Artificial intelligence-driven power grid production management organization structure optimization method

Abstract

The invention provides an artificial intelligence-driven power grid production management organization structure optimization method, and relates to the technical field of power grid production management, and the method comprises the steps: constructing a global data sensing network and an edge calculation infrastructure; the method comprises the following steps: establishing an intelligent execution terminal, carrying out automatic inspection operation on power grid management, establishing a track according to a power grid equipment concentrated area planning state, installing an inspection robot to carry out regular inspection along a preset track, and collecting electric power information of each power grid equipment; developing an intelligent decision-making deduction engine and a knowledge graph system; establishing a dynamic self-adaptive hybrid decision execution unit; through deep fusion of a global sensing network and edge calculation, the problem of response lag caused by traditional dependence on manual inspection and hierarchical reporting is solved, the inspection robot is adopted to complete the inspection task of a power grid equipment concentrated area, the purpose of equipment inspection in a relatively high range is achieved, and the inspection efficiency is improved. Partial discharge detection processing can be flexibly carried out on power grid equipment at different positions.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid production management, and specifically to an artificial intelligence-driven power grid production management organizational structure optimization method. Background Art

[0002] Optimizing the organizational structure of power grid production management refers to systematic reforms that improve the efficiency, safety, and economy of power grid operations by adjusting the management structure, resource allocation, and process systems of power companies in production and operations. With the large-scale integration of new energy, the marketization of electricity, and the acceleration of digital transformation, traditional hierarchical management models are gradually showing problems such as delayed response and insufficient coordination. Optimization measures focus on building a multi-dimensional coordination mechanism. Their core goal is to establish an efficient management system that adapts to the needs of the new power system, balances safety, efficiency, and sustainable development, and provides a solid organizational foundation for energy transformation.

[0003] In existing technologies, power grid production management mainly relies on manual inspections and hierarchical reporting. This solution will lead to response delays and low efficiency in locating faults. On the other hand, inspections of areas where power grid equipment is concentrated still rely mainly on manual implementation. This solution has high risks and low efficiency. Conventional inspection robots find it difficult to avoid ground obstacles. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the purpose of the present invention is to provide an artificial intelligence-driven power grid production management organizational structure optimization method to solve the problems raised in the above-mentioned background technology. The present invention compresses the data collection-analysis delay from hours to milliseconds through the deep integration of the global perception network and edge computing, solving the response lag problem caused by traditional reliance on manual inspections and hierarchical reporting. Inspection robots are used to complete inspection tasks in areas where power grid equipment is concentrated. Combined with the two track structures of the bottom track and the obstacle-crossing track, it can ensure that the inspection robot can move in the presence of various obstacles on the ground, and achieve the purpose of equipment inspection in a higher range, and can flexibly perform local discharge detection and processing on power grid equipment in different locations.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an artificial intelligence-driven power grid production management organizational structure optimization method, comprising the following steps:

[0006] S1. Build a global data perception network and edge computing infrastructure, focusing on multi-source data integration to establish an intelligent perception system covering all aspects of transmission, transformation, and distribution.

[0007] S2. Establish an intelligent execution terminal to automate inspection operations for power grid management. Build tracks based on the planned status of the centralized area of ​​power grid equipment, and install inspection robots to conduct regular inspections along the preset tracks and collect power information for each power grid device.

[0008] S3. Develop an intelligent decision-making engine and knowledge graph system, and build a dynamic decision-making center based on digital twins and game theory optimization;

[0009] S4. Establish a dynamic and adaptive hybrid decision-making execution unit, reconstruct the task execution mechanism of human-machine collaboration, and divide the multi-level response system according to the event level;

[0010] S5. Restructure the flexible organizational structure and cross-departmental collaboration mechanism, build a matrix-style dynamic organization, establish an organizational agility assessment system, quantify departmental collaborative effectiveness through a collaborative response index, and directly link it to performance appraisals;

[0011] S6. Build a model sharing platform to implement and verify the model, promote the automatic iteration and upgrade of the system, and simultaneously establish a model performance degradation early warning mechanism.

[0012] Furthermore, in step S1, high-precision IoT sensors, inspection drone clusters, and meteorological satellite data receiving stations are deployed to form a multimodal data warehouse that is updated at the minute level. Edge computing equipment is deployed at key nodes such as substations and new energy stations to run lightweight AI models under the federated learning framework.

[0013] Furthermore, in step S3, a 1:1 virtual mirror system is built using the physical information model of the power grid, and an LSTM-GAN hybrid model is integrated to predict the load fluctuation and equipment failure probability distribution in the next 72 hours. With the help of the subject collaborative optimization algorithm, the interests of power generators, transmission and transformation operators, and dispatching centers are modeled as an incomplete information game.

[0014] Furthermore, in step S4, the AI ​​agent directly dispatches the inspection robot to perform standardized operations, simultaneously generates digital work orders and archives them in the blockchain evidence storage system, and simultaneously presents the three-dimensional heat map of the fault impact domain, the inventory distribution of spare parts, and the real-time positioning of the repair team through the augmented reality interface. The AI ​​generates multiple sets of disposal plans, which include success rate estimates and cost accounting.

[0015] Furthermore, the track includes a bottom track and an obstacle-crossing track, and both the bottom track and the obstacle-crossing track are used to provide mobile guidance for the inspection robot, and the obstacle-crossing track needs to be built according to the obstacles on the ground. When there is a pipeline obstruction or a small power grid equipment obstruction on the ground, the bottom track is extended to the side of the obstacle, and then the obstacle-crossing track is built and supported by columns.

[0016] Furthermore, after the inspection robot moves along the bottom track and moves to one side of the obstacle, it is lifted from the bottom track to the obstacle track through the docking platform, and the number of layers and height of the obstacle track are increased according to the height of the obstacle.

[0017] Furthermore, by starting the electric lifting rod, the transmission plate and the top plate are driven to move up and down, and the top plate drives the bottom lifting plate to be lifted or lowered through the connecting column. By lifting the lifting plate, the power system and the driving roller are lifted until the lifting driving roller is pressed from the docking platform surface of the bottom track to the bottom surface of the docking platform of the upper obstacle track.

[0018] Furthermore, the electric lifting rod provides pressure for the driving roller to press against the docking platform of the obstacle crossing track. At this time, the power system is controlled in reverse to make the driving roller roll along the docking platform of the obstacle crossing track to the inner side of the moving channel of the obstacle crossing track, and the electric telescopic rod is controlled to retract, so as to achieve the purpose of lifting the entire inspection robot.

[0019] Furthermore, it also includes leakage monitoring of ground cable lines: after starting the first motor, the rotating arm at the end is driven to rotate through the driving shaft, and the rotating arm controls the first motor, the pressing wheel, the conductive column and the connecting plate to rotate synchronously. The pressing wheel is used to press on the ground and contact the surface of the cable line passed over, and is used to detect and process the leakage status of the ground cable, and transmit the current signal to the partial discharge detector through the conductive column and the wire at the rear end of the conductive column.

[0020] Furthermore, it also includes a detection and correction process for the inspection robot: the first motor drives the rotating arm to reach a horizontal state, and the second motor controls the connecting plate to move toward the rear end until the positioning column is embedded in the interior of the positioning slot. The interior of the positioning slot is conical, and the positioning column is pressed against the innermost area of ​​the positioning slot to achieve the positioning of the entire connecting plate, assisting the first motor to control the entire rotating arm to reach a completely horizontal state. Each time the discharge detection process of one of the power grid equipment is completed, the detection and correction process of the inspection robot is achieved through the cooperation process of the positioning column and the positioning slot.

[0021] Beneficial effects of the present invention:

[0022] 1. This AI-driven approach to optimizing the organizational structure of power grid production management reduces data collection and analysis latency from hours to milliseconds through the deep integration of a global perception network and edge computing. This addresses the response lag caused by traditional reliance on manual inspections and hierarchical reporting. Through a hybrid decision-making mechanism of AI agents and human experts, it forms an agile closed loop of prediction, decision-making, execution, and feedback, ensuring the coordinated evolution of technological change and organizational capabilities.

[0023] 2. This AI-driven method for optimizing the organizational structure of power grid production management uses inspection robots to complete inspection tasks in areas where power grid equipment is concentrated. Combined with two track structures, the bottom track and the obstacle-crossing track, it ensures that the inspection robot can move in the presence of various obstacles on the ground and achieve the purpose of equipment inspection within a larger range.

[0024] 3. The inspection robot used in this artificial intelligence-driven power grid production management organizational structure optimization method can detect the leakage status of lines laid on the ground and fixed areas on the ground through the detection module at the end. It can also flexibly perform partial discharge detection and processing on power grid equipment at different locations, and can quickly and efficiently locate the monitoring position of the detection module itself. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of the artificial intelligence-driven power grid production management organizational structure optimization method of the present invention;

[0026] Figure 2 This is a diagram showing the composition of key deployed devices used in the artificial intelligence-driven power grid production management organizational structure optimization method of the present invention;

[0027] Figure 3 This is a diagram showing the connection between the inspection robot and the track of the present invention;

[0028] Figure 4 It is a structural diagram of the track portion of the present invention;

[0029] Figure 5 This is a schematic structural diagram of the inspection robot part of the present invention;

[0030] Figure 6 This is a schematic structural diagram of the driving mechanism of the inspection robot of the present invention;

[0031] Figure 7 It is a structural schematic diagram of the mobile trolley part of the present invention;

[0032] Figure 8 It is a structural diagram of the detection module part of the present invention;

[0033] Figure 9 for Figure 7 Enlarged view of area A in the middle;

[0034] In the figure: 1. Bottom track; 2. Obstacle crossing track; 3. Column; 4. Inspection robot; 5. Moving channel; 6. Front convex plate; 7. Docking platform; 8. Lifting plate; 9. Moving trolley; 10. Driving mechanism; 11. Detection module; 12. Power system; 13. Driving roller; 14. Connecting column; 15. Top plate; 16. Transmission plate; 17. Support wheel; 18. Rear baffle; 19. Front plate; 20. Partial discharge detector; 21. Electric lifting rod; 22. First motor; 23. Drive shaft; 24. Rotating arm; 25. Second motor; 26. Screw; 27. Pressing wheel; 28. Conductive ring; 29. ​​Electrode column; 30. Conductive column; 31. Connecting plate; 32. Wire pipe; 33. Threaded sleeve; 34. Positioning column; 35. Extension plate; 36. Positioning groove. DETAILED DESCRIPTION

[0035] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0036] See also Figures 1 to 9 , the present invention provides the following technical solution: an artificial intelligence-driven power grid production management organizational structure optimization method, comprising the following steps:

[0037] S1. Build a global data perception network and edge computing infrastructure. Focusing on multi-source data fusion, establish an intelligent perception system covering all aspects of power generation, transmission, transformation, and distribution. Deploy high-precision IoT sensors, inspection drone clusters, and meteorological satellite data receiving stations to form a multimodal data warehouse with minute-level updates. Deploy edge computing equipment at key nodes such as substations and new energy stations, and run lightweight AI models under the federated learning framework.

[0038] S2. Establish an intelligent execution terminal to perform automated inspection operations for power grid management. Build a track based on the planned status of the centralized area of ​​power grid equipment, and install an inspection robot 4 to perform regular inspections along the preset track and collect power information for each power grid device.

[0039] S3. Develop an intelligent decision-making engine and knowledge graph system. Build a dynamic decision-making hub based on digital twins and game theory optimization. Use the physical information model of the power grid to build a 1:1 virtual mirror system. Integrate an LSTM-GAN hybrid model to predict load fluctuations and equipment failure probability distributions over the next 72 hours. Leverage a collaborative optimization algorithm to model the interests of power generators, transmission and distribution operators, and dispatch centers as an incomplete information game.

[0040] S4. Establish a dynamic and adaptive hybrid decision-making execution unit, restructure the task execution mechanism for human-machine collaboration, and divide the multi-level response system according to the incident level. The AI ​​agent directly dispatches the inspection robot 4 to perform standardized tasks, simultaneously generates digital work orders and archives them in the blockchain evidence storage system. The augmented reality interface simultaneously presents a three-dimensional heat map of the fault impact domain, spare parts inventory distribution, and the real-time location of the repair team. The AI ​​generates multiple sets of treatment plans, including success rate estimates and cost accounting;

[0041] S5. Restructure the flexible organizational structure and cross-departmental collaboration mechanism, build a matrix-style dynamic organization, establish an organizational agility assessment system, quantify departmental collaborative effectiveness through a collaborative response index, and directly link it to performance appraisals;

[0042] S6. Build a model sharing platform to implement and verify the model, promote the automatic iteration and upgrade of the system, and simultaneously establish a model performance degradation early warning mechanism.

[0043] In this embodiment, a patrol robot 4 used in the above-mentioned step S2 and a track structure suitable for the patrol robot 4 are also provided. The track structure includes a bottom track 1 and an obstacle crossing track 2. The bottom track 1 and the obstacle crossing track 2 both include a moving channel 5, a front protrusion plate 6 and a docking platform 7, and the docking platform 7 is arranged at both ends of the bottom track 1 and the obstacle crossing track 2. A moving channel 5 is opened inside the bottom track 1 and the obstacle crossing track 2. A front protrusion plate 6 is welded to the front end of the moving channel 5, and columns 3 are welded to the back of both ends of the obstacle crossing track 2.

[0044] Specifically, the bottom track 1 and obstacle crossing track 2 provided in this embodiment are both used to provide movement guidance functions for the inspection robot 4, and the obstacle crossing track 2 needs to be built according to the obstacles on the ground. For example, if there is a pipeline obstruction or a small power grid equipment obstruction on the ground, the bottom track 1 can be extended to the side of the obstacle, and then the obstacle crossing track 2 can be built and supported by the column 3. After the inspection robot 4 moves along the bottom track 1 and moves to the side of the obstacle, it is lifted from the bottom track 1 to the obstacle crossing track 2 through the docking platform 7, and the number of layers and height of the obstacle crossing track 2 are increased according to the height of the obstacle.

[0045] The inspection robot 4 includes a driving mechanism 10, a mobile cart 9 and a detection module 11. The driving mechanism 10 includes a lifting plate 8, a connecting column 14 and a top plate 15. The surface of the lifting plate 8 is welded with a connecting column 14, and the top plate 15 is welded to the top of the connecting column 14. The surface of the lifting plate 8 is screwed with a power system 12, and the rear end of the power system 12 is inserted with a driving roller 13. The side of the top plate 15 is welded with a transmission plate 16, and the top of the mobile cart 9 is welded with an electric lifting rod 21. The top of the electric lifting rod 21 is screwed to the bottom of the transmission plate 16.

[0046] By starting the electric lifting rod 21, the transmission plate 16 and the top plate 15 are driven to move up and down. The top plate 15 drives the bottom lifting plate 8 to be lifted or lowered through the connecting column 14. By lifting the lifting plate 8, the power system 12 and the driving roller 13 are lifted until the driving roller 13 is lifted from the surface of the docking platform 7 of the bottom track 1 to the bottom surface of the docking platform 7 of the upper obstacle track 2. The electric lifting rod 21 provides pressure for the driving roller 13 to press against the docking platform 7 of the obstacle track 2. At this time, the power system 12 is controlled in reverse to make the driving roller 13 roll along the docking platform 7 of the obstacle track 2 to the inner side of the moving channel 5 of the obstacle track 2, and the electric telescopic rod is controlled to retract to achieve the purpose of lifting the entire inspection robot 4.

[0047] A front end plate 19 and a rear end plate are welded on the surface of the mobile trolley 9, and an extension plate 35 is integrally formed at the end of the rear end plate. A positioning groove 36 is provided at the end of the extension plate 35. The detection module 11 includes a first motor 22, a drive shaft 23, a rotating arm 24, a second motor 25, a pressing wheel 27, a conductive column 30 and an electrode column 29. The first motor 22 is screwed to the surface of the rear end plate, and the output end of the first motor 22 is inserted with a drive shaft 23, and the end of the drive shaft 23 is screwed with a rotating arm 24, and the end shaft of the rotating arm 24 is connected to a pressing wheel 27, and a conductive ring 28 is embedded in the side of the pressing wheel 27, and a conductive column 30 is inserted in the middle of the pressing wheel 27. The electric column 30, the front end of the conductive column 30 is installed with the electrode column 29, the rear end of the conductive column 30 is installed with the connecting plate 31, the end of the connecting plate 31 is provided with a threaded sleeve 33, the surface of the rotating arm 24 is screwed with the second motor 25, the output end of the second motor 25 is inserted with a screw rod 26, the screw rod 26 passes through the inside of the threaded sleeve, and a positioning column 34 and a wire tube 32 are welded on the back of the connecting plate 31. The surface of the mobile trolley 9 is screwed with the partial discharge detector 20, the surface of the conductive column 30 is partially connected to the conductive ring 28 through the line, and the conductive column 30 is partially connected to the partial discharge detector 20 after the wire passes through the wire tube 32.

[0048] After the first motor 22 is started, the driving shaft 23 drives the rotating arm 24 at the end to rotate. The rotating arm 24 controls the first motor 22, the pressing wheel 27, the conductive column 30 and the connecting plate 31 to rotate synchronously. The pressing wheel 27 is used to press on the ground and contact the surface of the cable line passed over. It is used to detect and process the leakage status of the ground cable and transmit the current signal to the partial discharge detector 20 through the conductive column 30 and the wires at the rear end of the conductive column 30.

[0049] After the rotating arm 24 is rotated by the first motor 22, the second motor 25 is started, and the second motor 25 drives the screw rod 26 to rotate. The screw rod 26 cooperates with the threaded sleeve 33 to drive the entire connecting plate 31 to perform translational movement. The connecting plate 31 pulls the conductive column 30 at one end to perform telescopic movement in the middle position of the pressing wheel 27, so that the electrode column 29 can partially lean against the surface of the adjacent power grid equipment. The partial discharge detector 20 at the rear end detects the current state of the surface of the contacted power grid equipment through the wires, the conductive column 30 and the electrode column 29.

[0050] By controlling the first motor 22 to drive the rotating arm 24 to reach a horizontal state, the second motor 25 controls the connecting plate 31 to move toward the rear end until the positioning column 34 is embedded in the interior of the positioning groove 36. The interior of the positioning groove 36 is in a conical state. The positioning column 34 is pressed against the innermost area of ​​the positioning groove 36 to achieve the positioning of the entire connecting plate 31, thereby assisting the first motor 22 to control the entire rotating arm 24 to reach a completely horizontal state. Each time the discharge detection process of one of the power grid equipment is completed, the detection and correction process of the inspection robot 4 is achieved through the cooperation process of the positioning column 34 and the positioning groove 36.

[0051] The basic principles, main features and advantages of the present invention are shown and described above. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0052] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. An artificial intelligence-driven method for optimizing the organizational structure of power grid production management, characterized in that: The following steps are involved: S1. Build a global data perception network and edge computing infrastructure, focusing on multi-source data integration to establish an intelligent perception system covering all aspects of transmission, transformation, and distribution. S2. Establish an intelligent execution terminal to automate inspection operations for power grid management. Build tracks based on the planned status of the centralized area of ​​power grid equipment, and install inspection robots to conduct regular inspections along the preset tracks and collect power information for each power grid device. S3. Develop an intelligent decision-making engine and knowledge graph system, and optimize and build a dynamic decision-making center based on digital twins and game theory; S4. Establish a dynamic and adaptive hybrid decision-making execution unit, reconstruct the task execution mechanism of human-machine collaboration, and divide the multi-level response system according to the event level; S5. Restructure the flexible organizational structure and cross-departmental collaboration mechanism, build a matrix-style dynamic organization, establish an organizational agility assessment system, quantify departmental collaborative effectiveness through a collaborative response index, and directly link it to performance appraisals; S6. Build a model sharing platform to implement and verify the model, promote the automatic iteration and upgrade of the system, and simultaneously establish a model performance degradation early warning mechanism.

2. The artificial intelligence-driven power grid production management organizational structure optimization method according to claim 1, characterized in that: In step S1, high-precision IoT sensors, inspection drone clusters, and meteorological satellite data receiving stations are deployed to form a multimodal data warehouse that is updated at the minute level. Edge computing equipment is deployed at key nodes of substations and new energy stations to run lightweight AI models under the federated learning framework.

3. The artificial intelligence-driven power grid production management organizational structure optimization method according to claim 2, characterized in that: In step S3, a 1:1 virtual mirror system is built using the physical information model of the power grid, and an LSTM-GAN hybrid model is integrated to predict the load fluctuation and equipment failure probability distribution in the next 72 hours. With the help of the subject collaborative optimization algorithm, the interests of power generators, transmission and transformation operators, and dispatching centers are modeled as an incomplete information game.

4. The artificial intelligence-driven power grid production management organizational structure optimization method according to claim 3 is characterized by: In step S4, the AI ​​agent directly dispatches the inspection robot to perform standardized operations, simultaneously generates digital work orders and archives them in the blockchain evidence storage system, and simultaneously presents the three-dimensional heat map of the fault impact domain, the inventory distribution of spare parts, and the real-time positioning of the repair team through the augmented reality interface. The AI ​​generates multiple sets of disposal plans, which include success rate estimates and cost accounting.

5. The artificial intelligence-driven power grid production management organizational structure optimization method according to claim 1, characterized in that: The track includes a bottom track and an obstacle-crossing track, and both the bottom track and the obstacle-crossing track are used to provide mobile guidance for the inspection robot. The obstacle-crossing track needs to be built according to the obstacles on the ground. When there is a pipeline obstruction or a small power grid equipment obstruction on the ground, the bottom track is extended to the side of the obstacle, and then the obstacle-crossing track is built and supported by columns.

6. The artificial intelligence-driven power grid production management organizational structure optimization method according to claim 5, characterized in that: After the inspection robot moves along the bottom track and moves to one side of the obstacle, it is lifted from the bottom track to the obstacle crossing track through the docking platform, and the number of layers and height of the obstacle crossing track are increased according to the height of the obstacle.

7. The artificial intelligence-driven power grid production management organizational structure optimization method according to claim 5, characterized in that: By starting the electric lifting rod, the transmission plate and the top plate are driven to move up and down. The top plate drives the bottom lifting plate to be lifted or lowered through the connecting column. By lifting the lifting plate, the power system and the driving roller are lifted until the lifting driving roller is pressed from the docking platform surface of the bottom track to the bottom surface of the docking platform of the upper obstacle track.

8. The artificial intelligence-driven power grid production management organizational structure optimization method according to claim 7, characterized in that: The electric lifting rod provides pressure for the driving roller to press against the docking platform of the obstacle crossing track. At this time, the power system is controlled in reverse to make the driving roller roll along the docking platform of the obstacle crossing track to the inner side of the moving channel of the obstacle crossing track, and the electric telescopic rod is controlled to retract to achieve the purpose of lifting the entire inspection robot.

9. The artificial intelligence-driven power grid production management organizational structure optimization method according to claim 7, characterized in that: It also includes leakage monitoring of ground cable lines: after starting the first motor, the rotating arm at the end is driven to rotate through the drive shaft, and the rotating arm controls the first motor, the pressing wheel, the conductive column and the connecting plate to rotate synchronously. The pressing wheel is used to press on the ground and contact the surface of the cable line passed over, and is used to detect and process the leakage status of the ground cable, and transmit the current signal to the partial discharge detector through the conductive column and the wire at the rear end of the conductive column.

10. The artificial intelligence-driven power grid production management organizational structure optimization method according to claim 8, characterized in that: It also includes a detection and correction process for the inspection robot: the first motor drives the rotating arm to reach a horizontal state, and the second motor controls the connecting plate to move toward the rear end until the positioning column is embedded in the interior of the positioning slot. The interior of the positioning slot is conical, and the positioning column is pressed against the innermost area of ​​the positioning slot to achieve the positioning of the entire connecting plate, assisting the first motor to control the entire rotating arm to reach a completely horizontal state. Each time the discharge detection process of one of the power grid equipment is completed, the detection and correction purpose of the inspection robot is achieved through the cooperation of the positioning column and the positioning slot.

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