Artificial intelligence-based RPA method for grid digitalization process monitoring

Abstract

An artificial intelligence-based RPA method for grid digitalization process monitoring, comprising the following steps: constructing an RPA-based grid digitalization process tracking system; performing RPA-based grid digitalization process tracking control; performing grid digitalization process authority management; and testing the operation effect and reliability of the system. In this way, corresponding tracking control can be implemented across independent software architecture layers to achieve grid digitalization process authority management, thereby ensuring data security of a grid digitalization process; by means of a tracking time consumption test, a tracking effect test and a system stability test, a grid digitalization process tracking control software algorithm is optimized, improving the tracking performance of the system.

Description

An RPA power grid digital process monitoring method based on artificial intelligence TECHNICAL FIELD

[0001] The present application relates to the technical field of marketing management monitoring, and particularly relates to an RPA power grid digital process monitoring method based on artificial intelligence. BACKGROUND

[0002] With the continuous development of China's economy, the research and application of China's management system in the field of power grid power marketing have also gradually increased. The positive application of a mature and universal management system in the management of power grid power marketing projects not only reflects the characteristics and particularity of power grid power enterprises, but also may have a good influence on the construction of marketing management projects, and continuously improves the management experience and efficiency of marketing projects.

[0003] According to data, the existing marketing management system lacks effective monitoring measures, cannot intelligently analyze the power consumption of users, and cannot make the services of power supply enterprises more close to users. When problems occur in the power supply line, they cannot be discovered and processed in the first time. At the same time, the charging situation of electricity charges cannot be analyzed in real time, the funds of power supply enterprises cannot be guaranteed, and the situation of electricity charges and electricity leakage cannot be prevented.

[0004] Chinese patent document CN116720688A discloses a "national power grid power marketing management monitoring system". It includes a business management module, a marketing management module, a monitoring management module and a system maintenance module. The business management module is mainly responsible for the handling of daily power consumption business, customer service and other businesses. The monitoring management module monitors the data information of the business management module and the marketing management module in real time. The monitoring management module includes a power supply monitoring unit, a power consumption monitoring unit, a quality monitoring unit and a special monitoring unit. The power consumption monitoring unit includes a power consumption area, a power consumption cost area and a power consumption price area. Through the power consumption area, the power consumption cost area, the power consumption price area, the power consumption behavior analysis area and the payment behavior analysis area, the above technical solution lacks the optimization of the software algorithm for tracking and controlling the power grid digital process.

[0005] SUMMARY

[0006] The application mainly solves the technical problem of lacking of algorithm optimization of power grid digital process tracking control software in the original technical solution, and provides an RPA power grid digital process monitoring method based on artificial intelligence, constructs an RPA-based power grid digital process tracking system, and implements corresponding tracking control between various independent software structure levels through the RPA-based power grid digital process tracking control method, simultaneously realizes power grid digital process permission management process design, ensures the data security of the power grid digital process, realizes the algorithm optimization of the power grid digital process tracking control software through tracking time-consuming test, tracking effect test and system stability test, and improves the system tracking performance.

[0007] The above technical problems of the application are mainly solved by the following technical scheme: the application comprises the following steps:

[0008] S1 constructing an RPA-based power grid digital process tracking system;

[0009] S2 performing RPA-based power grid digital process tracking control;

[0010] S3 performing power grid digital process permission management;

[0011] S4 performing system operation effect and reliability test.

[0012] The RPA technology-based power grid digital process tracking control system is constructed, the power grid digital process architecture is designed based on the RPA technology, and the system modules are designed based on the architecture, so as to complete the system modular design. In combination with the design result, the power grid digital process tracking control software algorithm is further designed to improve the system tracking performance. Finally, the overall design of the power grid digital process tracking control system is completed according to the designed modules and corresponding software, and the system has excellent tracking effect and long-term development prospect.

[0013] As a preferred, the step S1 comprises constructing a logic architecture of the RPA-based power grid digital design, and the logic architecture comprises an artificial intelligence layer for realizing power grid data conversion, an RPA process automation layer for simulating manual operation, and a scheduling monitoring and analyzing layer for coordinating RPA robots.

[0014] The artificial intelligence layer mainly completes the conversion of power grid data through various technologies such as computer vision and voice recognition, and then transmits the converted data to the RPA for automatic processing. The AI technology is combined to further improve the automation level of the power grid digital process.

[0015] After introducing the RPA into the RPA process automation layer, the RPA robot can effectively simulate manual operation according to the power grid digital process processing rules, and complete operations such as power grid digital process data transmission, RPA robot automatic opening and closing of service system, etc.

[0016] In the process of grid digitalization tracking and monitoring, the dispatch monitoring analysis layer is needed to coordinate and schedule the RPA robots. This layer includes multiple functional areas, such as log security audit, log recording and tracking, real-time monitoring, permission control, etc.

[0017] As a preferred, the step S1 further comprises constructing a grid digitalization process tracking control system module, and the grid digitalization process function mainly includes effective checking of data reports and verification of grid power.

[0018] The electric energy intelligent verification module effectively displays the number, time, creation and modification time of the grid digitalization process operation task, uses FPGA chips and DSP processors to process data, and carries out more in-depth checking and analysis of data reports, so as to complete efficient management of grid digitalization process data reports.

[0019] The grid power monitoring module mainly uses ACS712 current sensors and PT100 voltage sensors to collect real-time operation data of grid equipment, and carries out statistics and display of grid power generation types, monthly power, annual power, etc.

[0020] The version synchronization control module defines the interaction specification of the grid digitalization process by using XML, so that the heterogeneous system digitalization docking process between the grids can be asynchronously identified. At the same time, the jump relationship and control content description of each node in the grid digitalization process can also be described, and the communication mode between the process interfaces of the master station and the substation of the grid distributed system can also be standardized, so that the digitalization docking process between the grid distributed systems can be asynchronously adapted.

[0021] The node state tracking module realizes the tracking of the grid digitalization process by using the standardized process instance form to record the grid digitalization node flow process, but the node process should be the node flow process after the grid digitalization process is initiated. After recording, the grid distributed system master station state file and the substation state file are synchronized, so as to realize the node state tracking after the grid digitalization business process instance jumps between nodes.

[0022] The process tracking log module records the full-cycle log tracking information of each node project and operation change content in the grid digitalization process in the grid digitalization full-time business process, and uniformly completes the log integration management of the grid digitalization heterogeneous system according to the recording results.

[0023] As preferred, the grid digitalization process tracking control system module mainly comprises a version synchronization control module, a node state tracking module and a process tracking log module, and further comprises an electric energy intelligent checking module for displaying grid digitalization process operation tasks and a grid power monitoring module for collecting real-time operation data of grid equipment.

[0024] As preferred, the step S2 specifically comprises constructing a main program based on the RPA technology, performing program control tracking and control software, and setting a tracking control target and calculating a dynamic execution index of the control target when implementing corresponding tracking control.

[0025] In the formula, J represents a target dynamic execution index, d represents a monitoring range, i.e. a region monitored and controlled in the tracking control system, S represents a change monitoring distance, and x represents a preset time.

[0026] As preferred, after obtaining the target actual dynamic execution index, it is taken as an actual tracking control limit standard of the grid digitalization process, and an RPA monitoring target corresponding to the target actual dynamic execution index is set in the RPA monitoring target, and the RPA monitoring target corresponding to the target actual dynamic execution index is added to the system.

[0027] In the grid digitalization process, the main program is a background program, which realizes system functions according to a formulated sequential process. The main program first realizes initialization, then enters the main program module, tracks the remaining function modules in a continuous cycle, and implements interrupt response. After entering the interrupt response, it enters the auxiliary tracking sub-program again, sets a constant value in the grid by determining the actual running quantity of the grid digitalization process, so as to avoid greater errors in tracking the grid digitalization process.

[0028] As preferred, the step S3 specifically comprises placing the RPA robot cluster in a fixed server, and using OA to send emails to users to establish services. After receiving the email, the RPA identifies the identity information of the current user, receives the grid digitalization business process submitted by the user, performs allocation processing and execution on the business, and finally submits the data after verifying and checking the executed tasks.

[0029] Since the power grid power supply personnel all have OA addresses, no additional service terminal needs to be set up. According to the specific OA address, the power grid digital process data can be effectively protected, and the attack threat to the RPA robot cluster can be reduced. If the user identity in the power grid system changes, a new OA address can be applied for through the RPA robot cluster system, and the original business can be implemented. After the user sends an email to the RPA robot using the OA, the RPA needs to identify the identity information of the current user. After receiving the power grid digital business process submitted by the user, the RPA robot performs allocation processing and execution on the business, and finally checks and verifies the executed task. When the executed business process data is correct, the data can be submitted, thereby completing the authorization management of the power grid digital process.

[0030] As preferred, the step S4 comprises tracking time-consuming test, and data tracking comparison test is carried out on the power grid digital process data in the scheduling monitoring analysis layer. During the operation of the power grid digitalization, only one type of power grid digital process data is effectively tracked, and the amount of power grid digital process data is set. The tracking time of one type of power grid digital process data is compared. The higher the tracking time, the lower the system tracking efficiency, and the lower the tracking time, the higher the system tracking efficiency.

[0031] Through simulation experiment analysis and verification, it can be known that wavelet packet decomposition can effectively decompose the high and low frequency information of the fault signal, and the energy moment representing the transient fault characteristic value is obtained through four-layer wavelet packet decomposition. The training parameters obtained based on the QPSO optimized RBF neural network are optimal, and the correct identification of the fault can be realized under different conditions, and the identification efficiency advantage is significant.

[0032] As preferred, the step S4 further comprises tracking effect test. A certain regional power grid is taken as an experimental object, and steady-state data in the power grid digital process are further collected. According to the collection result, different systems are used to effectively track the frequency waveform of the steady-state data, and the tracking result is compared with the actual steady-state data frequency waveform. If the tracking result is close to the actual result, it means that the tracking deviation between the two is small, and the system tracking performance is high. If the tracking result is far from the actual result, it means that the system tracking performance is low.

[0033] System stability test can also be carried out. When the power grid monitoring module monitors the power grid digital process data, too much data will increase the system load, causing the system to be prone to unstable operation. According to the power grid digital process data monitored by the module, system load comparison test is carried out by using system 1, system 2 and system 3. The higher the load, the greater the impact on the system operation, the worse the stability of the system, the lower the load, the smaller the impact on the system operation, and the better the stability of the system.

[0034] The beneficial effects of the present application are: through the power grid digital process tracking control method based on RPA, corresponding tracking control is implemented between each independent software structure level, while realizing the power grid digital process permission management process design, ensuring the data security of the power grid digital process, through tracking time-consuming test, tracking effect test and system stability test, realizing the optimization of power grid digital process tracking control software algorithm, and improving the system tracking performance. BRIEF DESCRIPTION OF DRAWINGS

[0035] Fig. 1 is a schematic connection structure block diagram of the present application.

[0036] Fig. 2 is a main program tracking control flow chart of the present application.

[0037] Fig. 3 is a power grid digital process permission management flow chart of the present application.

[0038] Fig. 4 is a power grid digital process steady-state data tracking waveform diagram of the present application.

[0039] Fig. 5 is a system load comparison diagram of the present application.

[0040] Fig. 6 is a RPA monitoring database architecture diagram of the present application. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the technical scheme of the present application will be further described in detail below through examples, and combined with the drawings. It should be understood that the specific embodiments described here are only the best embodiment of the present application, which is used to explain the present application, and does not limit the protection scope of the present application. All other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0042] Before discussing the example embodiments in more detail, it should be mentioned that some example embodiments are described as processes or methods depicted as flow diagrams. Although the flow diagrams describe the operations (or steps) as sequential processes, many of the operations (or steps) can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The processes can be terminated when their operations are completed, but can also have additional steps not included in the figures; the processes can correspond to methods, functions, routines, subroutines, etc.

[0043] The power marketing mainly refers to that the power enterprise propagates and popularizes a diversified marketing mode of the related power business such as power quantity, power price execution, line loss management and electricity charge management through a special network platform. Compared with the traditional marketing mode, the information-based power marketing has stronger flexibility and adaptability, the processing and feedback speed of the business is very fast, and the error generation probability is greatly reduced, and the marketing effect is improved. In practice, the inspection work of the power marketing is also very key, and a more comprehensive and systematic scheme needs to be built to improve the quality of the power marketing inspection work. The application of the RPA technology further deepens the implementation of the power marketing inspection work, expands the actual application range of the inspection system by determining the breakthrough point of the inspection work, ensures more accurate and efficient power marketing inspection monitoring work, and improves the overall monitoring effect.

[0044] The technical solutions of the present application will be further specifically described below by means of embodiments and in combination with the drawings.

[0045] Embodiment: An RPA power grid digital process monitoring method based on artificial intelligence, comprising the following steps:

[0046] S1, a power grid digital process tracking system based on RPA is constructed, as shown in FIG. 2. The logical architecture of the power grid digital design based on RPA is constructed, which includes an artificial intelligence layer for realizing power grid data conversion, an RPA process automation layer for simulating manual operation, and a scheduling monitoring and analysis layer for coordinating RPA robots.

[0047] The power grid digital process tracking system based on RPA is constructed, and the corresponding tracking control is implemented between each independent software structure level through the power grid digital process tracking control method based on RPA. At the same time, the power grid digital process permission management process design is realized to ensure the data security of the power grid digital process. Through tracking time-consuming test, tracking effect test and system stability test, the power grid digital process tracking control software algorithm optimization is realized, and the system tracking performance is improved.

[0048] The artificial intelligence layer mainly completes the conversion of the power grid data through various technologies such as computer vision and voice recognition, and then transmits the converted data to the RPA for automatic processing. The AI technology is combined to further improve the automation level of the power grid digital process.

[0049] After introducing RPA into the RPA process automation layer, the RPA robot can effectively simulate manual operation according to the power grid digital process processing rules, complete the power grid digital process data transmission, and automatically open and close the service system and other operations of the RPA robot.

[0050] When the power grid digital process tracking and monitoring is carried out, the dispatching monitoring and analysis layer needs to be used to coordinate and schedule the RPA robot. This layer includes multiple functional areas, such as log security audit, log recording and tracking, real-time monitoring, permission control, etc.

[0051] The power grid digital process tracking control system module is constructed, and the power grid digital process function mainly includes effective checking of data reports and correction verification of power grid power. The power grid digital process tracking control system module mainly includes a version synchronization control module, a node state tracking module and a process tracking log module, and also includes an electric energy intelligent checking module for displaying power grid digital process operation tasks and a power grid power monitoring module for collecting real-time operation data of power grid equipment. The power grid digital process tracking control is mainly realized from three aspects of version control, state synchronization monitoring and process log tracking.

[0052] The electric energy intelligent checking module effectively displays the number, time, creation and modification time of the power grid digital process operation task, uses FPGA chips and DSP processors to process data, and carries out more in-depth checking and analysis on data reports, so as to complete efficient management of power grid digital process data reports.

[0053] The power grid power monitoring module mainly uses ACS712 current sensors and PT100 voltage sensors to collect real-time operation data of power grid equipment, and carries out statistics and display on power grid power generation types, monthly power, annual power, etc.

[0054] The version synchronization control module uses XML to define the interaction specification of the power grid digital process, so that the heterogeneous system digital connection process between the power grids can be asynchronously identified. At the same time, the jump relationship and control content description of each node in the power grid digital process can also be described, and the communication mode between the process interfaces of the master station and the substation of the power grid distributed system can also be standardized, so that the digital connection process between the power grid distributed systems can be asynchronously adapted.

[0055] When the power grid digital process tracking is realized, the node state tracking module needs to use the standardized process instance form to record the power grid digital node flow process, but the node process should be the node flow process after the power grid digital process is initiated. After the recording is completed, the power grid distributed system master station state file and the substation state file are synchronized, so as to realize the node state tracking after the power grid digital business process instance jumps between nodes.

[0056] The process tracking log module records the full-cycle log tracking information of each node project and operation change content in the power grid digital process in the power grid digital full-time business process, and uniformly completes the log integration management of the power grid digital heterogeneous system according to the recording results.

[0057] S2 carries out RPA-based power grid digital process tracking control, as shown in FIG. 2, specifically including, constructing a main program based on RPA technology, carrying out program control tracking and control software, and when implementing corresponding tracking control, a tracking control target needs to be set up, and a dynamic execution index of the control target is calculated:

[0058] In the formula, J represents the target dynamic execution index; d represents the monitoring range, that is, the area monitored and controlled in the tracking control system; S represents the change monitoring distance; and x represents the preset time.

[0059] Based on the above calculation, the target actual dynamic execution index can be obtained, which is taken as the actual tracking control limit standard of the power grid digital process, and the RPA monitoring target corresponding to the target actual dynamic execution index is set in the RPA monitoring target, and the RPA monitoring target corresponding to the target actual dynamic execution index is added to the system software design. The power grid digital process tracking control process is further designed through the above description, as shown in FIG. 2.

[0060] After the target actual dynamic execution index is obtained, it is taken as the actual tracking control limit standard of the power grid digital process, and the RPA monitoring target corresponding to the target actual dynamic execution index is set in the RPA monitoring target, and the RPA monitoring target corresponding to the target actual dynamic execution index is added to the system.

[0061] In the power grid digital process, the main program is a background program, which realizes system functions according to the formulated sequence process. The main program first realizes initialization, then enters the main program module, simultaneously tracks the remaining function modules during continuous circulation, and implements interrupt response. After entering the interrupt response, it enters the auxiliary tracking subprogram again, and by determining the actual running quantity of the power grid digital process, a fixed value is set in the power grid to avoid greater errors in tracking the power grid digital process.

[0062] S3 carries out power grid digital process permission management, specifically including, placing RPA robot clusters in fixed servers, and using OA to send emails to users to establish services, RPA identifies the identity information of the current user after receiving the email, receives the power grid digital business process submitted by the user, implements allocation processing and execution on the business, and finally submits the data after verifying and checking the executed tasks. The power grid digital process permission management process is shown in FIG. 3.

[0063] Since the power grid power supply personnel all have OA addresses, there is no need to additionally set up service terminals. According to the specific OA address, the power grid digital process data can be effectively protected, and the attack threat to the RPA robot cluster can be reduced. If the user identity in the power grid system changes, a new OA address can be applied for through the RPA robot cluster system, and the original business can be implemented. After the user sends an email to the RPA robot using the OA, the RPA needs to identify the identity information of the current user. After receiving the power grid digital business process submitted by the user, the RPA robot performs allocation processing and execution on the business, and finally checks and verifies the executed task. When the executed business process data is correct, the data can be submitted, thereby completing the authorization management of the power grid digital process.

[0064] S4 carries out system operation effect and reliability test.

[0065] The power grid digital process tracking control system based on the RPA technology is constructed, the power grid digital process architecture is designed based on the RPA technology, and the system modules are designed based on this, so as to complete the modular design of the system. Combined with the design result, the power grid digital process tracking control software algorithm is further designed to improve the tracking performance of the system. Finally, according to the designed modules and corresponding software, the overall design of the power grid digital process tracking control system is completed, and the system has excellent tracking effect and long-term development prospect.

[0066] Tracking time consumption test, data tracking comparison test is carried out on the power grid digital process data in the dispatching monitoring analysis level. During the power grid digital operation, only one kind of power grid digital process data is effectively tracked, and the amount of power grid digital process data is set. The tracking time consumption of one kind of power grid digital process data is compared respectively. The higher the tracking time consumption is, the lower the system tracking efficiency is, and the lower the tracking time consumption is, the higher the system tracking efficiency is.

[0067] The specific test results are shown in Table 1. It is found from the data in Table 1 that as the power grid digital process data increases, the tracking time consumption of the three systems also increases. When the power grid digital process data reaches 700, the overall time consumption of system 1 reaches 85 ms, and compared with system 2 and system 3, the tracking efficiency of this system is the best. The final tracking time of system 2 and system 3 is 120 ms and 140 ms respectively, and the tracking time consumption is high, which indicates that the tracking efficiency of system 2 and system 3 is low.

[0068] Table 1 Comparison test of tracking time consumption of different systems

[0069] Tracking effect test, taking a certain regional power grid as the experimental object, further collecting the steady-state data in the digital process of the power grid, according to the collection results, using different systems to track the frequency waveform of the steady-state data effectively, and comparing the tracking results with the actual steady-state data frequency waveform, if the tracking results are similar to the actual results, it means that the tracking deviation between the two is small, and the system tracking performance is high, if the tracking results are far from the actual results, it means that the system tracking performance is low. The detailed test results are shown in Figure 4.

[0070] As can be seen from Figure 4, the tracking results of system 1 have no deviation from the actual steady-state data frequency waveform, which means that the tracking performance of system 1 is excellent. While in the tracking process of system 2 and system 3, the tracking waveform is quite different from the actual waveform, which means that the tracking performance of the two systems needs to be further improved.

[0071] System stability test, when the power grid monitoring module monitors the data of the power grid digital process, too much data will increase the system load, which will cause the system to run unstable. According to the power grid digital process data monitored by the module, system 1, system 2 and system 3 are used to carry out system load comparison test, the higher the load, the greater the impact on system operation, the worse the stability of the system, the lower the load, the smaller the impact on system operation, the better the stability of the system. As can be seen from Figure 5, with the increase of monitoring data, the three systems all show an upward trend. The overall upward trend of system 1 is relatively slow, maintaining at about 10%, while the overall upward trend of system 2 and system 3 is relatively fast, and the system load is higher than that of system 1. This verifies that the influence of monitoring data on system load of system 1 is small, and the system design effect is good.

[0072] Through simulation experiment analysis and verification, it can be known that wavelet packet decomposition can effectively decompose the high and low frequency information of fault signal, and the energy moment representing the transient fault characteristic value is obtained through four-layer wavelet packet decomposition. The training parameters obtained based on QPSO optimized RBF neural network are optimal, and the correct identification of fault can be realized under different conditions, and the identification efficiency is significantly advantageous.

[0073] Based on the architecture of Figure 1, an RPA monitoring database can also be designed, as shown in Figure 6, according to the process of the functional module, the corresponding database architecture is created. According to the hierarchical structure of the monitoring module and the design of the RPA monitoring database, the inspection of the power marketing process based on RPA technology is finally completed.

[0074] Embodiment

[0075] 1 RPA technology-based power grid digital process tracking system modular design

[0076] 1.1 Logic architecture of RPA-based power grid digital design

[0077] The power grid digital process architecture based on RPA technology is composed of three levels, namely, artificial intelligence layer, RPA process automation layer and dispatching monitoring and analysis layer. The architecture is shown in Figure 1.

[0078] (1) Artificial intelligence layer

[0079] The artificial intelligence layer mainly converts power grid data through computer vision, voice recognition and other technologies, and then transmits the converted data to RPA for automatic processing. The AI technology is combined to further improve the automation level of power grid digital processes.

[0080] (2) RPA process automation layer

[0081] After introducing RPA into the power grid process automation layer, RPA robots can effectively simulate manual operations according to the rules of power grid digital process processing, complete power grid digital process data transmission, RPA robot automatic opening and closing of service systems, and other operations.

[0082] (3) Dispatching monitoring and analysis layer

[0083] When tracking and monitoring power grid digital processes, the dispatching monitoring and analysis layer is needed to coordinate and dispatch RPA robots. This layer includes multiple functional areas, such as log security audit, log recording and tracking, real-time monitoring, and permission control.

[0084] 1.2 Module composition of power grid digital process tracking control system

[0085] According to the power grid digital process architecture based on RPA technology, the hardware of the power grid digital process tracking control system is further designed. The functions of the power grid digital process mainly include effective verification of data reports and verification of power grid power.

[0086] (1) Electric energy intelligent verification module

[0087] The electric energy intelligent verification module effectively displays the number, time, creation and modification time of power grid digital process tasks. It uses FPGA chips and DSP processors to process data and conducts in-depth verification and analysis of data reports, thereby achieving efficient management of power grid digital process data reports.

[0088] (2) Power grid power monitoring module

[0089] The power grid power monitoring module mainly uses ACS712 current sensors and PT100 voltage sensors to collect real-time operation data of power grid equipment and performs statistics and display on power generation types, monthly power, annual power and other parameters in the power grid.

[0090] (3) Version synchronization control module

[0091] The version synchronization control module defines the interaction specification of the power grid digital process by using XML, so that the heterogeneous system digital connection process between power grids can be asynchronously identified. At the same time, the jump relationship and control content description of each node in the power grid digital process can be described, and the communication mode between the process interfaces of the master station and the substation of the power grid distributed system can be standardized, so that the digital connection process between the power grid distributed systems can be asynchronously adapted.

[0092] (4) Node state tracking module

[0093] When implementing the power grid digital process tracking, the standardized process instance form is used to record the power grid digital node flow process, but the node process should be the node flow process after the power grid digital process is initiated. After recording, the power grid distributed system master station state file and the substation state file are synchronized, so as to realize the node state tracking of the power grid digital business process instance after jumping between nodes.

[0094] (5) Process tracking log module

[0095] In the power grid digital full-time business process, the full-cycle log tracking information of each node project and operation change content in the power grid digital process is recorded, and the log integration management of the power grid digital heterogeneous system is uniformly completed according to the recording results.

[0096] In the above established modules, the power grid digital process tracking control is mainly realized from three aspects of version control, state synchronization monitoring and process log tracking.

[0097] 2 Key software algorithm design of power grid digital process tracking control system

[0098] 2.1 Power grid digital process tracking control process based on RPA technology

[0099] After completing the system module design, the software algorithm needs to be further designed. A main program is constructed based on RPA technology, which is used for program control tracking and control software. At the same time, each software structure level is independent of each other, and when implementing the corresponding tracking control, the tracking control target needs to be set, and the dynamic execution index of the control target is calculated:

[0100] In the formula: J represents the target dynamic execution index; d represents the monitoring range, that is, the area monitored and controlled in the tracking control system; S represents the change monitoring distance; x represents the preset time.

[0101] Based on the above calculation, the target actual dynamic execution index can be obtained, which is used as the actual tracking control limit standard of the power grid digital process, and the RPA monitoring target corresponding to the target actual dynamic execution index is set in the RPA monitoring target, and the RPA monitoring target corresponding to the target actual dynamic execution index is added to the system software design. The tracking control process of the power grid digital process is further designed according to the above description, as shown in Figure 2.

[0102] After obtaining the target actual dynamic execution index, it is used as the actual tracking control limit standard of the power grid digital process, and the RPA monitoring target corresponding to the target actual dynamic execution index is set in the RPA monitoring target, and the RPA monitoring target corresponding to the target actual dynamic execution index is added to the system.

[0103] In the power grid digital process, the main program belongs to the background program, and the system function is realized according to the established sequence process. The main program first realizes initialization, then enters the main program module, and simultaneously tracks the remaining function modules during continuous circulation, and implements interrupt response. After entering the interrupt response, it enters the auxiliary tracking subprogram again, and by determining the actual running quantity of the power grid digital process, a fixed value is set in the power grid to avoid greater errors in tracking the power grid digital process.

[0104] 2.2 Design of power grid digital process permission management process

[0105] When tracking and controlling the power grid digital system using RPA technology, the RPA robot cluster needs to be placed in a fixed server, and OA is used to send emails to users to establish services. Since the power supply personnel all have OA addresses, there is no need to set up additional service terminals.

[0106] According to the specific OA address, the power grid digital process data can be effectively protected, and the attack threat to the RPA robot cluster can be reduced. If the user identity in the power grid system changes, a new OA address can be applied through the RPA robot cluster system, and the original business can be implemented.

[0107] After the user sends an email to the RPA robot using OA, the RPA needs to identify the identity information of the current user, receives the power grid digital business process submitted by the user, and the RPA robot performs allocation processing and execution on the business, and finally checks and verifies the executed task. When the executed business process data is correct, the data can be submitted, and thus the authorization management of the power grid digital process is completed. The power grid digital process permission management process is shown in Figure 3.

[0108] Compared with traditional information systems, RPA adopts a non-intrusive mode that does not affect the original IT infrastructure, can avoid conversion of existing system interfaces or functions during implementation, and can access the current system in the same way as a human being by following existing security and data integrity standards without threatening system security. RPA is easy to deploy and its process solutions can be customized, which can effectively reduce costs and improve the flexibility of financial process scaling.

[0109] RPA has a significant and long-term impact on the digital transformation of enterprise finance. As virtual labor, RPA can effectively improve the quality of financial data and the efficiency of financial management and business processing in power enterprises, ensuring the smooth development of enterprise financial informatization; and promote the standardization of enterprise financial management.

[0110] In order to solve the problems of poor tracking performance and low system running efficiency of the power grid digital process tracking control system, the application designs an RPA power grid digital process monitoring method based on artificial intelligence. Based on the RPA technology, the power grid digital process architecture is designed, and the system modules are designed based on this, so as to complete the modular design of the system. Combined with the design result, the power grid digital process tracking control software algorithm is further designed to improve the tracking performance of the system. Finally, according to the designed modules and corresponding software, the overall design of the power grid digital process tracking control system is completed, and the system has excellent tracking effect and long-term development prospect. Moreover, the real-time monitoring function is newly added in the system, so that the managers can more intuitively control various work, break the environmental restrictions of the traditional monitoring system, and provide more powerful support for the fine management of the whole process of power marketing service.

[0111] A person of ordinary skill in the art can understand that all or part of the steps in the above-mentioned various methods of the embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer readable storage medium, which can include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0112] The specific embodiments described herein are merely illustrative of the principles of this application. Numerous modifications and adaptations will be readily apparent to those skilled in the art without departing from the spirit of the application. Accordingly, the scope of the application should be determined by the appended claims and equivalents thereof, rather than by the description of the embodiments described above.

Claims

1. An AI-based RPA (Robotic Process Automation) method for monitoring digital processes in power grids, characterized in that: Includes the following steps: S1 builds a power grid digital process tracking system based on RPA; S2 performs RPA-based digital process tracking and control of the power grid; S3 performs access control for digital power grid processes; S4 is used to test the system's performance and reliability.

2. The AI-based RPA (Robotic Process Automation) method for monitoring digital processes in a power grid, as described in claim 1, is characterized in that... Step S1 includes constructing a logical architecture for RPA-based digital power grid design. The logical architecture includes an artificial intelligence layer for realizing power grid data conversion, an RPA process automation layer for simulating manual operations, and a scheduling, monitoring, and analysis layer for coordinating RPA robots.

3. The AI-based RPA (Robotic Process Automation) method for monitoring digital processes in power grids according to claim 1 or 2, characterized in that, Step S1 also includes building a power grid digital process tracking and control system module. The power grid digital process function mainly includes effective verification of data reports and verification of power grid electricity.

4. The AI-based RPA method for monitoring digital processes in power grids according to claim 1, characterized in that, The power grid digital process tracking and control system module mainly includes a version synchronization control module, a node status tracking module, and a process tracking log module. It also includes an intelligent power verification module that displays the power grid digital process operation tasks and a power grid power monitoring module that collects real-time operating data of power grid equipment.

5. The AI-based RPA method for monitoring digital processes in power grids according to claim 1, characterized in that, Step S2 specifically includes: constructing a main program based on RPA technology, performing program control tracking and control software, and when implementing corresponding tracking control, setting tracking control targets and calculating the dynamic execution index of the control targets: In the formula: J represents the target dynamic execution index; d represents the monitoring range, i.e. the area monitored and controlled in the tracking and control system; S represents the change monitoring distance; and x represents the preset time.

6. The AI-based RPA method for monitoring digital processes in a power grid, as described in claim 5, is characterized in that... After the target actual dynamic execution index is obtained, it is used as the actual tracking and control limit standard of the power grid digital process. RPA monitoring targets corresponding to the target actual dynamic execution index are set in the RPA monitoring targets, and the RPA monitoring targets corresponding to the target actual dynamic execution index are added to the system.

7. The AI-based RPA method for monitoring digital processes in power grids according to claim 1, characterized in that, Step S3 specifically includes placing the RPA robot cluster in a fixed server, using OA to send emails to users to establish services, receiving the emails, identifying the current user's identity information, receiving the power grid digitalization business process submitted by the user, allocating and processing the business, executing it, and finally verifying and checking the executed tasks before submitting the data.

8. The AI-based RPA method for monitoring digital processes in power grids according to claim 1, characterized in that, Step S4 includes a tracking time test. Within the scheduling and monitoring analysis level, a data tracking comparison test is conducted on the digital power grid process data. During the digital operation of the power grid, only one type of digital power grid process data is effectively tracked. The amount of digital power grid process data is set, and the tracking time of one type of digital power grid process data is compared. The higher the tracking time, the lower the system tracking efficiency, and the lower the tracking time, the higher the system tracking efficiency.

9. A power grid digital process monitoring method based on artificial intelligence according to claim 1 or 8, characterized in that, Step S4 also includes a tracking effect test. Taking a regional power grid as the experimental object, steady-state data in the digital process of the power grid is further collected. Based on the collection results, different systems are used to effectively track the frequency waveform of the steady-state data, and the tracking results are compared with the actual steady-state data frequency waveform. If the tracking results are similar to the actual results, it indicates that the tracking deviation between the two is small and the system tracking performance is high. If the tracking result differs greatly from the actual result, it indicates that the system's tracking performance is low.

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