Single-phase disconnection fault data capturing method based on RPA robot

Abstract

The invention provides a single-phase disconnection fault data capturing method based on an RPA robot. The data capturing method specifically comprises the steps of setting a data capturing demand according to a current single-phase disconnection fault range and historical single-phase disconnection fault processing data; an access system is set based on the data capture requirement, and a capture subprogram is selected according to the corresponding access system; historical grabbing information of the RPA robot is obtained, and a corresponding execution RPA robot is matched based on the corresponding grabbing subprogram; and each RPA robot executes data grabbing based on the corresponding grabbing subprogram and performs integration processing to obtain current single-phase disconnection fault data. According to the method, manual operation can be simulated through the RPA robot, so that data in a power system lacking a data direct acquisition function can be grabbed, data required by fault positioning can be accurately positioned and analyzed, the query efficiency of single-phase disconnection fault data is further improved, and the positioning efficiency of subsequent single-phase disconnection faults is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault handling, and particularly to a method for capturing single-phase disconnection fault data based on an RPA robot. Background Art

[0002] In the power system, the distribution network plays a key role in distributing electric energy to users. However, due to the complex structure of the distribution network, numerous devices, and variable operating environments, single-phase disconnection faults occur frequently. Such faults not only cause power outages for users but also pose a threat to the safe and stable operation of the power grid. Therefore, it is necessary to ensure the timely identification and handling of single-phase disconnection faults to ensure the safety and stability of the power grid operation.

[0003] Traditional single-phase disconnection fault location requires manual query of single-phase disconnection fault data such as public and special transformer equipment information to mark the fault point on the single-line diagram, and then narrow the fault range based on the marked single-line diagram to achieve the location of single-phase disconnection faults. Moreover, among the single-phase disconnection fault data, the public and special transformer equipment information, as an important feature for analyzing disconnection faults, is mainly stored in the distribution network area four system and the power consumption acquisition system. Such systems lack the function of directly collecting data and require manual login to query and call information. Therefore, in the scenario of a large number of public and special transformer users in the distribution network, the query efficiency of manual information query is low, and the subsequent single-phase disconnection fault location efficiency cannot be guaranteed. Summary of the Invention

[0004] The purpose of the present invention is to overcome the drawback of low query efficiency by manual data query when facing the query of single-phase disconnection fault data such as public and special transformer equipment information lacking the function of directly collecting data, and provide a method for capturing single-phase disconnection fault data based on an RPA robot. By simulating human operations with the RPA robot, it is possible to capture data in a power system lacking the function of directly collecting data, and analyze the current data capture requirements for single-phase disconnection faults to select a suitable RPA robot and accurately set the capture subroutine of the RPA robot, thereby accurately locating and analyzing the required data, further improving the query efficiency of single-phase disconnection fault data, and ensuring the subsequent single-phase disconnection fault location efficiency.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] An automatic method for capturing single-phase disconnection fault data based on an RPA robot includes:

[0007] Setting data capture requirements according to the current single-phase disconnection fault range and historical single-phase disconnection fault handling data;

[0008] Setting an access system based on the data capture requirements and selecting a capture subroutine according to the corresponding access system;

[0009] Obtain the historical scraping information of the RPA robot, and match the corresponding executing RPA robot based on the corresponding scraping subroutine.

[0010] Each RPA robot executes data scraping based on the corresponding scraping subroutine, and integrates and processes the scraped data according to the data scraping requirements to obtain the current single-phase broken wire fault data.

[0011] Single-phase broken wire fault data such as public special transformer equipment information is stored in the distribution network area four system and the power consumption collection system lacking the function of direct data acquisition. Relevant data extraction can only be realized after logging in to the system through the account password. The RPA robot can simulate human operations to interact with the computer interface, and this kind of human operation can be recorded and replayed to realize the automated processing of data. Therefore, using the RPA robot to imitate the system login and data scraping operations of access systems such as the distribution network area four system can realize the automated data scraping of single-phase broken wire fault data without manual participation, improving the query efficiency of information query. And analyze according to the current data scraping requirements of single-phase broken wire faults to select a suitable RPA robot and set the scraping subroutine specifically to quickly locate the data position that needs to be scraped, further improving the query efficiency of information query and ensuring the positioning efficiency of subsequent single-phase broken wire faults.

[0012] Further, setting the data scraping requirements according to the current single-phase broken wire fault range and historical single-phase broken wire fault handling data includes:

[0013] Divide the historical single-phase broken wire fault handling data to determine the fault call data for each broken wire fault.

[0014] Cluster the fault call data according to the fault type of each broken wire fault to determine the fault call data type for each fault type.

[0015] Determine the associated operating lines based on the current single-phase broken wire fault range, and determine the broken wire fault type and broken wire fault frequency of the corresponding historical broken wire faults for each associated operating line according to the historical single-phase broken wire fault handling data.

[0016] Determine the corresponding fault probability according to the broken wire fault type and broken wire fault frequency of the corresponding historical broken wire faults for each associated operating line.

[0017] Taking each associated operating line as the data scraping target, determine the data type and scraping weight to be scraped for each associated operating line according to the corresponding fault probability and the fault call data type of all broken wire fault types.

[0018] Further, setting up an access system based on the data scraping requirements and selecting a scraping subroutine according to the corresponding access system includes:

[0019] Determining the associated running lines for data scraping and the corresponding data types based on the data scraping requirements;

[0020] Determining the access system according to the data type and selecting the scraping subroutine for each access system according to the corresponding access system type;

[0021] Obtaining the data transmission relationship between each access system and each associated running line, and setting the parameters of the corresponding scraping subroutine based on the data transmission relationship, the data type, and the scraping weight of each associated running line.

[0022] Further, setting the parameters of the corresponding scraping subroutine based on the data transmission relationship, the data type, and the scraping weight of each associated running line includes:

[0023] Setting the access system of the scraping subroutine based on the data transmission relationship;

[0024] Setting the scraping data type and data scraping keywords of the scraping subroutine according to the data type of each associated running line;

[0025] Setting the data conversion rule, naming rule, and data verification rule according to the data format of each scraping data in the corresponding access system;

[0026] Setting the scraping frequency and scraping data volume of each scraping data according to the corresponding scraping weight.

[0027] Further, obtaining the historical scraping information of the RPA robot and matching the corresponding executing RPA robot based on the corresponding scraping subroutine includes:

[0028] Obtaining the scraping subroutines executed by each RPA robot and the effective data ratio of the scraping data thereof based on the historical scraping information of the RPA robot;

[0029] Taking the parameters of the scraping subroutine of each access system as the matching parameters, matching the set scraping subroutine with the scraping subroutines executed by each RPA robot, and determining the matching degree between each RPA robot and the scraping subroutine of each access system;

[0030] Scoring each RPA robot based on the matching degree and the effective data ratio of the scraping data, and selecting the executing RPA robot of the scraping subroutine of each access system according to the scoring result.

[0031] Further, after determining the matching degree between each RPA robot and the scraping subroutine of each access system, the following is also executed:

[0032] Compare the matching degree between each RPA robot and the scraping subroutine of each access system with a preset matching threshold;

[0033] Based on the comparison result, filter out the access systems whose matching degree between the corresponding scraping subroutine and each RPA robot is lower than the preset matching threshold;

[0034] Select the RPA robot with the highest proportion of valid data in the corresponding scraped data as the RPA robot for executing the scraping subroutine of the corresponding filtered access system.

[0035] Furthermore, the data scraping by each RPA robot based on the corresponding scraping subroutine includes:

[0036] Configure the RPA robot based on the corresponding scraping subroutine and configure the data source connection information of the RPA robot according to the corresponding access system;

[0037] Based on the data source connection information, establish a data source connection between the RPA robot and the access system, and the PRA robot performs data scraping according to the set scraping subroutine.

[0038] Furthermore, the data source connection information includes database connection parameters, web access permissions, and API interfaces with the access system.

[0039] Furthermore, the data scraping by each RPA robot based on the corresponding scraping subroutine further includes:

[0040] The RPA robot determines the database storing the corresponding scraped data in the access system according to the corresponding scraped data type based on the corresponding scraping subroutine;

[0041] According to the corresponding data scraping keywords, obtain the corresponding scraped data in the database according to the set scraping frequency and scraping data volume, and perform data verification on the obtained scraped data according to the data verification rules;

[0042] Perform format conversion on the scraped data that passes the data verification according to the data conversion rules, and name the format-converted scraped data according to the naming rules.

[0043] Furthermore, the data scraping by each RPA robot based on the corresponding scraping subroutine and integrating and processing the scraped data according to the data scraping requirements further includes:

[0044] Divide all the scraped data according to the naming of the scraped data to determine the operation data of each associated operation line;

[0045] Construct a single-line diagram within the current single-phase open-circuit fault range according to the location information and topological connection relationship of each associated operating line, and label the single-line diagram based on the operating data corresponding to each associated operating line.

[0046] The beneficial effects of the present invention are:

[0047] Single-phase open-circuit fault data such as public and special transformer equipment information is stored in the distribution network area four system and power consumption collection system lacking direct data acquisition function. Relevant data extraction can only be achieved after logging into the system with an account password. The RPA robot can simulate human operations to interact with the computer interface, and such human operations can be recorded and replayed to achieve automated data processing. Therefore, the RPA robot is used to imitate the system login and data scraping operations for access systems such as the distribution network area four system, realizing automated data scraping for single-phase open-circuit fault data without manual participation, improving the query efficiency of information query. And analyze according to the current data scraping requirements for single-phase open-circuit faults to select a suitable RPA robot and specifically set scraping subroutines to quickly locate the data positions that need to be scraped, further improving the query efficiency of information query and ensuring the location efficiency of subsequent single-phase open-circuit faults. Description of the Drawings

[0048] Figure 1 It is a schematic flow diagram of the present invention. Detailed Embodiments

[0049] The present invention will be further described below with reference to the drawings and embodiments.

[0050] Embodiment:

[0051] The automatic data scraping method for single-phase open-circuit fault data based on RPA robot, as Figure 1 shown, includes:

[0052] Set data scraping requirements according to the current single-phase open-circuit fault range and historical single-phase open-circuit fault handling data;

[0053] Set an access system based on the data scraping requirements and select a scraping subroutine according to the corresponding access system;

[0054] Obtain the historical scraping information of the RPA robot and match the corresponding executing RPA robot based on the corresponding scraping subroutine;

[0055] Each RPA robot executes data scraping based on the corresponding scraping subroutine and integrates and processes the scraped data according to the data scraping requirements to obtain the current single-phase open-circuit fault data.

[0056] The single-phase open-circuit fault data involved in single-phase open-circuit fault location mainly include the information of public and special transformers, such as the voltage and current data of public transformers, the voltage and current information of special transformers, and the voltage and current information of intelligent switches. These are important features for analyzing open-circuit faults. Among them, the voltage and current information of public transformers and intelligent switches are stored in the distribution network area 4 system, which is accessed through a web page. The corresponding data cannot be directly obtained. After logging in and verification, data extraction is required. The voltage and current information of special transformers is stored in the power consumption collection system of marketing. Lack of direct data acquisition function, after manual login, the corresponding data needs to be queried.

[0057] RPA (Robotic Process Automation) robots can interact with computer interfaces by simulating human operations on the keyboard and mouse, such as clicking, scrolling, inputting, etc. These operations can be recorded and replayed, enabling automated data processing. This way of simulating human operations allows RPA robots to perform data scraping on web pages or systems like humans, without the need for in-depth programming or modification of the system. Therefore, using RPA robots to replace manual work for logging in to the distribution network area 4 system and the power consumption collection system and data scraping can significantly improve the information query efficiency for single-phase open-circuit fault data.

[0058] Since single-phase open-circuit faults account for a relatively small proportion among the fault events occurring during the operation of the distribution network, their related data also account for a relatively small proportion in the data stored in access systems such as the distribution network area 4 system and the power consumption collection system. Therefore, to ensure that RPA robots can quickly locate the corresponding data, precise data scraping requirements are set according to the current single-phase open-circuit fault range, and then appropriate RPA robots are selected and corresponding scraping subroutines are set to ensure that RPA robots can quickly locate the required data when performing data scraping.

[0059] Considering that the simulated operations of RPA robots can be recorded and reproduced, RPA robots are selected based on the corresponding historical scraping information, and then they execute the corresponding scraping subroutines. Instead of setting up RPA robots from scratch every time, modifications are made based on historical operations, further improving the data scraping efficiency of RPA robots.

[0060] In addition to the information of public and special transformer equipment, single-phase open-circuit faults also need to be located by combining other relevant data, such as power factor, load capacity, etc. Considering that the types of power equipment set on different operating lines are different and the environments are different, the types of single-phase open-circuit faults that may occur may also vary, and the types of data involved are also different. In order to ensure the data availability and effectiveness of data capture, according to the current impact of single-phase open-circuit faults and combined with the historical single-phase open-circuit fault handling data, the current data capture requirements are accurately set.

[0061] Specifically, setting the data capture requirements according to the current single-phase open-circuit fault range and historical single-phase open-circuit fault handling data includes:

[0062] Divide the historical single-phase open-circuit fault handling data to determine the fault call data for each open-circuit fault.

[0063] Cluster the fault call data according to the fault types of each open-circuit fault to determine the fault call data types for each fault type.

[0064] Determine the associated operating lines based on the current single-phase open-circuit fault range, and determine the open-circuit fault types and open-circuit fault frequencies of the historical open-circuit faults corresponding to each associated operating line according to the historical single-phase open-circuit fault handling data.

[0065] Determine the corresponding fault probabilities according to the open-circuit fault types and open-circuit fault frequencies of the historical open-circuit faults corresponding to each associated operating line.

[0066] Taking each associated operating line as the data capture target, determine the data types and capture weights to be captured for each associated operating line according to the corresponding fault probabilities and the fault call data types for all open-circuit fault types.

[0067] When dividing the historical single-phase open-circuit fault handling data, it can be divided in terms of time or event dimensions to determine the fault call data for each open-circuit fault, so as to determine the key information such as the occurrence time, location, and cause of each open-circuit fault, providing an accurate data source for subsequent clustering analysis and data capture.

[0068] Through clustering analysis, similar fault types can be grouped into one category, and the typical characteristics and data types of each fault type can be determined, so as to determine all data types available for single-phase open-circuit fault analysis, providing data support for subsequent data capture requirements setting.

[0069] Specifically, when clustering the fault call data according to the fault type of each disconnection fault and determining the fault call data types of each fault type, relevant features related to the fault type, such as equipment status, environmental factors, human factors, etc., can be extracted from the fault call data first, and then the features can be clustered through clustering algorithms such as K-means and hierarchical clustering to form different fault types. Finally, according to the clustering results, the typical features and data types of each fault type can be determined.

[0070] Although precise positioning cannot be achieved, before data capture, the received fault alarm information and the large-scale line patrol operations of the power supply station can obtain a rough range of single-phase disconnection faults. By analyzing the associated operating lines involved in this range, the possible single-phase disconnection fault types generated on the associated operating lines can be determined, and then the available part of the data in the data generated on the associated operating lines can be determined, thereby optimizing the quality of the captured data.

[0071] Moreover, considering that the operating conditions of different operating lines are different and the probabilities of single-phase disconnection faults occurring are different, and the amount of data captured is generally limited. To ensure that effective data can be captured, the capture weights for data capture are set according to the corresponding fault probabilities to adjust the corresponding data capture amounts, and the relevant data of the associated operating lines with high fault probabilities are captured preferentially.

[0072] After determining the data capture requirements, the specific data types to be captured can be determined. Through their data types, the specific access systems for storing the corresponding data can be determined, and then specific capture subroutines can be selected in combination with the data characteristics of the corresponding access systems. The capture subroutines at least include the relevant steps of the operation processes such as logging in and verifying the corresponding access systems.

[0073] Specifically, setting the access system based on the data capture requirements and selecting the capture subroutine according to the corresponding access system includes:

[0074] Determining the associated operating lines for data capture and the corresponding data types based on the data capture requirements;

[0075] Determining the access system according to the data type and selecting the capture subroutine for each access system according to the corresponding access system type;

[0076] Obtaining the data transmission relationships between each access system and each associated operating line, and setting the parameters of the corresponding capture subroutine based on the data transmission relationships, the data types, and the capture weights of each associated operating line.

[0077] The data types stored in each access system are fixed. Through the data types, the access systems for which data capture is required can be determined, and then the corresponding capture subroutines can be determined, which record the basic information for logging in to the corresponding access system and capturing data from it.

[0078] Different types of data in different lines will be stored in different access systems. Therefore, it is necessary to determine the data transmission relationship between each access system and each associated operating line, and then determine the specific data to be captured from each access system. Combining with the corresponding capture weights, the specific data capture volume is set to ensure the efficient and accurate implementation of single-phase disconnection fault location.

[0079] However, the specific details of these data captures may not be adapted to the corresponding parameters in the captured subroutines. Therefore, it is necessary to further set the parameters of the captured subroutines according to the currently determined data capture requirements to ensure the data capture execution effect of the RPA robot.

[0080] Specifically, setting the parameters of the corresponding captured subroutines based on the data transmission relationship and the data types and capture weights of each associated operating line includes:

[0081] Setting the access system of the captured subroutine based on the data transmission relationship;

[0082] Setting the captured data type and data capture keywords of the captured subroutine according to the data type of each associated operating line;

[0083] Setting the data conversion rules, naming rules, and data verification rules according to the data format of each captured data in the corresponding access system;

[0084] Setting the capture frequency and capture data volume of each captured data according to the corresponding capture weight.

[0085] The data conversion rules include data type conversion, data unit conversion, etc.

[0086] And in order to facilitate the identification and use of data in subsequent processing and analysis, a unified naming rule is set for each captured data. The naming rule includes at least the name of the associated operating line, the name of the data point, the timestamp format, and the data storage path, etc.

[0087] The formulation of the data verification rule is to ensure the accuracy and integrity of the captured data, and to screen out missing data and abnormal data, which can be specifically set according to the data type.

[0088] And in order to optimize the captured data quality of each data capture operation, the capture frequency and capture data volume of each captured data are set according to the corresponding capture weight. The higher the capture weight, the higher the fault probability. Correspondingly, the set capture frequency is higher and the capture data volume is also larger.

[0089] After determining the data capture requirements, combined with the historical capture information of the RPA robot, select a suitable RPA robot.

[0090] Specifically, obtaining the historical scraping information of the RPA robot and matching the corresponding executing RPA robot based on the corresponding scraping subroutine includes:

[0091] Obtaining the effective data ratio of the scraping subroutines executed by each RPA robot and their scraping data based on the historical scraping information of the RPA robot;

[0092] Using the parameters of the scraping subroutines of each access system as matching parameters, matching the set scraping subroutines with the scraping subroutines executed by each RPA robot, and determining the matching degree between each RPA robot and the scraping subroutines of each access system;

[0093] Scoring each RPA robot based on the matching degree and the effective data ratio of the scraping data, and selecting the executing RPA robot for the scraping subroutines of each access system according to the scoring results.

[0094] Among them, the corresponding historical scraping information can be extracted from the log system or database of the RPA robot. The historical scraping information includes the name of the scraping subroutine executed by the RPA robot, the execution time, the amount of scraped data, and the number of effective data. Furthermore, when each RPA robot executes each scraping subroutine, the corresponding effective data ratio can be calculated. The effective data ratio can intuitively reflect the data scraping ability of the RPA robot, and the effective data ratio = the number of effective data / the total amount of scraped data.

[0095] List the parameters of the scraping subroutines of each access system, such as data type, scraping keywords, and data conversion rules, etc., match them with the corresponding scraping subroutines, and determine whether the RPA robot has executed scraping subroutines with similar scraping data requirements.

[0096] Among them, the matching degree between each RPA robot and the scraping subroutines of each access system can intuitively reflect the amount of parameter adjustment operations required when executing the corresponding scraping subroutines. The lower the matching degree, the higher the amount of parameter adjustment operations, and the longer the required preprocessing time, which will reduce the subsequent data scraping efficiency. Combining with the effective data ratio that can display the data scraping ability, the best RPA robot for executing the scraping subroutines of each access system can be selected.

[0097] Specifically, the specific score can be obtained by weighted summation. If there are similar scores, the stability, load conditions, etc. of the RPA robot can be used to assist in the selection.

[0098] After determining the matching degree between each RPA robot and the scraping subroutines of each access system, the following is also executed:

[0099] Compare the matching degree between each RPA robot and the scraping subroutine of each access system with a preset matching threshold;

[0100] Based on the comparison results, filter out the access systems for which the matching degree between the corresponding scraping subroutine and each RPA robot is lower than the preset matching threshold;

[0101] Select the RPA robot with the highest proportion of valid data in the corresponding scraped data as the RPA robot for executing the scraping subroutine of the corresponding filtered access system.

[0102] For the scraping subroutines with relatively low matching degrees, a large number of parameter adjustments are required for any RPA robot, and the difference in the pre - preparation time is not much. Therefore, directly select the RPA robot with the highest proportion of valid data to reduce the corresponding calculation amount and optimize the selection efficiency of the RPA robot.

[0103] After determining the RPA robot for one access system, remove it from the list of selectable RPA robots for other access systems to avoid selection conflicts of RPA robots.

[0104] After determining the corresponding executing RPA robot, each RPA robot performs data scraping based on the corresponding scraping subroutine, including:

[0105] Configure the RPA robot based on the corresponding scraping subroutine and configure the data source connection information of the RPA robot according to the corresponding access system;

[0106] Based on the data source connection information, establish a data source connection between the RPA robot and the access system, and the PRA robot performs data scraping according to the set scraping subroutine.

[0107] The data source connection information includes database connection parameters with the access system, web access permissions, and API interfaces.

[0108] Each RPA robot performing data scraping based on the corresponding scraping subroutine further includes:

[0109] The RPA robot determines the database storing the corresponding scraped data in the access system according to the corresponding scraped data type based on the corresponding scraping subroutine;

[0110] According to the corresponding data scraping keywords, obtain the corresponding scraped data in the database according to the set scraping frequency and scraping data volume, and perform data verification on the obtained scraped data according to the data verification rules;

[0111] Perform format conversion on the scraped data passing the data verification according to the data conversion rules, and name the format - converted scraped data according to the naming rules.

[0112] After retrieving the required data from the corresponding access system, data verification is performed to remove missing data and abnormal data, and the retrieved data after data verification is subjected to format conversion to ensure output in a unified data format. Also, for the purpose of facilitating identification and search, the retrieved data after format conversion is named, which can effectively improve the analysis efficiency of subsequent fault location analysis.

[0113] Each of the RPA robots performs data retrieval based on the corresponding retrieval subroutine, and integrates and processes the retrieved data according to the data retrieval requirements, further including:

[0114] Divide all the retrieved data according to the naming of the retrieved data to determine the operation data of each associated operation line;

[0115] Construct a single-line diagram within the scope of the current single-phase open-circuit fault according to the location information and topological connection relationship of each associated operation line, and label the single-line diagram according to the operation data corresponding to each associated operation line.

[0116] After retrieving the data related to the single-phase open-circuit fault, display it in the form of a single-line diagram to facilitate the subsequent rapid location of the single-phase open-circuit fault.

[0117] The above-described embodiments are only a preferred solution of the present invention, and do not impose any form of limitation on the present invention. There are other variations and modifications without exceeding the technical solutions described in the claims.

Claims

1. The method for automatically capturing single-phase line break fault data based on RPA robot is characterized by: include: Set data capture requirements based on the current single-phase disconnection fault range and historical single-phase disconnection fault handling data; Set up access systems based on data capture requirements and select capture subroutines according to the corresponding access systems; Obtain the historical crawling information of the RPA robot and match the corresponding execution RPA robot based on the corresponding crawling subroutine; Each RPA robot executes data capture based on the corresponding capture subroutine, and integrates and processes the captured data according to the data capture requirements to obtain the current single-phase line break fault data.

2. The single-phase disconnection fault data capture method based on the RPA robot according to claim 1 is characterized in that: The data capture requirements are set according to the current single-phase disconnection fault range and historical single-phase disconnection fault handling data, including: The historical single-phase disconnection fault handling data is divided to determine the fault call data of each disconnection fault; the fault call data is clustered according to the fault type of each disconnection fault to determine the type of fault call data for each fault type; Determine the associated operating lines based on the current single-phase disconnection fault range, and determine the disconnection fault type and disconnection fault frequency of each associated operating line corresponding to the historical disconnection fault according to the historical single-phase disconnection fault handling data; Determine the corresponding fault probability according to the line disconnection fault type and line disconnection fault frequency corresponding to the historical line disconnection fault of each associated operating line; Taking each associated running line as the data capture target, the data type and capture weight for each associated running line are determined respectively according to the corresponding fault probability and the fault call data type corresponding to all line break fault types.

3. The single-phase disconnection fault data capture method based on RPA robot according to claim 2 is characterized in that: The step of setting up an access system based on data capture requirements and selecting a capture subroutine according to the corresponding access system includes: Determine the associated operation routes and corresponding data types for data capture based on data capture requirements; Determine the access system according to the data type, and select the crawling subroutine of each access system according to the corresponding access system type; The data transmission relationship between each access system and each associated operation line is obtained, and the parameters of the corresponding capture subroutine are set based on the data transmission relationship and the data type and capture weight of each associated operation line.

4. The single-phase disconnection fault data capture method based on the RPA robot according to claim 3 is characterized in that: The setting of the parameters of the corresponding grabbing subroutine based on the data transmission relationship and the data type and grabbing weight of each associated running line includes: An access system for setting up a crawling subroutine based on a data transmission relationship; According to the data type of each associated running line, set the capture data type and data capture keyword of the capture subroutine; According to the data format of each captured data in the corresponding access system, set data conversion rules, naming rules and data verification rules; Set the crawling frequency and amount of each type of crawled data according to the corresponding crawling weight.

5. The single-phase disconnection fault data capture method based on RPA robot according to claim 4 is characterized in that: The obtaining of historical crawling information of the RPA robot and matching the corresponding execution RPA robot based on the corresponding crawling subroutine include: Based on the historical crawling information of the RPA robot, obtain the crawling subroutines executed by each RPA robot and the valid data ratio of its crawled data; Using the parameters of the crawling subprogram of each access system as matching parameters, the set crawling subprogram is matched with the crawling subprogram executed by each RPA robot to determine the matching degree between each RPA robot and the crawling subprogram of each access system; Each RPA robot is scored based on the matching degree and the proportion of valid data in the captured data, and the RPA robot executing the capture subroutine of each access system is selected according to the scoring results.

6. The single-phase disconnection fault data capture method based on RPA robot according to claim 5 is characterized in that: After determining the matching degree between each RPA robot and each access system's crawling subroutine, the following is further performed: Compare the matching degree between each RPA robot and each crawling subroutine connected to the system with the preset matching threshold; Based on the comparison results, select access systems whose matching degree between the corresponding crawling subprogram and each RPA robot is lower than the preset matching threshold; The RPA robot with the highest percentage of valid data in the corresponding captured data is selected as the RPA robot that executes the captured subroutine of the corresponding access system.

7. The single-phase disconnection fault data capture method based on RPA robot according to claim 6 is characterized in that: Each RPA robot performs data crawling based on the corresponding crawling subroutine, including: Configure the RPA robot based on the corresponding crawling subroutine, and configure the data source connection information of the RPA robot according to the corresponding access system; Based on the data source connection information, a data source connection is established between the RPA robot and the access system, and the RPA robot performs data crawling according to the set crawling subroutine.

8. The single-phase disconnection fault data capture method based on RPA robot according to claim 7 is characterized in that: The data source connection information includes database connection parameters, web page access permissions and API interfaces with the access system.

9. The single-phase disconnection fault data capture method based on RPA robot according to claim 4 is characterized in that: Each RPA robot performs data crawling based on a corresponding crawling subroutine, further comprising: Based on the corresponding crawling subroutine, the RPA robot determines the database that stores the corresponding crawled data in the access system according to the corresponding crawled data type; According to the corresponding data crawling keywords, the corresponding crawling data is obtained in the database according to the set crawling frequency and crawling data volume, and the obtained crawling data is verified according to the data verification rules; The format of the captured data that has passed the data verification is converted according to the data conversion rules, and the captured data after the format conversion is named according to the naming rules.

10. The single-phase disconnection fault data capture method based on RPA robot according to claim 9 is characterized in that: Each RPA robot performs data capture based on the corresponding capture subroutine, and integrates and processes the captured data according to the data capture requirements, and also includes: According to the naming of captured data, all captured data are divided to determine the running data of each associated running line; According to the location information and topological connection relationship of each associated operating line, a single-line diagram within the current single-phase line break fault range is constructed, and the single-line diagram is annotated according to the operating data corresponding to each associated operating line.

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