Cable quality detection method, device and electronic equipment

Abstract

The present invention provides a cable quality detection method, device and electronic device, the method comprising: obtaining cable data of each cable in a target detection area, the cable data refers to data corresponding to each influencing factor affecting the cable quality; clustering each cable based on the cable data to obtain multiple data classes, selecting the cluster center of each data class, and marking the cable corresponding to the cluster center as a typical cable; obtaining the current carrying capacity test data of the typical cable in each data class, inputting the current carrying capacity test data of the typical cable in each data class and the cable data of the typical cable in each data class into a preset quality detection model, and obtaining the quality detection results of the typical cable in each data class; determining the quality detection results of each cable in the target detection area based on the quality detection results of the typical cable in each data class. The present invention can improve the quality detection efficiency of the cable.

Description

Technical Field

[0001] The present invention belongs to the technical field of power cables, and more specifically, relates to a cable quality detection method, device and electronic equipment. Background Art

[0002] At present, there is no technical means to control the quality of cable production at the cable construction site, and operators work entirely based on experience. Therefore, how to effectively detect cable quality has become an urgent problem to be solved in this field.

[0003] In the prior art, there have been many solutions for detecting the quality of cables using quality detection models. However, the number of cables laid is huge, and using quality detection models to detect each cable requires a lot of computing resources, which affects the efficiency of cable quality detection. Summary of the invention

[0004] The object of the present invention is to provide a cable quality detection method, device and electronic equipment to improve the efficiency of cable quality detection.

[0005] A first aspect of an embodiment of the present invention provides a cable quality detection method, comprising:

[0006] Acquire cable data of each cable in the target detection area, wherein the cable data refers to data corresponding to each influencing factor affecting the cable quality;

[0007] Clustering each cable based on the cable data to obtain multiple data classes, selecting a cluster center of each data class, and marking the cable corresponding to the cluster center as a typical cable;

[0008] Obtain the current carrying capacity test data of typical cables in each data class, input the current carrying capacity test data of typical cables in each data class and the cable data of typical cables in each data class into a preset quality inspection model, and obtain the quality inspection results of typical cables in each data class;

[0009] The quality inspection results of each cable in the target inspection area are determined based on the quality inspection results of typical cables in each data class.

[0010] In a possible implementation, the quality inspection result of the cable is a fault state of the cable; and determining the quality inspection result of each cable in the target inspection area based on the quality inspection result of a typical cable in each data class includes:

[0011] If the quality inspection result of a typical cable shows that the typical cable is in a fault state, all cables in the data class where the typical cable belongs are determined to be in a fault state;

[0012] If the quality inspection result of a typical cable shows that the typical cable is not in a fault state, the quality inspection result of each cable is determined according to the distance between each cable and the typical cable corresponding to the cable;

[0013] Among them, the typical cable of the data class where a certain cable is located is the typical cable corresponding to the cable, and the distance of a certain cable from the typical cable corresponding to the cable refers to the distance between the cable data corresponding to the cable and the cluster center in the data class where the cable is located.

[0014] In a possible implementation, determining the quality inspection result of each cable according to the distance between each cable and a typical cable corresponding to the cable includes:

[0015] For each data class, the following steps are performed:

[0016] The distance of a certain cable from the typical cable corresponding to the cable is recorded as the typical distance of the cable;

[0017] For each cable in the target data class, the cable data corresponding to each cable and the current carrying capacity test data of the typical cable in the target data class are input into the preset quality detection model in order from large to small typical distances, until a cable that is not in a fault state is detected, and the cable that is not in a fault state is recorded as the target cable, and the typical distance of the target cable is recorded as the target distance;

[0018] The cables whose typical distance in the target data class is not greater than the target distance are determined to be in a non-fault state, and the cables whose typical distance in the target data class is greater than the target distance are determined to be in a fault state.

[0019] In a possible implementation, determining the quality inspection result of each cable according to the distance between each cable and a typical cable corresponding to the cable includes:

[0020] The distance of a certain cable from the typical cable corresponding to the cable is recorded as the typical distance of the cable;

[0021] A cable whose typical distance is greater than a preset distance value is determined to be in a fault state, and a cable whose typical distance is not greater than the preset distance value is determined to be in a non-fault state.

[0022] In a possible implementation, the quality inspection result of the cable is the quality level of the cable; and determining the quality inspection result of each cable in the target inspection area based on the quality inspection result of a typical cable in each data class includes:

[0023] The quality level of the typical cables in each data class is taken as the quality level of the cables in each data class.

[0024] In a possible implementation, the quality detection model includes a weight coefficient selection model and a neural network model; the current carrying capacity test data of typical cables in each data class and the cable data of typical cables in each data class are input into a preset quality detection model to obtain the quality detection results of typical cables in each data class, including:

[0025] Inputting the current carrying capacity test data of typical cables in each data class into the weight coefficient selection model to determine the weight coefficient corresponding to the typical cables in each data class;

[0026] The weight coefficients corresponding to the typical cables in each data class and the cable data of the typical cables in each data class are input into the neural network model to obtain the quality inspection results of the typical cables in each data class.

[0027] In a possible implementation, the weight coefficient selection model is used to output a corresponding weight coefficient value according to input current carrying capacity test data;

[0028] The initial weight coefficient of the neural network model is empty, and the neural network model is used to use the input weight coefficient as the weight coefficient of this neural network model and then output the corresponding quality detection result according to the input cable data.

[0029] A second aspect of an embodiment of the present invention provides a cable quality detection device, including:

[0030] A data acquisition module, used to acquire cable data of each cable in the target detection area, wherein the cable data refers to data corresponding to various influencing factors affecting the cable quality;

[0031] A data clustering module, used for clustering each cable based on the cable data to obtain multiple data classes, selecting a cluster center of each data class, and marking the cable corresponding to the cluster center as a typical cable;

[0032] The quality inspection module is used to obtain the current carrying capacity test data of the typical cables in each data class, input the current carrying capacity test data of the typical cables in each data class and the cable data of the typical cables in each data class into a preset quality inspection model, and obtain the quality inspection results of the typical cables in each data class;

[0033] The quality determination module is used to determine the quality detection results of each cable in the target detection area based on the quality detection results of typical cables in each data class.

[0034] According to a third aspect of an embodiment of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned cable quality detection method when executing the computer program.

[0035] According to a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned cable quality detection method are implemented.

[0036] The cable quality detection method, device and electronic device provided by the embodiments of the present invention have the following beneficial effects:

[0037] Different from the prior art, the present invention provides a rapid quality inspection scheme for a large number of cables, that is, a clustering processing scheme is used to select typical cables from the same type of cables, and the quality inspection results of the same type of cables are obtained by quality inspection of the typical cables. Since there is no need to perform current carrying capacity tests on all cables, and there is no need to calculate the quality inspection model for the cable data of all cables, the present invention can effectively improve the quality inspection speed of cables. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic diagram of a process flow of a cable quality detection method provided by an embodiment of the present invention;

[0040] Figure 2 A structural block diagram of a cable quality detection device provided by an embodiment of the present invention;

[0041] Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0043] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0044] Please refer to Figure 1 , Figure 1 A schematic diagram of a flow chart of a cable quality detection method provided by an embodiment of the present invention, the method comprising:

[0045] S101: Acquire cable data of each cable in a target detection area, where the cable data refers to data corresponding to various influencing factors that affect cable quality.

[0046] In this embodiment, the cable data includes but is not limited to the current data of the cable, the temperature data of the environment where the cable is located, the humidity data of the environment where the cable is located, the production parameter data of the cable, etc.

[0047] S102: Clustering each cable based on the cable data to obtain multiple data classes, selecting a cluster center of each data class, and marking the cable corresponding to the cluster center as a typical cable.

[0048] In this embodiment, the cable data of each cable may be clustered to obtain multiple data classes, and the cable corresponding to the cable data serving as the cluster center may be marked as a typical cable.

[0049] Among them, this mark is used to instruct operators to perform current carrying capacity tests on typical cables of each data class.

[0050] S103: Obtain the current carrying capacity test data of typical cables in each data class, input the current carrying capacity test data of typical cables in each data class and the cable data of typical cables in each data class into a preset quality inspection model, and obtain the quality inspection results of typical cables in each data class.

[0051] In this embodiment, the current carrying capacity test data of the typical cable is also the current carrying capacity of the typical cable. The quality inspection of the typical cable can be performed by inputting the current carrying capacity of the typical cable and the cable data of the typical cable into a preset quality inspection model.

[0052] S104: Determine the quality inspection result of each cable in the target inspection area based on the quality inspection result of the typical cables in each data class.

[0053] In this embodiment, according to the clustering characteristics, the quality inspection results of typical cables in each data class can be used to infer the quality inspection results of other cables in each data class, thereby reducing the number of cable quality inspections and improving the cable quality inspection efficiency.

[0054] It can be concluded from the above that, different from the prior art, the embodiment of the present invention provides a rapid quality detection solution for a large number of cables, that is, a clustering processing solution is used to select typical cables from the same type of cables, and the quality detection results of the same type of cables are obtained by performing quality detection on the typical cables. Since there is no need to perform current carrying capacity tests on all cables, and there is no need to calculate the cable data of all cables using the quality detection model, the embodiment of the present invention can effectively improve the quality inspection speed of cables.

[0055] In a possible implementation, the quality inspection result of the cable is the fault state of the cable. The quality inspection result of each cable in the target inspection area is determined based on the quality inspection result of a typical cable in each data class, including:

[0056] If the quality inspection result of a typical cable shows that the typical cable is in a fault state, all cables in the data class where the typical cable belongs are determined to be in a fault state.

[0057] If the quality inspection result of a typical cable shows that the typical cable is not in a fault state, the quality inspection result of each cable is determined according to the distance between each cable and the typical cable corresponding to the cable.

[0058] Among them, the typical cable of the data class where a certain cable is located is the typical cable corresponding to the cable, and the distance of a certain cable from the typical cable corresponding to the cable refers to the distance between the cable data corresponding to the cable and the cluster center (that is, the cable data corresponding to the typical cable) in the data class where the cable is located.

[0059] In this embodiment, if a typical cable is a faulty cable, all cables in the data class to which the typical cable belongs are determined as faulty cables. If a typical cable is not a faulty cable, the quality inspection results of other cables need to be determined based on the distance between other cables and the typical cable.

[0060] In a possible implementation, determining the quality test result of each cable according to the distance between each cable and a typical cable corresponding to the cable includes:

[0061] For each data class, the following steps are performed:

[0062] The distance between a certain cable and the typical cable corresponding to the cable is recorded as the typical distance of the cable.

[0063] For each cable in the target data class, the cable data corresponding to each cable and the current carrying capacity test data of the typical cable in the target data class are input into the preset quality inspection model in order from large to small typical distances until a cable that is not in a faulty state is detected, and the cable that is not in a faulty state is recorded as the target cable, and the typical distance of the target cable is recorded as the target distance.

[0064] The cables whose typical distance in the target data class is not greater than the target distance are determined to be in a non-fault state, and the cables whose typical distance in the target data class is greater than the target distance are determined to be in a fault state.

[0065] That is to say, in this embodiment, in a certain data class, cables that are farther away from the typical cables will be preferentially selected for quality inspection until a cable that is not in a faulty state (i.e., the target cable) is detected. Finally, cables whose typical distances are within the target distance are determined to be normal cables (i.e., cables in a non-faulty state), and cables whose typical distances are greater than the target distance are determined to be in a faulty state (according to this solution, cables whose typical distances are greater than the target distance are all quality inspected, and the quality inspection results are all faulty states).

[0066] In a possible implementation, determining the quality test result of each cable according to the distance between each cable and a typical cable corresponding to the cable includes:

[0067] The distance between a certain cable and the typical cable corresponding to the cable is recorded as the typical distance of the cable.

[0068] A cable whose typical distance is greater than a preset distance value is determined to be in a fault state, and a cable whose typical distance is not greater than the preset distance value is determined to be in a non-fault state.

[0069] In this embodiment, in order to further improve the quality detection speed of the cable, the cable whose typical distance is greater than the preset distance value can be directly determined as a fault state.

[0070] In a possible implementation, the quality inspection result of the cable is the quality level of the cable. The quality inspection result of each cable in the target inspection area is determined based on the quality inspection result of a typical cable in each data class, including:

[0071] The quality level of the typical cables in each data class is taken as the quality level of the cables in each data class.

[0072] In this embodiment, the quality inspection result of the cable can be reflected in the form of quality level. In order to achieve rapid inspection, the quality level of the typical cable can be directly used as the quality level of other cables in the same data class (that is, cables in the same data class except the typical cable).

[0073] In a possible implementation, the quality detection model includes a weight coefficient selection model and a neural network model. The current carrying capacity test data of typical cables in each data class and the cable data of typical cables in each data class are input into the preset quality detection model to obtain the quality detection results of typical cables in each data class, including:

[0074] The current carrying capacity test data of typical cables in each data class is input into the weight coefficient selection model to determine the weight coefficient corresponding to the typical cables in each data class.

[0075] The weight coefficients corresponding to the typical cables in each data class and the cable data of the typical cables in each data class are input into the neural network model to obtain the quality inspection results of the typical cables in each data class.

[0076] In a possible implementation, the weight coefficient selection model is used to output a corresponding weight coefficient value according to the input current carrying capacity test data. That is, a mapping relationship table of current carrying capacity test data and weight coefficient values ​​is pre-stored in the weight coefficient selection model, and the weight coefficient selection model outputs a corresponding weight coefficient value according to the received current carrying capacity test data. The weight coefficient value is used as the weight coefficient value of each layer of the subsequent neural network model.

[0077] In a possible implementation, the initial weight coefficient of the neural network model is empty, and the neural network model is used to use the input weight coefficient as the weight coefficient of the neural network model and then output the corresponding quality detection result according to the input cable data.

[0078] That is to say, the weight coefficient value output by the weight coefficient selection model is used as the weight coefficient value of the neural network model, and then the cable data is input into the neural network model of this weight coefficient value to realize quality detection. In this embodiment, different current carrying capacity test data corresponds to neural networks with different weight coefficients, which can effectively improve the accuracy of quality detection.

[0079] Corresponding to the cable quality detection method of the above embodiment, Figure 2 This is a structural block diagram of a cable quality detection device provided by an embodiment of the present invention. For ease of description, only the parts related to the embodiment of the present invention are shown. Figure 2 The cable quality detection device 20 includes: a data acquisition module 21, a data clustering module 22, a quality detection module 23, and a quality determination module 24.

[0080] The data acquisition module 21 is used to acquire the cable data of each cable in the target detection area, and the cable data refers to the data corresponding to each influencing factor affecting the cable quality.

[0081] The data clustering module 22 is used to cluster the cables based on the cable data to obtain multiple data classes, select the cluster center of each data class, and mark the cable corresponding to the cluster center as a typical cable.

[0082] The quality inspection module 23 is used to obtain the current carrying capacity test data of typical cables in each data class, input the current carrying capacity test data of typical cables in each data class and the cable data of typical cables in each data class into a preset quality inspection model, and obtain the quality inspection results of typical cables in each data class.

[0083] The quality determination module 24 is used to determine the quality detection results of each cable in the target detection area based on the quality detection results of typical cables in each data type.

[0084] In a possible implementation, the quality detection result of the cable is a fault state of the cable. The quality determination module 24 is specifically used for:

[0085] If the quality inspection result of a typical cable shows that the typical cable is in a fault state, all cables in the data class where the typical cable belongs are determined to be in a fault state.

[0086] If the quality inspection result of a typical cable shows that the typical cable is not in a fault state, the quality inspection result of each cable is determined according to the distance between each cable and the typical cable corresponding to the cable.

[0087] Among them, the typical cable of the data class where a certain cable is located is the typical cable corresponding to the cable, and the distance of a certain cable from the typical cable corresponding to the cable refers to the distance between the cable data corresponding to the cable and the cluster center in the data class where the cable is located.

[0088] In a possible implementation, the quality determination module 24 is specifically configured to:

[0089] For each data class, the following steps are performed:

[0090] The distance between a certain cable and the typical cable corresponding to the cable is recorded as the typical distance of the cable.

[0091] For each cable in the target data class, the cable data corresponding to each cable and the current carrying capacity test data of the typical cable in the target data class are input into the preset quality inspection model in order from large to small typical distances until a cable that is not in a faulty state is detected, and the cable that is not in a faulty state is recorded as the target cable, and the typical distance of the target cable is recorded as the target distance.

[0092] The cables whose typical distance in the target data class is not greater than the target distance are determined to be in a non-fault state, and the cables whose typical distance in the target data class is greater than the target distance are determined to be in a fault state.

[0093] In a possible implementation, the quality determination module 24 is specifically configured to:

[0094] The distance between a certain cable and the typical cable corresponding to the cable is recorded as the typical distance of the cable.

[0095] A cable whose typical distance is greater than a preset distance value is determined to be in a fault state, and a cable whose typical distance is not greater than the preset distance value is determined to be in a non-fault state.

[0096] In a possible implementation, the quality detection result of the cable is the quality level of the cable. The quality determination module 24 is specifically used for:

[0097] The quality level of the typical cables in each data class is taken as the quality level of the cables in each data class.

[0098] In a possible implementation, the quality detection model includes a weight coefficient selection model and a neural network model. The quality detection module 23 is specifically used for:

[0099] The current carrying capacity test data of typical cables in each data class is input into the weight coefficient selection model to determine the weight coefficient corresponding to the typical cables in each data class.

[0100] The weight coefficients corresponding to the typical cables in each data class and the cable data of the typical cables in each data class are input into the neural network model to obtain the quality inspection results of the typical cables in each data class.

[0101] In a possible implementation, the weight coefficient selection model is used to output a corresponding weight coefficient value according to input current carrying capacity test data.

[0102] The initial weight coefficient of the neural network model is empty. The neural network model is used to take the input weight coefficient as the weight coefficient of this neural network model and then output the corresponding quality detection result according to the input cable data.

[0103] See also Figure 3 , Figure 3 FIG. 1 is a schematic block diagram of an electronic device provided by an embodiment of the present invention. Figure 3The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to perform the following operations to perform the functions of the modules / units in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 24 are shown.

[0104] It should be understood that in the embodiment of the present invention, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0105] The input device 302 may include a touch panel, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0106] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0107] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiment of the present invention can execute the implementation methods described in the first and second embodiments of the cable quality detection method provided in the embodiment of the present invention, and can also execute the implementation methods of the electronic device described in the embodiment of the present invention, which will not be repeated here.

[0108] In another embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, wherein the program instructions are executed by a processor to implement all or part of the processes in the above-mentioned embodiment method, and may also be completed by instructing the relevant hardware through the computer program, wherein the computer program may be stored in a computer-readable storage medium, and wherein the computer program may be executed by the processor to implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the contents contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signal and telecommunication signal.

[0109] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0110] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0112] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0113] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0114] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0115] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A cable quality detection method, characterized in that: include: Acquire cable data of each cable in the target detection area, wherein the cable data refers to data corresponding to each influencing factor affecting the cable quality; Clustering each cable based on the cable data to obtain multiple data classes, selecting a cluster center of each data class, and marking the cable corresponding to the cluster center as a typical cable; Obtain the current carrying capacity test data of typical cables in each data class, input the current carrying capacity test data of typical cables in each data class and the cable data of typical cables in each data class into a preset quality inspection model, and obtain the quality inspection results of typical cables in each data class; Determine the quality inspection results of each cable in the target inspection area based on the quality inspection results of typical cables in each data class; The quality detection model includes a weight coefficient selection model and a neural network model; the current carrying capacity test data of typical cables in each data class and the cable data of typical cables in each data class are input into the preset quality detection model to obtain the quality detection results of typical cables in each data class, including: Inputting the current carrying capacity test data of typical cables in each data class into the weight coefficient selection model to determine the weight coefficient corresponding to the typical cables in each data class; The weight coefficients corresponding to the typical cables in each data class and the cable data of the typical cables in each data class are input into the neural network model to obtain the quality inspection results of the typical cables in each data class.

2. The cable quality detection method according to claim 1, characterized in that: The quality inspection result of the cable is the fault state of the cable; the quality inspection result of each cable in the target inspection area is determined based on the quality inspection result of the typical cable in each data class, including: If the quality inspection result of a typical cable shows that the typical cable is in a fault state, all cables in the data class where the typical cable belongs are determined to be in a fault state; If the quality inspection result of a typical cable shows that the typical cable is not in a fault state, the quality inspection result of each cable is determined according to the distance between each cable and the typical cable corresponding to the cable; Among them, the typical cable of the data class where a certain cable is located is the typical cable corresponding to the cable, and the distance of a certain cable from the typical cable corresponding to the cable refers to the distance between the cable data corresponding to the cable and the cluster center in the data class where the cable is located.

3. The cable quality detection method according to claim 2, characterized in that: Determining the quality inspection result of each cable according to the distance between each cable and the typical cable corresponding to the cable includes: For each data class, the following steps are performed: The distance of a certain cable from the typical cable corresponding to the cable is recorded as the typical distance of the cable; For each cable in the target data class, the cable data corresponding to each cable and the current carrying capacity test data of the typical cable in the target data class are input into the preset quality detection model in order from large to small typical distances, until a cable that is not in a fault state is detected, and the cable that is not in a fault state is recorded as the target cable, and the typical distance of the target cable is recorded as the target distance; The cables whose typical distance in the target data class is not greater than the target distance are determined to be in a non-fault state, and the cables whose typical distance in the target data class is greater than the target distance are determined to be in a fault state.

4. The cable quality detection method according to claim 2, characterized in that: Determining the quality inspection result of each cable according to the distance between each cable and the typical cable corresponding to the cable includes: The distance of a certain cable from the typical cable corresponding to the cable is recorded as the typical distance of the cable; A cable whose typical distance is greater than a preset distance value is determined to be in a fault state, and a cable whose typical distance is not greater than the preset distance value is determined to be in a non-fault state.

5. The cable quality detection method according to claim 1, characterized in that: The quality inspection result of the cable is the quality level of the cable; the quality inspection result of each cable in the target inspection area is determined based on the quality inspection result of the typical cable in each data class, including: The quality level of the typical cables in each data class is taken as the quality level of the cables in each data class.

6. The cable quality detection method according to claim 1, characterized in that: The weight coefficient selection model is used to output a corresponding weight coefficient value according to the input current carrying capacity test data; The initial weight coefficient of the neural network model is empty, and the neural network model is used to use the input weight coefficient as the weight coefficient of this neural network model and then output the corresponding quality detection result according to the input cable data.

7. A cable quality detection device, characterized in that: include: A data acquisition module, used to acquire cable data of each cable in the target detection area, wherein the cable data refers to data corresponding to various influencing factors affecting the cable quality; A data clustering module, used for clustering each cable based on the cable data to obtain multiple data classes, selecting a cluster center of each data class, and marking the cable corresponding to the cluster center as a typical cable; The quality inspection module is used to obtain the current carrying capacity test data of the typical cables in each data class, input the current carrying capacity test data of the typical cables in each data class and the cable data of the typical cables in each data class into a preset quality inspection model, and obtain the quality inspection results of the typical cables in each data class; A quality determination module, used to determine the quality inspection results of each cable in the target inspection area based on the quality inspection results of typical cables in each data class; Among them, the quality detection model includes a weight coefficient selection model and a neural network model; the quality detection module is also used to input the current carrying capacity test data of typical cables in each data class into the weight coefficient selection model to determine the weight coefficient corresponding to the typical cables in each data class; the weight coefficient corresponding to the typical cable in each data class and the cable data of the typical cable in each data class are input into the neural network model to obtain the quality detection results of the typical cables in each data class.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, 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 6 are implemented.

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