Electric power data management method and system based on RPA robot

Abstract

The invention relates to the technical field of power data management, and discloses a power data management method and system based on an RPA robot. The method comprises the steps that an RPA robot simulates manual operation, and tasks such as collection, arrangement, analysis and report generation of electric power data are automatically completed. According to the method and the system, an adaptive data acquisition algorithm, a data association integration algorithm, an intelligent task scheduling algorithm, a complex event processing algorithm and an intelligent interface adaptation algorithm are specially designed, so that the accuracy, the efficiency and the intelligent level of data processing are improved. According to the technical scheme, the efficiency, accuracy and timeliness of power data management can be effectively improved, the working quality and efficiency are improved, burden reduction and efficiency improvement of a basic level are assisted, and digital transformation of the power industry is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power data management, and in particular to a power data management method and system based on an RPA robot. Background Art

[0002] RPA is the abbreviation of Robotic Process Automation, which refers to robotic software that can simulate human operations in digital devices such as computers, and use and integrate existing technologies to reduce repetitive, tedious, and large-scale manual work tasks to achieve business process automation.

[0003] RPA has the characteristics of anthropomorphism, non-intrusion, zero business integration, and short implementation cycle. It simulates human operations through computer programming or auxiliary software, automatically executes process tasks according to rules designed by humans, replaces or assists humans in completing related computer operations, effectively improves employee work efficiency and business service level, and alleviates the workload of grassroots.

[0004] The inventors of the present invention have discovered that in the electric power industry, data management and analysis are of great significance for ensuring the stable operation of the power system, optimizing resource allocation, and improving operational efficiency. However, with the continuous growth of the amount of electric power data, and with the increase in repetitive and tedious work tasks such as daily duty management, report preparation, data transmission and reception, document processing, business flow, and exception analysis for grassroots personnel in various units, traditional data management methods have gradually exposed problems such as low efficiency, high error rate, and high cost. In order to effectively support the efficient execution of daily work tasks in various business areas, we understand the daily work of business personnel through visits, surveys, and on-the-job learning, and conduct summary and analysis. Relying on the existing system, through the integration of various technologies through RPA, robots can simulate manual operations to solve various repetitive, tedious, and large-scale work tasks, realize business process automation, and effectively help the company reduce burdens, reduce costs, and increase efficiency, and accelerate the company's digital transformation.

[0005] While some power data management systems and methods exist, they still suffer from numerous deficiencies in data capture, processing, analysis and modeling, report generation, and distribution. Data capture is cumbersome and error-prone, data processing is inefficient, analysis and modeling methods are limited and lack depth, and report generation and distribution lack flexibility. These issues severely hinder the effective utilization and value discovery of power data. Summary of the Invention

[0006] The purpose of this invention is to provide an electric power data management method and system based on RPA robots, which uses automation technology to improve the data processing efficiency and accuracy of the power department, reduce the burden on grassroots staff, optimize electric power business processes, and promote the digital transformation of electric power enterprises.

[0007] The technical solution of the present invention comprises the following steps:

[0008] A power data management method based on RPA robots includes the following steps:

[0009] S1: The RPA robot simulates manual operations to obtain relevant data from the power-related system interface and performs format conversion and cleaning operations on the obtained data to remove invalid or redundant information.

[0010] S2: Integrate, compare and calculate the data processed by S1 to generate statistical summary results;

[0011] S3: Automatically executes repetitive tasks such as account operations, batch operations, and report generation through an intelligent task scheduling mechanism.

[0012] S4: Use RPA robots to set up warning nodes, handle complex events, and push warning information to users;

[0013] S5: Through intelligent interface adaptation technology, seamless connection with the existing power system is achieved.

[0014] Preferably, an adaptive data acquisition algorithm is introduced in step S1, which uses convolutional neural network technology in deep learning to identify and simulate changes in the user interface and automatically adjusts the data acquisition strategy according to the recognition results.

[0015] Preferably, in step S2, graph database technology is used to integrate data from different sources through entity relationships to improve data relevance and analysis depth.

[0016] Preferably, in step S4, an intelligent task scheduling algorithm is used to dynamically adjust the task execution order according to the task priorities and resource consumption, thereby optimizing resource allocation and improving task execution efficiency.

[0017] Preferably, in step S4, an intelligent task scheduling algorithm is used to quickly identify and respond to warning events to improve the real-time and accuracy of warnings. The specific intelligent task scheduling algorithm is as follows:

[0018] Divide the work orders into levels 1-3 according to the fault type and define the binary variable z i , if task i is a suburban rural task, then z i =1, otherwise 0; set the timeout warning threshold A i , respectively assign A i =(30+30z i )min、(35+35z i )min、(40+40z i )min;

[0019] Define the decision variable, xij : Indicates whether to assign work order i to worker j, where i = 1, 2, ..., n, n is the number of orders, j = 1, 2, ..., m, m is the number of workers, d ij : represents the distance from worker j to work order i, t ij : represents the time required for worker j to receive the order and arrive at work order i;

[0020] Taking minimizing the number of timeout warnings as the objective function,

[0021]

[0022] Where (t ij -A i ) + Indicates that when t ij More than A i When t ij -A i , otherwise it is 0.

[0023] Definition constraints: Considering manpower and equipment as resources, there are R types of resources, and the total available amount of each resource at any time is R k , the demand of task i for resource k is r ik ; For any time t, if

[0024]

[0025] Then x ij =1, otherwise x ij =0;

[0026] where y i (t) is an indicator function, if task i is using resources at time t, then y i )t)=1, otherwise 0;

[0027] For each fault work order i, if t ij -A i If ≥0, a warning SMS will be sent to the substation manager, director, and deputy director simultaneously.

[0028] Preferably, in the data collection process of step S1, if the network connection is interrupted or the system response times out, a preset number of retries will be performed. If the retries fail, an error message will be recorded and a backup data collection solution will be started.

[0029] Preferably, when encountering data format mismatch, data missing or calculation error during the data collection process in step S1, data repair will be automatically attempted according to preset rules. If the repair fails, the abnormal data and error information will be recorded and the relevant business personnel will be notified.

[0030] Preferably, when a system failure or operation conflict occurs during the execution of the automatic task in step S3, the current operation will be stopped and the executed part of the task will be rolled back, and then the task will be tried again. If multiple retries fail, the system administrator will be notified.

[0031] An electric power data management system based on RPA robots adopts a modular design, is compatible with multiple operating systems and browsers, can simulate manual operations and adapt to changes in different system interfaces, has task scheduling and management functions, can process multiple tasks in parallel, and uses data encryption and transmission mechanisms to ensure data security.

[0032] A computer program product includes a code of computer-executable instructions stored on a computer-readable medium.

[0033] As can be seen from the above technical solution, the present invention has the following innovations: 1. The present invention improves the operating efficiency and reliability of the power system through an intelligent task scheduling algorithm, classifies work orders according to fault type and sets different timeout warning thresholds, quickly identifies and responds to warning events, enhances the real-time and accuracy of warnings, and defines decision variables and objective functions to minimize the number of timeout warnings, ensuring timely processing of tasks. At the same time, it optimizes resource allocation through constraint conditions and improves the utilization of human and equipment resources;

[0034] 2. When a work order of the present invention times out and is not processed, the system will automatically send an early warning SMS to the management to ensure that the problem is quickly addressed and resolved, thereby enhancing the responsiveness and service level of the power system;

[0035] 3. This invention uses RPA robots to automatically acquire and process power data, greatly improving data processing efficiency while effectively ensuring data accuracy and avoiding errors and delays that may occur in manual processing.

[0036] 4. The present invention adopts adaptive data acquisition algorithm and intelligent interface algorithm, which enables it to achieve perfect seamless connection with the existing system without modifying the existing system, greatly reducing the cost and difficulty of system integration. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1This is a flow chart of an RPA robot-based power data management method of the present invention;

[0039] Figure 2 It is a flow chart of power data management of the present invention.

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0042] The present invention provides an RPA-based power data management method and system, which are described in detail below.

[0043] Example 1

[0044] A power data management method based on RPA robots includes the following steps:

[0045] S1: The RPA robot simulates manual operations to obtain relevant data from the power-related system interface and performs format conversion and cleaning operations on the obtained data to remove invalid or redundant information. It introduces an adaptive data collection algorithm that uses convolutional neural network technology in deep learning to identify and simulate changes in the user interface and automatically adjusts the data collection strategy based on the recognition results. During the data collection process, if the network connection is interrupted or the system response timeout occurs, a preset number of retries will be performed. If the retry fails, the error information will be recorded and the backup data collection plan will be activated. If the data format mismatch, data missing or calculation error is encountered during the data collection process, the data will be automatically repaired according to the preset rules. If the repair fails, the abnormal data and error information will be recorded and the relevant business personnel will be notified.

[0046] S2: Integrate, compare, and calculate the data processed in S1 to generate statistical summary results; use graph database technology to integrate data from different sources through entity relationships to improve data relevance and analysis depth;

[0047] S3: Automatically executes repetitive tasks such as account operations, batch operations, and report generation through an intelligent task scheduling mechanism. If a system failure or operation conflict occurs during automatic task execution, the current operation will be stopped, the executed portion of the task will be rolled back, and the task will be attempted again. If multiple retries fail, the system administrator will be notified.

[0048] S4: Use RPA robots to set up early warning nodes, handle complex events, and push early warning information to users. Through intelligent task scheduling algorithms, the order of task execution is dynamically adjusted based on task priority and resource consumption, thereby optimizing resource allocation and improving task execution efficiency.

[0049] S5: Through intelligent interface adaptation technology, seamless connection with the existing power system is achieved.

[0050] Example 2

[0051] The specific intelligent task scheduling algorithm is as follows:

[0052] Divide the work orders into levels 1-3 according to the fault type and define the binary variable z i , if task i is a suburban rural task, then z i =1, otherwise 0; set the timeout warning threshold A i , respectively assign A i =(30+30z i )min、(35+35z i )min、(40+40z i )min;

[0053] Define the decision variable, x ij : Indicates whether to assign work order i to worker j, where i = 1, 2, ..., n, n is the number of orders, j = 1, 2, ..., m, m is the number of workers, d ij : represents the distance from worker j to work order i, t ij : represents the time required for worker j to receive the order and arrive at work order i;

[0054] Taking minimizing the number of timeout warnings as the objective function,

[0055]

[0056] Where (t ij -A i ) + Indicates that when t ij More than A i When t ij -A i , otherwise it is 0.

[0057] Definition constraints: Considering manpower and equipment as resources, there are R types of resources, and the total available amount of each resource at any time is R k , the demand of task i for resource k is r ik ; For any time t, if

[0058]

[0059] Then x ij =1, otherwise x ij =0;

[0060] where y i (t) is an indicator function, if task i is using resources at time t, then y i (t) = 1, otherwise 0;

[0061] For each fault work order i, if t ij -A i If the value is ≥0, an early warning SMS message will be sent to the substation manager, director, and deputy director simultaneously. The RPA robot selects a suitable template from the preset SMS template library, accurately fills in the key information of the work order (such as work order number, fault location, estimated repair time, etc.) into the SMS content, and then pushes the SMS message to the mobile phones of the substation manager, director, and deputy director simultaneously through a stable and reliable SMS gateway to ensure that relevant personnel obtain the work order information as soon as possible.

[0062] Example 3

[0063] An electric power data management system based on RPA robots adopts a modular design and is compatible with multiple operating systems and browsers. It can simulate manual operations and adapt to changes in different system interfaces. It has task scheduling and management functions, can process multiple tasks in parallel, and uses data encryption and transmission mechanisms to ensure data security. The system provides users with a simple, intuitive, and easy-to-use operation interface. Users can easily start RPA robots to perform tasks by simply clicking buttons and setting parameters. Users can also view the detailed progress and final results of task execution at any time, such as the number of completed invoice issuance tasks and the report generation status of data statistics and summary tasks.

[0064] Example 4

[0065] Power data management based on RPA robots uses highly accurate keyboard and mouse simulation technology to obtain the required data from various power-related system interfaces, just like real-life operations. For example, when obtaining power outage information, it can accurately log in to the "Proactive Repair Work Order Query" interface of the power supply service command platform and accurately obtain work order data that meets the requirements according to preset query conditions. When obtaining customer information, it can quickly locate and query relevant contact information and other detailed data from the Marketing 2.0 system based on the user number. It supports the collection of multiple common data formats, whether it is text-based work order descriptions, tabular power consumption data, or contract documents in the form of document scans. After collection, it uses built-in intelligent algorithms to perform preliminary data processing, such as automatically identifying and removing invalid characters, duplicate content, and other redundant information in the data, while converting the data into a unified format that is convenient for subsequent processing.

[0066] Deeply integrate various types of collected data. For example, correlate and integrate user electricity consumption data obtained from the electricity information collection system with basic user information in the marketing system to conduct more comprehensive analysis. During the comparison operation, compare the real-time collected data with the preset standard data range or historical data to promptly identify abnormal data fluctuations or potential problems.

[0067] In terms of calculation operations, in the statistical summary of metering management level data, it is possible to accurately calculate the data of multiple indicator basic tables derived from the consumption and procurement system according to complex calculation formulas to obtain accurate statistical summary results, such as calculating the average, peak, and valley values ​​of electricity consumption in different regions and time periods. When summarizing the statistical summary of abnormal data management, advanced algorithms are used to automatically decompress the classified business scenario rule data, and based on detailed business logic, comprehensive and accurate calculation and statistical summary are carried out according to dimensions such as business scenario, rule name, and each unit;

[0068] In addition, with the help of OCR technology, it can quickly and accurately identify text information in document scans, such as the amount, title, tax number and other key content on invoices. It then uses NLP technology to understand and process text data, realizing automated processing of document data such as invoices and contracts, such as automatically extracting key information from invoices and entering it into the financial system.

[0069] For repetitive tasks, such as asset data processing in financial work, RPA robots can automatically delete unreasonable empty cards in batches from the ERP system, eliminating the need for manual operation, greatly improving processing efficiency. In marketing work, it can automatically issue invoices for more than 20,000 photovoltaic users in the city. Based on preset invoice templates and user electricity consumption data, accurate invoice content can be quickly generated.

[0070] In terms of 24 / 7 on-call support, once a fault ticket is generated and dispatched, the RPA robot can immediately initiate monitoring, tracking the ticket status in real time. If a timeout or anomaly is detected, prompt prompts or escalation actions will be taken.

[0071] When entering power outage information, the operating status of the power system is continuously monitored. Once a power outage is detected, relevant information such as the time, location, and scope of the power outage are automatically entered according to preset rules to ensure timely recording and updating of power outage information.

[0072] Example 5

[0073] In the attack IP detection task of the intrusion prevention (IPS) system and firewall system, the RPA robot automatically logs into the system according to the set time period (such as every hour or every half hour), accurately executes the query operation of the attack IP, and after obtaining the attack IP list, securely reads the IP address segment information of the city and county companies from the system configuration file and performs a detailed comparison. For IPs that are not on the whitelist, a prominent pop-up reminder will immediately pop up on the monitoring terminal, and detailed information about the attack IP, such as IP address, attack time, attack type, etc., will be displayed. These key information will be fully recorded in the security log for subsequent in-depth security analysis and timely processing.

[0074] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, which may include: read-only memory, random access memory, disk or CD, etc.

[0075] The above describes in detail the RPA-based power data management method and system provided in an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only intended to help understand the method and core concept of the present invention. At the same time, for those skilled in the art, the specific implementation methods and application scope may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A power data management method based on RPA robot, characterized in that: The following steps are involved: S1: The RPA robot simulates manual operations to obtain relevant data from the power-related system interface and performs format conversion and cleaning operations on the obtained data to remove invalid or redundant information. S2: Integrate, compare and calculate the data processed by S1 to generate statistical summary results; S3: Automatically executes repetitive tasks such as account operations, batch operations, and report generation through an intelligent task scheduling mechanism. S4: Use RPA robots to set up warning nodes, handle complex events, and push warning information to users; S5: Through intelligent interface adaptation technology, seamless connection with the existing power system is achieved.

2. The power data management method based on RPA robot according to claim 1 is characterized in that: In step S1, an adaptive data acquisition algorithm is introduced, which uses the convolutional neural network technology in deep learning to identify and simulate changes in the user interface and automatically adjust the data acquisition strategy according to the recognition results.

3. The power data management method based on RPA robot according to claim 1 is characterized in that: In step S2, graph database technology is used to integrate data from different sources through entity relationships to improve data relevance and analysis depth.

4. The power data management method based on RPA robot according to claim 1 is characterized in that: In step S4, an intelligent task scheduling algorithm is used to dynamically adjust the task execution order according to the task priority and resource consumption, thereby optimizing resource allocation and improving task execution efficiency.

5. The power data management method based on RPA robot according to claim 4 is characterized in that: In step S4, an intelligent task scheduling algorithm is used to quickly identify and respond to warning events to improve the real-time and accuracy of warnings. The specific intelligent task scheduling algorithm is as follows: Divide the work orders into levels 1-3 according to the fault type and define the binary variable z i , if task i is a suburban rural task, then z i =1, otherwise 0; set the timeout warning threshold A i , respectively assign A i =(30+30z i )min、(35+35z i )min、(40+40z i )min; Define the decision variable, x ij : Indicates whether to assign work order i to worker j, where i = 1, 2, ..., n, n is the number of orders, j = 1, 2, ..., m, m is the number of workers, d ij : represents the distance from worker j to work order i, t ij : represents the time required for worker j to receive the order and arrive at work order i; Taking minimizing the number of timeout warnings as the objective function, Where (t ij -A i ) + Indicates that when t ij More than A i When t ij -A i , otherwise it is 0. Definition constraints: Considering manpower and equipment as resources, there are R types of resources, and the total available amount of each resource at any time is R k , the demand of task i for resource k is r ik ; For any time t, if Then x ij =1, otherwise x ij =0; where y i (t) is an indicator function, if task i is using resources at time t, then y i (t) = 1, otherwise 0; For each fault work order i, if t ij -A i If ≥0, a warning SMS will be sent to the substation manager, director, and deputy director simultaneously.

6. The power data management method based on RPA robot according to claim 1 is characterized by: During the data collection process of step S1, if the network connection is interrupted or the system response times out, a preset number of retries will be performed. If the retries fail, an error message will be recorded and a backup data collection plan will be started.

7. The power data management method based on RPA robot according to claim 1 is characterized in that: In step S1, when encountering data format mismatch, data missing or calculation errors during the data collection process, data repair will be automatically attempted according to the preset rules. If the repair fails, the abnormal data and error information will be recorded and the relevant business personnel will be notified.

8. The power data management method based on RPA robot according to claim 1 is characterized in that: If a system failure or operation conflict occurs during the execution of the automatic task in step S3, the current operation will be stopped and the executed part of the task will be rolled back. Then, the task will be tried again. If multiple retries fail, the system administrator will be notified.

9. An electric power data management system based on an RPA robot according to any one of claims 1 to 8, characterized in that: The system adopts a modular design and is compatible with multiple operating systems and browsers. It can simulate manual operations and adapt to changes in different system interfaces. It has task scheduling and management functions, can process multiple tasks in parallel, and uses data encryption and transmission mechanisms to ensure data security.

10. A computer program product, characterized in that The program product comprises a code of computer-executable instructions of the method according to any one of claims 1 to 8, the code being stored on a computer-readable medium.

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