A method for intelligently detecting electrical properties of cables

Abstract

The present invention discloses an intelligent detection method for the electrical performance of a cable, and relates to the technical field of electrical performance detection. The method comprises: determining a batch of target cables, and constructing target electrical performance constraints; connecting a scheme configuration library, and configuring an electrical performance detection scheme; setting a cable standard detection platform according to a standard detection scheme, and detecting the target cables one by one; dividing the full amount of standard detection data sets, and performing random sampling according to the division results and a preset sampling ratio; setting an electric quantity extreme detection platform according to an extreme detection scheme, and detecting the target cables one by one; and obtaining the comprehensive electrical performance detection results of the batch target cables. The method solves the technical problems that the existing cable electrical performance detection relies on manual operation and simple test equipment, which is not only inefficient, but also easily affected by human factors, resulting in the difficulty in ensuring the accuracy and reliability of the electrical performance detection results, and achieves the technical effect of improving the accuracy and efficiency of the cable electrical performance detection.

Description

Technical Field

[0001] The present application relates to the field of electrical performance detection technology, and specifically to an intelligent detection method for electrical performance of cables. Background Art

[0002] In today's information and intelligent world, cables are important infrastructure for power transmission and information communication. The stability and reliability of their electrical performance are crucial to the safe operation of power systems and the smooth operation of information networks. Since cables may be affected by many factors during production, installation, and operation, such as material quality, process level, environmental factors, etc., their electrical performance may fluctuate or become abnormal. Accurate and efficient testing of the electrical performance of cables has become a key link. However, with the continuous increase in cable types and the continuous expansion of application areas, the requirements for cable electrical performance testing are also getting higher and higher. Traditional cable electrical performance testing often relies on manual operation and simple testing equipment, which is not only inefficient, but also easily affected by human factors, resulting in difficulty in ensuring the accuracy and consistency of the test results, and unable to meet the development needs of the modern cable industry.

[0003] Therefore, at present, the technologies related to cable electrical performance testing rely on manual operation and simple testing equipment, which is not only inefficient but also easily affected by human factors, resulting in technical problems such as difficulty in ensuring the accuracy and reliability of electrical performance test results. Summary of the invention

[0004] The present application provides an intelligent detection method for the electrical properties of cables. By adopting technical means such as setting up a detection platform and clustering and dividing data sets, the present application solves the technical problem that the existing cable electrical properties detection relies on manual operation and simple testing equipment, which is not only inefficient but also easily affected by human factors, resulting in the difficulty in ensuring the accuracy and reliability of the electrical properties detection results. The present application realizes the automated and precise detection of the electrical properties of cables, achieving the technical effect of improving the accuracy and efficiency of the cable electrical properties detection.

[0005] The present application provides an intelligent detection method for electrical performance of a cable, the method comprising: determining a batch of target cables, the batch of target cables comprising a plurality of target cables to be detected, having a unified electrical performance requirement, and constructing a target electrical performance constraint based on the electrical performance requirement; connecting a scheme configuration library, inputting the electrical performance requirement into the scheme configuration library, configuring an electrical performance detection scheme, the electrical performance detection scheme comprising a standard detection scheme and an extreme detection scheme, and establishing a scheme association relationship between the standard detection scheme and the extreme detection scheme; setting a cable standard detection platform according to the standard detection scheme, detecting a plurality of target cables to be detected one by one, and obtaining a full standard detection data set; dividing the full standard detection data set according to the detection data, randomly selecting the batch of target cables according to the division result and a preset sampling ratio, and obtaining a plurality of selected target cables; setting an electrical extreme detection platform according to the extreme detection scheme, detecting a plurality of selected target cables one by one, obtaining a selected extreme detection data set, and constructing a full extreme detection data set in combination with the scheme association relationship; obtaining a comprehensive electrical performance detection result of the batch of target cables based on the target electrical performance constraint, the full standard detection data set, and the full extreme detection data set.

[0006] In a possible implementation, a scheme association relationship between a standard detection scheme and an extreme detection scheme is established, and the following processing is performed: a relationship mining network is constructed; the standard detection scheme and the extreme detection scheme are input into the relationship mining network, scheme association analysis is performed, and relationship mining results are obtained; and a scheme association relationship is generated based on the relationship mining results.

[0007] In a possible implementation, a relationship mining network is constructed and the following processing is performed: historical cable electrical performance test data is obtained, wherein the historical cable electrical performance test data includes multiple groups of historical standard test data and multiple groups of historical extreme test data; wherein the historical standard test data has a corresponding standard test scheme identifier, and the historical extreme test data has an extreme test scheme identifier; relationship mining of the multiple groups of historical standard test data and the multiple groups of historical extreme test data is performed, and a relationship mining network is obtained according to the relationship mining results and the scheme identifiers.

[0008] In a possible implementation, a relationship mining network is obtained based on the relationship mining results and the scheme identifier, and the following processing is also performed: based on the historical cable electrical performance test data, historical cable appearance test data and historical mechanical performance test data are obtained; in combination with the historical cable appearance test data and the historical mechanical performance test data, relationship mining of multiple groups of historical standard test data and multiple groups of historical extreme test data is performed, and a relationship mining network is obtained based on the relationship mining results and the scheme identifier.

[0009] In a possible implementation, a full standard detection data set is obtained, and the following processing is also performed: according to the standard detection scheme, the detection parameter configuration of the cable standard detection platform is determined, and based on the detection parameter configuration, the detection unit setting of the cable standard detection platform is adjusted; multiple target cables to be detected are sorted to form a cable detection queue, and according to the cable detection queue, multiple target cables are detected one by one using the cable standard detection platform, and the corresponding standard detection data is output after each target cable is detected; the standard detection data of each target cable is summarized to construct a full standard detection data set.

[0010] In a possible implementation, a full extreme detection dataset is constructed in combination with the scheme association relationship, and the following processing is also performed: a full standard detection dataset is extracted, and a simulated full standard detection dataset is generated based on the scheme association relationship; the simulated full standard detection dataset is optimized and corrected according to the selected extreme detection dataset to obtain a full extreme detection dataset.

[0011] In a possible implementation, comprehensive electrical performance test results of a batch of target cables are obtained, and the following processing is also performed: the full amount of standard test data sets are evaluated according to the target electrical performance constraints to obtain a first electrical performance test result; the full amount of extreme test data sets are evaluated according to the target electrical performance constraints to obtain a second electrical performance test result; the first electrical performance test result and the second electrical performance test result are weightedly fused to obtain a comprehensive electrical performance test result.

[0012] In a possible implementation, after obtaining the comprehensive electrical performance test results of the batch target cables, the following processing is also performed: establishing actual electrical performance constraints for the batch target cables based on the comprehensive electrical performance test results; configuring work risk warnings for the batch target cables based on the actual electrical performance constraints, and operating and maintaining the target cables based on the work risk warnings.

[0013] Through the intelligent detection method of the electrical performance of cables proposed in this application, it is planned to determine the batch target cables and construct the target electrical performance constraints; connect the scheme configuration library and configure the electrical performance detection scheme; set up the cable standard detection platform according to the standard detection scheme, and detect the target cables one by one; divide the full standard detection data set, and randomly select according to the division results and the preset sampling ratio; set up the electric quantity extreme detection platform according to the extreme detection scheme, and detect the target cables one by one; obtain the comprehensive electrical performance detection results of the batch target cables. It solves the technical problems that the existing cable electrical performance detection relies on manual operation and simple test equipment, which is not only inefficient, but also easily affected by human factors, resulting in the difficulty in ensuring the accuracy and reliability of the electrical performance detection results, and realizes the automation and precision detection of the cable electrical performance, achieving the technical effect of improving the accuracy and efficiency of the cable electrical performance detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the method according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of a flow chart of a method for intelligently detecting the electrical properties of a cable provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of a process for obtaining a full set of standard test data in a method for intelligently detecting electrical properties of a cable provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0020] The present application embodiment provides a method for intelligently detecting the electrical properties of a cable. Figure 1 As shown, the method includes:

[0021] Step S100, determining a batch of target cables, the batch of target cables including multiple target cables to be tested, having a unified electrical performance requirement, and constructing a target electrical performance constraint based on the electrical performance requirement. A batch of cables that need to be tested for electrical performance is selected as the target object, that is, multiple target cables to be tested constitute a batch of target cables, and multiple target cables have a unified electrical performance requirement, that is, they all meet certain electrical characteristic standards, and a target electrical performance constraint is constructed based on the electrical performance requirements of the target cables. Specifically, according to the electrical performance requirements that the target cables need to meet, a series of specific electrical performance indicators are formulated as constraints to guide the electrical performance testing of the cables, ensuring that the test results can accurately reflect whether the cables meet the predetermined electrical performance requirements. The target electrical performance constraints may include the resistance value, insulation strength, withstand voltage level, transmission efficiency, etc. of the cables. For example, for power transmission cables, it is necessary to specify that their resistance value is within a certain range to ensure the efficiency of power transmission; for communication cables, it is necessary to specify their insulation strength and withstand voltage level to ensure the stability and safety of signal transmission; when constructing the target electrical performance constraint, it is also necessary to comprehensively consider factors such as the material, structure, manufacturing process, and actual application environment of the cables to ensure the rationality and effectiveness of the constraints.

[0022] Step S200, connect the solution configuration library, input the electrical performance requirements into the solution configuration library, configure the electrical performance detection scheme, the electrical performance detection scheme includes a standard detection scheme and an extreme detection scheme, and establish a scheme association relationship between the standard detection scheme and the extreme detection scheme. Connecting the solution configuration library refers to docking the electrical performance requirements of the target cable with the pre-established cable electrical performance detection scheme configuration library. The configuration library usually contains a variety of detection schemes for different cable types, application scenarios and performance requirements, which can comprehensively and accurately evaluate the electrical performance of the cable. Inputting the electrical performance requirements into the solution configuration library refers to retrieving, selecting and configuring appropriate detection schemes in the configuration library according to the specific electrical performance requirements of the target cable. Specifically, the electrical performance requirements are analyzed, the matching and screening of the schemes in the solution configuration library are performed, and the electrical performance detection schemes are configured, including standard detection schemes and extreme detection schemes. Among them, the standard detection scheme is formulated according to the general standards and specifications of the cable industry, and is used to evaluate the electrical performance of the cable under normal working conditions. , covering the basic electrical performance parameters and test methods of the cable, which can ensure the basic quality and reliability of the cable; the extreme detection scheme is a detection scheme designed for the electrical performance of the cable under extreme conditions. It is used to test the performance of the cable under environmental conditions beyond the normal working range, including extreme environmental factors such as high temperature, low temperature, high humidity, high pressure, or the performance of the cable under abnormal working conditions such as overload and short circuit. Establishing the scheme association relationship between the standard detection scheme and the extreme detection scheme means logically connecting the two schemes, which is helpful to comprehensively evaluate the electrical performance of the cable, which can not only ensure the stability of the performance of the cable under normal working conditions, but also predict the performance of the cable under extreme conditions, and can have a more comprehensive understanding of the performance characteristics of the cable.

[0023] In a possible implementation, step S200 further includes step S210, constructing a relationship mining network. Construct a relationship mining network that automatically mines and identifies the relationship between the standard detection scheme and the extreme detection scheme, analyzes the intrinsic relationship between the two detection schemes, and discovers the correlation, complementarity or potential differences between the two. It also includes step S220, inputs the standard detection scheme and the extreme detection scheme into the relationship mining network, performs scheme association analysis, and obtains the relationship mining result. All relevant data of the standard detection scheme and the extreme detection scheme are input into the constructed relationship mining network, which may include a detailed description of the scheme, test parameters, experimental conditions, expected results, etc. The relationship mining network will analyze these data, evaluate the similarities, differences and potential mutual influences between different schemes, so as to identify the complex relationship between them. Finally, the relationship mining network will output a relationship mining result, which is a detailed description of the association relationship between the standard detection scheme and the extreme detection scheme, including under which parameters or conditions the two schemes are similar, under which conditions they have obvious differences, and the impact of these differences on the detection of cable electrical properties. It also includes step S230, generating a scheme association relationship based on the relationship mining result. Based on the relationship results mined from the network, the logical connection between the standard detection scheme and the extreme detection scheme is further constructed and clarified. For example, based on the mining results, it can be determined in which cases the standard detection scheme should be used first, under which extreme conditions the extreme detection scheme needs to be supplemented or strengthened, and how to combine the two schemes to obtain more accurate detection results.

[0024] In a possible implementation, step S210 further includes step S211, obtaining historical cable electrical performance test data, wherein the historical cable electrical performance test data includes multiple groups of historical standard test data and multiple groups of historical extreme test data. The historical cable electrical performance test data includes multiple groups of historical standard test data and multiple groups of historical extreme test data. The historical standard test data refers to the data obtained by performing electrical performance tests in accordance with the general standards and specifications of the cable industry under normal working conditions; the historical extreme test data refers to the data obtained by performing electrical performance tests on the cable under extreme conditions. It also includes step S212, wherein the historical standard test data has a corresponding standard test scheme identifier, and the historical extreme test data has an extreme test scheme identifier. The historical standard test data has a corresponding standard test scheme identifier, which means that each group of standard test data can be traced back to the standard test scheme it adopts. This identifier may be a unique code, number or name used to identify and distinguish different standard test schemes; similarly, the historical extreme test data also has an extreme test scheme identifier, which is used to identify and distinguish different extreme test schemes to ensure that each group of extreme test data can be accurately matched with its corresponding test scheme. The method further includes step S213, performing relationship mining of multiple sets of historical standard test data and multiple sets of historical extreme test data, and obtaining a relationship mining network according to the relationship mining results and the scheme identification. Using data mining to perform relationship mining of multiple sets of historical standard test data and multiple sets of historical extreme test data is helpful to understand the actual application effects of standard test schemes and extreme test schemes in different cable electrical performance tests, as well as the relationship patterns between them. After completing the relationship mining, a network model that can reflect the relationship between the standard test scheme and the extreme test scheme is constructed according to the mined association results and scheme identification information, i.e., the relationship mining network, which not only shows the direct association between different schemes, but also may reveal the indirect connection and mutual influence between them, which helps us optimize the selection and combination of test schemes and improve the accuracy and efficiency of cable electrical performance testing.

[0025] In a possible implementation, step S213 further includes step S213-1, obtaining historical cable appearance inspection data and historical mechanical performance inspection data based on historical cable electrical performance inspection data. In addition to the electrical performance data of the cable, historical inspection data of the cable in terms of appearance and mechanical performance are further collected. These data are important components of cable quality inspection and can fully reflect the performance and quality status of the cable. Specifically, the historical cable appearance inspection data mainly includes information on the appearance characteristics, size, and identification of the cable, such as whether the insulation layer of the cable is uniform and undamaged, whether the sheath is complete, and whether the identification is clear; the historical mechanical performance inspection data refers to the performance data of the cable in the mechanical performance test, and the mechanical performance test includes the test of tensile strength, bending performance, wear resistance, impact resistance and other indicators. It also includes step S213-2, combining the historical cable appearance inspection data and the historical mechanical performance inspection data, performing relationship mining of multiple groups of historical standard inspection data and multiple groups of historical extreme inspection data, and obtaining a relationship mining network based on the relationship mining results and the scheme identifier. Standard test data and extreme test data may not be well mapped because the appearance size and mechanical properties of different cables will also have an impact. Based on appearance, mechanical properties, and standard tests, extreme tests are mapped together to construct a relationship mining network. When only the standard test plan is input, the impact of cable appearance and mechanical properties is still considered.

[0026] Step S300, set up a cable standard testing platform according to the standard testing scheme, test multiple target cables to be tested one by one, and obtain a full standard testing data set. According to the known standard testing scheme, set up a cable standard testing platform, which usually includes the required test equipment, instruments and tools, configured and calibrated according to the requirements of the standard testing scheme to ensure the accuracy and reliability of the test results, and put multiple target cables to be tested into the standard testing platform one by one for testing. During the test, the platform will conduct a comprehensive evaluation of the electrical properties, appearance, mechanical properties and other aspects of the cable according to the parameters and steps in the standard testing scheme, which may include voltage testing, insulation resistance testing, appearance inspection, mechanical strength testing, etc. After the test is completed, the platform will automatically record and save the test results of each target cable, forming a full standard testing data set, which contains detailed test data of all cables to be tested under the standard testing scheme. By obtaining the full standard testing data set, we can fully and objectively understand the performance and quality status of each target cable.

[0027] In one possible implementation, Figure 2As shown, step S300 further includes step S310, determining the detection parameter configuration of the cable standard detection platform according to the standard detection scheme, and adjusting the detection unit setting of the cable standard detection platform based on the detection parameter configuration. According to the known standard detection scheme, it is necessary to determine the specific detection items and test requirements that the cable standard detection platform needs to perform, which may include parameters of electrical performance testing (such as voltage range, current size, insulation resistance value, etc.), criteria for appearance inspection (such as dimensional tolerance, identification clarity, etc.) and conditions for mechanical performance testing (such as stretching rate, bending angle, etc.), based on these detection parameters and test requirements, determine the detection parameter configuration of the cable standard detection platform, including selecting appropriate test equipment, sensors and measuring instruments, and setting the working mode and parameter range of these equipment to ensure that they can accurately and reliably perform the required test tasks, and adjust the detection unit setting of the cable standard detection platform according to the detection parameter configuration, wherein the detection unit is a module on the platform responsible for performing specific test tasks, and needs to be configured and adjusted according to the requirements of the detection parameters, including adjusting the clamping method of the test fixture, changing the model of the sensor and measuring instrument, setting the parameters and algorithm of the test software, etc. The step also includes step S320, in which a plurality of target cables to be inspected are sorted to form a cable inspection queue, and a cable standard inspection platform is used to inspect the plurality of target cables one by one according to the cable inspection queue, and corresponding standard inspection data is output after the inspection of each target cable is completed. The multiple target cables to be detected are sorted according to certain rules to form a cable detection queue, and then according to the cable detection queue, the multiple target cables are detected one by one using the cable standard detection platform, and the corresponding standard detection data is output after each target cable detection is completed. Specifically, the target cables to be detected are sorted to form a cable detection queue to ensure the orderliness and efficiency of cable detection. The cable detection queue is an ordered list, which lists all target cables to be detected according to the sorting rules, which is helpful for the planned management and scheduling of cable detection. According to the cable detection queue, the target cables are detected one by one using the already set cable standard detection platform, which means that each target cable is placed in the detection platform for detection in sequence according to the order in the queue. The detection platform will test the cable for various indicators according to the preset standard detection scheme, including electrical performance, appearance, mechanical performance and other aspects. After each target cable is detected, the detection platform will automatically record and output the corresponding standard detection data, which is an important basis for cable performance and quality evaluation, including voltage, current, insulation resistance, size, appearance description, mechanical strength and other specific test results. It also includes step S330, summarizing the standard detection data of each target cable to construct a full standard detection data set.All relevant test data generated by each target cable during the standard testing process are collected and integrated to form a complete data set, namely the full standard test data set, including various performance indicators of the cable, such as voltage, current, insulation resistance, appearance size, mechanical strength, etc., which together reflect the performance characteristics and quality status of the cable. The purpose of constructing the full standard test data set is to form a data set that comprehensively and accurately reflects the performance status of all target cables, and provide important data support for subsequent data analysis, quality assessment, cable selection and use. It can be used as the basis for cable quality control, and can also be used for comparison and trend analysis of cable performance.

[0028] Step S400, divide the full standard test data set according to the test data, randomly select the batch of target cables according to the division results and the preset sampling ratio, and obtain multiple selected target cables. The full standard test data set is divided, that is, clustered according to the specific characteristics or conditions of the cable test data, which may include the performance index range of the cable, the severity of the appearance defects, the strength of the mechanical properties, etc. The full standard test data set is divided into multiple subsets, each subset represents a set of cables with common characteristics, and the batch of target cables is randomly selected according to the division results and the preset sampling ratio. A certain number of cables are extracted from each subset according to the preset ratio, that is, multiple selected target cables, wherein the preset extraction ratio is a pre-set ratio value, which determines the ratio of cables extracted from each subset. Representative samples are selected from the full standard test data set by random sampling for subsequent detailed analysis or further testing to ensure that the selected target cables are both diverse and can reflect the performance characteristics and distribution of the entire batch of cables.

[0029] Step S500, according to the extreme detection scheme, an electric quantity extreme detection platform is set, and multiple selected target cables are detected one by one to obtain a selected extreme detection data set, and a full extreme detection data set is constructed in combination with the scheme association relationship. According to the requirements of the extreme detection scheme, the electric quantity extreme detection platform is set, which may involve more complex test equipment, more stringent test environment and more sophisticated test steps to simulate the extreme conditions that the cable may encounter in actual application. The electric quantity extreme detection platform is used to detect multiple selected target cables one by one. By testing the performance of multiple selected target cables under extreme conditions, more data about the behavior of the cable under extreme conditions can be obtained. During the detection process, the platform will automatically record and save the extreme detection data of each selected target cable to form a selected extreme detection data set, which includes various performance indicators of the cable under extreme conditions, such as voltage stability, current carrying capacity, insulation strength, etc. Finally, in combination with the association between the previous standard detection scheme and the extreme detection scheme, the selected extreme detection data set is associated and integrated with the full standard detection data set to construct a full extreme detection data set.

[0030] In a possible implementation, step S500 further includes step S510, extracting a full standard test data set, and generating a simulated full standard test data set based on the scheme association relationship. The full standard test data set is extracted, including the complete test results obtained for all target cables under the standard test scheme, and a simulated full standard test data set is generated based on the scheme association relationship, that is, the full standard test data set is simulated and expanded to obtain more possible test data, and a simulated full standard test data set is generated. It also includes step S520, optimizing and correcting the simulated full standard test data set according to the selected extreme test data set to obtain a full extreme test data set. Although the simulated full standard detection dataset is generated based on the real detection data and the association between the schemes, it may still have some deviations or deficiencies. By introducing the detection data under actual extreme conditions, we can adjust and improve the simulated data to make it closer to the actual situation. The generated simulated full standard detection dataset is optimized and corrected using the obtained selected extreme detection dataset, which may include recalculating the simulated data, correcting certain parameters, adjusting the data distribution, etc. By constantly comparing and correcting the selected extreme detection dataset, the accuracy and reliability of the simulated data are gradually improved. The optimized and corrected simulated full standard detection dataset is combined with the selected extreme detection dataset to form the full extreme detection dataset.

[0031] Step S600, based on the target electrical performance constraint, the full standard test data set and the full extreme test data set, obtain the comprehensive electrical performance test results of the batch target cables. Based on the target electrical performance constraint, the full standard test data set and the full extreme test data set, obtain the comprehensive electrical performance test results of the batch target cables. Specifically, under the condition of meeting the target electrical performance constraint, comprehensively analyze and evaluate the full standard test data set and the full extreme test data set of the batch target cables to obtain the comprehensive electrical performance test results of the batch target cables, comprehensively and objectively evaluate the electrical performance of the batch target cables, and provide a scientific basis for the selection, use and maintenance of the cables.

[0032] In a possible implementation, step S600 further includes step S610, evaluating the full standard test data set according to the target electrical performance constraint to obtain a first electrical performance test result. According to the target electrical performance constraint, each piece of data in the full standard test data set is compared and evaluated item by item. Specifically, the electrical performance data of each cable is compared with the constraint condition to determine whether it meets the standard, for example, to check whether the voltage test value of the cable is within the allowable voltage range, whether the current carrying capacity meets the requirements, whether the resistance value is within the specified range, etc., and the evaluation results are summarized to form the first electrical performance test result. It also includes step S620, evaluating the full extreme test data set according to the target electrical performance constraint to obtain a second electrical performance test result. The test data under these extreme conditions are evaluated according to the previously set target electrical performance constraint. Specifically, the data in the full extreme test data set is compared with the target electrical performance constraint item by item, that is, the voltage stability, current carrying capacity, resistance change, insulation performance and other indicators of each cable under extreme conditions are checked to be still within the allowable range, and the evaluation results of all cables under extreme conditions are summarized to form the second electrical performance test result. It also includes step S630, weighted fusion of the first electrical performance test result and the second electrical performance test result to obtain a comprehensive electrical performance test result. The weight of each test result is clarified. For the first electrical performance test result (the test result under standard conditions), if the standard conditions can well represent the state of the cable in normal use, then its weight may be relatively high; for the second electrical performance test result (the test result under extreme conditions), since extreme conditions can better expose potential problems of the cable, its weight may also be very important, but the specific weight depends on the degree of correlation between the extreme conditions and the actual application scenario; according to the determined weight, the two test results are weighted fused to form a comprehensive electrical performance test result.

[0033] In a possible implementation, step S600 also includes step S650, establishing actual electrical performance constraints for batch target cables according to the comprehensive electrical performance test results. Based on the obtained comprehensive electrical performance test results, the electrical performance standards and requirements that should be met in actual use are set for the batch target cables. Specifically, according to the comprehensive electrical performance test results, the performance of the cables in various electrical performance indicators is analyzed, including checking the specific data of the cables in terms of voltage stability, current carrying capacity, resistance change, insulation performance, etc., and the gap between these data and the target electrical performance constraints. Combined with the actual application requirements and working environment of the cables, actual electrical performance constraints are formulated for the batch target cables, which may include more specific voltage ranges, current carrying capacity requirements, resistance limits, insulation strength standards, etc., to ensure that the cables can work stably and reliably in actual use. It also includes step S660, configuring the work risk warning of the batch target cables based on the actual electrical performance constraints, and operating and maintaining the target cables according to the work risk warning. The actual electrical performance constraints are used to set up cable work risk monitoring and early warning, and the operation and maintenance management of the target cables is carried out accordingly. Specifically, based on the established actual electrical performance constraints, work risk early warnings are configured for batches of target cables. The system can monitor the electrical performance data of the cables in real time and compare them with the actual electrical performance constraints. Once it is found that the electrical performance data of the cables exceeds the constraint range or abnormal changes occur, the system will immediately trigger the early warning mechanism, such as issuing an audible and visual alarm, sending text messages or email notifications, etc. Based on the work risk early warning, the operation and maintenance personnel can carry out operation and maintenance management of the target cables. If the inspection finds that the cable does have problems, the operation and maintenance personnel need to take appropriate measures to repair or replace it to ensure that the cable returns to normal working condition.

[0034] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for intelligent detection of electrical properties of a cable, characterized in that: The method comprises: Determine a batch of target cables, the batch of target cables including a plurality of target cables to be detected, having a uniform electrical performance requirement, and construct a target electrical performance constraint based on the electrical performance requirement; Connecting to a solution configuration library, inputting the electrical performance requirements into the solution configuration library, configuring an electrical performance detection solution, wherein the electrical performance detection solution includes a standard detection solution and an extreme detection solution, and establishing a solution association relationship between the standard detection solution and the extreme detection solution; According to the standard detection scheme, a cable standard detection platform is set up, and multiple target cables to be detected are detected one by one to obtain a full standard detection data set; Dividing the full standard test data set according to the test data, and randomly selecting the batch of target cables according to the division result and a preset sampling ratio to obtain a plurality of selected target cables; According to the extreme detection scheme, an electric quantity extreme detection platform is set up, and multiple selected target cables are detected one by one to obtain a selected extreme detection data set, and a full extreme detection data set is constructed based on the scheme association relationship; Based on the target electrical performance constraints, the full set of standard test data sets and the full set of extreme test data sets, comprehensive electrical performance test results of batch target cables are obtained.

2. The method for intelligent detection of electrical properties of a cable according to claim 1, characterized in that: Establish the scheme association relationship between the standard detection scheme and the extreme detection scheme, including: Build a relationship mining network; Inputting the standard detection scheme and the extreme detection scheme into the relationship mining network, performing scheme association analysis, and obtaining relationship mining results; Generate solution association relationships based on the relationship mining results.

3. The method for intelligently detecting the electrical properties of a cable according to claim 2, characterized in that: Build a relationship mining network, including: Acquire historical cable electrical performance test data, wherein the historical cable electrical performance test data includes multiple groups of historical standard test data and multiple groups of historical extreme test data; Among them, the historical standard detection data has a corresponding standard detection scheme identifier, and the historical extreme detection data has an extreme detection scheme identifier; Relationship mining of multiple groups of historical standard detection data and multiple groups of historical extreme detection data is performed, and a relationship mining network is obtained according to the relationship mining results and solution identifiers.

4. The method for intelligently detecting the electrical properties of a cable according to claim 3, characterized in that: The method further comprises: According to the historical cable electrical performance test data, obtain the historical cable appearance test data and historical mechanical performance test data; In combination with the historical cable appearance inspection data and the historical mechanical property inspection data, relationship mining of multiple groups of historical standard inspection data and multiple groups of historical extreme inspection data is performed, and a relationship mining network is obtained according to the relationship mining results and solution identifiers.

5. The method for intelligently detecting the electrical properties of a cable according to claim 1, characterized in that: Get the full set of standard test data, including: Determine the detection parameter configuration of the cable standard detection platform according to the standard detection scheme, and adjust the detection unit settings of the cable standard detection platform based on the detection parameter configuration; Arrange multiple target cables to be tested to form a cable testing queue, and test multiple target cables one by one using a cable standard testing platform according to the cable testing queue, and output corresponding standard testing data after each target cable is tested; Summarize the standard inspection data of each target cable and construct a full standard inspection data set.

6. The method for intelligently detecting the electrical properties of a cable according to claim 1, characterized in that: Combine the scheme associations to build a full extreme detection dataset, including: Extracting a full set of standard test data, and generating a simulated full set of standard test data based on the scheme association relationship; The simulated full standard detection data set is optimized and corrected according to the selected extreme detection data set to obtain the full extreme detection data set.

7. The method for intelligently detecting the electrical properties of a cable according to claim 1, characterized in that: Obtain comprehensive electrical performance test results of batch target cables, including: Evaluate the full set of standard test data according to the target electrical performance constraint to obtain a first electrical performance test result; Evaluate the entire extreme detection data set according to the target electrical performance constraint to obtain a second electrical performance detection result; The first electrical performance test result and the second electrical performance test result are weightedly fused to obtain a comprehensive electrical performance test result.

8. The method for intelligently detecting the electrical properties of a cable according to claim 1, characterized in that: The method further comprises: Establishing actual electrical performance constraints of batch target cables according to the comprehensive electrical performance test results; Based on the actual electrical performance constraints, a work risk warning for the batch of target cables is configured, and the target cables are operated and maintained according to the work risk warning.

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