Power distribution gateway test method and system based on deep learning, medium and terminal

Abstract

The invention discloses a power distribution gateway test method and system based on deep learning, a medium and a terminal, and the method comprises the steps: collecting data generated by a tested power distribution gateway in a test process when the tested power distribution gateway is tested through an operation and maintenance debugging tool, and enabling the data to serve as test data; performing clustering analysis on all the test data, and classifying the test data according to a clustering analysis result to form a test sample database of a plurality of manufacturers; evaluating a local environment observation state of the operation and maintenance debugging tool after executing each test action based on the test sample databases of the plurality of manufacturers by adopting an MAAC algorithm; and adopting an MAAC algorithm, analyzing a test execution strategy of the operation and maintenance debugging tool based on the evaluation result, and testing the tested power distribution gateway according to the test execution strategy. According to the method, the MAAC algorithm is adopted, deep mining is carried out on test sample databases of multiple manufacturers, and test execution strategies suitable for power distribution gateways of different manufacturers and models are generated.

Description

Technical Field

[0001] The present invention relates to the field of equipment testing, and in particular to a power distribution gateway testing method, system, medium and terminal based on deep learning. Background Art

[0002] In recent years, information technology, big data technology and artificial intelligence technology have developed rapidly, and human society has gradually entered an intelligent era marked by the interconnection of all things. As a key technology for realizing the interconnection of all things, the core of the Internet of Things is to build an interconnection system of all things through sensor equipment, communication technology and Internet technology, and realize the intelligent panoramic interaction of people, machines and things. The use of Internet of Things technology is conducive to the revolutionary upgrading of the industry and has been widely used in various industries. As far as the power industry is concerned, the Internet of Things built by using cutting-edge technologies such as "big cloud, things, mobile, intelligence and chain" has realized the transformation of traditional power grids to digital power grids, promoted the deep integration of power grid physical information systems, and was conducive to the interconnection of "source, grid, load and storage" equipment.

[0003] With the advancement of the construction of the power distribution Internet of Things, a large number of power distribution gateways are connected to the Internet of Things platform. However, the current on-site operation and maintenance testing work is still mainly based on single-unit testing, without networking, the on-site testing instruments have single functions, and the on-site work is heavily dependent on the work experience of personnel. The quality of work varies greatly, fault diagnosis depends on the technical support of suppliers, and diagnostic testing requires rich experience to deal with. The efficiency of fault diagnosis is low, and it is difficult to improve quality and efficiency, which seriously restricts the development of digitalization and intelligence of distribution networks. Summary of the invention

[0004] The embodiments of the present invention provide a distribution gateway testing method, system, medium and terminal based on deep learning, which adopts a deep learning algorithm to deeply mine the test sample database of multiple manufacturers containing observation data, and then generate a test execution strategy suitable for distribution gateways of different manufacturers and models to expand the applicable scenarios of the test execution strategy.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a power distribution gateway testing method based on deep learning, comprising:

[0006] When using the operation and maintenance debugging tool to test the tested power distribution gateway, collecting data generated by the tested power distribution gateway during the test process as test data;

[0007] Performing cluster analysis on all the test data, and classifying each of the test data according to the cluster analysis result to form a test sample database of multiple manufacturers;

[0008] Using the MAAC algorithm, based on the test sample databases of the multiple manufacturers, the local environment observation state of the operation and maintenance debugging tool after executing each test action is evaluated;

[0009] The MAAC algorithm is adopted to analyze the test execution strategy of the operation and maintenance debugging tool based on the evaluation results of the local environment observation status after the operation and maintenance debugging tool executes each test action, and the tested power distribution gateway is tested according to the test execution strategy of the operation and maintenance debugging tool.

[0010] In implementing the embodiment of the present invention, when using the operation and maintenance debugging tool to test the tested power distribution gateway, the data generated by the tested power distribution gateway during the test process is collected as test data, and then all the test data are clustered and analyzed. According to the cluster analysis results, each test data is classified and processed, and data belonging to different manufacturers are intelligently identified to form a test sample database of multiple manufacturers, so as to provide sample data support for the deep learning of the operation and maintenance debugging tool, solve the long-standing problem that test data cannot be effectively mined and extracted, effectively meet the pertinence of field test cases brought by massive samples and the accuracy of diagnostic results, and facilitate the subsequent summary of common problems of different manufacturers. Then, the MAAC algorithm, a deep learning algorithm, is used to evaluate the local environmental observation state after the operation and maintenance debugging tool executes each test action based on the test sample database of multiple manufacturers. ,Then, the MAAC algorithm is used to analyze the test execution strategy of the operation and maintenance debugging tool based on the evaluation results of the local environment observation state after the operation and maintenance debugging tool executes each test action, and the distribution gateway under test is tested in accordance with the test execution strategy of the operation and maintenance debugging tool, thereby forming a targeted test plan for manufacturers and common problems. Through the MAAC algorithm, the test sample databases of multiple manufacturers can be deeply mined, and then the local environment observation state after the operation and maintenance debugging tool executes each test action can be evaluated, and the test execution strategy of the operation and maintenance debugging tool can be obtained based on the evaluation result analysis, so that the test execution strategy can adapt to the distribution gateways of different manufacturers and models, and then adapt to different test requirements and environmental changes, provide users with a wider range of test coverage, and reduce unnecessary repeated tests, and improve test efficiency.

[0011] As a preferred solution, the MAAC algorithm is used to analyze the test execution strategy of the operation and maintenance debugging tool based on the evaluation results of the local environment observation state after the operation and maintenance debugging tool executes each test action, and the tested power distribution gateway is tested according to the test execution strategy of the operation and maintenance debugging tool, specifically:

[0012] The evaluation results of the local environment observation state after the operation and maintenance debugging tool executes each test action are input into the intelligent agent action network, so that the intelligent agent action network outputs the corresponding test instructions; wherein the intelligent agent action network is obtained by updating the network parameters of the initial action network using the gradient ascent method; the initial action network is pre-built using the MAAC algorithm;

[0013] Sending the test instruction to the operation and maintenance debugging tool through the manager;

[0014] When the operation and maintenance debugging tool receives the test instruction, the operation and maintenance debugging tool obtains the test execution strategy of the operation and maintenance debugging tool based on the test instruction, and tests the tested power distribution gateway according to the test execution strategy of the operation and maintenance debugging tool.

[0015] The preferred solution of the embodiment of the present invention is to input the local environment observation status evaluation result of the operation and maintenance debugging tool into the intelligent agent action network so that the intelligent agent action network outputs the corresponding test instructions, and the intelligently generated test instructions are sent to the operation and maintenance debugging tool through the manager so that the operation and maintenance debugging tool performs the test tasks based on the test instructions. This automated process reduces the intermediate links and improves the test efficiency and accuracy. In addition, the initial action network is pre-built based on the MAAC algorithm, and then the network parameters of the initial action network are updated using the gradient ascent method to obtain the intelligent agent action network, so that the intelligent agent action network can learn from historical test data and optimize the network parameters, thereby continuously improving the performance of the intelligent agent action network, so as to further optimize the test execution strategy and improve the test effect.

[0016] As a preferred solution, the MAAC algorithm is used to evaluate the local environment observation state of the operation and maintenance debugging tool after executing each test action based on the test sample database of the multiple manufacturers, specifically:

[0017] Inputting the data contained in the test sample databases of the multiple manufacturers into the agent evaluation network, so that the agent evaluation network outputs an estimated value of the state-action evaluation function corresponding to each test action as an evaluation result of the local environment observation state after the operation and maintenance debugging tool executes each test action;

[0018] Among them, the test sample database of multiple manufacturers contains several test actions of multiple manufacturers and the local environment observation state after executing each test action; the intelligent agent evaluation network is obtained by iteratively updating the parameters of the initial evaluation network with the goal of minimizing the loss function; the initial evaluation network is pre-built using the MAAC algorithm.

[0019] A preferred solution for implementing the embodiment of the present invention is to pre-build an initial evaluation network based on the MAAC algorithm, so that the rating network has adaptability, so as to cope with the testing requirements and environmental changes of different manufacturers, and then handle complex testing scenarios and changing testing environments. Then, with the goal of minimizing the loss function, the parameters of the initial evaluation network are continuously iteratively updated to obtain the intelligent agent evaluation network. This data-driven optimization method can learn from a large amount of historical test data and continuously improve the accuracy and reliability of the evaluation.

[0020] As a preferred solution, cluster analysis is performed on all the test data, and according to the cluster analysis result, each test data is classified to form a test sample database of multiple manufacturers, specifically:

[0021] Classifying all the test data into manufacturer data according to the pre-input multiple manufacturer names to build an initial database of multiple manufacturers;

[0022] Using the K-means algorithm, cluster analysis is performed according to the distance between each two test data, and according to the cluster analysis results, each of the test data is classified into a corresponding initial database;

[0023] After all the test data are classified, the current initial database of each manufacturer is used as the test sample database of each manufacturer.

[0024] A preferred solution for implementing the embodiment of the present invention is to perform preliminary manufacturer data classification on all test data based on multiple pre-input manufacturer names, and then use the K-means algorithm to perform cluster analysis based on the distance between each two test data. This allows classification based on the intrinsic characteristics of the data to ensure the accuracy and efficiency of data classification. Based on the clustering analysis results of the K-means algorithm, each test data is classified into a corresponding initial database, thereby constructing a test sample database for multiple manufacturers, which provides a structured and standardized data foundation for subsequent testing and analysis.

[0025] As a preferred solution, when the tested power distribution gateway is tested using the operation and maintenance debugging tool, data generated by the tested power distribution gateway during the test process is collected as test data, specifically:

[0026] When using the operation and maintenance debugging tool to execute the current test item to test the tested power distribution gateway, it is determined in real time whether the current test item is the last test item;

[0027] If so, fill the test related information into the preset test report template, generate the corresponding test report and give the corresponding test conclusion; wherein the test related information includes the test data and standard data corresponding to each test item, the absolute error between the test data and the standard data, and the preset threshold value corresponding to the absolute error;

[0028] If not, the index moves to the next test item, so that the operation and maintenance debugging tool continues to execute the next test item, tests the tested power distribution gateway and collects data generated by the tested power distribution gateway during the test process.

[0029] The preferred solution of the embodiment of the present invention is to use the operation and maintenance debugging tool to execute the current test project to test the tested power distribution gateway, and determine in real time whether the current test project is the last test item. If the current test project is the last test item, the test-related information is filled into the preset test report template in real time to generate the corresponding test report, thereby improving the speed and accuracy of report generation. The standardized test process and report generation method ensure the consistency and repeatability of each test, which helps to perform the same test at different time points and in different environments, and obtain results in a consistent format for comparison. In addition, the test-related information includes the test data, standard data, absolute error and preset threshold corresponding to each test item. The detailed records ensure the comprehensiveness and traceability of the test results, which helps to facilitate subsequent analysis and optimization.

[0030] As a preferred solution, when the tested power distribution gateway is tested using the operation and maintenance debugging tool, data generated by the tested power distribution gateway during the test process is collected as test data, specifically:

[0031] When the operation and maintenance debugging tool is used to execute the current test project to test the tested power distribution gateway, data generated by the tested power distribution gateway during the test process is collected in real time as test data corresponding to the current test project;

[0032] Calculate the absolute error between the test data corresponding to the current test item and the standard data corresponding to the current test item, and determine whether the calculated absolute error is greater than a preset threshold;

[0033] If yes, the test conclusion of the current test item is determined to be unqualified;

[0034] If not, the test conclusion of the current test item is determined to be qualified.

[0035] A preferred scheme for implementing the embodiment of the present invention is to calculate the absolute error between the test data corresponding to the current test item and the standard data corresponding to the current test item, and determine whether the calculated absolute error is greater than a preset threshold. If so, the test conclusion of the current test item is determined to be unqualified; otherwise, the test conclusion of the current test item is determined to be qualified. Through the above-mentioned real-time data collection and error judgment means, the test results can be fed back instantly to help operators identify unqualified test items in time, which helps to quickly locate and solve problems and reduce troubleshooting time.

[0036] In order to solve the same technical problem, an embodiment of the present invention further provides a power distribution gateway testing system based on deep learning, comprising:

[0037] A data acquisition module is used to collect data generated by the tested power distribution gateway during the test process as test data when the tested power distribution gateway is tested using the operation and maintenance debugging tool;

[0038] A cluster analysis module, used to perform cluster analysis on all the test data, and classify each test data according to the cluster analysis result to form a test sample database of multiple manufacturers;

[0039] A state evaluation module, used to evaluate the local environment observation state after the operation and maintenance debugging tool executes each test action based on the test sample database of the multiple manufacturers by using the MAAC algorithm;

[0040] The strategy analysis module is used to adopt the MAAC algorithm to analyze the test execution strategy of the operation and maintenance debugging tool based on the evaluation results of the local environment observation state after the operation and maintenance debugging tool executes each test action, and test the tested power distribution gateway according to the test execution strategy of the operation and maintenance debugging tool.

[0041] As a preferred solution, the strategy analysis module specifically includes:

[0042] The instruction generation unit is used to input the evaluation results of the local environment observation state after the operation and maintenance debugging tool executes each test action into the intelligent agent action network, so that the intelligent agent action network outputs the corresponding test instructions; wherein the intelligent agent action network is obtained by updating the network parameters of the initial action network using the gradient ascent method; the initial action network is pre-built using the MAAC algorithm;

[0043] An instruction sending unit, used for sending the test instruction to the operation and maintenance debugging tool through the manager;

[0044] An instruction response unit is used to obtain the test execution strategy of the operation and maintenance debugging tool based on the test instruction when the operation and maintenance debugging tool receives the test instruction, and test the tested power distribution gateway according to the test execution strategy of the operation and maintenance debugging tool.

[0045] In order to solve the same technical problem, the present invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution gateway testing method based on deep learning.

[0046] In order to solve the same technical problem, the present invention also provides a terminal, including a processor, a memory and a computer program stored in the memory; wherein the computer program can be executed by the processor to implement the power distribution gateway testing method based on deep learning. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 : A flowchart of a power distribution gateway testing method based on deep learning provided in Example 1 of the present invention;

[0048] Figure 2 : A structural diagram of a power distribution gateway testing system based on deep learning provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Embodiment one:

[0051] Please refer to Figure 1 , a power distribution gateway testing method based on deep learning is provided in an embodiment of the present invention, the method includes steps S1 to S4, and each step is specifically as follows:

[0052] Step S1, when using the operation and maintenance debugging tool to test the tested power distribution gateway, collect the data generated by the tested power distribution gateway during the test process as test data.

[0053] In this embodiment, in order to implement the test of the power distribution gateway under test, the operation and maintenance debugging tool is first used to edit the test case and start the test so that the test case data is sent to the tester driver module through the protocol. Then, the tester driver module controls the hardware module to output the test data to the corresponding terminal device according to the parsed test case data. The terminal device collects the received test data and sends the data to the power distribution gateway under test through RS485, IEC104, MQTT and other protocols. Then, the power distribution gateway under test sends the test data to the operation and maintenance debugging tool through the MQTT protocol. The operation and maintenance debugging tool compares and analyzes it with the standard data, automatically forms an electronic test report, and finally stores the electronic report in the test report library for access anytime and anywhere. After the test is started, according to the pre-edited test case, the test software calls and executes the test script to start executing the first test item. After the execution of this test item is completed, the test start index moves to the next item to be tested. When the test item switches from the start state to the pause state, the test device stops executing the test case and saves the current test section data. After clicking Start Execution, the test case can continue to execute, thereby realizing automatic control of the test process. Among them, the test case is the test execution strategy.

[0054] It should be noted that the operation and maintenance debugging tool includes a device cluster and a computing engine. Among them, the device cluster can perform data export, device control, and device management, and the computing engine can perform test case editing, test case execution, data collection and analysis, test report analysis, test log management, and deep learning management. Specifically, data export is to export and view the data saved in the test process; device control is to control the test process of this device; device management is to manage the IP address, port number and other information of the tested power distribution gateway; test case editing is to write the operation and maintenance debugging case of the power distribution gateway; test case execution is to control the output test volume of the hardware device according to the written test case; data collection and analysis is to analyze whether the data collected by the tested device is qualified or not, and at the same time, the test process data is stored in the database for deep learning analysis; test report management is to automatically fill in the test report according to the data collection and analysis results, and save it as a word or PDF electronic version; test log management is to record the test process; deep learning management can mine and classify test data, establish a test sample database, and establish an intelligent test strategy based on deep learning, intelligently identify data from different manufacturers, summarize common problems of different manufacturers, and automatically generate targeted test cases.

[0055] As a preferred solution, step S1 may include steps S11 to S13, each of which is specifically as follows:

[0056] Step S11, when using the operation and maintenance debugging tool to execute the current test item to test the tested power distribution gateway, it is determined in real time whether the current test item is the last test item; if so, step S12 is executed; if not, step S13 is executed.

[0057] Step S12, fill the test related information into the preset test report template, generate the corresponding test report and give the corresponding test conclusion.

[0058] The test-related information includes test data corresponding to each test item, standard data, an absolute error between the test data and the standard data, and a preset threshold value corresponding to the absolute error.

[0059] In this embodiment, the test data, standard data, error, set threshold and other information are filled into the preset test report template to generate a test report in the format specified by the user, and give the test conclusion. The test report supports word and PDF formats; the test conclusion includes whether each test item is qualified, and the reasons for the unqualified test items.

[0060] As an example, when telemetry fails, the test data, standard data and error size are given, and the reasons for the failure of the test are judged to include one or more of the following: wrong time point number (misalignment), wrong phase sequence connection (one phase value is very large, and the other phase value is zero) and inaccurate accuracy (greater than the error range), etc. As another example, when telesignaling and remote control fail, the reasons for the failure of the test can be judged to include one or more of the following: wrong point number (misalignment), control pulse interval (no command received) and command format error (single or double point position), etc.

[0061] Step S13, the index moves to the next test item, so that the operation and maintenance debugging tool continues to execute the next test item, tests the tested power distribution gateway and collects data generated by the tested power distribution gateway during the test process.

[0062] As a preferred solution, step S1 may further include steps S14 to S17, each of which is specifically as follows:

[0063] Step S14, when the operation and maintenance debugging tool is used to execute the current test project to test the power distribution gateway under test, data generated by the power distribution gateway under test during the test process is collected in real time as test data corresponding to the current test project.

[0064] Step S15, calculate the absolute error between the test data corresponding to the current test item and the standard data corresponding to the current test item, and determine whether the calculated absolute error is greater than a preset threshold; if so, execute step S16; if not, execute step S17.

[0065] Step S16, determining that the test conclusion of the current test item is unqualified.

[0066] Step S17, determining that the test conclusion of the current test item is qualified.

[0067] Step S2, performing cluster analysis on all test data, and classifying each test data according to the cluster analysis result to form a test sample database of multiple manufacturers.

[0068] As a preferred solution, step S2 includes step S21 to step S23, and each step is specifically as follows:

[0069] Step S21, classifying all test data by manufacturer data according to the pre-input multiple manufacturer names to construct an initial database of multiple manufacturers.

[0070] Step S22, using the K-means algorithm, performs cluster analysis according to the distance between every two test data, and classifies each test data into a corresponding initial database according to the cluster analysis result.

[0071] It should be noted that the core idea of ​​the K-means algorithm is to select k data from n data sets as the original cluster center points, and combine the distance between the remaining nk data and these k data to determine which category it should belong to; then obtain the average value of the data in each category, redefine the cluster center, and repeat the above steps until the objective function converges. Based on this, the operation and maintenance debugging tool can realize the classification management of equipment data from different manufacturers, establish a sample library and test library based on cluster learning analysis, and use the protection trip control bit, zero-sequence fault detection control bit, automatic reclosing simulation control bit, secondary reclosing simulation control bit, protection trip time (milliseconds), zero-sequence protection trip time (milliseconds), automatic reclosing time (milliseconds), secondary reclosing time (milliseconds), post-acceleration time (milliseconds), one-stage overcurrent setting (A), one-stage zero-sequence overcurrent setting (A), normal voltage value (V), and residual voltage value (V) as reference data to establish a fault test data sample library for each manufacturer's open circuit fault, short circuit fault, ground fault, polarity fault, etc.

[0072] In this embodiment, the K-means algorithm is used, the database of the nth manufacturer is used as the nth data set, and k data are selected from the n data sets as the initial clustering centers, and cluster analysis is performed on the databases of different manufacturers to further improve the databases of different manufacturers, so as to maximize the distance between different databases (classes) and ensure that the distance within the database (class) is minimized. Specifically, this step includes steps S221 to S224, and each step is as follows:

[0073] Step S221, randomly select k data from n data sets as initial cluster centers μ1, μ2, ..., μ k ∈R n .

[0074] Step S222, obtain the distance between the remaining data and the k data, and refer to formula (1) to clarify each remaining data x i Category C i .

[0075]

[0076] In the formula, x j Represents any one of the k data extracted.

[0077] Step S223: for all categories j, refer to formula (2) and reselect the center μ of each category j .

[0078]

[0079] Step S224: If the standard measurement function converges, that is, the optimal solution of the sum of squared errors within the cluster is reached (making the K-means algorithm evaluation function fitness (A[1], A[2], ..., A[n]) have the minimum value), the program stops, otherwise it returns to step S222. The calculation formula of the K-means algorithm evaluation function is shown in formula (3) (4).

[0080]

[0081] In the formula, Dist(x i , C k ) represents x i data and the center point C k The distance between. First determine the original value C k , and then calculate x i The value of x i The value of C k Optimize and finally obtain the best cluster center. Reduce the data center distance to achieve better convergence performance.

[0082]

[0083] In addition, the K-means algorithm evaluation function is improved by adding a weight factor W k , after weighted processing, calculate which sample category the sample is closest to. Among them, the weight factor W k For the expression of , please refer to formula (6).

[0084]

[0085] In the formula, ε k Represents standard deviation.

[0086] Assume the target is C k When k' is used, the function increment after k' is expressed as Δε k Among them, Δε k For the expression of , please refer to formula (7).

[0087]

[0088] It should be noted that introducing the above algorithm into the system can realize the classification management of data, establish a sample library and test library based on cluster learning analysis, and use the protection tripping control bit, zero-sequence fault detection control bit, automatic reclosing simulation control bit, secondary reclosing simulation control bit, protection tripping time (milliseconds), zero-sequence protection tripping time (milliseconds), automatic reclosing time (milliseconds), secondary reclosing time (milliseconds), post-acceleration time (milliseconds), one-stage overcurrent setting (A), one-stage zero-sequence overcurrent setting (A), normal voltage value (V), and residual voltage value (V) as reference data to establish a sample library of fault test data such as circuit breaker faults, short circuit faults, ground faults, polarity faults, etc. from various manufacturers.

[0089] Step S23, after completing the classification of all test data, the current initial database of each manufacturer is used as the test sample database of each manufacturer.

[0090] In this embodiment, after establishing the classification sample libraries of different manufacturers, during on-site operation and maintenance or retesting of the same manufacturer, targeted testing can be performed based on common errors of different manufacturers to reduce dependence on the manufacturer.

[0091] Step S3, using the MAAC algorithm, based on the test sample databases of multiple manufacturers, evaluates the local environment observation status after the operation and maintenance debugging tool executes each test action.

[0092] As a preferred solution, step S3 includes step S31, which is specifically as follows:

[0093] Step S31, input the data contained in the test sample databases of multiple manufacturers into the intelligent agent evaluation network, so that the intelligent agent evaluation network outputs the estimated value of the state-action evaluation function corresponding to each test action as the evaluation result of the local environment observation state after the operation and maintenance debugging tool executes each test action.

[0094] Among them, the test sample database of multiple manufacturers contains several test actions of multiple manufacturers and the observation status of the local environment after executing each test action; the intelligent agent evaluation network is obtained by iteratively updating the parameters of the initial evaluation network with the goal of minimizing the loss function; the initial evaluation network is pre-built using the MAAC algorithm.

[0095] It should be noted that the estimated value of the state-action evaluation function corresponding to the test action is used to measure the pros and cons of the strategy of the intelligent agent to execute the test action, and can evaluate the common faults of the distribution gateway and the equipment of different manufacturers. Among them, the manager of the intelligent operation and maintenance debugging tool, the test system of the Huangjingshi operation and maintenance debugging tool, the environment changes state over time, the intelligent agent observes these states, and tests common faults and distribution gateways of different manufacturers in real time according to the new observation information, so as to achieve targeted testing.

[0096] In this embodiment, the input of the agent evaluation network is the local environment observation state o and the tester test action a, and its output is the state-action evaluation function that measures the quality of the agent's corresponding action strategy. The estimated value of the network parameter θ is the target network parameter θ. The evaluation network of the agent includes the main evaluation network and the target evaluation network. The role of the target evaluation network is to improve the stability and convergence of the evaluation network. At the same time, in order to encourage the agent to actively explore the unknown space and avoid premature convergence to non-optimal strategies, the evaluation network also adds a policy entropy term, that is,

[0097] Specifically, the expression of the loss function is shown in formula (8). The loss function is used to quantify the value of network evaluation. and the expected cumulative reward y m The difference between them. Where, referring to formula (9), the expected cumulative reward y m It can be calculated by combining the Bellman equation with the policy entropy term. The evaluation network is iteratively updated by minimizing the loss function L(θ), and then referring to formula (10), the evaluation network parameter θ is iteratively updated by minimizing the loss function to obtain an evaluation network that can more accurately evaluate the test strategy of the tester. Specifically, in the centralized training stage, the rough quality of the current test of each tester is measured according to the observed state and the test items of the tester, and the accuracy and stability of the test are continuously improved.

[0098]

[0099] Where M represents the total number of agents in the entire system; E[·] represents the expected value; and D represents the experience replay buffer, which is used to store the experience data generated by the interaction between the agent and the environment. r represents the reward value, Represents the newly observed state after executing the action; represents the target action network parameter of agent m; β represents the regularization system, which is used to control the importance of entropy; η Q To evaluate the learning rate of the network.

[0100] Step S4, using the MAAC algorithm, based on the evaluation results of the local environment observation status after the operation and maintenance debugging tool executes each test action, analyze the test execution strategy of the operation and maintenance debugging tool, and test the distribution gateway under test according to the test execution strategy of the operation and maintenance debugging tool.

[0101] As a preferred solution, step S4 includes step S41 to step S43, and each step is specifically as follows:

[0102] Step S41, inputting the evaluation results of the local environment observation state after the operation and maintenance debugging tool executes each test action into the intelligent agent action network, so that the intelligent agent action network outputs the corresponding test instruction.

[0103] Among them, the intelligent agent action network is obtained by updating the network parameters of the initial action network using the gradient ascent method; the initial action network is pre-built using the MAAC algorithm.

[0104] It should be noted that the input of the action network of agent m is the local observation state o m , whose output is the tester test instruction a m , the parameters to be optimized are the action network parameters and target action network parameters Similar to the agent evaluation network, the agent action network also introduces the target network and policy entropy terms.

[0105] In this embodiment, the MAAC algorithm uses the policy gradient method to train the action network. See formula (13). The agent updates its own action network parameters based on the gradient ascent method. A better test execution strategy for the operation and maintenance debugging tool manager is obtained. Specifically, in the centralized training phase, the operation and maintenance debugging tool manager continuously updates the test execution strategy of the operation and maintenance debugging tool according to the current environment observation status and the test items of the operation and maintenance debugging tool. The policy gradient expression can be found in equations (11) and (12).

[0106]

[0107] In the formula, Indicates the selection of test execution strategy Reward expectations; is a scoring function, which represents the gradient of the logarithm of the strategy probability to the parameter, and is used to quantify the impact of changes in network parameters on the strategy; It is expressed as the advantage function of agent m, which is used to measure the current test instruction a m The value of is better than the average level, thus guiding to find a better action; b(o,a M\m ) represents the expected Q-values ​​of all the actions of agents except agent m; is the policy entropy term; λ is the regularization coefficient; η μ is the learning rate of the action network.

[0108] After updating the agent evaluation network and the agent action network, the parameters of the target evaluation network and the target action network of the operation and maintenance debugging tool are also updated accordingly. For details, please refer to formulas (14) and (15).

[0109]

[0110] In the formula, represents the parameter of the target evaluation network; τ<<1 represents the soft update coefficient; Represents the parameters of the target action network. Repeat the above network parameter update process until the network parameters θ of the agent evaluation network and the network parameters of the agent action network are To achieve the best.

[0111] Step S42: Send the test instruction to the operation and maintenance debugging tool through the manager.

[0112] In this embodiment, the intelligent agent action network is used to obtain the test execution strategy of the operation and maintenance debugging tool. In the training stage, the operation and maintenance debugging tool uses the current environmental observation status and the test items of the tester to train the test execution strategy of the operation and maintenance debugging tool. In the execution stage, the manager of the operation and maintenance debugging tool issues test instructions to each numerical distribution network test based on the output results of the action network, realizing the common faults of the distribution gateway and the targeted testing of equipment from different manufacturers.

[0113] Step S43, when the operation and maintenance debugging tool receives the test instruction, the operation and maintenance debugging tool obtains the test execution strategy of the operation and maintenance debugging tool based on the test instruction, and tests the tested power distribution gateway according to the test execution strategy of the operation and maintenance debugging tool.

[0114] In this embodiment, the data of different manufacturers are classified according to the test data, and then the common errors of different manufacturers can be mined and analyzed based on the MAAC algorithm, a deep learning algorithm, to generate targeted test plans for manufacturers and common faults. According to the targeted test plans for manufacturers and common faults, the current manufacturer is automatically tested, so that when similar errors are encountered in on-site operation and maintenance tests, the problem can be handled without relying on the equipment manufacturer, and the next time the same manufacturer sends the same equipment for inspection, a targeted test plan for the common errors of the manufacturer can be automatically generated according to the previous data mining results for automatic testing, thereby improving the test directionality and efficiency.

[0115] Please refer to Figure 2 , is a schematic diagram of the structure of a power distribution gateway test system based on deep learning provided by an embodiment of the present invention, the system includes a data acquisition module M1, a cluster analysis module M2, a state evaluation module M3 and a strategy analysis module M4, and the specific modules are as follows:

[0116] The data acquisition module M1 is used to collect data generated by the tested power distribution gateway during the test process as test data when the tested power distribution gateway is tested using the operation and maintenance debugging tool;

[0117] The cluster analysis module M2 is used to perform cluster analysis on all test data and classify each test data according to the cluster analysis results to form a test sample database of multiple manufacturers;

[0118] The state evaluation module M3 is used to evaluate the local environment observation state after the operation and maintenance debugging tool executes each test action based on the test sample database of multiple manufacturers using the MAAC algorithm;

[0119] The strategy analysis module M4 is used to adopt the MAAC algorithm to analyze the test execution strategy of the operation and maintenance debugging tool based on the evaluation results of the local environment observation status after the operation and maintenance debugging tool executes each test action, and test the distribution gateway under test according to the test execution strategy of the operation and maintenance debugging tool.

[0120] As a preferred solution, the strategy analysis module M4 specifically includes an instruction generation unit 41, an instruction sending unit 42 and an instruction response unit 43, and the details of each unit are as follows:

[0121] The instruction generation unit 41 is used to input the evaluation results of the local environment observation state after the operation and maintenance debugging tool executes each test action into the intelligent agent action network, so that the intelligent agent action network outputs the corresponding test instruction; wherein the intelligent agent action network is obtained by updating the network parameters of the initial action network using the gradient ascent method; the initial action network is pre-built using the MAAC algorithm;

[0122] The instruction sending unit 42 is used to send the test instruction to the operation and maintenance debugging tool through the manager;

[0123] The instruction response unit 43 is used to obtain the test execution strategy of the operation and maintenance debugging tool based on the test instruction when the operation and maintenance debugging tool receives the test instruction, and test the tested power distribution gateway according to the test execution strategy of the operation and maintenance debugging tool.

[0124] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0125] An embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a distribution gateway testing method based on deep learning as described in Example 1.

[0126] An embodiment of the present invention also provides a terminal, including a processor, a memory, and a computer program stored in the memory; wherein the computer program can be executed by the processor to implement a power distribution gateway testing method based on deep learning as described in Example 1.

[0127] Preferably, the computer program can be divided into one or more modules / units (such as computer program, computer program), one or more modules / units are stored in the memory and executed by the processor to complete the present invention. One or more modules / units can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal.

[0128] The processor can be a central processing unit (CPU), or 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. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor. The processor is the control center of the terminal, and various parts of the terminal are connected using various interfaces and lines.

[0129] The memory mainly includes a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function, etc., and the data storage area can store related data, etc. In addition, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, and a flash card (Flash Card), etc., or the memory can also be other volatile solid-state storage devices.

[0130] It should be noted that the above-mentioned terminal may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that the above-mentioned terminal is merely an example and does not constitute a limitation on the terminal. It may include more or fewer components, or a combination of certain components, or different components.

[0131] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0132] The present invention provides a power distribution gateway testing method, system, medium and terminal based on deep learning. When using an operation and maintenance debugging tool to test the tested power distribution gateway, data generated by the tested power distribution gateway during the test process is collected as test data, and then all test data are clustered and analyzed. According to the cluster analysis results, each test data is classified and processed, and data belonging to different manufacturers are intelligently identified to form a test sample database of multiple manufacturers, so as to provide sample data support for the deep learning of the operation and maintenance debugging tool, solve the problem that test data cannot be effectively mined and extracted for a long time, effectively meet the pertinence of field test cases brought by massive samples and the accuracy of diagnostic results, and facilitate the subsequent summary of common problems of different manufacturers. Then, the MAAC algorithm, a deep learning algorithm, is used to evaluate the operation and maintenance debugging tool to perform various test actions based on the test sample database of multiple manufacturers. The local environment observation state after the operation and maintenance debugging tool executes each test action is then evaluated, and the MAAC algorithm is then used to analyze the test execution strategy of the operation and maintenance debugging tool. In accordance with the test execution strategy of the operation and maintenance debugging tool, the tested power distribution gateway is tested, thereby forming a targeted test plan for manufacturers and common problems. Through the MAAC algorithm, the test sample databases of multiple manufacturers can be deeply mined, and then the local environment observation state after the operation and maintenance debugging tool executes each test action is evaluated. The test execution strategy of the operation and maintenance debugging tool is obtained based on the evaluation result analysis, so that the test execution strategy can adapt to the power distribution gateways of different manufacturers and models, and then adapt to different test requirements and environmental changes, provide users with a wider range of test coverage, reduce unnecessary repeated testing, and improve test efficiency.

[0133] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A power distribution gateway testing method based on deep learning, characterized in that: include: When using the operation and maintenance debugging tool to test the tested power distribution gateway, collecting data generated by the tested power distribution gateway during the testing process as test data; Performing cluster analysis on all the test data, and classifying each of the test data according to the cluster analysis result to form a test sample database of multiple manufacturers; Using the MAAC algorithm, based on the test sample databases of the multiple manufacturers, the local environment observation state of the operation and maintenance debugging tool after executing each test action is evaluated; The MAAC algorithm is adopted to analyze the test execution strategy of the operation and maintenance debugging tool based on the evaluation results of the local environment observation status after the operation and maintenance debugging tool executes each test action, and the tested power distribution gateway is tested according to the test execution strategy of the operation and maintenance debugging tool.

2. A power distribution gateway testing method based on deep learning as claimed in claim 1, characterized in that: The MAAC algorithm is adopted to analyze the test execution strategy of the operation and maintenance debugging tool based on the evaluation results of the local environment observation state after the operation and maintenance debugging tool executes each test action, and the tested power distribution gateway is tested according to the test execution strategy of the operation and maintenance debugging tool, specifically: The evaluation results of the local environment observation state after the operation and maintenance debugging tool executes each test action are input into the intelligent agent action network, so that the intelligent agent action network outputs the corresponding test instructions; wherein the intelligent agent action network is obtained by updating the network parameters of the initial action network using the gradient ascent method; the initial action network is pre-built using the MAAC algorithm; Sending the test instruction to the operation and maintenance debugging tool through the manager; When the operation and maintenance debugging tool receives the test instruction, the operation and maintenance debugging tool obtains the test execution strategy of the operation and maintenance debugging tool based on the test instruction, and tests the tested power distribution gateway according to the test execution strategy of the operation and maintenance debugging tool.

3. A power distribution gateway testing method based on deep learning as claimed in claim 1, characterized in that: The MAAC algorithm is used to evaluate the local environment observation state of the operation and maintenance debugging tool after executing each test action based on the test sample database of the multiple manufacturers, specifically: Inputting the data contained in the test sample databases of the multiple manufacturers into the agent evaluation network, so that the agent evaluation network outputs an estimated value of the state-action evaluation function corresponding to each test action as an evaluation result of the local environment observation state after the operation and maintenance debugging tool executes each test action; Among them, the test sample database of multiple manufacturers contains several test actions of multiple manufacturers and the local environment observation state after executing each test action; the intelligent agent evaluation network is obtained by iteratively updating the parameters of the initial evaluation network with the goal of minimizing the loss function; the initial evaluation network is pre-built using the MAAC algorithm.

4. A power distribution gateway testing method based on deep learning as claimed in claim 1, characterized in that: The cluster analysis is performed on all the test data, and the test data is classified according to the cluster analysis result to form a test sample database of multiple manufacturers, specifically: Classifying all the test data into manufacturer data according to the pre-input multiple manufacturer names to build an initial database of multiple manufacturers; Using the K-means algorithm, cluster analysis is performed according to the distance between each two test data, and according to the cluster analysis results, each of the test data is classified into a corresponding initial database; After all the test data are classified, the current initial database of each manufacturer is used as the test sample database of each manufacturer.

5. A power distribution gateway testing method based on deep learning as claimed in claim 1, characterized in that: When the tested power distribution gateway is tested using the operation and maintenance debugging tool, data generated by the tested power distribution gateway during the test process is collected as test data, specifically: When using the operation and maintenance debugging tool to execute the current test item to test the tested power distribution gateway, it is determined in real time whether the current test item is the last test item; If so, fill the test related information into the preset test report template, generate the corresponding test report and give the corresponding test conclusion; wherein the test related information includes the test data and standard data corresponding to each test item, the absolute error between the test data and the standard data, and the preset threshold value corresponding to the absolute error; If not, the index moves to the next test item, so that the operation and maintenance debugging tool continues to execute the next test item, tests the tested power distribution gateway and collects data generated by the tested power distribution gateway during the test process.

6. A power distribution gateway testing method based on deep learning as claimed in claim 1, characterized in that: When the tested power distribution gateway is tested using the operation and maintenance debugging tool, data generated by the tested power distribution gateway during the test process is collected as test data, specifically: When the operation and maintenance debugging tool is used to execute the current test project to test the tested power distribution gateway, data generated by the tested power distribution gateway during the test process is collected in real time as test data corresponding to the current test project; Calculate the absolute error between the test data corresponding to the current test item and the standard data corresponding to the current test item, and determine whether the calculated absolute error is greater than a preset threshold; If yes, the test conclusion of the current test item is determined to be unqualified; If not, the test conclusion of the current test item is determined to be qualified.

7. A power distribution gateway testing system based on deep learning, characterized in that: include: A data acquisition module is used to collect data generated by the tested power distribution gateway during the test process as test data when the tested power distribution gateway is tested using the operation and maintenance debugging tool; A cluster analysis module, used to perform cluster analysis on all the test data, and classify each test data according to the cluster analysis result to form a test sample database of multiple manufacturers; A state evaluation module, used to evaluate the local environment observation state after the operation and maintenance debugging tool executes each test action based on the test sample database of the multiple manufacturers by using the MAAC algorithm; The strategy analysis module is used to adopt the MAAC algorithm to analyze the test execution strategy of the operation and maintenance debugging tool based on the evaluation results of the local environment observation state after the operation and maintenance debugging tool executes each test action, and test the tested power distribution gateway according to the test execution strategy of the operation and maintenance debugging tool.

8. A power distribution gateway testing system based on deep learning as claimed in claim 7, characterized in that: The strategy analysis module specifically includes: The instruction generation unit is used to input the evaluation results of the local environment observation state after the operation and maintenance debugging tool executes each test action into the intelligent agent action network, so that the intelligent agent action network outputs the corresponding test instructions; wherein the intelligent agent action network is obtained by updating the network parameters of the initial action network using the gradient ascent method; the initial action network is pre-built using the MAAC algorithm; An instruction sending unit, used for sending the test instruction to the operation and maintenance debugging tool through the manager; An instruction response unit is used to obtain the test execution strategy of the operation and maintenance debugging tool based on the test instruction when the operation and maintenance debugging tool receives the test instruction, and test the tested power distribution gateway according to the test execution strategy of the operation and maintenance debugging tool.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a distribution gateway testing method based on deep learning as described in any one of claims 1 to 6.

10. A terminal, characterized in that: It comprises a processor, a memory and a computer program stored in the memory; wherein the computer program can be executed by the processor to implement a power distribution gateway testing method based on deep learning as described in any one of claims 1 to 6.

Images