Pumped storage power station intelligent construction management and control system based on 5G private network

Abstract

The invention provides a pumped storage power station intelligent construction management and control system based on a 5G private network, and the system comprises a 5G private network module which is used for deploying a 5G base station position and an edge calculation node, and dynamically adjusting the network flow; the data sensing module is used for collecting multi-source data through a sensor network; the data center module is used for fusing the sensor data, the BIM data and the GIS data, performing preprocessing, establishing a three-dimensional model of the pumped storage power station, and updating the three-dimensional model in combination with multi-source data; the construction management and control module is used for analyzing the construction progress and the equipment operation state according to the three-dimensional model and generating intelligent decision support data; wherein data are transmitted among the modules through a 5G private network module. According to the system, through cooperative work of all the modules, 5G base station deployment is optimized, a 5G private network is applied to the intelligent construction management and control system, multi-source data is fused to analyze and decide construction information, a construction management and control scheme is generated, and therefore effective management and control over intelligent construction of the pumped storage power station are achieved.

Description

Technical Field

[0001] This invention relates to the field of pumped storage power stations, and in particular to an intelligent construction and control system for pumped storage power stations based on a 5G private network. Background Technology

[0002] Pumped storage power stations are an important form of electricity storage. They convert excess electrical energy into potential energy and store it in high-level reservoirs during off-peak hours, releasing it during peak hours to meet grid demand, thus achieving peak shaving, frequency regulation, and supply-demand balancing. With the integration of social informatization and industrialization, pumped storage power stations are gradually developing towards intelligent operation, and digital construction has become a trend. The intelligent construction of pumped storage power stations requires real-time data collection and monitoring of construction equipment, personnel, and the environment; remote operation and scheduling of construction equipment; efficient transmission of large amounts of sensor data and video monitoring data; and construction optimization and risk warning. These requirements place high demands on the low latency, high bandwidth, and high reliability of communication networks. Traditional communication methods are insufficient to meet the needs of intelligent construction and refined management of pumped storage power stations. Introducing a 5G private network is an effective way to solve this problem, but many technical challenges remain in practical applications.

[0003] Pumped storage power stations are typically built in remote, mountainous areas with complex terrain, such as hills, tunnels, and underground caverns. Their construction is characterized by long construction periods, large scale, numerous uncertainties, and difficult risk management. The planning and layout of 5G private networks requires comprehensive consideration of factors such as coverage, signal strength, and data transmission rates. Blindly increasing the number of base stations is not only costly but may also cause interference problems. How to reasonably control the scale of base stations and optimize the network structure while meeting business needs is a technical challenge. Furthermore, the construction of pumped storage power stations involves many aspects, with a wide variety of sensors and diverse data formats. It is necessary to collect, clean, and semantically model multi-source heterogeneous data, and integrate it with BIM and GIS models to achieve real-time synchronization between the engineering entity and the digital twin. Summary of the Invention

[0004] The main objective of this invention is to provide an intelligent construction management and control system for pumped storage power stations based on a 5G private network, which solves the problems of optimizing the deployment of 5G base stations and multi-source data fusion to achieve intelligent construction management and control.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: an intelligent construction and control system for pumped storage power stations based on a 5G private network, comprising: The 5G private network module is used to deploy 5G base station locations and edge computing nodes, and dynamically adjust network traffic; The data sensing module is used to collect multi-source data through a sensor network; The data center module is used to integrate sensor data, BIM data, and GIS data, and preprocess them to build a 3D model of the pumped storage power station, and update the 3D model by combining multi-source data. It also includes a construction management module, which analyzes the construction status based on the 3D model and generates a construction management plan; the various modules transmit data to each other via a 5G private network module.

[0006] In the preferred scheme, the method for deploying 5G base stations includes: obtaining a three-dimensional model of the pumped storage power station and the design scheme of the 5G base station; generating a three-dimensional electromagnetic field distribution model based on the signal strength and coverage of the 5G base station to obtain signal blind spots and weak coverage areas; and determining the location and number of 5G base stations by optimizing the deployment of 5G base stations to achieve full signal coverage and minimize interference.

[0007] In the preferred scheme, the methods for optimizing 5G base station deployment include: S1. Based on the topography, underground structure, equipment, power and communication requirements data of the pumped storage power station, candidate grids are divided across the entire power station area, and the core parameters of each grid are calculated, including: topographic elevation difference factor. Mountain shading coefficient underground coverage weight Penetration loss coefficient Equipment interference coefficient and power synergy factors ; S2. Construct a quadratic unconstrained binary optimization model integrating the parameters of the pumped storage power station, with the objective function as follows: ; in, , ∈{0,1} represents the base station deployment status. Baseline coverage cost for base stations Let λ be the basic interference coefficient between base stations, and λ be the coverage quality weighting factor; constraints are also set as follows: Coverage constraints: ; Interference constraints: ; Underground cover priority constraints: ; Capacity constraints: ; In the formula, This represents the basic coverage capability of base station i for grid j. For the minimum coverage requirement of grid j, This represents the maximum permissible interference value for the base station. The minimum threshold for underground cover. This represents the total number of grid cells in the underground area. C represents the maximum number of terminals that can be accessed by the base station, and C represents the total terminal demand. S3. Transform the optimization model into the Hamiltonian of the quantum annealing algorithm: ; In the formula, , The Pauli-Z operator for qubits; the optimal solution for base station deployment is obtained through quantum annealing; and an intelligent metadata management method based on metadata management and intelligent reasoning is proposed.

[0008] In the preferred scheme, the mountain shading coefficient The calculation rule is: when there is no mountain obstruction =1; when there is a mountain blocking the view. Where γ is the mountain density coefficient, Let θ be the distance to grid ij, and θ be the elevation angle. Underground Coverage Weight The calculation formula is: Where k is the importance coefficient, Let i be the number of critical devices within grid i. The area is the grid area. Equipment interference coefficient The calculation rule is: outside the safe distance =1; within the safe distance ,in Let ρ be the distance between grid i and critical device k, and let ρ be the interference attenuation index.

[0009] Electricity Coordination Factor The calculation rule is: when power cannot be supplied from the power station =1; when power can be supplied from a power station Where μ is the profit coefficient, Let i be the distance from the substation to grid i, and D be the power supply radius of the substation.

[0010] In a preferred embodiment, the method for deploying edge computing nodes includes: constructing a hypergraph of edge nodes and the relationships between nodes, where each node represents an edge computing node and each edge represents the connection relationship between nodes; constructing a hypergraph neural network model for model training; optimizing the collaborative computing between nodes through multi-layer propagation; and obtaining a deployment scheme for edge computing nodes.

[0011] In the preferred scheme, an industrial ring network is also deployed in parallel within the 5G private network module, and core control data is transmitted simultaneously in the 5G private network and the industrial ring network, forming a dual-link backup. Set up a link health detection mechanism to collect the transmission latency and packet loss rate of the two links in real time. When any link's indicators exceed the standard, it will be automatically marked as an "abnormal link". When the 5G private network is interrupted by obstructions from mountains or underground factories, the link is immediately switched to industrial ring network transmission to ensure uninterrupted data transmission. For areas with weak signals, a drone relay blind spot filling trigger mechanism is set up. When the signal strength is detected to be less than the set threshold, the drone is automatically dispatched to hover at the designated location to act as a signal relay and fill the signal blind spot. After the link switch, the current transmission link type, the reason for the switch, and the estimated recovery time are pushed to the management and control platform in real time.

[0012] In the preferred embodiment, a sensor network is deployed in the construction area of ​​the pumped storage power station to acquire multi-source data, including information data of engineering objects in the construction area. By utilizing edge computing nodes to preprocess and synchronize multi-source data, a unified information model is established to convert multi-source data with different protocols and formats into a standardized information model. Sensor networks include environmental monitoring sensors used to monitor the construction site environment and acquire environmental monitoring data; Structural monitoring sensors are used to monitor the health status of structures and acquire structural deformation data; Equipment monitoring sensors are used to monitor the operating status of construction equipment and obtain equipment maintenance data; Personnel monitoring sensors are used to locate the position of construction workers in real time and obtain personnel location data; Material tracking sensors are used to track the flow and inventory of materials and to acquire material data. And smart terminal devices, used to acquire images and video data from the construction site.

[0013] In the preferred embodiment, BIM data is used to provide distribution information of buildings and internal components, while GIS data is used to provide spatial information of the construction site. Methods for establishing a three-dimensional model of a pumped storage power station include: Transform BIM data and GIS data into the same coordinate system to obtain BIM and GIS point cloud data; The BIM and GIS point cloud data are segmented separately to form multiple hyperscale units; Extract the feature information of each hypermorphic unit and match the hypermorphic units of BIM and GIS point cloud data; The matched hypermorphic units are spatially superimposed and their attributes are stitched together to generate a 3D model that integrates BIM and GIS data.

[0014] In a preferred embodiment, a dynamic mapping relationship is established between multi-source data and engineering objects in the 3D model, and the 3D model is updated in real time.

[0015] In the preferred embodiment, the construction management module includes an earthwork control module for obtaining a management plan for earthwork construction; and a tunnel construction module for establishing a tunnel construction fault prediction model and obtaining a management plan for tunnel construction.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) This system optimizes the deployment of 5G base stations through the collaborative work of various modules, applies 5G private network to the intelligent construction management and control system, integrates multi-source data to analyze and make decisions on construction information, generates construction management and control schemes, and thus achieves effective management and control of intelligent construction of pumped storage power stations. (2) Through refined environmental modeling, multi-objective joint optimization and the adoption of advanced quantum heuristic solution algorithms, the scientific, accurate, economical and highly reliable 5G base station deployment scheme in the specific complex scenario of pumped storage power station has been realized. It can effectively overcome terrain obstruction, reduce signal blind spots, suppress interference within the system, and provide a stable, high-quality and fully covered 5G network foundation for construction management. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the overall structure of the present invention.

[0018] Figure 2 This is a structural diagram of the electromagnetic signal intensity distribution of the present invention.

[0019] Figure 3 This is a network bandwidth demand distribution structure diagram of the present invention.

[0020] Figure 4 This is a structural diagram of the sensor array of the present invention. Detailed Implementation

[0021] like Figures 1-4 As shown, a smart construction and control system for pumped storage power stations based on a 5G private network includes a 5G private network module for deploying 5G base station locations and edge computing nodes, and dynamically adjusting network traffic. The data sensing module is used to collect multi-source data through a sensor network; The data center module is used to integrate sensor data, BIM data, and GIS data, and preprocess them to build a 3D model of the pumped storage power station, and update the 3D model by combining multi-source data. It also includes a construction management module, which analyzes construction progress and equipment operating status based on a 3D model to generate intelligent decision support data; among these, data is transmitted between the various modules via a 5G private network module.

[0022] This solution, based on the geographical characteristics and construction needs of the pumped storage power station construction area, deploys 5G base stations through 5G private network modules to ensure comprehensive and stable network coverage; deploys edge computing nodes in key construction areas to achieve localized data processing; and allocates network resources according to priority, such as construction command transmission, video monitoring data, and sensor data from high to low priority, dynamically adjusting network traffic to optimize resource allocation and ensure real-time transmission of critical data.

[0023] Multiple sensors are installed at various locations within the construction area to construct a comprehensive sensor network, enabling real-time acquisition of parameters such as temperature, pressure, vibration, humidity, and displacement. A data sensing module collects multi-source data, ensuring the frequency and accuracy of data acquisition meet construction requirements. This multi-source data is then transmitted to a data center module via a 5G private network module, facilitating the effective aggregation of data and providing a basis for subsequent construction decision-making.

[0024] The data center module stores and manages all relevant data to enable data to be collected once and used everywhere. Preferably, distributed data storage technology is used to improve data storage capacity and read / write performance, ensuring high data availability.

[0025] Based on the updated 3D model from the data center module, the construction management module performs precise analysis of the construction status, including comparing the actual construction progress with the planned progress, monitoring deviations in the construction process in real time, and promptly identifying potential schedule risks; it also monitors and evaluates the operating status of construction equipment in real time, monitoring key performance indicators such as temperature, pressure, and vibration to determine whether the equipment is operating normally; and it intelligently generates a construction management plan by combining the results of construction progress and equipment operating status monitoring with historical construction data, industry standards, and expert experience.

[0026] This system utilizes the collaborative work of its various modules to apply the 5G private network to the intelligent construction management and control system, thereby enabling intelligent construction of pumped storage power stations.

[0027] In the preferred scheme, the method for deploying 5G base stations includes: obtaining a three-dimensional model of the pumped storage power station and the design scheme of the 5G base station; generating a three-dimensional electromagnetic field distribution model based on the signal strength and coverage of the 5G base station to obtain signal blind spots and weak coverage areas; and determining the location and number of 5G base stations by optimizing the deployment of 5G base stations to achieve full signal coverage and minimize interference.

[0028] In the preferred scheme, the methods for optimizing 5G base station deployment include: S1. Based on the topography, underground structure, equipment, power and communication requirements data of the pumped storage power station, candidate grids are divided across the entire power station area, and the core parameters of each grid are calculated, including: topographic elevation difference factor. Mountain shading coefficient underground coverage weight Penetration loss coefficient Equipment interference coefficient and power synergy factors ; Among them, topographic elevation difference factor This is the difference between the elevation of candidate grid i for a 5G base station and the average elevation of its corresponding pumped storage power station sub-region. It is used to quantify the impact of terrain undulations on 5G signal propagation path loss and is a core parameter adapted to the characteristics of large elevation differences between the upper and lower reservoirs and significant elevation differences between underground and above-ground areas in pumped storage power stations. The calculation formula is: ; in, The elevation of the center point of candidate grid i needs to be extracted from the 1:500 high-precision digital elevation model (DEM) data of the pumped storage power station. The elevation of the underground area is based on the elevation of the main powerhouse entrance on the ground as 0, and the elevation of the underground grid is recorded as a negative value. The average elevation of the sub-region to which candidate grid i belongs is not the average elevation of the entire power station area. S2. Construct a quadratic unconstrained binary optimization model integrating the parameters of the pumped storage power station, with the objective function as follows: ; in, , ∈{0,1} represents the base station deployment status. Baseline coverage cost for base stations Let λ be the basic interference coefficient between base stations, and λ be the coverage quality weighting factor; constraints are also set as follows: Coverage constraints: ; Interference constraints: ; Underground cover priority constraints: ; Capacity constraints: ; In the formula, This represents the basic coverage capability of base station i for grid j. For the minimum coverage requirement of grid j, This represents the maximum permissible interference value for the base station. The minimum threshold for underground cover. This represents the total number of grid cells in the underground area. C represents the maximum number of terminals that can be accessed by the base station, and C represents the total terminal demand. S3. Transform the optimization model into the Hamiltonian of the quantum annealing algorithm: ; In the formula, , The Pauli-Z operator for qubits; the optimal solution for base station deployment is obtained through quantum annealing; and an intelligent metadata management method based on metadata management and intelligent reasoning is proposed.

[0029] In the preferred scheme, the mountain shading coefficient The calculation rule is: when there is no mountain obstruction =1; when there is a mountain blocking the view. Where γ is the mountain density coefficient, Let θ be the distance to grid ij, and θ be the elevation angle. Underground Coverage Weight The calculation formula is: Where k is the importance coefficient, Let i be the number of critical devices within grid i. The area is the grid area. Equipment interference coefficient The calculation rule is: outside the safe distance =1; within the safe distance ,in Let ρ be the distance between grid i and critical device k, and let ρ be the interference attenuation index.

[0030] Electricity Coordination Factor The calculation rule is: when power cannot be supplied from the power station =1; when power can be supplied from a power station Where μ is the profit coefficient, Let i be the distance from the substation to grid i, and D be the power supply radius of the substation.

[0031] Another approach to optimizing 5G base station deployment includes: S1. Divide the signal blind spots and weak coverage areas into multiple grids, with each grid serving as a candidate location for a 5G base station; S2. Establish a quadratic unconstrained bivariate optimization model, which can be expressed as: ,in, For 5G base station deployment status, when =1 indicates deployment. =0 indicates no deployment; J ij Let be the interference coefficient between base stations i and j; ℎ i Let i be the coverage cost of base station i; S3. Simulating the quantum annealing process involves adjusting the Hamiltonian to obtain the globally optimal solution, leading to an optimized deployment scheme for 5G base stations. The solution process includes initializing the qubit states to represent candidate base station locations; simulating the quantum annealing process through quantum gate operations; calculating the energy of each qubit state; and selecting the state with the lowest energy as the optimal solution to obtain the optimized deployment scheme for 5G base stations. The optimized deployment scheme for 5G base stations is then simulated again as a three-dimensional electromagnetic field distribution model to evaluate whether signal blind spots and weak coverage areas meet the construction requirements of pumped storage power stations. If not, the quantum annealing solution is repeated.

[0032] In the preferred embodiment, the method for deploying edge computing nodes includes: constructing a hypergraph of edge nodes and their relationships, where each node represents an edge computing node and each edge represents a connection between nodes; constructing a hypergraph neural network model for model training; optimizing collaborative computing between nodes through multi-layer propagation; and obtaining a deployment scheme for the edge computing nodes. Preferably, the hypergraph can be represented as: ,in, It is a set of nodes, where each node represents a computing resource; The hypergraph is a set of hyperedges, where each hyperedge represents a collaborative computation task; the hypergraph neural network model can be represented as: ,in, For nodes In the layer, It is a super-edge The weight matrix, It is a bias term. It is an activation function.

[0033] In the preferred scheme, network traffic data is acquired in real time based on multiple 5G base stations and edge computing nodes, and network traffic allocation is adjusted using software-defined networking.

[0034] In the preferred embodiment, a sensor network is deployed in the construction area of ​​the pumped storage power station to acquire multi-source data, including information data of engineering objects in the construction area. Edge computing nodes are used to preprocess and synchronize the multi-source data in time to establish a unified information model, which is used to convert data of different protocols and formats into a standardized information model.

[0035] In the preferred embodiment, the sensor network includes environmental monitoring sensors for monitoring the construction site environment and acquiring environmental monitoring data; Structural monitoring sensors are used to monitor the health status of structures and acquire structural deformation data; Equipment monitoring sensors are used to monitor the operating status of construction equipment and obtain equipment maintenance data; Personnel monitoring sensors are used to locate the position of construction workers in real time and obtain personnel location data; Material tracking sensors are used to track the flow and inventory of materials and to acquire material data. And smart terminal devices, used to acquire images and video data from the construction site.

[0036] In the preferred embodiment, BIM data is used to provide distribution information of buildings and internal components, while GIS data is used to provide spatial information of the construction site. Methods for establishing a three-dimensional model of a pumped storage power station include: Transform BIM data and GIS data into the same coordinate system to obtain BIM and GIS point cloud data; The BIM and GIS point cloud data are segmented separately to form multiple hyperscale units; Extract the feature information of each hypermorphic unit and match the hypermorphic units of BIM and GIS point cloud data; The matched hypermorphic units are spatially superimposed and their attributes are stitched together to generate a 3D model that integrates BIM and GIS data.

[0037] In the preferred embodiment, a dynamic mapping relationship is established between multi-source data and engineering objects in the 3D model, and the 3D model is updated in real time.

[0038] In the preferred embodiment, the construction management module includes an earthwork control module for obtaining management and control plans for earthwork construction; and a tunnel construction module for establishing a tunnel construction fault prediction model and obtaining management and control plans for tunnel construction.

[0039] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A smart construction and control system for pumped storage power stations based on a 5G private network, characterized by: include: The 5G private network module is used to deploy 5G base station locations and edge computing nodes, and dynamically adjust network traffic; The data sensing module is used to collect multi-source data through a sensor network; The data center module is used to integrate sensor data, BIM data, and GIS data, and preprocess them to build a 3D model of the pumped storage power station, and update the 3D model by combining multi-source data. It also includes a construction management module, which analyzes the construction status based on the 3D model and generates a construction management plan; the various modules transmit data to each other via a 5G private network module.

2. The intelligent construction and control system for pumped storage power stations based on a 5G private network as described in claim 1, characterized in that: The methods for deploying 5G base stations include: obtaining a 3D model of the pumped storage power station and the design scheme of the 5G base station; generating a 3D electromagnetic field distribution model based on the signal strength and coverage of the 5G base station to obtain signal blind spots and weak coverage areas; and determining the location and number of 5G base stations by optimizing the deployment of 5G base stations to achieve full signal coverage and minimize interference.

3. The intelligent construction and control system for pumped storage power stations based on a 5G private network as described in claim 2, characterized in that: Methods to optimize 5G base station deployment include: S1. Based on the topography, underground structure, equipment, power and communication requirements data of the pumped storage power station, candidate grids are divided across the entire power station area, and the core parameters of each grid are calculated, including: topographic elevation difference factor. Mountain shading coefficient underground coverage weight Penetration loss coefficient Equipment interference coefficient and power synergy factors ; S2. Construct a quadratic unconstrained binary optimization model integrating the parameters of the pumped storage power station, with the objective function as follows: ； in, , ∈{0,1} represents the base station deployment status. Baseline coverage cost for base stations Let λ be the basic interference coefficient between base stations, and λ be the coverage quality weighting factor; constraints are also set as follows: Coverage constraints: ; Interference constraints: ; Underground cover priority constraints: ; Capacity constraints: ; In the formula, This represents the basic coverage capability of base station i for grid j. For the minimum coverage requirement of grid j, This represents the maximum permissible interference value for the base station. The minimum threshold for underground cover. This represents the total number of grid cells in the underground area. C represents the maximum number of terminals that can be accessed by the base station, and C represents the total terminal demand. S3. Transform the optimization model into the Hamiltonian of the quantum annealing algorithm: ； In the formula, , The Pauli-Z operator for qubits; the optimal solution for base station deployment is obtained through quantum annealing; and an intelligent metadata management method based on metadata management and intelligent reasoning is proposed.

4. The intelligent construction and control system for pumped storage power stations based on a 5G private network as described in claim 3, characterized in that: Mountain shading coefficient The calculation rule is: when there is no mountain obstruction =1; when there is a mountain blocking the view. Where γ is the mountain density coefficient, Let θ be the distance to grid ij, and θ be the elevation angle. Underground Coverage Weight The calculation formula is: Where k is the importance coefficient, Let i be the number of critical devices within grid i. The area is the grid area. Equipment interference coefficient The calculation rule is: outside the safe distance =1; within the safe distance ,in Let ρ be the distance between grid i and critical device k, and let ρ be the interference attenuation index. Electricity Coordination Factor The calculation rule is: when power cannot be supplied from the power station =1; when power can be supplied from a power station Where μ is the profit coefficient, Let i be the distance from the substation to grid i, and D be the power supply radius of the substation.

5. The intelligent construction and control system for pumped storage power stations based on a 5G private network as described in claim 1, characterized in that: The methods for deploying edge computing nodes include: constructing a hypergraph of edge nodes and the relationships between nodes, where each node represents an edge computing node and each edge represents the connection between nodes; constructing a hypergraph neural network model for model training; optimizing the collaborative computing between nodes through multi-layer propagation; and obtaining a deployment scheme for edge computing nodes.

6. The intelligent construction and control system for pumped storage power stations based on a 5G private network as described in claim 1, characterized in that: 5G... In the private network module, an industrial ring network is also deployed in parallel, and core control data is transmitted simultaneously on the 5G private network and the industrial ring network, forming a dual-link backup; Set up a link health detection mechanism to collect the transmission latency and packet loss rate of the two links in real time. When any link's indicators exceed the standard, it will be automatically marked as an "abnormal link". When the 5G private network is interrupted by obstructions from mountains or underground factories, the link is immediately switched to industrial ring network transmission to ensure uninterrupted data transmission. For areas with weak signals, a drone relay blind spot filling trigger mechanism is set up. When the signal strength is detected to be less than the set threshold, the drone is automatically dispatched to hover at the designated location to act as a signal relay and fill the signal blind spot. After the link switch, the current transmission link type, the reason for the switch, and the estimated recovery time are pushed to the management and control platform in real time.

7. The intelligent construction and control system for pumped storage power stations based on a 5G private network as described in claim 1, characterized in that: A sensor network is deployed in the construction area of ​​the pumped storage power station to acquire multi-source data, including information on engineering objects in the construction area. By utilizing edge computing nodes to preprocess and synchronize multi-source data, a unified information model is established to convert multi-source data with different protocols and formats into a standardized information model. Sensor networks include environmental monitoring sensors used to monitor the construction site environment and acquire environmental monitoring data; Structural monitoring sensors are used to monitor the health status of structures and acquire structural deformation data; Equipment monitoring sensors are used to monitor the operating status of construction equipment and obtain equipment maintenance data; Personnel monitoring sensors are used to locate the position of construction workers in real time and obtain personnel location data; Material tracking sensors are used to track the flow and inventory of materials and to acquire material data. And smart terminal devices, used to acquire images and video data from the construction site.

8. The intelligent construction and control system for pumped storage power stations based on a 5G private network as described in claim 1, characterized in that: BIM data is used to provide information on the distribution of buildings and their internal components, while GIS data is used to provide spatial information about the construction site. Methods for establishing a three-dimensional model of a pumped storage power station include: Transform BIM data and GIS data into the same coordinate system to obtain BIM and GIS point cloud data; The BIM and GIS point cloud data are segmented separately to form multiple hyperscale units; Extract the feature information of each hypermorphic unit and match the hypermorphic units of BIM and GIS point cloud data; The matched hypermorphic units are spatially superimposed and their attributes are stitched together to generate a 3D model that integrates BIM and GIS data.

9. The intelligent construction and control system for pumped storage power stations based on a 5G private network as described in claim 1 or 8, characterized in that: A dynamic mapping relationship is established between multi-source data and engineering objects in the 3D model, and the 3D model is updated in real time.

10. The intelligent construction and control system for pumped storage power stations based on a 5G private network according to claim 1, characterized in that: The construction management module includes an earthwork control module, which is used to obtain management and control plans for earthwork construction; and a tunnel construction module, which is used to establish a tunnel construction fault prediction model and obtain management and control plans for tunnel construction.

Images