status of the faulty cable.

[0097] Figure 3 A schematic diagram of the structure of a cable fault prediction device provided in an embodiment of the present application. Figure 3 As shown, the device may include an acquisition module 301 and a processing module 302 .

[0098] Specifically, the acquisition module 301 is used to acquire historical operation data of the target cable, wherein the historical operation data includes temperature, current, humidity, vibration frequency, vibration amplitude, and partial discharge amount;

[0099] The processing module 302 is used to determine the correlation between each historical operation data based on the Pearson correlation coefficient, obtain a correlation characteristic matrix, and determine the first eigenvalue of the correlation characteristic matrix; determine the covariance between each historical operation data, obtain a covariance matrix, and determine the second eigenvalue of the covariance matrix; determine whether the target cable has a potential fault based on the historical operation data, the first eigenvalue and the second eigenvalue.

[0100] Based on the above embodiment, optionally, the processing module 302 is further used to determine the operating state value δ of the target cable according to the following formula; and determine whether the target cable has a potential fault according to the operating state value δ;

[0101]

[0102] Wherein, t represents the historical monitoring period, α represents the average temperature of the target cable within time t, β represents the average humidity of the target cable within time t, I represents the average current of the target cable within time t, P represents the average vibration frequency of the target cable within time t, F represents the average vibration amplitude of the target cable within time t, and Jmax represents the maximum partial discharge amount of the target cable within time t, A represents the first characteristic value, and B represents the second characteristic value.

[0103] Based on the above embodiment, optionally, the processing module 302 is further configured to determine that a potential fault exists in the target cable if the operating status value is greater than or equal to a preset threshold.

[0104] Based on the above embodiment, optionally, the device further includes: a sending module;

[0105] The acquisition module 301 is also used to acquire the importance of the target cable when it is determined that the target cable has a potential fault;

[0106] A sending module is used to send a first warning message to a terminal held by a target maintenance personnel when the importance of the target cable exceeds a preset importance, and the first warning message is used to remind the target maintenance personnel to inspect the target cable within a first preset time; when the importance of the target cable is less than the preset importance, a second warning message is sent to the terminal held by the target maintenance personnel, and the second warning message reminds the target maintenance personnel to inspect the target cable within a second preset time; wherein the first preset time is less than the second preset time.

[0107] Based on the above embodiment, optionally, the acquisition module 301 is also used to obtain the work planning time of all maintenance personnel within a preset range, and select the maintenance personnel whose work planning time meets the first preset time and / or the second preset time requirements as the target maintenance personnel.

[0108] On the basis of the above embodiment, optionally, the acquisition module 301 is also used to query the importance of the target cable according to a preset first mapping relationship; wherein the first mapping relationship includes the corresponding relationship between each cable and the importance of each cable; if found, the queried importance is used as the importance of the target cable; if not found, the scope affected by the target cable and the number of users affected within the corresponding scope and the associated equipment are determined; based on the number of users and the associated equipment, the importance of the target cable is determined, and the first mapping relationship is updated according to the importance of the target cable.

[0109] Based on the above embodiment, optionally, the processing module 302 is also used to build a digital twin model based on the basic cable data; obtain the operating status data of each cable collected by the sensor, and input the operating status data into the digital twin model for visual display, and differentiate and display the target cable in the digital twin model after the target cable fails.

[0110] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 4 As shown, the device includes a processor 40, a memory 41, an input device 42 and an output device 43; the number of the processor 40 in the device can be one or more. Figure 4 A processor 40 is taken as an example; the processor 40, memory 41, input device 42 and output device 43 in the device can be connected by a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0111] The memory 41, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the fault prediction method for cables in the embodiment of the present application (for example, the acquisition module 301 and the processing module 302 in the fault prediction device for cables). The processor 40 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 41, that is, realizing the above-mentioned fault prediction method for cables.

[0112] The memory 41 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created during the washing reminder process, etc. In addition, the memory 41 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 41 may further include a memory remotely arranged relative to the processor 40, and these remote memories may be connected to the device / terminal / server via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0113] The input device 42 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the electronic device. The output device 43 may include a display device such as a display screen.

[0114] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0115] Acquire historical operation data of the target cable, wherein the historical operation data includes temperature, current, humidity, vibration frequency, vibration amplitude, and partial discharge amount;

[0116] Determine the correlation between each historical operation data based on the Pearson correlation coefficient, obtain a correlation characteristic matrix, and determine the first eigenvalue of the correlation characteristic matrix;

[0117] Determine the covariance between each historical operation data, obtain a covariance matrix, and determine the second eigenvalue of the covariance matrix;

[0118] It is determined whether the target cable has a potential fault according to the historical operation data, the first characteristic value, and the second characteristic value.

[0119] The cable fault prediction device, electronic device, and computer-readable storage medium provided in the above embodiments can execute the cable fault prediction method provided in any embodiment of the present application, and have the corresponding functional modules and beneficial effects of executing the method. For technical details not described in detail in the above embodiments, please refer to the cable fault prediction method provided in any embodiment of the present application.

[0120] Through the above description of the implementation method, the technicians in the relevant field can clearly understand that the present application can be implemented with the help of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0121] It is worth noting that the various units and modules included in the above embodiments are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application.

[0122] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing 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 appended claims.

Claims

1. A cable fault prediction method, characterized in that: include: Acquire historical operation data of the target cable, wherein the historical operation data includes temperature, current, humidity, vibration frequency, vibration amplitude, and partial discharge amount; Determine the correlation between each historical operation data based on the Pearson correlation coefficient, obtain a correlation characteristic matrix, and determine the first eigenvalue of the correlation characteristic matrix; Determine the covariance between each historical operation data, obtain a covariance matrix, and determine the second eigenvalue of the covariance matrix; It is determined whether the target cable has a potential fault according to the historical operation data, the first characteristic value, and the second characteristic value.

2. The method according to claim 1, characterized in that The determining, according to the historical operation data, the first characteristic value, and the second characteristic value, whether the target cable has a potential fault includes: Determine the operating state value δ of the target cable according to the following formula; Wherein, t represents the historical monitoring period, α represents the average temperature of the target cable within time t, β represents the average humidity of the target cable within time t, I represents the average current of the target cable within time t, P represents the average vibration frequency of the target cable within time t, F represents the average vibration amplitude of the target cable within time t, and J max represents the maximum partial discharge at the output side end of the target cable within time t, A represents the first characteristic value, and B represents the second characteristic value; According to the running status value, it is determined whether the target cable has a potential fault.

3. The method according to claim 2, characterized in that The step of determining, according to the operating status value, whether the target cable has a potential fault comprises: If the operating status value is greater than or equal to a preset threshold, it is determined that a potential fault exists in the target cable.

4. The method according to claim 1, characterized in that: Also includes: If it is determined that the target cable has a potential fault, obtaining the importance of the target cable; If the importance of the target cable exceeds a preset importance, a first warning message is sent to a terminal held by a target maintenance personnel, wherein the first warning message is used to remind the target maintenance personnel to inspect the target cable within a first preset time; If the importance of the target cable is less than the preset importance, a second warning message is sent to the terminal held by the target maintenance personnel, and the second warning message user reminds the target maintenance personnel to inspect the target cable within a second preset time; wherein the first preset time is less than the second preset time.

5. The method according to claim 4, characterized in that The target maintenance personnel are obtained through the following process: The work planning time of all maintenance personnel within a preset range is obtained, and a maintenance personnel whose work planning time meets the first preset time and / or the second preset time requirements is selected as the target maintenance personnel.

6. The method according to claim 4, characterized in that The obtaining the importance of the target cable comprises: Query the importance of the target cable according to a preset first mapping relationship; wherein the first mapping relationship includes a corresponding relationship between each cable and the importance of each cable; If found, the found importance is used as the importance of the target cable; If not found, determining the scope affected by the target cable and the number of users and associated devices affected within the corresponding scope; The importance of the target cable is determined according to the number of users and the associated devices, and the first mapping relationship is updated according to the importance of the target cable.

7. The method according to claim 1, characterized in that Also includes: Build a digital twin model based on basic cable data; The operating status data of each cable collected by the sensor is obtained, and the operating status data is input into the digital twin model for visual display, and the target cable is displayed in a differentiated manner in the digital twin model after the target cable fails.

8. A cable fault prediction device, characterized in that: include: An acquisition module, used to acquire historical operation data of a target cable, wherein the historical operation data includes temperature, current, humidity, vibration frequency, vibration amplitude, and partial discharge amount; A processing module, used for determining the correlation between each historical operation data based on the Pearson correlation coefficient, obtaining a correlation characteristic matrix, and determining a first eigenvalue of the correlation characteristic matrix; Determine the covariance between each historical operation data, obtain a covariance matrix, and determine the second eigenvalue of the covariance matrix; It is determined whether the target cable has a potential fault according to the historical operation data, the first characteristic value, and the second characteristic value.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images