Intelligent checking method and system for virtual loop of substation configuration file

Abstract

The invention discloses a substation configuration file virtual loop intelligent checking method and system. The method comprises the steps that a to-be-checked virtual loop and corresponding equipment data are determined according to a to-be-tested substation configuration file; according to the equipment data, obtaining an actual mapping relation between an auxiliary contact of each switch equipment in the virtual loop to be checked and an input terminal of each protection device; according to the equipment data and the actual mapping relation, the virtual connection state between the auxiliary contact of the switch equipment and the input terminal of the protection device is detected, and a virtual loop detection result of the to-be-detected transformer substation configuration file is obtained. According to the method, automatic detection of the virtual connection state between the auxiliary contact of the switch equipment and the input terminal of the protection device can be realized by utilizing the actual mapping relation and the equipment data, the conditions of wrong detection and missing detection are effectively avoided, the detection accuracy of the virtual loop of the substation configuration file is remarkably improved, and the detection efficiency is improved. And the device is suitable for different input terminal arrangement modes.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an intelligent verification method and system for virtual circuits of substation configuration files. Background Art

[0002] In the critical period when the substation automation system is moving towards intelligent depth and China is vigorously building a digital and intelligent strong power grid, digital technology has begun to be applied in the substation automation system. The virtual terminal circuit is a key component of the substation configuration file and also the core element for realizing the protection and control functions of the whole station. Its accuracy directly affects the safe and stable operation of the substation and even the entire power grid. Therefore, it is crucial to ensure the accuracy of the virtual terminal circuit.

[0003] The automatic verification technology for virtual terminal circuits has broad application prospects in business operations such as intelligent substation engineering design, construction commissioning, and operation and maintenance. However, currently, in the detection of virtual circuits in substation configuration files, a series of operation modes have been formed to ensure the correctness of virtual terminal circuits. For example, relying on the professional skills of business personnel to view and review, using visualization comparison tools to find version differences, conducting actual commissioning and test verification, etc. But these traditional methods have obvious defects. On the one hand, business personnel usually use the arrangement rules of input terminals summarized by operation experts to manually detect the virtual circuits in substation configuration files. However, the arrangement methods of input terminals of protection devices from different manufacturers are different, resulting in the arrangement rules of input terminals summarized based on experience being difficult to apply to all situations. And the high degree of manual participation also makes the work efficiency low, and it is difficult to comprehensively and accurately detect virtual circuit errors. On the other hand, although the relevant configuration technical standards for substation configuration files have been released, the integration and production links of substation configuration files of on-site intelligent substations are not standardized and errors occur frequently, making it difficult to conduct virtual circuit verification by finding version differences. In addition, the static manual verification technology for virtual circuits is difficult and the troubleshooting process is complex. Repeated on-site test verification not only consumes a large amount of time and resources but also cannot fundamentally guarantee the accuracy and efficiency of virtual circuit detection. It can be seen that there are many drawbacks in the existing method for detecting virtual circuits in substation configuration files based on manual detection. Summary of the Invention

[0004] The present invention provides an intelligent verification method and system for virtual circuits of substation configuration files to solve the technical problem that the existing technology cannot accurately and effectively detect the virtual circuits of substation configuration files.

[0005] To solve the above technical problem, in the first aspect of the embodiments of the present invention, an intelligent verification method for virtual circuits of substation configuration files is provided, including: Determine the virtual circuits to be verified and the device data corresponding to the virtual circuits to be verified according to the substation configuration file to be tested; Obtain the actual mapping relationship between the auxiliary contacts of each switching device in the virtual loop to be verified and the input terminals of each protection device according to the device data; Detect the virtual connection status between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual loop detection result of the substation configuration file to be measured.

[0006] As a preferred solution, the obtaining the actual mapping relationship between the auxiliary contacts of each switching device in the virtual loop to be verified and the input terminals of each protection device according to the device data specifically includes: Obtain the auxiliary contact status data of each switching device and the input terminal status data of each protection device according to the device data; According to the auxiliary contact status data and the input terminal status data, obtain the first status value of the auxiliary contact, the second status value of the input terminal, and the status change time difference between the auxiliary contact and the input terminal under each switching operation of the switching device. Match the first status value and the second status value according to the status change time difference and the preset signal transmission delay, and obtain the actual mapping relationship between each auxiliary contact and each input terminal in the virtual loop to be verified.

[0007] As a preferred solution, the detecting the virtual connection status between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtaining the virtual loop detection result of the substation configuration file to be measured specifically includes: Based on the switching device to be measured and the protection device to be measured with the actual mapping relationship, obtain the auxiliary contact status data of the switching device to be measured and the input terminal status data of the protection device to be measured from the device data; According to the auxiliary contact status data and the input terminal status data, obtain the status change time difference between the auxiliary contact of the switching device to be measured and the input terminal of the protection device to be measured under each switching operation of the switching device to be measured. When it is detected that the status change time difference is greater than the preset abnormal time threshold, determine that the virtual connection status between the auxiliary contact of the switching device to be measured and the input terminal of the protection device to be measured is abnormally virtual-connected; Obtain the virtual loop detection result according to the auxiliary contact of each switching device to be measured and the input terminal of the protection device to be measured whose virtual connection status is abnormally virtual-connected.

[0008] As a preferred solution, detecting the virtual connection state between the auxiliary contacts of the switchgear and the input terminals of the protection device according to the device data and the actual mapping relationship to obtain the virtual loop detection result of the substation configuration file to be measured specifically includes: Obtain the terminal physical position, terminal identifier, and contact function attribute of each of the input terminals from the device data; Input the terminal physical position, the terminal identifier, and the contact function attribute into a preset machine learning model to obtain the standard arrangement rule of the input terminals output by the machine learning model; wherein, the machine learning model is obtained by training based on a preset prior arrangement data set of input terminals; the standard arrangement rule of the input terminals is used to indicate the standard mapping relationship between each of the auxiliary contacts and each of the input terminals; Convert the actual mapping relationship and the standard mapping relationship into a first feature vector and a second feature vector respectively, and calculate the Euclidean distance between the first feature vector and the second feature vector; When it is detected that the Euclidean distance is greater than a preset Euclidean distance threshold, it is determined that there is at least one group of auxiliary contacts and input terminals with a virtual connection state of virtual connection abnormality in the virtual loop to be verified.

[0009] As a preferred solution, detecting the virtual connection state between the auxiliary contacts of the switchgear and the input terminals of the protection device according to the device data and the actual mapping relationship to obtain the virtual loop detection result of the substation configuration file to be measured specifically includes: Determine the connection relationship between each of the switchgears and each of the protection devices according to the actual mapping relationship; Obtain the first physical position of each of the switchgears and the second physical position of each of the protection devices from the device data; Generate a connection topology graph between each of the switchgears and each of the protection devices by using a graph theory algorithm according to the connection relationship, the first physical position, and the second physical position; Use the depth-first search algorithm to search for the target connection path between the switchgear and the protection device in the connection topology graph; Obtain the first state time series data of the switchgear and the second state time series data of the protection device in each of the target connection paths from the device data; Calculate the state change time series correlation degree between the switchgear and the protection device in each of the target connection paths by using a correlation calculation method according to the first state time series data and the second state time series data; Determine the connectivity probability between the switch device and the protection device in each of the target connection paths according to the preset corresponding relationship between the state change timing relevance and the connectivity probability; Input the path weight corresponding to the target connection path and the connectivity probability into a preset Bayesian network to obtain the virtual connection anomaly probability output by the Bayesian network; wherein, the path weight is determined based on the first physical location and the second physical location; the Bayesian network is trained based on the historical topology data between the switch device and the protection device; When it is detected that the virtual connection anomaly probability is greater than a preset probability threshold, determine that the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device in the target connection path is a virtual connection anomaly; Obtain the virtual loop detection result according to each group of auxiliary contacts and input terminals whose virtual connection state is a virtual connection anomaly.

[0010] As a preferred solution, the use of the depth-first search algorithm to search for the target connection path between the switch device and the protection device in the connection topology graph specifically includes: Determine the physical distance between each node in the connection topology graph according to the first physical location and the second physical location; wherein, the node is the switch device or the protection device; Determine the edge weight between each node according to the preset corresponding relationship between the physical distance and the edge weight; Use the depth-first search algorithm to traverse the nodes in the connection topology graph in descending order of the edge weight to obtain several alternative connection paths between the switch device and the protection device; Determine the path weight corresponding to each alternative connection path according to the product of the edge weights between each node in each alternative connection path, and use the alternative connection path with the largest path weight as the target connection path.

[0011] As a preferred solution, after the method detects the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device, it further includes: When it is detected that the virtual connection state between the auxiliary contact of any target switch device and the input terminal of any target protection device is a virtual connection anomaly, obtain the attribute parameters of each of the target switch devices from the device data; Generate the device feature identification value corresponding to each of the target switch devices according to the attribute parameters; The decision tree algorithm is used to normalize each of the device feature identification values, and based on the normalized device feature identification values, the virtual connection anomaly level corresponding to each of the target switch devices is determined according to the preset anomaly level classification threshold.

[0012] As a preferred solution, the attribute parameters include the operation duration, the maintenance interval, and the operation environment temperature; The generating of the device feature identification value corresponding to each of the target switch devices according to the attribute parameters specifically includes: According to a preset anomaly score comparison table, the first anomaly score corresponding to the operation duration of each of the target switch devices, the second anomaly score corresponding to the maintenance interval, and the third anomaly score corresponding to the operation environment temperature are respectively obtained; wherein, different operation durations, maintenance intervals, and operation environment temperatures and their corresponding anomaly scores are recorded in the anomaly score comparison table; The first anomaly score, the second anomaly score, and the third anomaly score are weighted and summed to obtain the device feature identification value corresponding to each of the target switch devices.

[0013] As a preferred solution, after the method determines the virtual connection anomaly level corresponding to each of the target switch devices according to the preset anomaly level classification threshold, it further includes: Adjusting the anomaly level classification threshold according to the anomaly occurrence frequency corresponding to each virtual connection anomaly level.

[0014] A second aspect of the embodiments of the present invention provides a virtual loop intelligent verification system for a substation configuration file, including: A data acquisition module, configured to determine a virtual loop to be verified and device data corresponding to the virtual loop to be verified according to a substation configuration file to be tested; An actual mapping relationship determination module, configured to obtain an actual mapping relationship between the auxiliary contacts of each switch device in the virtual loop to be verified and the input terminals of each protection device according to the device data; A virtual loop detection module, configured to detect the virtual connection state between the auxiliary contacts of the switch device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain a virtual loop detection result of the substation configuration file to be tested.

[0015] As a preferred solution, the actual mapping relationship determination module is configured to obtain an actual mapping relationship between the auxiliary contacts of each switch device in the virtual loop to be verified and the input terminals of each protection device according to the device data, specifically including: Obtain the auxiliary contact status data of each of the switch devices and the status data of the input terminals of each of the protection devices according to the device data; According to the auxiliary contact status data and the status data of the input terminals, obtain the first status value of the auxiliary contact, the second status value of the input terminal, and the status change time difference between the auxiliary contact and the input terminal under each switching operation of the switch device; Match the first status value and the second status value according to the status change time difference and a preset signal transmission delay to obtain the actual mapping relationship between each of the auxiliary contacts and each of the input terminals in the virtual circuit to be verified;

[0016] As a preferred solution, the virtual circuit detection module is used to detect the virtual connection status between the auxiliary contacts of the switch device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be measured, specifically including: Based on the switch device to be measured and the protection device to be measured with the actual mapping relationship, obtain the auxiliary contact status data of the switch device to be measured and the status data of the input terminals of the protection device to be measured from the device data; According to the auxiliary contact status data and the status data of the input terminals, obtain the status change time difference between the auxiliary contacts of the switch device to be measured and the input terminals of the protection device to be measured under each switching operation of the switch device to be measured; When it is detected that the status change time difference is greater than a preset abnormal time threshold, determine that the virtual connection status between the auxiliary contacts of the switch device to be measured and the input terminals of the protection device to be measured is abnormally virtual-connected; Obtain the virtual circuit detection result according to the auxiliary contacts of each group of switch devices to be measured and the input terminals of the protection devices to be measured with the virtual connection status being abnormally virtual-connected.

[0017] As a preferred solution, the virtual circuit detection module is used to detect the virtual connection status between the auxiliary contacts of the switch device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be measured, specifically including: Obtain the terminal physical position, terminal identifier, and contact function attribute of each of the input terminals from the device data; Inputting the terminal physical position, the terminal identifier and the contact function attribute into a preset machine learning model, obtaining the standard arrangement rule of the binary input terminals output by the machine learning model; wherein the machine learning model is obtained by training based on a preset binary input terminal prior arrangement data set; the standard arrangement rule of the binary input terminals is used to indicate the standard mapping relationship between each of the auxiliary contacts and each of the binary input terminals; Converting the actual mapping relationship and the standard mapping relationship into a first eigenvector and a second eigenvector respectively, and calculating the Euclidean distance between the first eigenvector and the second eigenvector; When it is detected that the Euclidean distance is greater than a preset Euclidean distance threshold, it is determined that there is at least one group of auxiliary contacts and input terminals whose virtual connection states are abnormal virtual connections in the virtual circuit to be checked.

[0018] As a preferred solution, the virtual circuit detection module is used to detect the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be tested, specifically including: Determining, according to the actual mapping relationship, a connection relationship between each of the switch devices and each of the protection devices; Acquire a first physical position of each of the switch devices and a second physical position of each of the protection devices from the device data; Generate a connection topology diagram between each of the switch devices and each of the protection devices using a graph theory algorithm according to the connection relationship, the first physical position, and the second physical position; Searching for a target connection path between the switch device and the protection device in the connection topology graph using a depth-first search algorithm; Acquire first state timing data of a switch device and second state timing data of a protection device in each of the target connection paths from the device data; Calculate the state change time series correlation between the switch device and the protection device in each of the target connection paths by using a correlation calculation method according to the first state time series data and the second state time series data; Determining the connectivity probability between the switch device and the protection device in each of the target connection paths according to the preset correspondence between the state change timing correlation and the connectivity probability; Input the path weight corresponding to the target connection path and the connectivity probability into a preset Bayesian network to obtain the virtual connection anomaly probability output by the Bayesian network; wherein, the path weight is determined based on the first physical location and the second physical location; the Bayesian network is trained based on the historical topology data between the switchgear and the protection device; When it is detected that the virtual connection anomaly probability is greater than a preset probability threshold, determine that the virtual connection state between the auxiliary contact of the switchgear in the target connection path and the input terminal of the protection device is a virtual connection anomaly; Obtain the virtual loop detection result according to each group of auxiliary contacts and input terminals with a virtual connection state of virtual connection anomaly.

[0019] As a preferred solution, the virtual loop detection module is used to search for the target connection path between the switchgear and the protection device in the connection topology diagram by using the depth-first search algorithm, specifically including: Determine the physical distance between each node in the connection topology diagram according to the first physical location and the second physical location; wherein, the node is the switchgear or the protection device; Determine the edge weight between each node according to the preset correspondence between the physical distance and the edge weight; Use the depth-first search algorithm to traverse the nodes in the connection topology diagram in the order of the edge weights from large to small, and obtain several alternative connection paths between the switchgear and the protection device; Determine the path weight corresponding to each alternative connection path according to the product of the edge weights between each node in each alternative connection path, and use the alternative connection path with the largest path weight as the target connection path.

[0020] Compared with the prior art, the beneficial effect of the embodiment of the present invention is that by obtaining the actual mapping relationship between the auxiliary contacts of each switchgear and the input terminals of each protection device in the virtual loop to be checked based on the device data corresponding to the virtual loop to be checked, the actual mapping relationship and device data can be used to realize the automatic detection of the virtual connection state between the auxiliary contacts of the switchgear and the input terminals of the protection device. Compared with the manual detection method, it effectively avoids the situation of misdetection and missed detection, significantly improves the detection accuracy of the virtual loop of the substation configuration file, and is applicable to different arrangements of input terminals. Description of the Drawings

[0021] Figure 1 It is a schematic flowchart of the intelligent verification method for the virtual loop of the substation configuration file in the embodiment of the present invention; Figure 2 It is a schematic structural diagram of the virtual loop intelligent checking system for substation configuration files in an embodiment of the present invention. Specific implementation manners

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0023] The substation configuration file involves complex connection relationships of many switching devices and protection devices. In the past operation of substations, situations such as misoperation of bus protection have occurred due to hidden dangers of virtual loops. As an example, due to the virtual connection error between the tripping of the main transformer branch protection of the bus differential protection and the opening input of the main transformer protection failure connection trip, when there is zero-sequence or negative-sequence overcurrent on the high-voltage side of the main transformer and the opening input of the failure connection trip is received, the main transformer failure connection trip protection misoperates and trips the three-side switches of the main transformer, resulting in power outage of some lines and having a great impact on the safe and stable operation of the power grid. With the development of intelligent substation technology, network and provincial companies have paid more and more attention to the hidden dangers of virtual loops. For example, the State Grid has carried out hidden danger investigations on virtual loops in operating intelligent substations in multiple provinces, and the Southern Grid has put forward strict requirements for the quality of configuration files in the infrastructure construction link. Under this background, the virtual loop intelligent checking method for substation configuration files proposed in the embodiments of the present invention has important practical significance.

[0024] When detecting the virtual loops of the substation configuration file of a substation, according to the virtual loop intelligent checking method for substation configuration files proposed in the embodiments of the present invention, after obtaining the device data and the actual mapping relationship, the virtual connection status between the auxiliary contacts of the switching devices and the opening input terminals of the protection devices is detected. During the detection process, various complex situations that may occur in actual operation are fully considered, and through a rigorous intelligent detection process, the accuracy and reliability of virtual loop detection are effectively improved, and the safe and stable operation of intelligent substations is effectively guaranteed.

[0025] Please refer to Figure 1 , the first aspect of the embodiments of the present invention provides a virtual loop intelligent checking method for substation configuration files, including the following steps S1 to S3: Step S1, determine the virtual loops to be checked and the device data corresponding to the virtual loops to be checked according to the substation configuration file to be measured; Step S2, obtain the actual mapping relationship between the auxiliary contacts of each switching device and the opening input terminals of each protection device in the virtual loops to be checked according to the device data; Step S3: Detect the virtual connection status between the auxiliary contacts of the switchgear and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual loop detection result of the to-be-tested substation configuration file.

[0026] Specifically, the substation configuration file, i.e., the SCD file (Substation Configuration Description), defines the mapping relationship between the position signals of the switchgear and the input terminals of the protection device. To accurately and effectively detect the virtual loops of the substation configuration file, in this embodiment, first, according to the to-be-tested substation configuration file, the to-be-verified virtual loops are determined and the corresponding device data is obtained. Since this embodiment of the present invention mainly detects the virtual connection status between the auxiliary contacts of the switchgear and the input terminals of the protection device, the obtained device data includes switchgear data and protection device data. It should be noted that the input quantity refers to the signal input of the protection device, which collects the position information of on-site switches, disconnectors, etc. It is the input potential determined according to the contact state. When the contact is closed, a low potential is input, and when the contact is open, a high potential is input. The switchgear in this embodiment includes circuit breakers, disconnectors, etc. This embodiment does not specifically limit the type of switchgear.

[0027] Furthermore, since the state change data of the auxiliary contacts of each switchgear and the input terminals of each protection device at different times can be determined based on the device data, considering that the state changes of the auxiliary contacts and the input terminals with a mapping relationship should be synchronous within the allowable signal transmission delay, this embodiment can determine the actual mapping relationship between the auxiliary contacts of each switchgear and the input terminals of each protection device.

[0028] Furthermore, since the actual mapping relationship reflects the virtual connection relationship between the auxiliary contacts and the input terminals, combined with relevant device data, such as the physical positions of the terminals, the state values of each auxiliary contact and the input terminal at different times, the physical positions of the switchgear and the protection device, etc., it is possible to determine whether there are auxiliary contacts and input terminals with abnormal virtual connections in the to-be-verified virtual loop, thereby realizing the detection of the virtual loop of the to-be-tested substation configuration file.

[0029] When actually using the intelligent verification method proposed in the embodiments of the present invention to detect the virtual circuits in the substation configuration file, the complex and changeable actual situation on site is fully considered. Currently, there are many non-standard aspects in the integration and production links of the substation configuration file at the intelligent substation site, which makes the error rate of virtual circuits remain high and difficult to troubleshoot. For example, in a certain actual project, the semantics of virtual terminals of some IED devices (Intelligent Electronic Devices) in the substation configuration file vary among different device manufacturers, which brings great difficulties to accurately parsing the internal information of the substation configuration file.

[0030] To solve these problems, in the process of detecting the virtual connection state, when obtaining the relevant information of the input terminal, in view of the non-standard and incomplete information in the substation configuration file, the technical path combining configuration file parsing, decoupling, reconstruction and human-computer interaction is adopted to deeply excavate and accurately understand the information such as the physical location of the terminal, the terminal identifier and the contact function attribute. Taking a 220 kV substation as an example, when processing its substation configuration file, through this technical path, the complex primary and secondary equipment configurations and their associated relationships are successfully sorted out, and the relevant attributes of each input terminal can be accurately identified, laying a solid foundation for subsequent detection.

[0031] In the link of determining the standard arrangement rule of the input terminals, the embodiments of the present invention can utilize a machine learning model trained based on a preset prior arrangement data set of input terminals, fully considering the differences of devices from different manufacturers. A large amount of data is collected from the actual operation records, covering the arrangement of input terminals under different voltage levels and wiring modes, and a rich prior arrangement data set is constructed. In view of the differences in the contact arrangement modes of switch devices from different manufacturers, such as the different arrangements of Manufacturer A and Manufacturer B, the machine learning model can effectively learn and output accurate standard arrangement rules, providing a reliable basis for judging whether the actual mapping relationship is abnormal.

[0032] Throughout the detection process, the basic requirements of the verification basis are always followed. The standard virtual circuit knowledge relied on is rich and diverse, and can adapt to various complex verification objects. Whether it is a substation of different voltage levels or a complex wiring mode including various design forms, it can be accurately dealt with. At the same time, through the unique identifier of the verification basis, it is ensured that the appropriate basis can be quickly and accurately matched during verification, greatly improving the accuracy and efficiency of detection.

[0033] In addition, it can be considered to introduce the idea of building a standardized model in the virtual circuit detection of substation configuration files. Based on artificial intelligence and big data technologies, deeply analyze the device data in the substation configuration files. By collecting a large number of substation configuration files of different types of substations, including device parameters, virtual circuit connection relationships, etc., build a standardized model covering the entire process of virtual circuit detection. This model clarifies the input data for virtual circuit detection, such as device data and actual mapping relationships; stipulates key detection steps, such as data preprocessing, mapping relationship matching, anomaly judgment, etc.; and determines the output results, such as virtual circuit detection results, anomaly reports, etc. Through the standardized model, the virtual circuit detection process is unified, reducing detection errors caused by human factors and improving detection efficiency. At the same time, use simulation verification and adaptive adjustment mechanisms to dynamically optimize the model according to the actual situations of different substations. For example, when encountering new device types or wiring methods, the model can automatically adjust parameters and detection logics to ensure accurate detection of the virtual circuit status and meet the virtual circuit detection requirements of substation configuration files in different scenarios.

[0034] In addition, the intelligent configuration generation method of test templates can also be used to design modular detection templates for the virtual circuit detection of substation configuration files. Among them, the test template is a standardized file used to describe specific test processes and steps, usually including input excitation quantities, judgment criteria, test steps, key judgment nodes, etc., and is customized according to different device configuration parameters and virtual circuit detection requirements. Decompose the virtual circuit detection process into multiple combinable small modules, such as data acquisition module, mapping relationship analysis module, anomaly detection module, etc. Use an automated template generation tool to quickly generate adapted detection scripts according to the device configuration parameters and virtual circuit detection requirements in the substation configuration file. The template supports dynamic loading and adjustment. When the substation configuration file changes, such as adding new devices or modifying virtual circuit connection relationships, the template can update the detection content in real time. In addition, the template system integrates the function of automatically generating reports, fills the key data in the detection process into the report template in real time, and ensures the accuracy and traceability of the report through version control. In this way, when detecting the virtual circuit of the substation configuration file, the detection preparation time can be greatly shortened, and the detection efficiency and flexibility can be improved.

[0035] In an alternative embodiment, the embodiment of the present invention supports automatically instantiating into a specific virtual loop detection outline after importing a substation configuration file based on a standard test template. The system has the ability to automatically adjust the test template according to the device configuration in the substation configuration file, and automatically match the input and output signal points, function configurations, protection settings, and test requirements of each device in the virtual loop. When detecting the virtual loop of the substation configuration file of a newly built substation, after the system reads the substation configuration file, it quickly identifies the switch device and protection device information therein, combines the standard test template with the actual device configuration, and automatically adjusts the test parameters and processes. For example, according to the arrangement method and function definition of the input terminals of different protection devices in the substation configuration file, it automatically adjusts the acquisition and judgment methods of the states of the input terminals during the detection process, realizes "one-key" rapid configuration and flexible adjustment, makes the detection process highly match the actual on-site requirements, and improves the accuracy of detection.

[0036] In an alternative embodiment, intelligent analysis and human-machine collaboration technologies are introduced during the virtual loop detection process. Using intelligent analysis technologies and real-time data acquisition means, the state data of the auxiliary contacts of switch devices and the input terminals of protection devices are monitored and automatically analyzed in real time. An abnormal detection model is established. Through learning a large amount of historical data, the state change pattern and time threshold of the virtual loop under normal conditions are determined. When it is detected that the time difference of state changes or other abnormal indicators exceed the preset range, the intelligent diagnosis engine quickly issues an alarm and gives a preliminary abnormal diagnosis conclusion. At the same time, the judgment experience of power industry experts on the virtual loop of the substation configuration file is integrated to build a knowledge base. In the face of complex abnormal situations, operators can refer to the information in the knowledge base, combine their own professional judgments, and work with the intelligent system to jointly determine the abnormal situations and treatment plans of the virtual loop. In addition, the user interface of the detection system is optimized to make its operation more convenient, and a remote collaboration function is added to facilitate experts and technicians in different regions to jointly participate in the detection and analysis of the virtual loop of the substation configuration file, improving the accuracy and reliability of detection.

[0037] The intelligent verification method for the virtual loop of the substation configuration file provided by the embodiment of the present invention can obtain the actual mapping relationship between the auxiliary contacts of each switch device and the input terminals of each protection device in the virtual loop to be verified based on the device data corresponding to the virtual loop to be verified, and can use this actual mapping relationship and device data to realize the automatic detection of the virtual connection state between the auxiliary contacts of the switch device and the input terminals of the protection device. Compared with the manual detection method, it effectively avoids the situations of misdetection and undetected, significantly improves the detection accuracy of the virtual loop of the substation configuration file, and is applicable to different arrangements of input terminals.

[0038] As a preferred solution, obtaining the actual mapping relationship between the auxiliary contacts of each switching device in the virtual loop to be verified and the input terminals of each protection device according to the device data specifically includes: Obtaining the auxiliary contact status data of each switching device and the input terminal status data of each protection device according to the device data; According to the auxiliary contact status data and the input terminal status data, obtaining the first status value of the auxiliary contact, the second status value of the input terminal, and the status change time difference between the auxiliary contact and the input terminal under each switching operation of the switching device; Matching the first status value and the second status value according to the status change time difference and a preset signal transmission delay to obtain the actual mapping relationship between each auxiliary contact and each input terminal in the virtual loop to be verified.

[0039] Specifically, for the auxiliary contact status data and the input terminal status data, the status values of the auxiliary contacts and the input terminals at different times are usually obtained at a fixed time interval of 5 milliseconds to clarify the status timing data of each auxiliary contact and input terminal. It can be understood that the switching device includes at least 2 auxiliary contacts and may experience an opening operation or a closing operation during each switching operation. When experiencing a closing operation, the normally open contact changes from the open state to the closed state, and the normally closed contact changes from the closed state to the open state. Exemplarily, the open state can be represented by the numerical value "0", and the closed state can be represented by the numerical value "1". Other numerical representation methods can also be used, which are not specifically limited in this embodiment. The status data of the input terminal reflects the opening and closing positions of the corresponding auxiliary contact. Therefore, the auxiliary contact status data and the input terminal status data will show a strong correlation. That is, when the auxiliary contact changes its status, the corresponding input terminal also changes its status, and the time difference between the two status change moments is within the allowable signal transmission delay, such as 10 milliseconds. Therefore, in this embodiment, obtaining the first status value of the auxiliary contact, the second status value of the input terminal, and the status change time difference between the auxiliary contact and the input terminal under each switching operation of the switching device is transformed into the time difference between the moment when the first status value changes and the moment when the second status value changes.

[0040] Further, within the time difference of the state change being within the signal transmission delay, match the first state value and the second state value. Exemplarily, assume that the open state is represented by the numerical value "0", and the closed state is represented by the numerical value "1", the signal transmission delay is 10 milliseconds, and the auxiliary contact changes to the open state at a certain moment, that is, the first state value is 0 at this time. Within 10 milliseconds after this moment, detect whether there is a second state value that synchronously changes and has a numerical value of 0. If so, determine that the first state value and the second state value match. To ensure the accuracy of the actual mapping relationship, it is necessary to match the first state value and the second state value for each switch operation. According to the number of successful matches and the total number of matches, the matching success rate can be determined, and the auxiliary contact and the input terminal with the highest matching success rate are selected, indicating that there is a mapping relationship between the two.

[0041] In an alternative embodiment, the embodiment of the present invention can also construct a standardized detection process. Through the standardized test case generation technology, its process generally includes: (1) Establish a fixed value list library, that is, establish a fixed value list library according to the regulations on the fixed value detection range and error requirements in the standard; (2) Establish a basic function template library, that is, establish a template for setting analog quantities during faults, a template for setting fault time, and a template for gradient setting associated with the fixed value, as well as templates for the output state of analog quantities before and after faults, input / output templates for switch quantities, and templates for delays before and after faults that have nothing to do with the fixed value, etc., for use when establishing test type templates; (3) Generate a test type template library, that is, generate a common test type template library according to the fixed value and in combination with the test methods encapsulated in the program, such as a virtual circuit test template; (4) Generate test instances, thereby constructing a standardized process for detecting virtual circuits in substation configuration files. When detecting virtual circuits in substation configuration files, based on a large amount of substation configuration file data of different types of substations, establish a standardized virtual circuit detection model. Just like establishing a fixed value list library, establish a key information library for substation configuration files, including device data, virtual circuit connection relationships, etc. According to the virtual circuit detection methods and principles, formulate standard detection steps and specifications. For example, stipulate the frequency and range of data collection, and clarify the basis and threshold for judging the virtual connection state. When actually detecting the virtual circuits in the substation configuration file of a certain 220 kV substation, according to the standardized process, first extract device data and actual mapping relationships from the substation configuration file, and then detect the virtual connection state one by one according to the standard judgment rules to ensure the consistency and accuracy of the detection process and avoid detection errors caused by human factors.

[0042] As a preferred solution, the detecting the virtual connection state between the auxiliary contact of the switching device and the input terminal of the protection device according to the device data and the actual mapping relationship to obtain the virtual circuit detection result of the substation configuration file to be tested specifically includes: Based on the switch device under test and the protection device under test with the actual mapping relationship, obtain the auxiliary contact status data of the switch device under test and the status data of the input terminal of the protection device under test from the device data; According to the auxiliary contact status data and the status data of the input terminal, obtain the difference in the status change time between the auxiliary contact of the switch device under test and the input terminal of the protection device under test for each switch operation of the switch device under test; When it is detected that the difference in the status change time is greater than the preset abnormal time threshold, determine that the virtual connection status between the auxiliary contact of the switch device under test and the input terminal of the protection device under test is a virtual connection anomaly; According to each set of auxiliary contacts of the switch device under test and the input terminals of the protection device under test with the virtual connection status being a virtual connection anomaly, obtain the virtual loop detection result.

[0043] Specifically, since the actual mapping relationship between the auxiliary contacts of each switch device and the input terminals of each protection device is clear, for the switch device under test and the protection device under test with the actual mapping relationship, obtain the corresponding auxiliary contact status data and input terminal status data respectively. At this time, the obtained auxiliary contact status data and input terminal status data should show strong correlation under normal virtual connection, that is, when the status of the auxiliary contact changes, the corresponding input terminal also changes its status, and the time difference between the two status change moments is within the allowable signal transmission delay. The virtual connection anomaly is mainly manifested as the asynchronous change of the status of the input terminal and the status of the auxiliary contact, that is, the status change moment of the input terminal lags behind the status change moment of the auxiliary contact, and the lag time exceeds the preset abnormal time threshold, such as 20 milliseconds. At this time, it is determined that the virtual connection status between the auxiliary contact of the switch device under test and the input terminal of the protection device under test is a virtual connection anomaly.

[0044] As a preferred solution, the detection of the virtual connection status between the auxiliary contact of the switch device and the input terminal of the protection device according to the device data and the actual mapping relationship to obtain the virtual loop detection result of the substation configuration file under test specifically includes: Obtain the terminal physical position, terminal identifier, and contact function attribute of each input terminal from the device data; Input the physical position of the terminal, the terminal identifier, and the contact function attribute into a preset machine learning model to obtain the standard arrangement rule of the input terminals output by the machine learning model; wherein, the machine learning model is obtained by training based on a preset prior arrangement dataset of input terminals; the standard arrangement rule of the input terminals is used to indicate the standard mapping relationship between each of the auxiliary contacts and each of the input terminals. Convert the actual mapping relationship and the standard mapping relationship into a first feature vector and a second feature vector respectively, and calculate the Euclidean distance between the first feature vector and the second feature vector. When it is detected that the Euclidean distance is greater than a preset Euclidean distance threshold, it is determined that there is at least one set of auxiliary contacts and input terminals with a virtual connection state of virtual connection anomaly in the virtual loop to be verified.

[0045] Specifically, the arrangement of the input terminals of the substation has a fixed pattern, which represents the connection rules with the auxiliary contacts of the switchgear. In the actual operation records, the terminal numbers are recorded according to the physical positions of the terminals. For example, the switch off-position terminals are located at positions 1 to 4, and the switch on-position terminals are located at positions 5 to 8. Each switchgear is configured with 4 normally open contacts and 4 normally closed contacts. The normally open contacts record the off-position state, and the normally closed contacts record the on-position state. The input terminal identifier is a combination of letters and numbers. For example, KA01 represents the normally open contact of the phase A switch off-position, and KB02 represents the normally closed contact of the phase B switch on-position. The contact function attributes are distinguished by the terminal identifier. There are differences in the contact arrangements of switchgears from different manufacturers. For example, for the switchgear of manufacturer A, the normally open contacts are configured at the physical positions 1 to 4 of the terminals, and the normally closed contacts are configured at the physical positions 5 to 8 of the terminals. While for the switchgear of manufacturer B, an alternating arrangement is adopted, that is, the normally open contacts are configured at the odd physical positions of the terminals, and the normally closed contacts are configured at the even physical positions of the terminals. As a result, the arrangements of the input terminals are also different. By statistically analyzing the distribution frequencies of the input terminals with different contact function attributes at the physical positions of the terminals, the occurrence probabilities of different contact function attributes at each physical position of the terminals are calculated. When the occurrence probability of a certain contact function attribute at a specific physical position of the terminal exceeds 80%, it is determined that this physical position of the terminal is the standard position of such input terminals. Based on this, in this embodiment, based on the arrangement rules of the input terminals of different manufacturers summarized by operation experts, they are converted into a prior arrangement data set of input terminals, and the machine learning model is trained so that the machine learning model can learn the standard positions of the input terminals with different contact function attributes. It can be understood that the prior arrangement data set of input terminals contains the terminal physical position labels, terminal identifier labels, and contact function attribute labels corresponding to each arrangement rule of the input terminals. Inputting the terminal physical positions, terminal identifiers, and contact function attributes of each input terminal in the device data corresponding to the virtual loop to be verified into the trained machine learning model can obtain the corresponding standard arrangement rule of the input terminals, which reflects the standard mapping relationship between the current auxiliary contacts and each input terminal.

[0046] Further, for the convenience of comparison, the actual mapping relationship and the standard mapping relationship are converted into the first feature vector and the second feature vector respectively. For example, the connection relationship between different auxiliary contacts and the input terminals can be encoded in a coding manner, thereby forming the first feature vector and the second feature vector respectively, and then calculating the Euclidean distance between the two feature vectors. It can be understood that the larger the Euclidean distance, the smaller the similarity between the two feature vectors, that is, there is a virtual connection abnormality in the actual mapping relationship. In this embodiment, when it is detected that the Euclidean distance is greater than the preset Euclidean distance threshold, such as 0.8, it is determined that there is at least one group of auxiliary contacts and input terminals with virtual connection abnormalities in the virtual circuit to be checked. For example, when the Euclidean distance is in the range of 0.8 to 0.9, it indicates that the position of the same type of contacts is wrong, and when the Euclidean distance is greater than 0.9, it indicates that different types of contacts are misconnected.

[0047] In an optional embodiment, the embodiment of the present invention can also realize compatibility detection of virtual circuits of substation configuration files of different manufacturers and different models of equipment. The format differences of substation configuration files of equipment from different manufacturers are analyzed, and a universal parsing interface is developed so that the detection system can read and process substation configuration files of various formats. Taking a regional power grid as an example, which contains equipment from multiple manufacturers, through a unified parsing interface, virtual circuit detection can be performed on substation configuration files of equipment from different manufacturers at the same time. During the detection process, just like controlling different testers, the detection parameters and methods are automatically adjusted according to the equipment type and the characteristics of the substation configuration file, so as to achieve efficient detection of virtual circuits of multiple devices and improve the applicability of the detection system.

[0048] As a preferred solution, the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device is detected according to the device data and the actual mapping relationship to obtain the virtual circuit detection result of the substation configuration file to be tested, specifically including: Determining, according to the actual mapping relationship, a connection relationship between each of the switch devices and each of the protection devices; Acquire a first physical position of each of the switch devices and a second physical position of each of the protection devices from the device data; Generate a connection topology diagram between each of the switch devices and each of the protection devices using a graph theory algorithm according to the connection relationship, the first physical position, and the second physical position; Searching for a target connection path between the switch device and the protection device in the connection topology graph using a depth-first search algorithm; Acquire first state timing data of a switch device and second state timing data of a protection device in each of the target connection paths from the device data; According to the first state time series data and the second state time series data, calculate the state change time series correlation degree between the switch device and the protection device in each of the target connection paths by using a correlation calculation method; According to the preset corresponding relationship between the state change time series correlation degree and the connectivity probability, determine the connectivity probability between the switch device and the protection device in each of the target connection paths; Input the path weight corresponding to the target connection path and the connectivity probability into a preset Bayesian network to obtain the virtual connection anomaly probability output by the Bayesian network; wherein, the path weight is determined based on the first physical location and the second physical location; the Bayesian network is trained based on the historical topology data between the switch device and the protection device; When it is detected that the virtual connection anomaly probability is greater than a preset probability threshold, determine that the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device in the target connection path is a virtual connection anomaly; According to each group of auxiliary contacts and input terminals with a virtual connection state of virtual connection anomaly, obtain the virtual loop detection result.

[0049] Specifically, in this embodiment, each switching device and each protection device are regarded as several nodes, and the connection relationship between the switching device and the protection device is regarded as an edge. A corresponding connection topology graph is generated through a graph theory algorithm. Further, since there may be multiple connection paths between the switching device and the protection device with a connection relationship, and the longer the connection path, the greater the signal transmission delay usually is, resulting in unstable virtual connections. In order to determine the optimal connection path between the switching device and the protection device, this embodiment uses a depth-first search algorithm to search for the target connection path between the switching device and the protection device in the connection topology graph. After determining the target connection path, the first state time series data of the switching device and the second state time series data of the protection device in each target connection path are obtained, and the correlation is calculated. It can be understood that the state change time series correlation can reflect the connectivity probability between the switching device and the protection device in the target connection path, that is, the probability that there is a path between these two nodes in the connection topology graph. Exemplarily, this embodiment pre-sets the corresponding relationship between the state change time series correlation and the connectivity probability. For example, when the state change time series correlation is higher than 0.9, the connectivity probability takes a value of 0.9; when the state change time series correlation is between 0.5 and 0.9, the connectivity probability takes a value of 0.6; when the state change time series correlation is lower than 0.5, the connectivity probability takes a value of 0.3. In order to analyze the virtual connection abnormality probability between the switching device and the protection device in the target connection path, this embodiment uses a pre-trained Bayesian network. It can be understood that this Bayesian network is trained using the historical topology data between the switching device and the protection device. The historical topology data includes the path weight label of the connection path between the switching device and the protection device, the connectivity probability between the switching device and the protection device, and the virtual connection status label between the switching device and the protection device. The path weight in this embodiment is determined based on the above-mentioned first physical position and the second physical position. The farther the switching device and the protection device are from each other, the smaller the path weight, indicating a higher signal transmission delay, and the lower the virtual connection abnormality probability between the two. Inputting the above-obtained connectivity probability and the path weight corresponding to the target connection path into the trained Bayesian network can obtain the virtual connection abnormality probability. When it is greater than a preset probability threshold, such as 0.8, it is determined that the virtual connection status between the auxiliary contact of the switching device and the input terminal of the protection device in the current target connection path is a virtual connection abnormality.

[0050] As a preferred solution, the using the depth-first search algorithm to search for the target connection path between the switching device and the protection device in the connection topology graph specifically includes: According to the first physical position and the second physical position, determine the physical distance between each node in the connection topology graph; wherein, the node is the switching device or the protection device; Determine the edge weights between each of the nodes according to the preset correspondence between the physical distances and the edge weights; Using the depth-first search algorithm, traverse the nodes in the connection topology graph in the order of the edge weights from large to small, and obtain several alternative connection paths between the switching device and the protection device; Determine the path weight corresponding to each of the alternative connection paths according to the product of the edge weights between the nodes in each of the alternative connection paths, and use the alternative connection path with the largest path weight as the target connection path.

[0051] Specifically, since there may be direct paths and indirect paths between the switching device and the protection device with a connection relationship. For example, a certain circuit breaker is indirectly connected to the protection device through a disconnector. Therefore, in this embodiment, it is necessary to obtain the physical distances between each node, which may be the physical distance between two switching devices, or the physical distance between the switching device and the protection device with a connection relationship. This embodiment does not make specific limitations here. Based on the preset different correspondences between the physical distances and the edge weights, for example, when the switching device and the protection device are installed in the same cabinet, the physical distance is less than 10 meters, and the edge weight of the connection is 0.8, indicating that the connection relationship is the most reliable; when the switching device and the protection device are installed in adjacent cabinets, the physical distance is in the range of 10 meters to 50 meters, and the edge weight of the connection is 0.6; when the switching device and the protection device are installed in different places, the physical distance exceeds 50 meters, and the edge weight of the connection is 0.4. Further, in the process of processing the depth optimization search algorithm, traverse the nodes in the connection topology graph in the order of the edge weights from large to small to obtain the connection path with the shortest distance as much as possible, so as to obtain several alternative connection paths between the switching device and the protection device. Since there may be two or more nodes in the alternative connection path, for example, a certain circuit breaker has two alternative connection paths. One is directly connected to the protection device with a connection relationship. At this time, the path weight is the edge weight between the circuit breaker and the protection device. The other is indirectly connected to the protection device with a connection relationship through a disconnector. At this time, the path weight is the product of the edge weight between the circuit breaker and the disconnector and the edge weight between the disconnector and the protection device. Sort the path weights corresponding to each alternative connection path, and use the alternative connection path with the largest path weight as the target connection path.

[0052] As a preferred solution, after detecting the virtual connection state between the auxiliary contact of the switching device and the input terminal of the protection device, the method further includes: When it is detected that the virtual connection state between the auxiliary contact of any target switch device and the input terminal of any target protection device is abnormal, the attribute parameters of each target switch device are obtained from the device data; Generating a device characteristic identification value corresponding to each of the target switch devices according to the attribute parameters; A decision tree algorithm is used to normalize each of the device characteristic identification values, and based on the normalized device characteristic identification values ​​and a preset abnormality level classification threshold, the virtual connection abnormality level corresponding to each of the target switch devices is determined.

[0053] Specifically, after detecting the existence of a virtual connection anomaly, this embodiment further identifies the level of the virtual connection anomaly so as to implement different countermeasures for different levels of virtual connection anomaly. First, it is necessary to obtain the attribute parameters of each target switch device with a virtual connection anomaly. It can be understood that the attribute parameters include operating time, maintenance interval, operating environment temperature, etc. These attribute parameters have a certain influence on the virtual connection anomaly. For example, with the increase of operating time and maintenance interval, the virtual connection anomaly of the current target switch device will increase, and with the increase of the operating environment temperature, it will affect the contact resistance and signal transmission delay of the target switch device, and at the same time, it will aggravate the contact wear, making the mechanism action speed slower. For example, for every 10 degrees increase in the operating environment temperature, the contact resistance of the auxiliary contact increases by 10%, and the signal transmission delay increases by 5 milliseconds. When operating in a high temperature environment above 35 degrees, the contact wear increases, and the mechanism action speed slows down. Therefore, this embodiment generates a device characteristic identification value corresponding to each target switch device based on these attribute parameters to characterize the overall device status of each target switch device.

[0054] Further, the decision tree algorithm is used to normalize the characteristic identification values ​​of each device. It can be understood that, based on the maximum and minimum values ​​of the characteristic identification values ​​of the device, the characteristic identification values ​​of each device can be normalized, and each characteristic identification value of the device can be mapped to a range between 0 and 1, and then the corresponding virtual connection abnormality level is determined according to the preset abnormality level classification threshold. Exemplarily, the abnormality level in this embodiment includes slight abnormality, moderate abnormality and severe abnormality, and the abnormality level classification threshold includes 0.3 and 0.7, that is, if the normalized characteristic identification value of the device is within the range of 0 to 0.3, it is a slight abnormality, if it is within the range of 0.3 to 0.7, it is a moderate abnormality, and if it exceeds 0.7, it is a severe abnormality.

[0055] As a preferred solution, the attribute parameters include operating time, maintenance interval and operating environment temperature; Generating the device characteristic identification value corresponding to each of the target switch devices according to the attribute parameters specifically includes: According to a preset abnormal score comparison table, respectively obtain a first abnormal score corresponding to the operation duration of each of the target switch devices, a second abnormal score corresponding to the maintenance interval, and a third abnormal score corresponding to the operation environment temperature; wherein, the abnormal score comparison table records the abnormal scores corresponding to different operation durations, maintenance intervals, and operation environment temperatures respectively. Perform weighted summation on the first abnormal score, the second abnormal score, and the third abnormal score to obtain the device characteristic identification value corresponding to each of the target switch devices.

[0056] Specifically, in order to effectively obtain the device characteristic identification value corresponding to each target switch device, this embodiment presets an abnormal score comparison table to clarify the abnormal scores corresponding to different operation durations, maintenance intervals, and operation environment temperatures respectively. For example, the abnormal score corresponding to an operation duration of 5 to 10 years is 0.4, the abnormal score corresponding to an operation duration of 10 to 15 years is 0.6, and the abnormal score corresponding to an operation duration of more than 15 years is 0.8. The abnormal score corresponding to a maintenance interval of 6 to 12 months is 0.4, the abnormal score corresponding to a maintenance interval of 12 to 18 months is 0.6, and the abnormal score corresponding to a maintenance interval of more than 18 months is 0.8. This embodiment will not elaborate further here. Perform weighted summation on the first abnormal score, the second abnormal score, and the third abnormal score to obtain the corresponding device characteristic identification value. The weight corresponding to each type of abnormal score can be set according to the influence degree of the current attribute parameter on the virtual connection abnormality. For example, if the operation duration has a higher influence degree on the virtual connection abnormality, the weights of the first abnormal score, the second abnormal score, and the third abnormal score are set to 0.4, 0.3, and 0.3 respectively. This embodiment does not make specific limitations here.

[0057] As a preferred solution, after the method determines the virtual connection abnormality level corresponding to each of the target switch devices according to the preset abnormal level classification threshold, it further includes: Adjust the abnormal level classification threshold according to the abnormal occurrence frequency corresponding to each type of virtual connection abnormality level.

[0058] Specifically, since the frequency of occurrence of exceptions corresponding to each virtual connection exception level can reflect the degradation degree of the current virtual connection, in order to process devices with virtual connection exceptions of different degrees in a timely manner, in this embodiment, based on the frequency of occurrence of exceptions corresponding to each virtual connection exception level, the current exception level classification threshold is adjusted, so that when evaluating the virtual connection exception level next time, a dynamic evaluation of the virtual connection exception level based on the frequency of occurrence of exceptions can be achieved based on the adjusted exception level classification threshold. Exemplarily, the virtual connection exception levels in this embodiment include minor exceptions, moderate exceptions, and severe exceptions, and the corresponding exception level classification thresholds include a first classification threshold and a second classification threshold. If minor exceptions occur frequently, it indicates that the virtual connection begins to deteriorate; if moderate exceptions occur frequently, it indicates that the virtual connection has deteriorated; if severe exceptions occur frequently, it indicates that the virtual connection is about to fail. In order to be able to process in a timely manner before the virtual connection deteriorates or even fails, this embodiment needs to adjust the exception level classification threshold. For example, if the occurrence frequency of minor exceptions exceeds 10 times, the first classification threshold is lowered by 0.1, indicating that the degradation degree of the current virtual connection has increased, and the judgment range of moderate exceptions needs to be correspondingly expanded, so as to be able to avoid the complete deterioration of the virtual connection in a timely manner. If the occurrence frequency of moderate exceptions exceeds 5 times, the second classification threshold is lowered by 0.2. If the occurrence frequency of severe exceptions exceeds 3 times, the second classification threshold is lowered by 0.3, indicating that there is a greater risk of failure of the current virtual connection, and the judgment range of severe exceptions needs to be correspondingly expanded, so as to be able to avoid the complete failure of the virtual connection in a timely manner, greatly improving the operation safety of the substation automation system.

[0059] Please refer to Figure 2 , the second aspect of the embodiment of the present invention provides a virtual loop intelligent verification system 100 for a substation configuration file, including: A data acquisition module 11, configured to determine a virtual loop to be verified and device data corresponding to the virtual loop to be verified according to a substation configuration file to be tested; An actual mapping relationship determination module 12, configured to obtain an actual mapping relationship between auxiliary contacts of each switching device and input terminals of each protection device in the virtual loop to be verified according to the device data; A virtual loop detection module 13, configured to detect a virtual connection state between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain a virtual loop detection result of the substation configuration file to be tested.

[0060] As a preferred solution, the actual mapping relationship determination module 12 is configured to obtain an actual mapping relationship between auxiliary contacts of each switching device and input terminals of each protection device in the virtual loop to be verified according to the device data, specifically including: Obtain the auxiliary contact status data of each of the switch devices and the status data of the input terminals of each of the protection devices according to the device data; According to the auxiliary contact status data and the status data of the input terminals, obtain the first status value of the auxiliary contact, the second status value of the input terminal, and the status change time difference between the auxiliary contact and the input terminal under each switching operation of the switch device; Match the first status value and the second status value according to the status change time difference and a preset signal transmission delay to obtain the actual mapping relationship between each of the auxiliary contacts and each of the input terminals in the virtual circuit to be verified;

[0061] As a preferred solution, the virtual circuit detection module 13 is configured to detect the virtual connection status between the auxiliary contacts of the switch device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be measured, specifically including: Based on the switch device to be measured and the protection device to be measured having the actual mapping relationship, obtain the auxiliary contact status data of the switch device to be measured and the status data of the input terminals of the protection device to be measured from the device data; According to the auxiliary contact status data and the status data of the input terminals, obtain the status change time difference between the auxiliary contacts of the switch device to be measured and the input terminals of the protection device to be measured under each switching operation of the switch device to be measured; When it is detected that the status change time difference is greater than a preset abnormal time threshold, determine that the virtual connection status between the auxiliary contacts of the switch device to be measured and the input terminals of the protection device to be measured is abnormally virtual-connected; Obtain the virtual circuit detection result according to the auxiliary contacts of the switch device to be measured and the input terminals of the protection device to be measured for each group with an abnormally virtual-connected virtual connection status;

[0062] As a preferred solution, the virtual circuit detection module 13 is configured to detect the virtual connection status between the auxiliary contacts of the switch device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be measured, specifically including: Obtain the terminal physical position, terminal identifier, and contact function attribute of each of the input terminals from the device data; Inputting the terminal physical position, the terminal identifier and the contact function attribute into a preset machine learning model, obtaining the standard arrangement rule of the binary input terminals output by the machine learning model; wherein the machine learning model is obtained by training based on a preset binary input terminal prior arrangement data set; the standard arrangement rule of the binary input terminals is used to indicate the standard mapping relationship between each of the auxiliary contacts and each of the binary input terminals; Converting the actual mapping relationship and the standard mapping relationship into a first eigenvector and a second eigenvector respectively, and calculating the Euclidean distance between the first eigenvector and the second eigenvector; When it is detected that the Euclidean distance is greater than a preset Euclidean distance threshold, it is determined that there is at least one group of auxiliary contacts and input terminals whose virtual connection states are abnormal virtual connections in the virtual circuit to be checked.

[0063] As a preferred solution, the virtual circuit detection module 13 is used to detect the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the substation configuration file to be tested, specifically including: Determining, according to the actual mapping relationship, a connection relationship between each of the switch devices and each of the protection devices; Acquire a first physical position of each of the switch devices and a second physical position of each of the protection devices from the device data; Generate a connection topology diagram between each of the switch devices and each of the protection devices using a graph theory algorithm according to the connection relationship, the first physical position, and the second physical position; Searching for a target connection path between the switch device and the protection device in the connection topology graph using a depth-first search algorithm; Acquire first state timing data of a switch device and second state timing data of a protection device in each of the target connection paths from the device data; Calculate the state change time series correlation between the switch device and the protection device in each of the target connection paths by using a correlation calculation method according to the first state time series data and the second state time series data; Determining the connectivity probability between the switch device and the protection device in each of the target connection paths according to the preset correspondence between the state change timing correlation and the connectivity probability; Input the path weight corresponding to the target connection path and the connectivity probability into a preset Bayesian network to obtain the virtual connection anomaly probability output by the Bayesian network; wherein, the path weight is determined based on the first physical location and the second physical location; the Bayesian network is trained based on the historical topology data between the switchgear and the protection device; When it is detected that the virtual connection anomaly probability is greater than a preset probability threshold, determine that the virtual connection state between the auxiliary contact of the switchgear in the target connection path and the input terminal of the protection device is a virtual connection anomaly; According to each group of auxiliary contacts and input terminals with a virtual connection state of virtual connection anomaly, obtain the virtual loop detection result.

[0064] As a preferred solution, the virtual loop detection module 13 is used to search for the target connection path between the switchgear and the protection device in the connection topology diagram by using the depth-first search algorithm, specifically including: According to the first physical location and the second physical location, determine the physical distance between each node in the connection topology diagram; wherein, the node is the switchgear or the protection device; According to the preset correspondence between the physical distance and the edge weight, determine the edge weight between each node; Use the depth-first search algorithm to traverse the nodes in the connection topology diagram in the order of the edge weight from large to small, and obtain several alternative connection paths between the switchgear and the protection device; According to the product of the edge weights between each node in each alternative connection path, determine the path weight corresponding to each alternative connection path, and use the alternative connection path with the largest path weight as the target connection path.

[0065] As a preferred solution, the virtual loop detection module 13 is further used for: When it is detected that the virtual connection state between the auxiliary contact of any target switchgear and the input terminal of any target protection device is a virtual connection anomaly, obtain the attribute parameters of each target switchgear from the device data; According to the attribute parameters, generate the device feature identification values corresponding to each target switchgear; Use the decision tree algorithm to normalize each device feature identification value, and according to the normalized device feature identification values, determine the virtual connection anomaly level corresponding to each target switchgear according to the preset anomaly level classification threshold.

[0066] As a preferred solution, the attribute parameters include operation duration, maintenance interval, and operating environment temperature; The virtual circuit detection module 13 is used to generate device characteristic identification values corresponding to each of the target switch devices according to the attribute parameters, specifically including: According to a preset abnormal score comparison table, respectively obtain a first abnormal score corresponding to the operation duration of each target switch device, a second abnormal score corresponding to the maintenance interval, and a third abnormal score corresponding to the operating environment temperature; wherein, different operation durations, maintenance intervals, and operating environment temperatures and their corresponding abnormal scores are recorded in the abnormal score comparison table; Perform weighted summation on the first abnormal score, the second abnormal score, and the third abnormal score to obtain the device characteristic identification values corresponding to each of the target switch devices.

[0067] As a preferred solution, the virtual circuit detection module 13 is further used for: Adjust the abnormal level classification threshold according to the abnormal occurrence frequency corresponding to each virtual connection abnormal level.

[0068] In an alternative embodiment, the embodiment of the present invention can introduce intelligent analysis and real-time data acquisition technology in virtual circuit verification. By establishing an abnormal detection model and an intelligent diagnosis engine, real-time monitoring and automatic analysis are performed on the status data of the auxiliary contacts of the switch device and the input terminals of the protection device. Integrate the judgment experience of power industry experts on the virtual circuits of the substation configuration file to construct a knowledge base. When an abnormal change in the virtual circuit status is detected, the intelligent diagnosis engine quickly analyzes and combines the information in the knowledge base to give accurate diagnosis conclusions and treatment suggestions. During the virtual circuit verification process of the substation configuration file in a certain substation, the intelligent system collects data in real time. When it is found that the time difference of the status change of a certain virtual circuit exceeds the normal range, the system quickly judges that there may be a connection failure, and retrieves the treatment methods of similar cases from the knowledge base to provide reference for the operation and maintenance personnel, improving the accuracy and reliability of virtual circuit verification.

[0069] The virtual circuit intelligent verification system 100 for substation configuration files provided by the embodiment of the present invention can obtain the actual mapping relationship between the auxiliary contacts of each switch device and the input terminals of each protection device in the virtual circuit to be verified based on the device data corresponding to the virtual circuit to be verified. It can utilize this actual mapping relationship and device data to realize the automatic detection of the virtual connection status between the auxiliary contacts of the switch device and the input terminals of the protection device. Compared with the manual detection method, it effectively avoids the situations of misdetection and missed detection, significantly improves the detection accuracy of the virtual circuits of the substation configuration file, and is applicable to different input terminal arrangement methods.

[0070] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A method for intelligently checking a virtual circuit of a substation configuration file, characterized in that: include: Determine the virtual circuit to be verified and the device data corresponding to the virtual circuit to be verified according to the configuration file of the substation to be tested; According to the device data, the actual mapping relationship between the auxiliary contacts of each switch device in the virtual circuit to be checked and the input terminals of each protection device is obtained; According to the device data and the actual mapping relationship, the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device is detected to obtain the virtual circuit detection result of the substation configuration file to be tested.

2. The method for intelligently checking the virtual circuit of a substation configuration file according to claim 1, characterized in that: The step of obtaining, according to the device data, an actual mapping relationship between the auxiliary contacts of each switch device in the virtual circuit to be verified and the binary input terminals of each protection device specifically includes: According to the device data, the auxiliary contact status data of each of the switch devices and the input terminal status data of each of the protection devices are obtained; According to the auxiliary contact state data and the binary input terminal state data, obtaining a first state value of the auxiliary contact, a second state value of the binary input terminal, and a state change time difference between the auxiliary contact and the binary input terminal at each switching action of the switch device; According to the state change time difference and the preset signal transmission delay, the first state value and the second state value are matched to obtain the actual mapping relationship between each of the auxiliary contacts and each of the binary input terminals in the virtual circuit to be checked.

3. The method for intelligently checking the virtual circuit of a substation configuration file according to claim 1, characterized in that: The detecting, according to the device data and the actual mapping relationship, the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device to obtain the virtual circuit detection result of the configuration file of the substation to be tested specifically includes: Based on the switch device to be tested and the protection device to be tested having the actual mapping relationship, the auxiliary contact state data of the switch device to be tested and the input terminal state data of the protection device to be tested are obtained from the device data; According to the auxiliary contact state data and the input terminal state data, obtaining the state change time difference between the auxiliary contact of the switch device under test and the input terminal of the protection device under test at each switching action of the switch device under test; When it is detected that the state change time difference is greater than a preset abnormal time threshold, the virtual connection state between the auxiliary contact of the switch device to be tested and the input terminal of the protection device to be tested is determined to be a virtual connection abnormality; The virtual circuit detection result is obtained according to each group of auxiliary contacts of the switch device to be tested and the input terminals of the protection device to be tested whose virtual connection status is abnormal virtual connection.

4. The method for intelligently checking the virtual circuit of a substation configuration file according to claim 1, characterized in that: The detecting, according to the device data and the actual mapping relationship, the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device to obtain the virtual circuit detection result of the configuration file of the substation to be tested specifically includes: Acquire the terminal physical position, terminal identifier and contact function attribute of each of the binary input terminals from the device data; Inputting the terminal physical position, the terminal identifier and the contact function attribute into a preset machine learning model, obtaining the standard arrangement rule of the binary input terminals output by the machine learning model; wherein the machine learning model is obtained by training based on a preset binary input terminal prior arrangement data set; the standard arrangement rule of the binary input terminals is used to indicate the standard mapping relationship between each of the auxiliary contacts and each of the binary input terminals; Converting the actual mapping relationship and the standard mapping relationship into a first eigenvector and a second eigenvector respectively, and calculating the Euclidean distance between the first eigenvector and the second eigenvector; When it is detected that the Euclidean distance is greater than a preset Euclidean distance threshold, it is determined that there is at least one group of auxiliary contacts and input terminals whose virtual connection states are abnormal virtual connections in the virtual circuit to be checked.

5. The method for intelligently checking the virtual circuit of a substation configuration file according to claim 1, characterized in that: The detecting, according to the device data and the actual mapping relationship, the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device to obtain the virtual circuit detection result of the configuration file of the substation to be tested specifically includes: Determining, according to the actual mapping relationship, a connection relationship between each of the switch devices and each of the protection devices; Acquire a first physical position of each of the switch devices and a second physical position of each of the protection devices from the device data; Generate a connection topology diagram between each of the switch devices and each of the protection devices using a graph theory algorithm according to the connection relationship, the first physical position, and the second physical position; Searching for a target connection path between the switch device and the protection device in the connection topology graph using a depth-first search algorithm; Acquire first state timing data of a switch device and second state timing data of a protection device in each of the target connection paths from the device data; Calculate the state change time series correlation between the switch device and the protection device in each of the target connection paths by using a correlation calculation method according to the first state time series data and the second state time series data; Determining the connectivity probability between the switch device and the protection device in each of the target connection paths according to the preset correspondence between the state change timing correlation and the connectivity probability; Inputting the path weight corresponding to the target connection path and the connectivity probability into a preset Bayesian network to obtain the virtual connection abnormality probability output by the Bayesian network; wherein the path weight is determined based on the first physical location and the second physical location; and the Bayesian network is obtained by training based on historical topology data between the switch device and the protection device; When it is detected that the probability of the virtual connection abnormality is greater than a preset probability threshold, the virtual connection state between the auxiliary contact of the switch device in the target connection path and the input terminal of the protection device is determined to be a virtual connection abnormality; The virtual circuit detection result is obtained according to each group of auxiliary contacts and input terminals whose virtual connection states are abnormal virtual connection.

6. The method for intelligently checking the virtual circuit of a substation configuration file according to claim 5, characterized in that: The step of searching the connection topology graph for a target connection path between the switch device and the protection device using a depth-first search algorithm specifically includes: Determine the physical distance between each node in the connection topology diagram according to the first physical location and the second physical location; wherein the node is the switch device or the protection device; Determining the edge weights between the nodes according to the preset correspondence between the physical distances and the edge weights; Using a depth-first search algorithm, traversing nodes in the connection topology graph in descending order of the edge weights, to obtain a plurality of alternative connection paths between the switch device and the protection device; The path weight corresponding to each of the candidate connection paths is determined according to the product of the edge weights between the nodes in each of the candidate connection paths, and the candidate connection path with the largest path weight is used as the target connection path.

7. The method for intelligently checking the virtual circuit of a substation configuration file according to claim 1, characterized in that: After detecting the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device, the method further includes: When it is detected that the virtual connection state between the auxiliary contact of any target switch device and the input terminal of any target protection device is abnormal, the attribute parameters of each target switch device are obtained from the device data; Generating a device characteristic identification value corresponding to each of the target switch devices according to the attribute parameters; A decision tree algorithm is used to normalize each of the device characteristic identification values, and based on the normalized device characteristic identification values ​​and a preset abnormality level classification threshold, the virtual connection abnormality level corresponding to each of the target switch devices is determined.

8. The method for intelligently checking the virtual circuit of a substation configuration file according to claim 7, characterized in that: The attribute parameters include operating time, maintenance interval and operating environment temperature; Generating the device characteristic identification value corresponding to each of the target switch devices according to the attribute parameters specifically includes: According to a preset abnormal score comparison table, a first abnormal score corresponding to the operating time of each target switch device, a second abnormal score corresponding to the maintenance interval, and a third abnormal score corresponding to the operating environment temperature are respectively obtained; wherein the abnormal score comparison table records the abnormal scores corresponding to different operating time, maintenance interval, and operating environment temperature; The first abnormality score, the second abnormality score and the third abnormality score are weightedly summed to obtain a device characteristic identification value corresponding to each of the target switch devices.

9. The method for intelligently checking the virtual circuit of a substation configuration file according to claim 7, characterized in that: After determining the virtual connection abnormality level corresponding to each of the target switch devices according to the preset abnormality level classification threshold, the method further includes: The abnormal level classification threshold is adjusted according to the abnormal occurrence frequency corresponding to each virtual connection abnormal level.

10. A substation configuration file virtual circuit intelligent verification system, characterized in that: include: A data acquisition module, used to determine the virtual circuit to be verified and the device data corresponding to the virtual circuit to be verified according to the configuration file of the substation to be tested; An actual mapping relationship determination module, used to obtain the actual mapping relationship between the auxiliary contacts of each switch device in the virtual circuit to be verified and the input terminals of each protection device according to the device data; The virtual circuit detection module is used to detect the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the substation configuration file to be tested.

11. The substation configuration file virtual circuit intelligent verification system according to claim 10, characterized in that: The actual mapping relationship determination module is used to obtain the actual mapping relationship between the auxiliary contacts of each switch device in the virtual circuit to be verified and the input terminals of each protection device according to the device data, specifically including: According to the device data, the auxiliary contact status data of each of the switch devices and the input terminal status data of each of the protection devices are obtained; According to the auxiliary contact state data and the binary input terminal state data, obtaining a first state value of the auxiliary contact, a second state value of the binary input terminal, and a state change time difference between the auxiliary contact and the binary input terminal at each switching action of the switch device; According to the state change time difference and the preset signal transmission delay, the first state value and the second state value are matched to obtain the actual mapping relationship between each of the auxiliary contacts and each of the binary input terminals in the virtual circuit to be checked.

12. The substation configuration file virtual circuit intelligent verification system according to claim 10, characterized in that: The virtual circuit detection module is used to detect the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be tested, specifically including: Based on the switch device to be tested and the protection device to be tested having the actual mapping relationship, the auxiliary contact state data of the switch device to be tested and the input terminal state data of the protection device to be tested are obtained from the device data; According to the auxiliary contact state data and the input terminal state data, obtaining the state change time difference between the auxiliary contact of the switch device under test and the input terminal of the protection device under test at each switching action of the switch device under test; When it is detected that the state change time difference is greater than a preset abnormal time threshold, the virtual connection state between the auxiliary contact of the switch device to be tested and the input terminal of the protection device to be tested is determined to be a virtual connection abnormality; The virtual circuit detection result is obtained according to each group of auxiliary contacts of the switch device to be tested and the input terminals of the protection device to be tested whose virtual connection status is abnormal virtual connection.

13. The substation configuration file virtual circuit intelligent verification system according to claim 10, characterized in that: The virtual circuit detection module is used to detect the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be tested, specifically including: Acquire the terminal physical position, terminal identifier and contact function attribute of each of the binary input terminals from the device data; Inputting the terminal physical position, the terminal identifier and the contact function attribute into a preset machine learning model, obtaining the standard arrangement rule of the binary input terminals output by the machine learning model; wherein the machine learning model is obtained by training based on a preset binary input terminal prior arrangement data set; the standard arrangement rule of the binary input terminals is used to indicate the standard mapping relationship between each of the auxiliary contacts and each of the binary input terminals; Converting the actual mapping relationship and the standard mapping relationship into a first eigenvector and a second eigenvector respectively, and calculating the Euclidean distance between the first eigenvector and the second eigenvector; When it is detected that the Euclidean distance is greater than a preset Euclidean distance threshold, it is determined that there is at least one group of auxiliary contacts and input terminals whose virtual connection states are abnormal virtual connections in the virtual circuit to be checked.

14. The substation configuration file virtual circuit intelligent verification system according to claim 10, characterized in that: The virtual circuit detection module is used to detect the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be tested, specifically including: Determining, according to the actual mapping relationship, a connection relationship between each of the switch devices and each of the protection devices; Acquire a first physical position of each of the switch devices and a second physical position of each of the protection devices from the device data; Generate a connection topology diagram between each of the switch devices and each of the protection devices using a graph theory algorithm according to the connection relationship, the first physical position, and the second physical position; Searching for a target connection path between the switch device and the protection device in the connection topology graph using a depth-first search algorithm; Acquire first state timing data of a switch device and second state timing data of a protection device in each of the target connection paths from the device data; Calculate the state change time series correlation between the switch device and the protection device in each of the target connection paths by using a correlation calculation method according to the first state time series data and the second state time series data; Determining the connectivity probability between the switch device and the protection device in each of the target connection paths according to the preset correspondence between the state change timing correlation and the connectivity probability; Inputting the path weight corresponding to the target connection path and the connectivity probability into a preset Bayesian network to obtain the virtual connection abnormality probability output by the Bayesian network; wherein the path weight is determined based on the first physical location and the second physical location; and the Bayesian network is obtained by training based on historical topology data between the switch device and the protection device; When it is detected that the probability of the virtual connection abnormality is greater than a preset probability threshold, the virtual connection state between the auxiliary contact of the switch device in the target connection path and the input terminal of the protection device is determined to be a virtual connection abnormality; The virtual circuit detection result is obtained according to each group of auxiliary contacts and input terminals whose virtual connection states are abnormal virtual connection.

15. The substation configuration file virtual circuit intelligent verification system according to claim 14, characterized in that: The virtual circuit detection module is used to search for a target connection path between the switch device and the protection device in the connection topology map using a depth-first search algorithm, specifically including: Determine the physical distance between each node in the connection topology diagram according to the first physical location and the second physical location; wherein the node is the switch device or the protection device; Determining the edge weights between the nodes according to the preset correspondence between the physical distances and the edge weights; Using a depth-first search algorithm, traversing nodes in the connection topology graph in descending order of the edge weights, to obtain a plurality of alternative connection paths between the switch device and the protection device; The path weight corresponding to each of the candidate connection paths is determined according to the product of the edge weights between the nodes in each of the candidate connection paths, and the candidate connection path with the largest path weight is used as the target connection path.

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