Mountain photovoltaic installation operation digital twin system and method

Abstract

The application discloses a mountain photovoltaic installation digital twin operation system and method, the system comprises a physical entity layer, a virtual twin layer, a twin data layer and a twin decision layer; the system collects the running state data of each operation equipment and the operation site environment information in the physical entity layer in real time, iteratively updates the data in the twin data layer, optimizes the operation process through the twin decision layer, and drives each operation equipment in the physical entity layer and the corresponding twin model in the virtual twin layer to complete the informatization management and automatic operation of mountain photovoltaic transportation and installation, so as to realize intelligent control of the mountain photovoltaic installation operation process and visual supervision of the operation site, improve the mountain photovoltaic installation operation efficiency, and improve the construction technical level of the mountain photovoltaic power station.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power plant technology, and in particular to a digital twin system and method for photovoltaic installation operations in mountainous areas. Background Technology

[0002] With rapid economic development, global energy demand has increased dramatically. The long-term reliance on traditional non-renewable energy sources such as coal and oil has led to ecological degradation and supply shortages, drawing widespread attention to green and renewable energy. As a green renewable energy source, solar photovoltaic (PV) power generation has experienced rapid development, with installed capacity continuously increasing. In recent years, my country has actively promoted PV power generation and encouraged the construction of mountain PV power stations in non-arable land areas. However, mountainous terrain is complex, with significant elevation changes and poor road conditions. Currently, the construction of mountain PV power stations requires extensive manual labor for tasks such as optimizing the layout of PV modules, selecting the location of PV support columns, and transporting and installing PV modules. During on-site operations, the automation level of equipment is low, making it difficult to efficiently complete multi-scenario operations with manual assistance; the level of information technology in process management is low, making it difficult to monitor the status of equipment in real time and accurately control the work process; and the level of visual supervision at the work site is low, making it difficult to effectively manage dynamic and complex changes in the work environment. The transportation and installation of mountain PV modules have not yet achieved a minimally invasive or unmanned operation, resulting in severe discontinuity in the work process, a large number of personnel involved in the work area, and low efficiency in transportation and installation—problems that urgently need to be addressed. Summary of the Invention

[0003] To address the aforementioned problems or shortcomings, this invention provides a digital twin system and method for mountain photovoltaic installation operations, enabling information-based management and automated operation of mountain photovoltaic transportation and installation, reducing risks in the mountain photovoltaic installation process, improving the efficiency of mountain photovoltaic installation operations, and thus promoting the advancement of mountain photovoltaic power station construction technology.

[0004] To solve the above-mentioned technical problems, the present invention is implemented as follows:

[0005] The present invention provides a digital twin operation system for mountain photovoltaic installation, which includes: a physical entity layer, a virtual twin layer, a data twin layer, and a decision twin layer, and connects the communication between each layer through a network.

[0006] The physical entity layer includes: drilling equipment, hoisting equipment, conveying monorail machine, conveying drone, mountain terrain and working environment, and provides the acquired key driving data to the corresponding twin model in the digital space through the data acquisition interface on each piece of equipment via control communication protocol.

[0007] The twin virtual layer includes a geometry module, a rule module, a constraint module, and an iteration module;

[0008] The geometric module is used to characterize the shape, size, and position coordinates of the twin models of drilling equipment, hoisting equipment, conveying monorail machine, conveying drone, mountain terrain, and working environment in the digital space.

[0009] The rule module is used to characterize the twin models of each piece of equipment in the geometry module and the physical properties and motion coupling relationships of the equipment.

[0010] The constraint module is used to characterize the twin models of each working equipment in the geometric module and the constraint relationships, boundary conditions, and collision conditions of the working equipment.

[0011] The iterative module uses intelligent algorithms to simulate and optimize the operation process of the twin model of the mountain environment and the twin model of each piece of equipment in the geometric module, based on the key driving data collected in the physical entity layer and the virtual simulation data in the digital space. The iterative results of the simulation optimization are then mapped to each piece of equipment in the physical entity layer for driving, thereby realizing the interaction between the operation process of each piece of equipment in the physical entity layer and the virtual and real scenes.

[0012] The twin data layer includes: physical acquisition data, virtual model data, virtual simulation data, and virtual-real driving data;

[0013] The physical data collected includes: the operation and status data of each piece of equipment in the physical entity layer, and the mountain environment monitoring data;

[0014] The virtual model data includes: the shape and size data and boundary constraint data of the three-dimensional models of each virtual operating equipment and the three-dimensional model of the mountain environment in the twin virtual body layer;

[0015] The virtual simulation data includes: data generated during the simulation optimization process of the three-dimensional models of each virtual operating equipment and the three-dimensional model of the mountain environment in the twin virtual body layer;

[0016] The virtual and real driving data includes: driving data and fault alarm data of each working equipment in the physical entity layer after simulation optimization in the digital space;

[0017] The twin decision-making layer includes: a navigation and positioning module, an intelligent optimization module, a logic control module, and a safety early warning module;

[0018] The navigation and positioning module is used to control the autonomous navigation or flight of each piece of work equipment. It acquires and integrates the coordinate data of the location of the work equipment and the operating status data of the work equipment in real time. After performing a unified coordinate transformation, it obtains the speed and direction control data of each piece of work equipment in the physical entity layer and provides them to the intelligent optimization module, the logic control module, and the safety warning module respectively.

[0019] The intelligent optimization module is used to optimize the operation paths of drilling equipment, hoisting equipment, conveying monorail machine and conveying drone based on the operation constraints of each operating equipment, mountain slope data, control data and environmental obstacle information, using particle swarm optimization algorithm.

[0020] The logic control module plans the sequence of actions of the drilling machine, hoisting machine, conveying monorail machine, conveying drone and its operating mechanism based on the control data.

[0021] The safety early warning module provides safety warnings for the operation of the virtual equipment based on the boundary conditions and collision conditions of each piece of equipment in the twin virtual layer, thereby improving the reliability of the operation of each piece of equipment in the physical entity layer.

[0022] The digital twin operation system for mountain photovoltaic installation described in this invention is also characterized in that the drilling equipment in the physical entity layer includes at least: a tracked vehicle, a sliding pneumatic drill bit, and an air compressor; the key drive data provided by the drilling equipment includes at least: the tracked vehicle's track wheel extension length, the tracked vehicle's travel speed and direction, the pneumatic drill bit's sliding stroke, the pneumatic drill bit's running speed, the air compressor's working pressure, and alarm and fault information;

[0023] The hoisting equipment includes at least: a tracked vehicle, a vehicle-mounted hopper, an air compressor, an articulated robot, and a photovoltaic panel gripping mechanism; the key drive data provided by the hoisting equipment includes at least: the extension length of the tracked vehicle's track wheels, the tracked vehicle's travel speed and direction, the number of photovoltaic panels in the vehicle-mounted hopper, the working pressure of the air compressor, the coordinates of the articulated robot's end effector, the vacuum pressure value for gripping the photovoltaic panels, and alarm and fault information.

[0024] The key drive data provided by the conveyor monorail machine includes at least: the conveyor monorail machine's travel speed, the conveyor monorail machine's position coordinates, the quantity of materials on the conveyor belt, and alarm and fault information.

[0025] The key driving data provided by the delivery drone includes at least: drone flight speed, drone spatial coordinates, number of loads, alarm and fault information;

[0026] The key driving data provided by the mountain terrain and working environment include at least: mountain slope data, photovoltaic brackets, photovoltaic columns, operators and obstacles.

[0027] Sensors are used to collect data on each piece of equipment and the working environment in the physical entity layer in real time, so as to update the changes in the equipment and working environment in the twin virtual entity layer in real time.

[0028] The physical acquisition data, virtual model data, virtual simulation data, and virtual-real driving data in the twin data layer are stored and updated using a MySQL cloud database.

[0029] The present invention provides a digital twin operation method for mountain photovoltaic installation, characterized in that it is applied to a digital twin system composed of a physical entity layer, a virtual twin layer, a data twin layer, and a decision twin layer, and includes the following steps:

[0030] Step 1: Establishing a mountain terrain model: Aerial photography by drones obtains point cloud data of the mountain terrain, which is used to construct a twin model of the mountain terrain, establish models of operators and obstacles in the mountain working environment, so as to realize the construction of the working environment;

[0031] Step 2, Equipment Model Establishment: Based on the actual work equipment drawings, use 3D modeling software to construct a twin model of the work equipment. Set geometric and motion parameters and boundary constraints for the key motion parts of the twin model of the work equipment, and correlate it with the data acquisition information of the actual work equipment to form a data association model.

[0032] Step 3: Integrate the mountain terrain twin model, the operation equipment twin model, and the operation environment information to form a digital twin photovoltaic operation scene that is consistent with the geodetic coordinate system of the physical entity layer. The relevant models and data in the virtual twin layer are dynamically updated in real time through information collected by sensors.

[0033] Step 4, Human-Machine Interaction Control: The program is written to create a visual interactive interface on the handheld terminal using a browser or APP, which is used to receive control commands and query information, and to display the simulation operation process information of the virtual operation equipment, the operating status of the on-site equipment, and alarm information.

[0034] Step 5: Based on different slope areas and slope orientations, arrange photovoltaic power generation components to maximize power generation. Use digital processing methods to process the mountain terrain twin model to mark the coordinates of the photovoltaic frame column installation points and extract the mountain terrain contour lines for the operation equipment to travel.

[0035] Step 6: Select the coordinates of the photovoltaic frame column installation location and the contour lines of the mountain terrain, and determine the slope difference between the adjacent contour lines of the mountain terrain and the selected contour lines. At the same time, combined with the boundary constraints of the operating equipment in the physical entity layer, the particle swarm algorithm is used to optimize the operating path of each operating equipment to obtain the optimal operating path.

[0036] Step 7: Based on the equipment information and site environment information provided by the physical entity layer, plan the sequence of actions of each piece of equipment to obtain the logical operation results, which can be used for simulation of each virtual equipment twin model in the twin virtual entity layer.

[0037] Step 8: In the digital twin operation scenario, using the actual equipment operation information provided by the physical entity layer and the data generated by the virtual twin layer, simulate the drilling operation, hoisting operation and material transportation operation in the current virtual operation process, and verify the consistency and reliability of the current virtual operation process.

[0038] Step 9: After the virtual operation process is verified, the simulation operation data information is converted into control commands to guide the automatic operation of each piece of equipment on site, and the operation information of each piece of equipment on site is synchronously fed back to the twin virtual layer virtual model.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] 1. This invention utilizes digital twin technology in the construction of mountain photovoltaic power stations. It establishes a digital twin model of the mountain environment and a digital twin model of the equipment for photovoltaic installation operations, unifies the coordinate system of the virtual and real mountain environment, and acquires real-time information on the photovoltaic installation site environment and equipment. This information is then mapped and driven to the corresponding mountain digital twin model and equipment digital twin model, enabling visualized management of the mountain photovoltaic installation site environment and equipment operation. This enhances the automation capabilities of the equipment and the information management capabilities of the operation, reduces labor costs, and improves operational efficiency.

[0041] 2. This invention utilizes digital twin technology to provide simulation functionality for photovoltaic installation operations in mountainous areas. It selects the coordinates of the photovoltaic installation support column positions, extracts the contour lines of the mountain where the photovoltaic installation is to be carried out, determines the slope differences between adjacent contour lines, and uses intelligent optimization algorithms to simulate and optimize the operating path of the twin model of the operation equipment in digital space. It corrects and verifies the operation process of the twin model, and the results are mapped to drive the operation of the physical equipment, thereby improving the ability to predict equipment failure risks on-site, reducing safety hazards, and enhancing the reliability of equipment operation. Attached Figure Description

[0042] Figure 1 This is a structural block diagram of a digital twin system for mountain photovoltaic installation operations in an embodiment of the present invention;

[0043] Figure 2 This is a flowchart of a digital twin system for mountain photovoltaic installation operations in an embodiment of the present invention. Detailed Implementation

[0044] In this embodiment, a digital twin system for mountain photovoltaic installation operations is provided. This system involves modeling of equipment and terrain within a visualized 3D work scene, data sensing and acquisition, simulation, iterative optimization, and intelligent control. Digital twin technology is used to achieve visualized monitoring and intelligent management of the mountain photovoltaic installation process, thereby solving existing technical challenges in mountain photovoltaic installation operations. Specifically, refer to... Figure 1 It includes: a physical entity layer, a twin virtual entity layer, a twin data layer, and a twin decision layer, which communicate through a network connection;

[0045] The physical entity layer includes: drilling equipment, hoisting equipment, conveyor monorail, conveyor drones, mountain terrain and operating environment.

[0046] The physical entity layer provides a data acquisition interface and, through control communication protocols, provides key driving data for the corresponding twin model in the digital space.

[0047] The drilling equipment includes at least: a tracked vehicle, a sliding pneumatic drill bit, and an air compressor; the data provided by the drilling equipment includes at least: the tracked vehicle's track wheel extension length, the tracked vehicle's travel speed and direction, the pneumatic drill bit's sliding stroke, the pneumatic drill bit's running speed, the air compressor's working pressure, and alarm and fault information.

[0048] The hoisting equipment includes at least: a tracked vehicle, a vehicle-mounted hopper, an air compressor, an articulated robot, and a photovoltaic panel gripping mechanism; the data provided by the hoisting equipment includes at least: the extension length of the tracked vehicle's track wheels, the tracked vehicle's travel speed and direction, the number of photovoltaic panels in the vehicle-mounted hopper, the working pressure of the air compressor, the coordinates of the articulated robot's end effector, the vacuum pressure value for gripping the photovoltaic panels, and alarm and fault information.

[0049] The data provided by the conveyor monorail machine includes at least: the travel speed of the conveyor monorail machine, the position coordinates of the conveyor monorail machine, the quantity of materials on the conveyor belt, and alarm and fault information.

[0050] The data provided by the delivery drone includes at least: drone flight speed, drone spatial coordinates, number of materials carried, and alarm and fault information.

[0051] Key driving data provided by the mountain topography and operating environment include at least: mountain slope data, photovoltaic brackets, photovoltaic columns, operators, and obstacles.

[0052] The twin virtual layer includes: a geometry module, a rule module, a constraint module, and an iteration module.

[0053] The geometry module is used to characterize the shape, size, and position coordinates of twin models of drilling equipment, hoisting equipment, conveyor monorail machines, conveyor drones, mountain terrain, and the working environment in digital space.

[0054] The rules module is used to characterize the twin models of each piece of equipment in the geometry module, as well as the physical properties and motion coupling relationships of the equipment.

[0055] The constraint module is used to characterize the twin models of each working equipment in the geometry module and the constraint relationships, boundary conditions, and collision conditions of the working equipment.

[0056] The iterative module uses intelligent algorithms to simulate and optimize the operation process of the twin models of the mountain environment and various operational equipment in the geometric module, based on the key driving data collected in the physical entity layer and the virtual simulation data in the digital space. The iterative results of the simulation optimization are then mapped to the operational equipment in the physical entity layer for driving, thereby realizing the interaction between the operation process of the operational equipment in the physical entity layer and the virtual and real scenes in the twin virtual entity layer.

[0057] The twin data layer includes: physical acquisition data, virtual model data, virtual simulation data, and virtual-real driving data.

[0058] The physical data collected includes: operational and status data of the equipment in the physical entity layer, and mountain environment monitoring data.

[0059] The virtual model data includes: shape and size data and boundary constraint data of the virtual equipment 3D model and the mountain environment 3D model in the twin virtual body layer.

[0060] The virtual simulation data includes data generated during the simulation optimization process of the virtual equipment 3D model and the mountain environment 3D model in the twin virtual body layer.

[0061] The virtual-real driving data includes: simulation-optimized data from the digital space, used for driving equipment and fault alarm data in the physical entity layer.

[0062] The twin decision-making layer includes: intelligent optimization module, logic control module, navigation and positioning module, and safety early warning module.

[0063] The navigation and positioning module is used to control the autonomous navigation or flight of each piece of equipment. It acquires and integrates the coordinate data of the location of the equipment and the operating status data of the equipment in real time. After performing a unified coordinate transformation, it obtains the speed and direction control data of each piece of equipment in the physical entity layer and provides them to the intelligent optimization module, the logic control module, and the safety early warning module respectively.

[0064] The intelligent optimization module is used to optimize the operation paths of drilling equipment, hoisting equipment, conveying monorail machines, and conveying drones based on the operational constraints of each piece of equipment, mountain slope data, control data, and environmental obstacle information, using a particle swarm optimization algorithm.

[0065] The logic control module plans the sequence of actions of drilling equipment, hoisting equipment, conveying monorail machine, conveying drone and its operating mechanism based on the control data.

[0066] The safety early warning module provides safety warnings for the operation of virtual equipment based on the boundary conditions and collision conditions of each piece of equipment in the twin virtual layer, thereby improving the reliability of the operation of each piece of equipment in the physical entity layer.

[0067] In the physical entity layer, sensors collect data on on-site equipment and operating environment information in real time, detect the operating status data of each piece of equipment in the physical entity layer in real time, monitor information on photovoltaic brackets, photovoltaic columns, operators, and obstacles in the operating environment in real time, and update the changes in equipment and operating environment in the twin virtual entity layer in real time.

[0068] In the physical entity layer, the operating equipment is equipped with an IoT gateway MQT-805, which supports GPS positioning and data acquisition and control of the on-site operating equipment via RS485, Modbus TCP or Ethernet / IP. Data is transmitted to the cloud platform for computing and simulation via a 4G wireless network.

[0069] The virtual twin layer allows for visualization and interaction via a browser or app on a handheld terminal, displaying and accessing virtual geometric models of the work equipment and mountain terrain, the status of the work equipment and the work process, as well as data in the twin data layer.

[0070] The operation process is simulated and optimized through intelligent optimization and logical control via cloud platform. Data drives the operation equipment model in the twin virtual layer, and the result mapping drives the operation equipment entity in the physical entity layer, realizing the interactive iteration of operation process and virtual and real scene in the physical entity layer and twin virtual layer.

[0071] The system uses a MySQL cloud database to store and update physical acquisition data, virtual model data, virtual simulation data, and virtual-real driving data in the twin data layer.

[0072] By constructing a digital twin 3D virtual scene for mountain photovoltaic installation operations, real-time information on the work site environment and equipment operation can be obtained. Data-driven twin models in the digital space enable visualized management of the mountain photovoltaic installation work site environment and equipment operation, enhancing the automation capabilities of the equipment and the information management capabilities of the operation, reducing the number of personnel in the work area, and improving work efficiency.

[0073] Based on the aforementioned digital twin operation system for mountain photovoltaic installation, in this embodiment, as follows: Figure 2 As shown, a digital twin operation method for mountain photovoltaic installation includes the following steps:

[0074] Step 1: Establishing a mountain terrain model: Aerial photography by drones obtains point cloud data of the mountain terrain. The mapping software Pix4Dmapper Pro processes the 3D point cloud data to construct a twin model of the mountain terrain. Models of operators and obstacles in the mountain working environment are then established to realize the construction of the working environment.

[0075] Step 2: Equipment Model Establishment: Based on the actual work equipment drawings, use 3D modeling software to construct a twin model of the work equipment. Set geometric and motion parameters and boundary constraints for the key motion parts of the twin model of the work equipment, and correlate it with the data acquisition information of the actual work equipment to form a data association model.

[0076] Step 3: Integrate the mountain terrain twin model, the operation equipment twin model, and the operation environment information to form a digital twin photovoltaic operation scene that is consistent with the geodetic coordinate system of the physical entity layer. Collect information through sensors and monitoring devices, and dynamically update the relevant models and data in the virtual twin layer in real time.

[0077] Step 4, Human-Machine Interaction Control: The program develops a visual interactive interface on the handheld terminal via a browser or APP to receive control commands and query information, and display information on the virtual equipment simulation operation process, the operating status of the on-site equipment, and alarm information.

[0078] Step 5: Based on different slope areas and slope orientations, arrange photovoltaic power generation components to maximize power generation. Use digital processing methods to process the mountain terrain twin model to mark the coordinates of the photovoltaic frame installation points and extract the mountain terrain contour lines for the operation equipment to travel on.

[0079] Step 6: Select the coordinates of the photovoltaic frame column installation location and the contour lines of the mountain terrain, and determine the slope difference between the adjacent contour lines of the mountain terrain and the selected contour lines. At the same time, combined with the boundary constraints of the operating equipment in the physical entity layer, the particle swarm algorithm is used to optimize the operating path of each operating equipment to obtain the optimal operating path.

[0080] Step 7: Based on the equipment and environment information provided by the physical entity layer, plan the sequence of actions of each piece of equipment to obtain the logical operation results, which can be used for simulation of the virtual equipment twin models in the twin virtual entity layer.

[0081] Step 8: In the digital twin operation scenario, use the actual equipment operation information provided by the physical entity layer and the data generated by the virtual twin layer to simulate drilling operations, hoisting operations and material transportation operations in the current virtual operation process, and verify the consistency and reliability of the current virtual operation process.

[0082] Step 9: After the virtual operation process is verified, the simulation operation data information is converted into control commands to guide the automatic operation of each piece of equipment on site, and the operation information of each piece of equipment on site is synchronously fed back to the twin virtual layer virtual model.

[0083] In summary, this invention provides a simulation operation function for mountain photovoltaic installation through digital twin technology. It establishes a twin model of the operation equipment and a twin model of the mountain terrain, collects data on the equipment operation process and information on the mountain terrain environment in real time, realizes the virtual-real mapping and interactive driving of the operation equipment, the mountain terrain environment and their corresponding twin models, verifies the operation process through the simulation operation mode in the digital space, and makes timely corrections in the digital space after discovering problems, thereby improving the automation level of equipment operation and the informatization level of the operation process, and improving the efficiency of mountain photovoltaic operation.

Claims

1. A digital twin operation system for mountain photovoltaic installation, characterized in that, include: The system consists of a physical entity layer, a virtual twin layer, a data twin layer, and a decision twin layer, and the communication between these layers is connected via a network. The physical entity layer includes: drilling equipment, hoisting equipment, conveying monorail machine, conveying drone, mountain terrain and working environment, and provides the acquired key driving data to the corresponding twin model in the digital space through the data acquisition interface on each piece of equipment via control communication protocol. The twin virtual layer includes a geometry module, a rule module, a constraint module, and an iteration module; The geometric module is used to characterize the shape, size, and position coordinates of the twin models of drilling equipment, hoisting equipment, conveying monorail machine, conveying drone, mountain terrain, and working environment in the digital space. The rule module is used to characterize the twin models of each piece of equipment in the geometry module and the physical properties and motion coupling relationships of the equipment. The constraint module is used to characterize the twin models of each working equipment in the geometric module and the constraint relationships, boundary conditions, and collision conditions of the working equipment. The iterative module uses intelligent algorithms to simulate and optimize the operation process of the twin model of the mountain environment and the twin model of each piece of equipment in the geometric module, based on the key driving data collected in the physical entity layer and the virtual simulation data in the digital space. The iterative results of the simulation optimization are then mapped to each piece of equipment in the physical entity layer for driving, thereby realizing the interaction between the operation process of each piece of equipment in the physical entity layer and the virtual and real scenes. The twin data layer includes: physical acquisition data, virtual model data, virtual simulation data, and virtual-real driving data; The physical data collected includes: the operation and status data of each piece of equipment in the physical entity layer, and the mountain environment monitoring data; The virtual model data includes: the shape and size data and boundary constraint data of the three-dimensional models of each virtual operating equipment and the three-dimensional model of the mountain environment in the twin virtual body layer; The virtual simulation data includes: data generated during the simulation optimization process of the three-dimensional models of each virtual operating equipment and the three-dimensional model of the mountain environment in the twin virtual body layer; The virtual and real driving data includes: driving data and fault alarm data of each working equipment in the physical entity layer after simulation optimization in the digital space; The twin decision-making layer includes: a navigation and positioning module, an intelligent optimization module, a logic control module, and a safety early warning module; The navigation and positioning module is used to control the autonomous navigation or flight of each piece of work equipment. It acquires and integrates the coordinate data of the location of the work equipment and the operating status data of the work equipment in real time. After performing a unified coordinate transformation, it obtains the control data of the speed and direction of each piece of work equipment in the physical entity layer and provides them to the intelligent optimization module, the logic control module, and the safety warning module respectively. The intelligent optimization module is used to optimize the operation paths of drilling equipment, hoisting equipment, conveying monorail machine and conveying drone based on the operation constraints of each operating equipment, mountain slope data, control data and environmental obstacle information, using particle swarm optimization algorithm. The logic control module plans the sequence of actions of the drilling machine, hoisting machine, conveying monorail machine, conveying drone and its operating mechanism based on the control data. The safety early warning module provides safety warnings for the operation of the virtual equipment based on the boundary conditions and collision conditions of each piece of equipment in the twin virtual layer, thereby improving the reliability of the operation of each piece of equipment in the physical entity layer.

2. The digital twin operation system for mountain photovoltaic installation according to claim 1, characterized in that, The drilling equipment in the physical entity layer includes at least: a tracked vehicle, a sliding pneumatic drill bit, and an air compressor; the key drive data provided by the drilling equipment includes at least: the tracked vehicle track wheel extension length, the tracked vehicle travel speed and direction, the pneumatic drill bit sliding stroke, the pneumatic drill bit running speed, the air compressor working pressure, and alarm and fault information; The hoisting equipment includes at least: a tracked vehicle, a vehicle-mounted hopper, an air compressor, an articulated robot, and a photovoltaic panel gripping mechanism; the key drive data provided by the hoisting equipment includes at least: the extension length of the tracked vehicle's track wheels, the tracked vehicle's travel speed and direction, the number of photovoltaic panels in the vehicle-mounted hopper, the working pressure of the air compressor, the coordinates of the articulated robot's end effector, the vacuum pressure value for gripping the photovoltaic panels, and alarm and fault information. The key drive data provided by the conveyor monorail machine includes at least: the conveyor monorail machine's travel speed, the conveyor monorail machine's position coordinates, the quantity of materials on the conveyor belt, and alarm and fault information. The key driving data provided by the delivery drone includes at least: drone flight speed, drone spatial coordinates, number of loads, alarm and fault information; The key driving data provided by the mountain terrain and working environment include at least: mountain slope data, photovoltaic brackets, photovoltaic columns, operators and obstacles.

3. The digital twin operation system for mountain photovoltaic installation according to claim 1, characterized in that, Sensors are used to collect data on each piece of equipment and the working environment in the physical entity layer in real time, so as to update the changes in the equipment and working environment in the twin virtual entity layer in real time.

4. The digital twin operation system for mountain photovoltaic installation according to claim 1, characterized in that: The physical acquisition data, virtual model data, virtual simulation data, and virtual-real driving data in the twin data layer are stored and updated using a MySQL cloud database.

5. A digital twin operation method for mountain photovoltaic installation, characterized in that, Applied to the digital twin system of claim 1, and comprising the following steps: Step 1: Establishing a mountain terrain model: Aerial photography by drones obtains point cloud data of the mountain terrain, which is used to construct a twin model of the mountain terrain, establish models of operators and obstacles in the mountain working environment, so as to realize the construction of the working environment; Step 2, Equipment Model Establishment: Based on the actual work equipment drawings, use 3D modeling software to construct a twin model of the work equipment. Set geometric and motion parameters and boundary constraints for the key motion parts of the twin model of the work equipment, and correlate it with the data acquisition information of the actual work equipment to form a data association model. Step 3: Integrate the mountain terrain twin model, the operation equipment twin model, and the operation environment information to form a digital twin photovoltaic operation scene that is consistent with the geodetic coordinate system of the physical entity layer. The relevant models and data in the virtual twin layer are dynamically updated in real time through information collected by sensors. Step 4, Human-Machine Interaction Control: The program is written to create a visual interactive interface on the handheld terminal using a browser or APP, which is used to receive control commands and query information, and to display the simulation operation process information of the virtual operation equipment, the operating status of the on-site equipment, and alarm information. Step 5: Based on different slope areas and slope orientations, arrange photovoltaic power generation components to maximize power generation. Use digital processing methods to process the mountain terrain twin model to mark the coordinates of the photovoltaic frame column installation points and extract the mountain terrain contour lines for the operation equipment to travel. Step 6: Select the coordinates of the photovoltaic frame column installation location and the contour lines of the mountain terrain, and determine the slope difference between the adjacent contour lines of the mountain terrain and the selected contour lines. At the same time, combined with the boundary constraints of the operating equipment in the physical entity layer, the particle swarm algorithm is used to optimize the operating path of each operating equipment to obtain the optimal operating path. Step 7: Based on the equipment information and site environment information provided by the physical entity layer, plan the sequence of actions of each piece of equipment to obtain the logical operation results, which can be used for simulation of each virtual equipment twin model in the twin virtual entity layer. Step 8: In the digital twin operation scenario, using the actual equipment operation information provided by the physical entity layer and the data generated by the virtual twin layer, simulate the drilling operation, hoisting operation and material transportation operation in the current virtual operation process, and verify the consistency and reliability of the current virtual operation process. Step 9: After the virtual operation process is verified, the simulation operation data information is converted into control commands to guide the automatic operation of each piece of equipment on site, and the operation information of each piece of equipment on site is synchronously fed back to the twin virtual layer virtual model.

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