Photovoltaic power station distributed construction management method and system based on load analysis

Abstract

The invention discloses a photovoltaic power station distributed construction management method and system based on load analysis, and the method comprises the steps: outputting a dynamic load intensity thermodynamic diagram according to the terrain elevation data, meteorological historical data and assembly mechanical parameters of a photovoltaic power station planning region; based on the dynamic load strength thermodynamic diagram, generating a construction zoning scheme for wind pressure resistance optimization; outputting a conflict-free construction resource allocation matrix according to the construction partitioning scheme; on the basis of the construction resource allocation matrix, construction parameters are dynamically corrected by adopting an adaptive particle swarm optimization algorithm, and an anti-deformation construction instruction set is generated; and driving the construction machinery to execute operation according to the anti-deformation construction instruction set, and iteratively updating the global load distribution map by using the reinforcement learning model to form a closed-loop construction management link. By utilizing the embodiment of the invention, the balance of construction precision and efficiency in a complex environment can be realized by fusing multi-source data, optimizing partition planning, intelligently scheduling equipment and controlling closed-loop quality.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of construction management, and in particular to a photovoltaic power station distributed construction management method and system based on load analysis. BACKGROUND

[0002] With the continuous expansion and complication of photovoltaic power station construction scale, the traditional construction management method faces many challenges in dealing with complex terrain, dynamic weather conditions and distributed construction needs. The existing technology mainly relies on static design drawings and manual experience for construction planning, which is difficult to respond to dynamic load changes such as sudden changes in wind speed and terrain undulations, resulting in frequent construction quality problems such as support deformation and component inclination deviation. Especially in the distributed construction scene, multiple devices are prone to time and space conflicts when working together, resulting in low construction efficiency and resource waste. Although some research attempts to introduce finite element analysis to optimize the construction scheme, it lacks the ability to dynamically integrate real-time load data, and the adjustment of construction parameters lags behind the actual working condition changes. SUMMARY

[0003] The purpose of the present application is to provide a photovoltaic power station distributed construction management method and system based on load analysis to solve the problems in the prior art, which can balance the construction precision and efficiency in complex environments by integrating multi-source data, optimizing zoning planning, intelligently scheduling equipment and closed-loop quality control.

[0004] One embodiment of the present application provides a photovoltaic power station distributed construction management method based on load analysis, the method comprising: According to the terrain elevation data, meteorological historical data and component mechanical parameters of the photovoltaic power station planning area, a multi-modal fusion deep neural network is used to fuse satellite remote sensing images and ground sensor time series data, and a dynamic load intensity heat map is output; Based on the dynamic load intensity heat map, a gradient-constrained spectral clustering algorithm is used to divide the construction sub-area, and the clustering center is dynamically adjusted according to the load extreme point and the terrain mutation feature, and an anti-wind pressure optimized construction zoning scheme is generated; According to the construction zoning scheme, combined with the real-time state data of the construction equipment, a space-time conflict resolution algorithm is used to generate an equipment scheduling path, the algorithm uses a dynamic time window constraint and a load balancing objective function to output a conflict-free construction resource allocation matrix; Based on the construction resource allocation matrix, a digital twin model is used to simulate the construction process, real-time collection of support deformation data and component inclination data is performed, and an adaptive particle swarm optimization algorithm is used to dynamically correct the construction parameters to generate an anti-deformation construction instruction set; According to the anti-deformation construction instruction set, the construction machinery is driven to perform operations, and the energy efficiency feedback data is analyzed synchronously through the edge computing node, the global load distribution atlas is iteratively updated by using the reinforcement learning model, and a closed-loop construction management link is formed.

[0005] Optionally, according to the terrain elevation data of the photovoltaic power station planning area, the meteorological historical data and the component mechanical parameters, a dynamic load intensity thermal map is output by a multimodal fusion deep neural network, which fuses satellite remote sensing images and ground sensor time series data, including: According to the satellite remote sensing image and the terrain elevation data, a terrain-component mapping grid is generated by a geometric registration algorithm, and a spatial grid is output after eliminating cloud interference; The spatial grid and the real-time wind speed, temperature and humidity data collected by the ground sensor are input into a spatio-temporal interpolation model, and a three-dimensional dynamic meteorological field is generated in combination with the meteorological historical data; The three-dimensional dynamic meteorological field and the component mechanical parameters are input into a multimodal fusion network, terrain texture features are extracted by residual convolution, meteorological time series patterns are extracted by bidirectional LSTM, and an initial load prediction map is output after fusion; The initial load prediction map is subjected to spatio-temporal noise filtering guided by optical flow, the intensity distribution is optimized according to the load correlation of adjacent grids, and a dynamically updated load intensity thermal map is generated.

[0006] Optionally, based on the dynamic load intensity thermal map, a gradient-constrained spectral clustering algorithm is used to divide the construction sub-area, the clustering center is dynamically adjusted according to the load extreme point and the terrain mutation feature, and an anti-wind pressure optimized construction zoning scheme is generated, including: According to the dynamic load thermal map, a gradient field is calculated, and a region with a load change rate exceeding a threshold value is extracted as a gradient mutation boundary; Based on the gradient mutation boundary, a region similarity matrix is constructed, and an adaptive Gaussian kernel function is used to quantify the load correlation strength of adjacent regions; The region similarity matrix is input into a spectral clustering algorithm, the initial clustering center position is constrained by the terrain elevation mutation point, and preliminary construction sub-area division is performed; The clustering center is dynamically adjusted according to the real-time wind speed prediction data, so that the high load area is offset to the low wind pressure direction, and the construction sub-area boundary is optimized; The optimized sub-area is subjected to finite element wind pressure simulation verification, and a construction zoning scheme meeting the critical wind pressure threshold is output.

[0007] Optionally, according to the construction zoning scheme, a time-space conflict resolution algorithm is used to generate a device scheduling path in combination with real-time state data of construction equipment, the algorithm outputs a conflict-free construction resource allocation matrix by using a dynamic time window constraint and a load balancing objective function, including: The construction zoning scheme is converted into a task node graph with time constraints, nodes of the task node graph including equipment type requirements, operation time length, and safety load threshold values; A device capability-position matrix is constructed according to device GPS positioning data and energy consumption logs, and the space-time cost of device movement to each node is calculated; Based on the task node graph and the device capability-position matrix, a dynamic time window is used to detect device path conflicts, and an initial path sequence is generated through priority scheduling; Taking load balancing as an objective function, a mixed integer programming solver is used to optimize the initial path sequence, and a device-task allocation matrix and a conflict-free scheduling path are output.

[0008] Optionally, based on the construction resource allocation matrix, a digital twin model is used to simulate the construction process, real-time acquisition of support deformation data and component inclination data is performed, an adaptive particle swarm optimization algorithm is used to dynamically correct construction parameters, and an anti-deformation construction instruction set is generated, including: The construction resource allocation matrix is mapped to the digital twin model, and the virtual construction machinery is driven to perform work according to the scheduling path; Real-time acquisition of support deformation sensor data and component inclination data is performed, and a millimeter-level precision deformation-inclination joint distribution graph is generated; According to the deformation-inclination joint distribution graph, deformation peak value, inclination uniformity, and progress deviation are defined as a multi-objective fitness function, and an adaptive particle swarm optimization algorithm is input; According to real-time deformation data, the search direction of the particle swarm is dynamically adjusted, and the optimal mechanical control parameters are solved under the constraints of construction specifications; The optimal mechanical control parameters are encoded into an anti-deformation construction instruction set, and the integrity and traceability of the instructions are ensured through blockchain signature.

[0009] Optionally, according to the anti-deformation construction instruction set, the construction machinery is driven to perform operations, and at the same time, energy efficiency feedback data is analyzed through an edge computing node, a reinforcement learning model is used to iteratively update a global load distribution atlas, and a closed-loop construction management link is formed, including: The anti-deformation construction instruction set is parsed into mechanical control signals, and a hydraulic system and a mechanical arm are driven to perform high-precision construction operations; Through the edge computing node, component installation error and mechanical energy consumption data are collected in real time, and energy efficiency-quality evaluation indexes are generated; Based on the energy efficiency-quality evaluation indexes, a hierarchical reinforcement learning model is trained, the upper layer optimizes load prediction parameters, the lower layer adjusts mechanical motion trajectories, the global load distribution atlas is iteratively updated, and a closed-loop construction management link is formed.

[0010] Another embodiment of the present application provides a photovoltaic power station distributed construction management system based on load analysis, the system comprising: a fusion module configured to fuse satellite remote sensing images and ground sensor time series data based on topographic elevation data of a photovoltaic power station planning area, meteorological historical data, and component mechanical parameters, and output a dynamic load intensity heat map through a multi-modal fusion deep neural network; a division module configured to divide a construction sub-area based on the dynamic load intensity heat map using a gradient-constrained spectral clustering algorithm, dynamically adjust clustering centers according to load extreme points and terrain mutation characteristics, and generate a wind pressure-resistant optimized construction zoning scheme; a generation module configured to generate a device dispatching path using a space-time conflict resolution algorithm based on the construction zoning scheme and combined with real-time state data of construction equipment, output a conflict-free construction resource allocation matrix through dynamic time window constraints and a load balancing objective function; a correction module configured to simulate a construction process through a digital twin model based on the construction resource allocation matrix, dynamically correct construction parameters in real time by collecting support deformation data and component inclination data using an adaptive particle swarm optimization algorithm, and generate a deformation-resistant construction instruction set; an update module configured to drive construction machinery to perform operations according to the deformation-resistant construction instruction set, synchronously analyze energy efficiency feedback data through an edge computing node, and iteratively update a global load distribution atlas using a reinforcement learning model to form a closed-loop construction management link.

[0011] Still another embodiment of the present application provides a storage medium having a computer program stored therein, wherein the computer program is configured to execute the method described in any of the above embodiments when running.

[0012] Still another embodiment of the present application provides an electronic device comprising a memory having a computer program stored therein and a processor configured to execute the computer program to perform the method described in any of the above embodiments.

[0013] Compared with the prior art, the photovoltaic power station distributed construction management method based on load analysis provided by the present application can output a dynamic load intensity heat map based on topographic elevation data of a photovoltaic power station planning area, meteorological historical data, and component mechanical parameters; generate a wind pressure-resistant optimized construction zoning scheme based on the dynamic load intensity heat map; output a conflict-free construction resource allocation matrix based on the construction zoning scheme; dynamically correct construction parameters using an adaptive particle swarm optimization algorithm based on the construction resource allocation matrix to generate a deformation-resistant construction instruction set; drive construction machinery to perform operations according to the deformation-resistant construction instruction set, synchronously analyze energy efficiency feedback data through an edge computing node, and iteratively update a global load distribution atlas using a reinforcement learning model to form a closed-loop construction management link, so that the construction precision and efficiency can be balanced in a complex environment by fusing multi-source data, optimizing zoning planning, intelligently scheduling equipment, and implementing closed-loop quality control. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 A hardware structure block diagram of a computer terminal of a photovoltaic power station distributed construction management method based on load analysis provided by the embodiment of the present application is provided. Figure 2 A flowchart of a photovoltaic power station distributed construction management method based on load analysis provided by the embodiment of the present application is provided. Figure 3 A structure diagram of a photovoltaic power station distributed construction management system based on load analysis provided by the embodiment of the present application is provided. DETAILED DESCRIPTION

[0015] The embodiments described below with reference to the drawings are exemplary and are only used to explain the present application and cannot be explained as a limitation of the present application.

[0016] The embodiment of the present application first provides a photovoltaic power station distributed construction management method based on load analysis, which can be applied to electronic equipment such as a computer terminal, specifically, a general computer, etc.

[0017] The following will be described in detail taking a computer terminal as an example. Figure 1 A hardware structure block diagram of a computer terminal of a photovoltaic power station distributed construction management method based on load analysis provided by the embodiment of the present application is provided. As shown in the figure, Figure 1 The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory can include a non-volatile storage medium and an internal memory.

[0018] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions which, when executed, can cause the processor to execute any kind of photovoltaic power station distributed construction management method based on load analysis.

[0019] The processor is used to provide computing and control capabilities to support the operation of the entire computer device.

[0020] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium, which, when executed by the processor, can cause the processor to execute any kind of photovoltaic power station distributed construction management method based on load analysis.

[0021] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that, Figure 1The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0022] It should be understood that the processor can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0023] Referring to Figure 2 The embodiments of the present application provide a photovoltaic power station distributed construction management method based on load analysis, which can include the following steps: S201, according to the terrain elevation data of the photovoltaic power station planning area, the meteorological historical data and the component mechanical parameters, through a multi-modal fusion deep neural network, satellite remote sensing images and ground sensor time series data are fused, and a dynamic load intensity thermal map is output; Specifically, the terrain-component mapping grid can be generated by a geometric registration algorithm according to the satellite remote sensing image and the terrain elevation data, and the spatial grid is output after eliminating the cloud interference. The satellite remote sensing image usually contains the spectral information and the terrain profile of the ground, but part of the area data may be missing due to cloud cover. The terrain elevation data is collected by laser radar (LiDAR) or interferometric synthetic aperture radar (InSAR), and provides accurate altitude information. The geometric registration algorithm is used to align the satellite image and the terrain data in the spatial coordinate system, and ensure the pixel-level matching of the two.

[0024] Data preprocessing and registration process: Satellite image cloud removal: Using multi-temporal satellite image fusion techniques, such as selecting consecutive three-day cloud-free area image segments, a complete land cover map is generated through sliding window mosaic. If a certain area is continuously covered by clouds (such as mountainous areas), historical synchronous images are called to fill in the gaps. For example, using 10-meter resolution images of the Sentinel-2 satellite, the time window is from January 1 to January 3, 2023, and the sliding window size is 512x512 pixels.

[0025] ‌Terrain elevation data alignment‌: Terrain data is usually stored in point cloud form and needs to be converted to raster format (such as GeoTIFF) with the same resolution as satellite images (10 meters). Through geometric registration algorithms (such as affine transformation), the latitude and longitude coordinates of satellite images are aligned with the UTM coordinate system (Universal Transverse Mercator) of terrain data. For example, the registration error is controlled within 0.5 pixels to ensure that the altitude and ground features are accurately matched.

[0026] ‌Terrain-component mapping grid generation‌: The standard size of photovoltaic components (such as 2 meters x 1 meter) is superimposed on the terrain grid to generate a terrain-component mapping grid. Each grid cell corresponds to a component installation location, recording the center point coordinates (X, Y, Z), where Z is the altitude. For example, a 1000x1000 meter planning area is divided into 500x500 grid cells (each cell is 2 meters x 2 meters).

[0027] ‌Cloud interference elimination techniques‌: ‌Cloud detection algorithm‌: Based on the ratio of shortwave infrared (SWIR) band to visible band (such as B11 / B04) of satellite images, a threshold (such as ratio > 0.8) is set to mark cloud pixels. For example, a certain area has a cloud coverage rate of 30%, which is marked as a repair area.

[0028] ‌Data filling strategy‌: For cloud-covered areas, nearest neighbor interpolation or historical data replacement is used. For example, a certain grid cell lacks data due to cloud cover, and the average elevation value of the adjacent 8 cells (such as an altitude of 352.6 meters) is used to fill in.

[0029] ‌Spatial grid output and verification‌: ‌Grid Format‌: Output as a GeoJSON file containing the latitude, longitude, elevation, slope, aspect, and cloud repair flag for each grid cell. For example, Grid ID_001 has the attributes {latitude 39.9°, longitude 116.5°, elevation 352 meters, slope 5°, aspect southeast, cloud repair: no}.

[0030] ‌Accuracy Verification‌: Randomly select 5% of the grid cells and compare them with field measurement data (such as differential GPS measurements) to ensure that the height error is ≤0.3 meters and the latitude and longitude offset is ≤0.0001 degrees.

[0031] ‌Dynamic Weather Field Generation‌: Input the spatial grid and real-time wind speed, temperature, and humidity data collected by ground sensors into the spatiotemporal interpolation model, combined with historical meteorological data to generate a three-dimensional dynamic weather field. ‌Ground Sensor Deployment‌: Deploy ground sensors in the photovoltaic power station area to transmit real-time data via a wireless sensor network (such as LoRaWAN). The spatiotemporal interpolation model combines the spatial grid location and sensor data to predict meteorological parameters in areas without deployed sensors.

[0032] ‌Sensor Data Collection and Fusion‌: ‌Sensor Types and Deployment‌: ‌Anemometer Installation‌: Install wind speed sensors (Anemometer) on the top of the support, and DHT22 temperature and humidity sensors at a height of 1.5 meters from the ground, with a density of one node per 100 meters. For example, deploy 121 sensors (11x11 grid) in a 1000x1000 meter area.

[0033] ‌Real-Time Data Preprocessing‌: ‌Remove Outliers‌: Remove outliers such as wind speed >30m / s or humidity >100%, and smooth the time series fluctuations using Kalman Filter. For example, if a sensor's instantaneous wind speed jumps to 25m / s, it is corrected to 18m / s after filtering.

[0034] ‌Spatiotemporal Interpolation Algorithm Execution‌: ‌Kriging Interpolation‌: ‌Quantify Spatial Correlation‌: Based on the Semivariogram, quantify spatial correlation to predict wind speed and temperature and humidity at unsampled points. For example, set the parameters of the Spherical Model: Range 200 meters, Nugget 0.1, Sill 1.5.

[0035] ‌Temporal Dimension Expansion‌: Introduce Time Decay Factor to blend historical meteorological data (e.g., 7-day hourly records) with real-time data. For example, the current time's interpolation weight is 0.8, and 1 hour ago's data weight is 0.2.

[0036] ‌Three-dimensional dynamic meteorological field construction‌: ‌Vertical stratification modeling‌: Stratify by height (e.g., 0 meters, 10 meters, 50 meters) to predict wind speed gradients at different altitudes. For example, ground wind speed is 5 m / s, 10 meters height is 6 m / s, and 50 meters height is 8 m / s.

[0037] ‌Dynamic update mechanism‌: Re-execute interpolation every 5 minutes to generate a time-series three-dimensional meteorological field. Output format is NetCDF file, containing timestamp, latitude, longitude, height, wind speed, temperature, humidity fields.

[0038] Input the three-dimensional dynamic meteorological field and component mechanical parameters into the multi-modal fusion network, extract terrain texture features through residual convolution, extract meteorological time series patterns through bidirectional LSTM, and output the initial load prediction map after fusion; ‌Component mechanical parameters‌ include photovoltaic panel stiffness (Stiffness), wind pressure resistance coefficient (Wind Load Coefficient), and support material yield strength (Yield Strength).‌Multi-modal fusion network‌ consists of residual convolutional neural network (ResNet) and bidirectional long short-term memory network (Bi-LSTM), which respectively process spatial and temporal features.

[0039] ‌Residual convolutional network (ResNet) design‌: ‌Input data format‌: Convert terrain grid data (elevation, slope) into grayscale images (512x512 pixels), and meteorological field data (wind speed, temperature, humidity) as multi-channel images (4 channels).

[0040] ‌Residual block structure‌: Each residual block contains two 3x3 convolution layers (Convolution Layer) and a skip connection (Skip Connection). For example, the first residual block's input is a 4-channel image, and the output is a 64-channel feature map.

[0041] ‌Terrain feature extraction‌: Capture terrain texture (e.g., steep slopes, gullies) through multiple layers of convolution, output 512-dimensional feature vectors.

[0042] ‌Bidirectional LSTM (Bi-LSTM) time series modeling‌: Input time series data: A time series of three-dimensional meteorological data (e.g., one frame every 5 minutes for the past hour), expanded into a time series vector by grid cell. For example, the wind speed sequence for a grid cell is [5.0, 5.2, 5.5, ..., 6.0] m / s.

[0043] Bidirectional Processing: The forward LSTM scans time series data from the past to the future, and the reverse LSTM scans from the future to the past to capture meteorological trends (such as an upward trend in wind speed). Output is a 256-dimensional time series feature vector.

[0044] Multimodal feature fusion and load prediction: Feature concatenation: Concatenate the 512-dimensional terrain features from ResNet and the 256-dimensional meteorological features from Bi-LSTM into a 768-dimensional joint feature vector.

[0045] ‌Fully connected layer prediction‌: Regress the load intensity (unit: kN / m) of each grid through three layers of fully connected layers (768→256→64→1) 2 For example, the predicted load of a grid is 1.2kN / m 2 .

[0046] ‌ Initial load prediction map generation‌: Renders the full grid prediction results as a heat map with a colormap from blue (low load) to red (high load).

[0047] The initial load prediction map is subjected to optical flow-guided spatiotemporal noise filtering, and the intensity distribution is optimized according to the correlation of loads in adjacent grids to generate a dynamically updated load intensity heat map.

[0048] Optical flow-guided filtering uses the load change direction (optical flow vector) of adjacent frames (temporal) and adjacent grids (spatial) to guide the filtering process, eliminating prediction noise and enhancing spatial continuity.

[0049] ‌Optical Flow Estimation‌: Optical flow algorithm selection: Lucas-Kanade method is used to calculate the optical flow vector (direction and magnitude) based on the time series change of the load prediction graph. For example, the load of a grid from time t to t+5 minutes is 1.2kN / m 2 Increased to 1.5kN / m 2 , the optical flow vector points to the northeast, with an amplitude of 0.3kN / m 2 .

[0050] ‌Optical flow field generation‌: The output is a vector field image, where each pixel represents the direction and magnitude of the load change.

[0051] ‌Spatiotemporal noise filtering implementation‌: ‌Spatial Filtering: Adaptive Gaussian Filter is used to adjust the filter kernel size based on the optical flow magnitude. For example, areas with high optical flow magnitude (high load variation) use a small kernel (3x3), while smooth areas use a large kernel (7x7).

[0052] ‌Temporal Filtering: Moving Average is applied to the load prediction map of the last 5 frames (25 minutes) to suppress random noise. For example, if a certain grid has a load of [1.2, 1.3, 1.25, 1.28, 1.3] in consecutive frames, the average is corrected to 1.27 kN / m 2 .

[0053] ‌Load Intensity Heatmap Optimization: ‌Edge-Preserving Enhancement: Bilateral Filter is used to sharpen the load boundaries in areas with terrain discontinuities (e.g., the intersection of a steep slope and a flat area), while smoothing out flat areas.

[0054] ‌Dynamic Update Mechanism: New data is received every 5 minutes, and the filtering process is re-executed to update the heatmap. The output is a Web Map Service (WMS) layer, supporting real-time visualization and API calls.

[0055] ‌Key Technology Examples and Parameter Descriptions: ‌LiDAR: Light Detection and Ranging, used for high-precision terrain mapping, with an elevation data error of ≤0.1 meters.

[0056] ‌UTM Coordinate System: Universal Transverse Mercator Projection, with zone numbers like Zone 50N, suitable for mid-latitude regions.

[0057] ‌LoRaWAN: Long Range Low Power Wireless Network, with a transmission distance of up to 10 kilometers, suitable for outdoor sensor deployment.

[0058] ‌ResNet Residual Block: Convolutional module with skip connections, alleviating the problem of gradient vanishing in deep networks.

[0059] ‌Bi-LSTM: Bidirectional Long Short-Term Memory Network, capable of capturing the forward and backward dependencies of time-series data.

[0060] The method first integrates multi-dimensional data sources, including terrain elevation, meteorological records and photovoltaic component mechanical properties, and realizes the spatio-temporal alignment and feature extraction of satellite remote sensing images and ground sensor data through a deep neural network. The multi-modal fusion network adopts a residual convolution structure to capture the spatial features of the terrain, and combines a bidirectional LSTM to process the temporal changes of the weather, finally generating a strength thermal map reflecting the distribution of dynamic loads such as wind pressure and snow load, breaking through the limitations of traditional static load analysis and realizing the fine prediction of the global dynamic load of photovoltaic power stations. The thermal map provides a data basis for subsequent construction zoning, ensuring that the construction plan matches the real environmental load and reducing the risk of structural failure from the source.

[0061] S202, based on the dynamic load strength thermal map, a gradient-constrained spectral clustering algorithm is used to divide the construction sub-area, the clustering center is dynamically adjusted according to the load extreme point and the terrain mutation feature, and an anti-wind pressure optimized construction zoning scheme is generated; Specifically, the gradient field can be calculated according to the dynamic load thermal map, and the area where the load change rate exceeds the threshold is extracted as the gradient mutation boundary; ‌Gradient Field‌ identifies areas of rapid load fluctuations by calculating the rate of change of dynamic load strength in space.‌Gradient mutation boundary‌ is the boundary between adjacent grid cells where the difference in load strength exceeds a pre-set threshold, representing the potential basis for construction zoning.

[0062] ‌Gradient Field Calculation Method‌: ‌Sobel Operator Application‌: The Sobel edge detection operator (a convolution-based image gradient calculation method) is used to calculate the gradient magnitude and direction of each grid cell on the three-dimensional dynamic load thermal map. For example, the horizontal direction convolution kernel is [-1, 0, 1; -2, 0, 2; -1, 0, 1], and the vertical direction is [-1, -2, -1; 0, 0, 0; 1, 2, 1], which calculates the X / Y direction gradient components respectively.

[0063] ‌Gradient Magnitude Formula‌: Gradient Magnitude = √(Gx 2 + Gy 2 ), where Gx is the X direction gradient and Gy is the Y direction gradient. For example, if Gx = 0.8 kN / m 2 ·m and Gy = 1.2 kN / m 2 ·m, then the gradient magnitude ≈ 1.44 kN / m 2 ·m (unit: load change per meter length).

[0064] ‌Gradient Direction Calculation‌: Gradient Direction Angle θ = arctan(Gy / Gx) is used to identify the load change trend (e.g. southeast, northwest).

[0065] Gradient Mutation Boundary Extraction: Threshold Setting: Based on historical data statistics, set the gradient amplitude threshold to 2 times the standard deviation of the normal load change rate (e.g., the normal gradient range of a certain area is 0-0.5 kN / m 2 ·m, the threshold is set to 0.5×2=1.0 kN / m 2 ·m).

[0066] Boundary Marking: Scan the gradient field and mark all grid cells with a gradient amplitude ≥1.0 kN / m 2 ·m, merge adjacent cells to form continuous boundary lines. For example, the boundary of a certain area consists of 3 discontinuous line segments with a total length of 120 meters.

[0067] Noise Filtering: Remove isolated small-area mutation regions (such as single grid cells or lines with length <5 meters), and retain the main mutation boundaries. For example, filter out isolated lines with length 3 meters, and retain boundaries with length ≥10 meters.

[0068] Verification and Visualization: Manual Sampling Verification: Randomly select 5% of the boundary line segments and compare them with actual terrain mutation points (such as steep slopes, ridge lines) to ensure that the boundary matches the actual terrain with a matching degree ≥90%.

[0069] Thermal Map Overlay Display: Overlay the gradient mutation boundary and load thermal map on the GIS platform to visually display the spatial distribution of high load gradient areas.

[0070] Based on the gradient mutation boundary, construct a regional similarity matrix, and use an adaptive Gaussian kernel function to quantify the correlation strength of adjacent regions; The regional similarity matrix (Similarity Matrix) is used to describe the correlation strength between different construction sub-regions, and is the input basis for spectral clustering algorithm. The adaptive Gaussian kernel function dynamically adjusts the kernel function parameters according to the gradient boundary, enhancing the similarity weight of local regions.

[0071] Regional Division and Labeling: Region Definition: Divide the photovoltaic power station planning area into N adjacent sub-regions (e.g., N=50), and each sub-region is surrounded by a gradient mutation boundary. For example, region A contains 100 grid cells and region B contains 85 cells.

[0072] Region Attribute Assignment: Record the average load intensity, area, terrain complexity (slope standard deviation) and other attributes of each sub-region. For example, region A has an average load of 1.5 kN / m 2 , an area of 2000 square meters, and a slope standard deviation of 8°.

[0073] Adaptive Gaussian Kernel Function Design: Kernel function formula: Similarity S(i,j)=exp(-d 2 (i,j) / (σ_iσ_j)), where d(i,j) is the Euclidean distance between regions i and j (integrated load intensity, slope difference), and σ_i is the adaptive bandwidth parameter of region i.

[0074] Bandwidth parameter σ adjustment: For regions adjacent to gradient mutation boundaries, reduce σ to enhance local similarity; for flat regions, increase σ to expand the correlation range. For example, boundary region σ=50 meters, flat region σ=200 meters.

[0075] Similarity matrix generation: Calculate the similarity scores of all region pairs to generate an N×N matrix. For example, the similarity between regions A and B is 0.7, A and C is 0.3, and B and C is 0.5.

[0076] Matrix optimization and sparsification: Sparse connection strategy: Only keep edges with similarity scores ≥0.5 (region association), reducing computational complexity. For example, from the original 50×50=2500 elements, 300 valid connections are selected.

[0077] Symmetry correction: Force the similarity matrix to be symmetric (S(i,j)=S(j,i)), avoiding clustering bias caused by one-way association.

[0078] Input the region similarity matrix into the spectral clustering algorithm, and constrain the initial clustering center position by the terrain elevation mutation point to perform preliminary construction sub-region division; Spectral clustering (Spectral Clustering) is a clustering method based on graph theory, which realizes data division through eigenvalue decomposition of Laplacian matrix. Terrain elevation mutation points (such as cliffs and gullies) are used as prior knowledge to constrain the clustering center position, ensuring that the construction partition matches the terrain risk area.

[0079] Laplacian matrix construction: Degree matrix (Degree Matrix): Diagonal matrix D, diagonal element D(i,i)=Σ_jS(i,j), representing the total similarity of region i. For example, D(A,A)=0.7+0.3+...+0.2=4.5 for region A.

[0080] Laplacian matrix calculation: Standardized Laplacian matrix L=I-D^(-1 / 2)SD^(-1 / 2), where I is the identity matrix. For example, the dimension of matrix L is 50×50.

[0081] Eigenvalue decomposition and clustering initialization: Eigenvector extraction: Perform eigenvalue decomposition on the matrix L and select the eigenvectors corresponding to the first k smallest eigenvalues ​​(k is the preset number of clusters, such as k=5). For example, the eigenvector dimension is 50×5.

[0082] Terrain-constrained cluster centers: Initial cluster centers are selected from the feature vector space, preferably near elevation abrupt changes. For example, in the area where the terrain abrupt changes (slope > 15°) are located, three centers are selected as initial centers.

[0083] ‌k-means clustering execution‌: Feature vector dimensionality reduction: Arrange the first k feature vectors into an N × k matrix by row and use it as input data. For example, 50 regions × 5 feature vectors.

[0084] K-means Iterative Optimization: Randomly initialize cluster centers (or specify terrain-constrained centers) and iteratively update the center positions until convergence. For example, after 10 iterations, if the cluster center moves less than 0.001, the calculation is stopped.

[0085] Initial construction sub-area division: Output cluster labels for each area, and merge areas with the same label into construction sub-areas. For example, label 1 corresponds to sub-areas A, C, and E, and label 2 corresponds to B and D.

[0086] Dynamically adjust cluster centers based on real-time wind speed forecast data to shift high-load areas toward low wind pressure and optimize construction sub-area boundaries; Real-time wind speed forecasts use meteorological models (such as WRF, Weather Research and Forecasting Model) to provide wind speed and direction data for the next hour. Low wind pressure areas refer to areas with low wind speeds or where the wind direction and terrain create a shielding effect, reducing construction risks.

[0087] Wind speed-load coupling analysis: Wind pressure calculation formula: Wind pressure P = 0.5 × ρ × v 2 ×C_p, where ρ is the air density (1.225 kg / m 3 ), v is the wind speed, C_p is the wind pressure coefficient (related to the module inclination angle). For example, when the wind speed is 10m / s, P=0.5×1.225×10 2 ×1.0≈61.25N / m 2 .

[0088] Load-wind pressure superposition: Superimpose the dynamic load intensity and wind pressure to generate a comprehensive stress distribution diagram. For example, the original load of a certain area is 1.2kN / m 2 Superimposed wind pressure 0.06kN / m 2Total load 1.26 kN / m 2 .

[0089] ‌Cluster center dynamic adjustment strategy‌: ‌Wind pressure risk assessment‌: Mark areas with total load exceeding the safety threshold (e.g., 1.5 kN / m 2 ) as high-risk areas and adjust the cluster center to move away from this area.

[0090] ‌Offset direction selection‌: Based on wind direction prediction (e.g., the dominant wind direction in the next hour is northwest), move the cluster center of the high-risk area to the southeast (leeward side). For example, the original center coordinates (X=100, Y=200) are adjusted to (X=105, Y=205).

[0091] ‌Offset amount calculation‌: Offset amount Δ = α × v 2 , where α is the adjustment coefficient (e.g., α = 0.001 m -1 s 2 ), and v is the predicted wind speed. For example, when v = 12 m / s, Δ = 0.001 × 12 2 ≈ 0.144 meters.

[0092] ‌Sub-region boundary optimization‌: ‌Boundary redivision‌: Based on the new cluster center, re-execute k-means clustering to update the construction sub-region boundary. For example, the boundary of the original sub-region A is contracted by 10 meters to the north, forming a new block A'.

[0093] ‌Continuity constraint‌: Ensure that the adjusted sub-region boundary is continuous to avoid fragmented segmentation. For example, merge isolated blocks with an area <500 square meters to adjacent sub-regions.

[0094] Perform finite element wind pressure simulation verification on the optimized sub-regions, output a construction zoning scheme that meets the critical wind pressure threshold.

[0095] ‌Finite element analysis (FEA, Finite Element Analysis)‌Through discretization of the structure model, simulate the stress distribution under wind pressure load, verify the wind pressure resistance performance of the construction zoning.‌Critical wind pressure threshold‌is determined by the mechanical parameters of photovoltaic components (such as support yield strength).

[0096] ‌Finite element model construction‌: ‌Geometric modeling‌: Based on the 3D terrain data (elevation, slope) of the construction sub-region, establish a support-component assembly model in ANSYS or COMSOL. For example, a sub-region model contains 500 support nodes and 2000 triangular mesh elements.

[0097] Material Property Definition: Set the bracket material as Q235 steel (elastic modulus 210 GPa, Poisson's ratio 0.3), and the component glass panel (elastic modulus 70 GPa, Poisson's ratio 0.23).

[0098] Boundary Condition Loading: Fix the bracket bottom (full constraint), and apply wind pressure loads on the component surface according to the wind pressure distribution map calculated in Step 4.

[0099] Wind Pressure Simulation and Stress Analysis: Static Solution: Calculate the stress distribution of the bracket under each wind pressure load and identify the maximum equivalent stress (Von Mises Stress). For example, the maximum stress in a certain area is 180 MPa, which is lower than the yield strength of Q235 steel, 235 MPa.

[0100] Critical Threshold Verification: If the maximum stress of all sub-regions is ≤ critical threshold (e.g., 200 MPa), the zoning scheme is considered qualified; otherwise, return to Step 4 to adjust the cluster centers again.

[0101] Simulation Result Optimization Iteration: High Stress Area Marking: Mark the sub-regions with maximum stress exceeding the threshold and generate a risk distribution map. For example, sub-region B has a maximum stress of 210 MPa and needs to be re-divided.

[0102] Zoning Scheme Update: According to the simulation results, further shift the cluster centers of high-risk areas and repeat Steps 4-5 until all regions pass the verification.

[0103] Key Technology Example and Parameter Description: Sobel Operator: Edge detection convolution kernel used to calculate image gradients.

[0104] k-means Clustering: Distance-based unsupervised clustering algorithm that iteratively optimizes center positions.

[0105] FEA (Finite Element Analysis): Numerical simulation method for structural mechanics analysis.

[0106] Q235 Steel: A carbon structural steel with a yield strength of 235 MPa.

[0107] Von Mises Stress: An equivalent strength indicator that combines multi-directional stresses.

[0108] The load mutation boundary is identified by the thermal map gradient field, combined with the terrain elevation change characteristics, and the improved spectral clustering algorithm is used to dynamically adjust the construction sub-regional division. The algorithm automatically classifies high load areas and steep terrain areas, and optimizes the cluster center position according to the real-time wind speed prediction, so that the partition boundary conforms to the wind pressure distribution law, realizes the wind pressure adaptive optimization of construction partition, and avoids the mechanical arrangement in high-risk areas. The dynamic adjustment mechanism improves the response ability of the partition scheme to sudden weather changes, and ensures the balance between construction safety and efficiency.

[0109] S203, according to the construction partition scheme, combining with the real-time state data of construction equipment, using a time-space conflict resolution algorithm to generate equipment scheduling path, the algorithm outputs a conflict-free construction resource allocation matrix through dynamic time window constraint and load balancing objective function; Specifically, the construction partition scheme can be converted into a task node graph with time constraints, and the nodes of the task node graph include equipment type requirements, operation time and safety load threshold; The task node graph with time constraints is to abstract the construction partition scheme of the photovoltaic power station into a network structure with time attributes, each node represents a construction task, and the edge represents the dependency relationship or movement path between tasks. The equipment type requirement refers to the type of machinery required to complete the task (such as cranes, forklifts, pile drivers); the operation time refers to the expected time consumption of the task; and the safety load threshold refers to the maximum mechanical load allowed in the region (unit: kN / m 2 ), to prevent ground collapse or support damage caused by overloading of construction equipment.

[0110] ‌Node attribute definition and mapping‌: ‌Construction partition mapping‌: Each construction sub-region (such as sub-region A, B) corresponds to one or more task nodes. For example, sub-region A needs to complete two tasks of support installation and component laying, and nodes A1 (support installation) and A2 (component laying) are created respectively.

[0111] ‌Equipment type requirement‌: Specify equipment according to process requirements. For example, support installation node A1 requires a crane (Crane) and a pile driver (Pile Driver), and component laying node A2 requires a forklift (Forklift) and a drone (Drone).

[0112] ‌Operation time calculation‌: Based on historical construction data and area estimation. For example, the operation time of support installation node A1 = area (500m 2 ) ÷ crane efficiency (50m 2 / h) = 10 hours.

[0113] ‌Safety Load Threshold Setting‌: Determined based on finite element simulation results (Step 3). For example, the foundation bearing capacity of sub-area A is 15 kN / m 2 , the safety threshold is set to 12 kN / m 2 (20% safety margin reserved).

[0114] ‌Time Constraint Modeling‌: ‌Task Dependency Relationship‌: Define the sequence between tasks. For example, the support installation node A1 must be completed before the component laying node A2.

[0115] ‌Time Window Constraint‌: Set the task executable time period combined with weather forecast data (such as rain to prohibit high-altitude operation). For example, the time window of the crane operation node A1 is 8:00-18:00 every day (avoiding the night strong wind period).

[0116] ‌Task Node Graph Generation and Verification‌: ‌Graph Structure Generation‌: Use a graph database (such as Neo4j) to store the relationship between nodes and edges. For example, node A1 is connected to A2, and the edge attribute is "must be started after A1 is completed".

[0117] ‌Logical Verification‌: Check if there is a circular dependency or time window conflict. For example, if the start time of node A2 is earlier than the end time of A1, an alarm is triggered and the time window is adjusted.

[0118] According to the device GPS positioning data and energy consumption log, construct the device capability-position matrix, calculate the space-time cost of the device moving to each node; ‌The device capability-position matrix is a two-dimensional table, with rows representing devices and columns representing task nodes, and element values representing the comprehensive cost of the device moving to the node and executing the task.‌Space-time cost‌ includes moving time, energy consumption cost and device capability matching degree.

[0119] ‌Device Capability Parameter Definition‌: ‌Mechanical Capability Dimensions‌: Load capacity (Max Load, unit: tons), moving speed (Speed, unit: km / h), energy efficiency (Energy Efficiency, unit: kWh / km). For example, the load capacity of the crane is 20 tons, the moving speed is 5 km / h, and the energy efficiency is 3 kWh / km.

[0120] ‌Device State Monitoring‌: Get real-time location (latitude and longitude coordinates) through GPS, record historical energy consumption data through energy consumption sensors. For example, the crane is currently located at (116.3°E, 39.9°N), and the average energy consumption in the past week is 25 kWh / h.

[0121] ‌Space-time Cost Calculation Model‌: ‌Travel time calculation: estimated based on the distance between the device's current location and the target node (calculated using the Haversine formula for spherical distance) and the moving speed. For example, the crane is 2 km away from node A1, and the travel time = 2 km ÷ 5 km / h = 0.4 hours (24 minutes).

[0122] ‌Energy consumption cost calculation: the energy consumption for moving = distance × energy consumption efficiency + operation energy consumption × operation duration. For example, the energy consumption for the crane to move to A1 = 2 km × 3 kWh / km + operation energy consumption 10 kWh / h × 10 hours = 6 + 100 = 106 kWh.

[0123] ‌Capability matching score: if the device's capability is lower than the task's requirement (e.g., insufficient load capacity), the matching score = 0; otherwise, the matching score = 1. For example, node A1 requires a load capacity ≥ 15 tons, and the crane has a capacity of 20 tons, so the matching score = 1.

[0124] ‌Device-node matrix construction: ‌Matrix filling: calculate the space-time cost and matching score for each device-node pair, and generate the matrix elements. For example, the space-time cost for the crane-device pair to node A1 = travel time 0.4 hours + energy consumption cost 106 kWh, and the matching score = 1.

[0125] ‌Matrix optimization: remove the device-node pairs with matching score = 0 (e.g., forklift cannot perform hoisting tasks) to reduce computational complexity.

[0126] Based on the task node graph and the device capability-location matrix, detect device path conflicts using dynamic time windows, and generate an initial path sequence through priority scheduling; ‌Dynamic time window (Dynamic Time Window) is the time period allocated to each device to perform tasks, which is updated in real time as the task progresses. Path conflict refers to multiple devices entering the same area within the same time window, causing spatial collision or load exceeding limit.

[0127] ‌Dynamic time window definition and update: ‌Initial time window allocation: allocate an initial task time window for each device based on the time constraints of the task node graph. For example, the crane's time window for task A1 is Day1 8:00-18:00.

[0128] ‌Real-time adjustment mechanism: if a device is delayed due to failure, its subsequent task time window is automatically postponed. For example, the crane is delayed by 2 hours at A1, and the subsequent task time window is postponed to Day1 10:00-20:00.

[0129] ‌Path conflict detection algorithm: ‌Space conflict detection‌: Identify devices that may enter the same geogrid (e.g., 10m x 10m) within the same time period through device path overlay analysis. For example, crane and forklift both enter grid (X=100, Y=200) at Day1 14:00-14:30.

[0130] ‌Load conflict detection‌: Calculate the total load of devices in the same area within the same time period, and flag a conflict if it exceeds the safety threshold. For example, crane (20 tons) and forklift (5 tons) have a total load of 25 tons in grid A, which exceeds the safety threshold of 20 tons.

[0131] ‌Priority scheduling strategy‌: ‌Priority rules‌: Set priorities based on task criticality, device energy efficiency, or contract terms (e.g., VIP customer priority). For example, scaffold installation tasks have higher priority than component laying, and cranes have higher priority than forklifts.

[0132] ‌Conflict resolution actions‌: ‌Delay execution‌: Low-priority devices wait for high-priority devices to complete their tasks before entering. For example, forklifts wait for cranes to leave grid A before entering.

[0133] ‌Path re-planning‌: Re-plan the detour path for low-priority devices. For example, forklifts change their original path to detour the north side road, increasing the moving distance by 50 meters but avoiding conflicts.

[0134] ‌Initial path sequence generation‌: ‌Sequence format‌: The order and timing of task execution for each device. For example, crane path sequence: A1 (Day1 8:00-8:00) → B1 (Day2 8:00-12:00); forklift path sequence: A2 (Day1 18:30-22:30) → B2 (Day2 13:00-17:00).

[0135] ‌Visualization output‌: Overlay device paths and time windows on the GIS platform, distinguishing device types by different colors, and visually displaying the initial scheduling plan.

[0136] Optimize the initial path sequence using a mixed integer programming solver with the objective function of load balancing, outputting the device-task assignment matrix and conflict-free scheduling paths.

[0137] Mixed Integer Programming (MIP) is a mathematical optimization method that handles both continuous variables (like time) and discrete variables (like device-task assignment). Load balancing refers to the process of distributing the load (task volume and energy consumption) among devices as evenly as possible, avoiding overloading some devices while others are idle.

[0138] Objective function definition: Load balancing indicator: Minimize the maximum load (Max Load) or load variance (LoadVariance) of all devices. For example, Objective Function = Minimize Max(Device1 Load, Device2 Load,..., DeviceN Load).

[0139] Load calculation method: Load = Σ (Task Duration × Device Energy Consumption Rate + Movement Energy Consumption). For example, Crane Load = 10 hours × 10 kWh / h + Movement Energy Consumption 6 kWh = 106 kWh.

[0140] Constraint setting: Time window constraint: Devices must arrive and perform tasks within the task time window. For example, Crane arrival time at Node A1 ≥ 8:00, departure time ≤ 18:00.

[0141] Resource uniqueness: The same task can only be performed by one device. For example, Node A1 can only be assigned to the crane or backup crane, and cannot be assigned repeatedly.

[0142] Safety load constraint: The total load of devices in the same area ≤ safety threshold. For example, the total weight of devices in Grid A ≤ 20 tons.

[0143] Solver configuration and execution: Solver selection: Commercial solvers such as Gurobi or open-source tools COIN-OR CBC are used to support MIP model solving.

[0144] Model input: Encode the objective function, constraints, and initial path sequence into a mathematical model. For example, define variable x_ij to represent whether device i performs task j (0 / 1 variable), and variable t_i to represent the task start time of device i (continuous variable).

[0145] Solution process: Iteratively search for feasible solutions and gradually approach the optimal solution. For example, the solver finds the optimal solution within 30 minutes, reducing the crane load from 106 kWh to 98 kWh, increasing the forklift load from 80 kWh to 85 kWh, and reducing the overall load variance by 40%.

[0146] Output results and applications: ‌Device-task allocation matrix‌: A table showing the list of tasks allocated to each device. For example, crane is allocated tasks A1, B1, C1; forklift is allocated tasks A2, B2, C2.

[0147] ‌Conflict-free scheduling path‌: Optimized device movement path and time window, ensuring no spatial or load conflicts. For example, crane and forklift work in grid A at different times, Day1 8:00-12:00 and Day1 14:00-18:00.

[0148] ‌Construction instruction issuance‌: Synchronize the allocation matrix and path to the automatic driving system of construction machinery (such as AGV navigation module), start the automated construction process.

[0149] ‌Key technology examples and parameter explanations‌: ‌Haversine formula‌: A formula for calculating the distance between two points on a sphere, input is latitude and longitude coordinates, output is kilometers.

[0150] ‌Neo4j‌: A graph database for storing node and edge relationship data.

[0151] ‌Gurobi‌: A commercial mathematical optimization solver that supports mixed integer programming, linear programming, etc.

[0152] ‌AGV (Automated Guided Vehicle)‌: An automated guided vehicle that travels along a predetermined path through a navigation system (such as laser or magnetic strip).

[0153] Convert the construction partition into a task node graph with time and space constraints, and build a movement cost model based on device GPS positioning and energy consumption data. The algorithm detects device path conflicts through dynamic time window, optimizes the scheduling sequence with load balancing as the goal, ensures that more resources are allocated to high-load areas while avoiding spatial and temporal overlap of device operations, solves the resource competition problem of large-scale distributed construction, and maximizes device utilization through mathematical optimization. Load balancing strategy prolongs the life of high-load devices, and time and space conflict reduction reduces the probability of construction interruption.

[0154] S204, based on the construction resource allocation matrix, simulate the construction process through the digital twin model, real-time collect support deformation data and component inclination data, adopt adaptive particle swarm optimization algorithm to dynamically correct construction parameters, generate anti-deformation construction instruction set; Specifically, the construction resource allocation matrix can be mapped to the digital twin model to drive virtual construction machinery to perform work according to the scheduling path; The Digital Twin Model is a virtual mirror of the physical construction process, achieving accurate simulation through real-time data synchronization. The construction resource allocation matrix contains device-task allocation relationships and scheduling paths, which need to be converted into mechanical action instructions in the virtual environment to drive the Digital Twin Model to simulate the construction process.

[0155] Construction and initialization of the Digital Twin Model: Three-dimensional scene modeling: Use Unreal Engine or Unity 3D engine to build a high-precision virtual construction site based on the BIM (Building Information Modeling) model of the construction area. For example, the geometric dimensions and material properties (such as elastic modulus, density) of the photovoltaic support are consistent with the physical entity, and the support model precision reaches millimeter level (±1mm).

[0156] Mechanical model import: Convert the CAD models of construction machinery such as cranes and forklifts into FBX format and add kinematic constraints (such as limiting the rotation angle of the boom to 0°~270°). For example, the hydraulic telescopic boom model of the crane contains 6 degrees of freedom (DOF) to simulate the motion trajectory of the real machine.

[0157] Resource allocation matrix mapping: Analyze the device-task relationship in the construction resource allocation matrix and convert task nodes (such as "Node A1 needs a crane to work from 8:00-10:00") into time series instructions. For example, the crane task "A1→B1" in the matrix is mapped to the virtual crane moving from coordinates (X=100, Y=200) to (X=150, Y=300).

[0158] Virtual construction machinery driving mechanism: Path trajectory generation: Based on the GPS coordinates (latitude and longitude) of the scheduling path, generate the moving path of the virtual machinery through coordinate conversion algorithms (such as UTM to local coordinate system). For example, the moving path of the crane is composed of a series of waypoints (Waypoint) with a spacing of 0.5 meters, and the path smoothing process uses Bézier Curve.

[0159] Action instruction analysis: Decompose the operation instructions (such as "install support") into mechanical arm movement, hydraulic pressure regulation, and other bottom-level control signals. For example, the "install support" instruction corresponds to a crane boom lowering speed of 0.2m / s and a gripper clamping force of 500N.

[0160] Real-time synchronization mechanism: Synchronize physical device sensor data (e.g., motor speed) to the digital twin model via OPC UA (Open Platform Communications Unified Architecture) protocol, ensuring virtual and real-world states are consistent. For example, crane hydraulic pressure sensor data is updated to the virtual model at a frequency of 100 Hz.

[0161] Simulation verification and exception handling: Collision detection: Enable a physics engine (e.g., NVIDIA PhysX) in the virtual environment to detect collisions between machinery and supports, terrain. For example, if the distance between the boom and the support is <0.1 meters, trigger an alarm and mark the path risk point.

[0162] Timing deviation correction: Compare the virtual progress (e.g., task A1 takes 9.5 hours) with the actual progress (10 hours), and automatically adjust the subsequent task time window. For example, after the virtual model detects the delay, it will postpone the start time of task B1 from 10:00 to 10:30.

[0163] Visual monitoring: Display mechanical load, energy consumption, and other indicators in the form of a heat map in the digital twin interface, supporting multi-perspective (top-down, side view, first-person) observation of construction details.

[0164] Real-time acquisition of support deformation sensor data and component inclination data to generate a millimeter-level precision deformation-inclination joint distribution map; Support deformation sensors are typically strain gauges or fiber Bragg grating sensors (FBG), deployed at key stress points of the support; Component inclination data is collected by gyroscopes or inclinometers, used to monitor the installation angle of photovoltaic panels. The deformation-inclination joint distribution map integrates both types of data, reflecting the coupling relationship between structural deformation and component posture during construction.

[0165] Sensor deployment and data acquisition: Deformation sensor arrangement: Install strain gauges at key locations such as support columns and beams, with a sensor every 2 meters. For example, a 20-meter support is equipped with 11 strain gauges, with a sampling frequency of 50 Hz and a measurement accuracy of ±0.01 mm.

[0166] Inclination sensor installation: Install MEMS (Micro-Electro-Mechanical Systems) inclination sensors at the four corners of photovoltaic components to measure X / Y axis inclination angles. For example, a component inclination measurement range of ±30° with a resolution of 0.01°.

[0167] Wireless transmission network: Zigbee or LoRa wireless network is used to aggregate sensor data to edge gateway. For example, each gateway covers a radius of 500 meters, supports simultaneous connection of 200 sensor nodes, and data transmission delay <100ms.

[0168] Data preprocessing and fusion: Noise filtering: Kalman filter is applied to the original deformation data to eliminate vibration interference (such as transient noise caused by mechanical operation). For example, the fluctuation range of the original data of a certain strain gauge is ±0.05mm, and after filtering, it is stabilized at ±0.01mm.

[0169] Space-time alignment: The time stamps of deformation data and inclination data are unified to GPS clock, and are aligned according to spatial grid (such as 1m×1m). For example, the deformation data (0.12mm) and inclination data (5.2°) of grid G001 are associated to the same time point (2023-10-01 10:00:00).

[0170] Anomaly value repair: Based on the data of adjacent sensors, the measurement value of the failed node is repaired. For example, when a certain inclination sensor fails, take the average value of the adjacent 4 sensor data (such as 5.0°, 5.1°, 5.3°, 5.2°→ repaired value is 5.15°).

[0171] Joint distribution map generation and visualization: Interpolation algorithm application: Kriging interpolation is used to convert discrete sensor data into continuous distribution map. For example, after interpolation, the deformation data generates a 0.1m×0.1m resolution deformation field, and the color mapping is from blue (0mm) to red (2mm).

[0172] Inclination vector superposition: The inclination direction and size are marked on the deformation heat map with arrows. For example, the deformation of a certain area is 0.5mm, and the inclination arrow points to the northwest direction, and the length represents the inclination angle 5°.

[0173] Dynamic update mechanism: The distribution map is refreshed every 5 seconds, and historical data backtracking (such as deformation trend animation in the past 1 hour) is supported. The output format is WebGL layer, which can be rendered in real time on the browser side.

[0174] According to the deformation-inclination joint distribution map, define deformation peak value, inclination uniformity and progress deviation as multi-objective fitness function, input adaptive particle swarm optimization algorithm; Multi-objective fitness function quantifies multiple dimensions of construction quality: Peak Deformation needs to be minimized to avoid structural damage; Tilt Uniformity needs to be maximized to ensure component efficiency; Schedule Deviation needs to be minimized to maintain project duration. Adaptive Particle Swarm Optimization (APSO) finds a balanced solution among multiple objectives by dynamically adjusting search parameters.

[0175] Fitness function definition: Peak Deformation calculation: Take the maximum deformation value among all grid cells. For example, the maximum deformation in the current distribution is 1.8 mm, which corresponds to the target value f1 = 1.8.

[0176] Tilt Uniformity measurement: Calculate the standard deviation of all component tilt angles. The smaller the standard deviation, the higher the uniformity. For example, the tilt angle data is [5.0°, 5.1°, 4.9°, 5.2°], the standard deviation ≈ 0.12°, and the target value f2 = 1 / 0.12 ≈ 8.33 (maximize).

[0177] Schedule Deviation evaluation: Compare the actual progress with the planned progress and calculate the absolute value of the deviation in hours. For example, a task planned to be completed in 10 hours actually took 10.5 hours, and the deviation f3 = 0.5.

[0178] Multi-objective weighted integration: Set weights according to engineering priorities (e.g., deformation weight w1 = 0.5, uniformity w2 = 0.3, schedule w3 = 0.2), fitness function F = w1f1 + w2(1 / f2) + w3*f3, which needs to be minimized.

[0179] Adaptive Particle Swarm Optimization algorithm configuration: Particle coding design: Each particle represents a set of mechanical control parameters (e.g., boom speed, clamping force). For example, particle = [speed 0.2 m / s, clamping force 500 N, hydraulic pressure 20 MPa].

[0180] Inertia weight adjustment: Dynamically reduce the inertia weight (Inertia Weight) according to the number of iterations, enhance global search in the early stage, and focus on local optimization in the later stage. For example, the initial weight w = 0.9, reduce 0.02 per generation, and the minimum is 0.4.

[0181] Learning factor optimization: Individual learning factor (C1) and social learning factor (C2) are adaptively adjusted according to the particle distribution density. For example, if the particle aggregation degree is high, increase C1 (focus on individual experience), otherwise increase C2 (focus on group experience).

[0182] ‌Algorithm execution and convergence control‌: ‌Population initialization‌: Randomly generate 50 particles with parameter ranges complying with mechanical operation specifications (e.g., speed 0.1~0.5m / s).

[0183] ‌Iterative update rule‌: Calculate particle fitness at each generation and update individual best (Pbest) and global best (Gbest). For example, if a particle's fitness decreases from 8.5 to 7.2, update its Pbest.

[0184] ‌Termination condition setting‌: Stop when the global best fitness changes by <1% for 10 consecutive generations or reaches the maximum number of iterations (e.g., 100 generations). Output the optimal parameter combination (e.g., speed 0.25m / s, holding force 480N).

[0185] Dynamically adjust particle swarm search direction based on real-time deformation data, and solve the optimal mechanical control parameters under construction specification constraints; ‌Real-time deformation data‌ is continuously input into the optimization algorithm through edge computing nodes, dynamically correcting the search direction to avoid local optima.‌ Construction specification constraints‌ include mechanical operation safety range (e.g., maximum lifting weight limit) and engineering standards (e.g., support installation precision requirements), which need to be processed through constraint handling techniques to ensure the effectiveness of the solution.

[0186] ‌Real-time data-driven search direction adjustment‌: ‌Deformation gradient analysis‌: Calculate the gradient field of the current deformation distribution map to identify high deformation growth areas (e.g., gradient >0.1mm / m). The optimization algorithm preferentially reduces the mechanical load parameters in these areas. For example, if the deformation gradient of area A is 0.15mm / m, reduce the crane operating speed in this area.

[0187] ‌Dynamic weight allocation‌: Adjust the fitness function weight based on the severity of deformation. For example, when the deformation peak value of a certain area exceeds 1.5mm, increase the deformation weight w1 from 0.5 to 0.7 to strengthen optimization of this target.

[0188] ‌Real-time update of particle swarm parameters‌: Adjust the particle velocity update formula based on deformation trend prediction (e.g., ARIMA model). For example, if the predicted deformation continues to increase, increase the search step size of particles towards low load parameters.

[0189] ‌Construction specification constraint handling techniques‌: ‌Hard constraint handling‌: Directly exclude solutions that violate safety specifications. For example, if a particle's crane speed parameter is 0.6m / s (exceeding the maximum allowed value of 0.5m / s), mark it as an invalid solution and regenerate it.

[0190] Soft Constraint Penalty: Apply a penalty term to solutions that slightly violate constraints. For example, if the clamping force is below 450 N, the fitness function increases a penalty term P = 10 * (450 - actual value), forcing the algorithm away from the violation region.

[0191] Feasible Solution Repair: Automatically correct parameters that partially violate constraints. For example, if the hydraulic pressure parameter is 25 MPa (exceeding the upper limit of 22 MPa), it is corrected to 22 MPa and the fitness is recalculated.

[0192] Multi-Objective Pareto Front Generation: Non-dominated Sorting: Sort particles by objective function values, preferentially retaining particles that are not dominated by other solutions. For example, the first layer contains three solutions with fitness F = 7.2, F = 7.5, and F = 7.8, none of which dominate each other.

[0193] Crowding Distance Calculation: Evaluate the distribution density of solutions in the objective space, preserving diversity. For example, a solution with a crowding distance of 0.15 indicates that there are fewer surrounding solutions, so it is preferentially retained.

[0194] Elitism Strategy: Retain the top 10% of optimal solutions in each generation to avoid losing high-quality solutions. For example, among 50 particles, 5 elite solutions are directly passed to the next generation.

[0195] Encode optimal mechanical control parameters into anti-deformation construction instruction sets, and ensure instruction integrity and traceability through blockchain signatures.

[0196] Anti-deformation construction instruction sets are the output of optimization algorithms, which need to be converted into executable instruction sequences for machines. Blockchain signatures use hash algorithms (such as SHA-256) and digital certificates to ensure that instructions cannot be tampered with during transmission and storage, and operation records can be traced.

[0197] Instruction Set Encoding and Formatting: Instruction Structure Design: Encapsulate control parameters, timestamps, and device IDs in JSON format. For example: { "device_id": "Crane-001", "timestamp": "2023-10-01T10:00:00Z", "commands": [ {"action": "move", "target": {"x": 100, "y": 200}, "speed":0.25}, {"action": "clamp", "force": 480, "duration": 60} ] }。

[0198] ‌Instruction Priority Tag: Set execution priority based on task urgency (0-9 levels). For example, bracket installation instructions have a priority of 7, and component debugging instructions have a priority of 3.

[0199] ‌Timing Synchronization Mechanism: Synchronize all device clocks through NTP (Network Time Protocol) to ensure instructions are executed according to the planned timing. For example, the timestamp error in the instructions is controlled within ±1ms.

[0200] ‌Blockchain Signature and Evidence Storage Process: ‌Hash Value Calculation: Perform SHA-256 hash operation on the instruction set JSON string to generate a unique digest (Digest). For example, the instruction set hash value is "a1b2c3...f6e5d4".

[0201] ‌Digital Signature Generation: Encrypt the hash value using the device private key (Private Key) to generate a digital signature (Digital Signature). For example, the private key is a 2048-bit RSA key, and the signature length is 256 bytes.

[0202] ‌Blockchain Chain: Package the instruction set, hash value, signature, and timestamp into a transaction and write it to the distributed ledger of Hyperledger Fabric blockchain. For example, the transaction ID is "TX-20231001-1000", and the block height is #12345.

[0203] ‌Instruction Issuance and Execution Verification: ‌Edge Node Verification: After receiving the instructions, the device verifies the validity of the signature using the public key and recalculates the hash value for consistency. For example, the public key is pre-installed in the device security chip (HSM).

[0204] ‌Abnormal Instruction Interception: If the hash value does not match or the signature is invalid, trigger an alarm and discard the instruction. For example, when tampered instructions are detected, automatically report to the monitoring center.

[0205] ‌Execution record backtracking: After the device completes the task, the execution result (such as actual time consumption, final deformation value) is signed and returned to the blockchain, forming a complete traceability chain. For example, the result record contains "Crane-001 completed task A1 at 2023-10-01T10:30:00Z with deformation 0.8mm".

[0206] ‌Key technology examples and parameter explanations: ‌UTM coordinate system (Universal Transverse Mercator): A global zoned projection coordinate system suitable for local area high-precision positioning.

[0207] ‌OPC UA protocol: An industrial automation communication protocol that supports cross-platform device data interaction.

[0208] ‌MEMS sensor (Micro-Electro-Mechanical Systems): Micro-electromechanical system for high-precision tilt measurement.

[0209] ‌SHA-256: Secure Hash Algorithm, generating a 256-bit (32-byte) unique data digest.

[0210] ‌Hyperledger Fabric: Enterprise-level blockchain framework supporting permission management and privacy protection.

[0211] The digital twin model maps the physical construction state in real time, and constructs a deformation-tilt joint distribution graph through millimeter-level deformation monitoring data. The adaptive particle swarm optimization algorithm takes the deformation peak value and tilt uniformity as the optimization objective, dynamically adjusts the mechanical control parameters under the constraints of construction specifications, generates anti-deformation operation instructions, realizes online optimization and risk pre-control of the construction process, and suppresses the structure deformation within the safety threshold. The combination of digital twin and optimization algorithm significantly improves the construction accuracy under complex terrain.

[0212] S205, according to the anti-deformation construction instruction set, drive the construction machinery to execute the operation, synchronize through the edge computing node to analyze the energy efficiency feedback data, use the reinforcement learning model to iteratively update the global load distribution atlas, form a closed-loop construction management link.

[0213] Specifically, the anti-deformation construction instruction set can be parsed into mechanical control signals to drive the hydraulic system and robotic arm to perform high-precision construction operations; The Anti-deformation Construction Instruction Set is a JSON-formatted instruction file verified by blockchain signature, containing device action parameters, target coordinates, and execution timing. The parsing process involves instruction decoding, coordinate conversion, and signal adaptation, ultimately generating pulse signals (PWM) or digital signals (Digital I / O) executable by the underlying mechanical control system.

[0214] ‌Instruction Parsing and Decoding‌: ‌Instruction Structure Parsing‌: The instruction set is encapsulated in JSON format, defining device ID, action type, target parameters, etc. in key-value pairs. For example: { "device_id": "HydraulicArm-001", "timestamp": "2023-10-01T10:00:00Z", "actions": [ {"type": "move_xy", "x": 150.25, "y": 200.75, "speed": 0.3}, {"type": "clamp", "force": 520, "duration": 5} ] }。 Each action field needs to be mapped to the corresponding control instruction. For example, move_xy corresponds to the two-dimensional plane movement of the robot arm, and clamp corresponds to the clamping action of the gripper.

[0215] ‌Blockchain Signature Verification‌: Use the device's pre-installed public key (Public Key) to verify the consistency of the instruction hash value (Hash Value) and digital signature (Digital Signature). For example, the hash algorithm uses SHA-256 (Secure Hash Algorithm 256-bit), and the signature algorithm uses ECDSA (Elliptic Curve Digital Signature Algorithm). If the verification fails, the instruction will be discarded and an alarm will be triggered.

[0216] ‌Control Signal Generation and Adaptation‌: ‌Coordinate Conversion‌: GPS coordinates (e.g., WGS-84 latitude and longitude) are converted to local engineering coordinate systems (e.g., UTM, Universal Transverse Mercator). For example, a target point (116.3°E, 39.9°N) is converted to UTM Zone 50N coordinates (X=324567, Y=4321098).

[0217] ‌Signal Type Adaptation‌: ‌Hydraulic System Control‌: Hydraulic flow is controlled by the opening of proportional valves, generating a 4-20mA analog signal. For example, a target pressure of 20MPa corresponds to a current signal of 16mA.

[0218] ‌Mechanical Arm Movement‌: Servo motors are driven using pulse width modulation (PWM) signals. For example, a rotation speed of 0.3m / s corresponds to a PWM duty cycle of 45%.

[0219] ‌Real-Time Feedback Loop‌: The position of the mechanical arm is monitored in real-time by Hall sensors and fed back to the PLC (Programmable Logic Controller). For example, if the actual position deviates from the target position by more than 2mm, a PID (Proportional-Integral-Derivative) control algorithm is triggered to adjust the motor speed.

[0220] ‌High Precision Execution and Safety Assurance‌: ‌Multi-Axis Synchronous Control‌: The X / Y / Z axes of the mechanical arm are driven synchronously by a motion controller, ensuring smooth trajectories. For example, the EtherCAT (Ethernet for Control Automation Technology) bus protocol is used to achieve multi-axis linkage, with a synchronization period of ≤1ms.

[0221] ‌Overload Protection Mechanism‌: The clamping force is monitored in real-time by pressure transducers, and if it exceeds a safety threshold (e.g., 600N), the pressure is immediately released and the action is interrupted. For example, the clamping force threshold is dynamically adjusted based on the mechanical parameters of the components to prevent fracturing of the photovoltaic panels.

[0222] ‌Precision Calibration‌: The laser tracker is used to calibrate the positioning accuracy of the robot regularly. For example, the repeat positioning accuracy after calibration is ±0.1mm, which meets the requirements of photovoltaic module installation.

[0223] The edge computing node collects real-time data on installation errors and mechanical energy consumption, and generates efficiency-quality evaluation indicators. ‌The edge computing node is an embedded device (such as NVIDIA Jetson TX2) deployed on the construction site, responsible for real-time processing of sensor data and generation of evaluation indicators.‌The efficiency-quality evaluation indicator (EQM) quantifies the energy consumption and installation quality of the construction process, providing training data for reinforcement learning models.

[0224] ‌Data collection and preprocessing‌: ‌Installation error measurement‌: ‌Visual measurement system‌: An industrial camera (such as Basler ace 2) is used to capture the installation position of the module, and the deviation between the actual coordinates and the design coordinates is calculated through the OpenCV (Open Source Computer Vision Library) image processing algorithm. For example, the image resolution is 2448×2048, and the measurement error is ±0.5mm.

[0225] ‌Laser range finder‌: Measures the distance error between modules through the TOF (Time-of-Flight) principle. For example, the design distance between adjacent modules is 1.5m, and when the actual distance is 1.48m, it is marked as a -0.02m deviation.

[0226] ‌Mechanical energy consumption statistics‌: ‌Electric energy metering module‌: Integrates ADE7953 chip to collect real-time device power consumption data (such as voltage, current, power factor). For example, the instantaneous power during crane operation is 15kW, and the cumulative energy consumption is 15kW×2h=30kWh.

[0227] ‌Hydraulic energy consumption calculation‌: Calculates the hydraulic system energy consumption based on flow meter and pressure sensor data. For example, flow 10L / min×pressure 20MPa×time 1h→energy consumption=(10×20×60) / 600=20kWh (assuming system efficiency 60%).

[0228] ‌Efficiency-quality indicator calculation‌: ‌Quality score (QS)‌: Define three sub-indicators for weighted calculation: ‌Position Error: Root Mean Square Error (RMSE) of all component coordinate deviations. For example, RMSE = 0.8mm, weight 40%.

[0229] ‌Tilt Consistency: Standard Deviation of component tilt angles. For example, Standard Deviation = 0.15°, weight 30%.

[0230] ‌Structural Integrity: Peak Deformation of the support. For example, Peak Deformation = 1.2mm, weight 30%.

[0231] Final QS = 0.4 × (1 - RMSE / 2mm) + 0.3 × (1 - StdDev / 0.5°) + 0.3 × (1 - PeakDeform / 2mm), value range 0-1 (1 is optimal).

[0232] ‌Efficiency Score (ES): Based on Energy per Unit Area and Completion Rate: ‌Energy Intensity: Total Energy Consumption (kWh) / Installed Area (m 2 ). For example, 30kWh / 50m 2 =0.6kWh / m 2 .

[0233] ‌Completion Rate: Number of tasks completed on time / Total number of tasks. For example, 8 out of 10 tasks completed on time → 80%.

[0234] Final ES = 0.7 × (1 - EnergyIntensity / 1kWh / m 2 ) + 0.3 × CompletionRate, value range 0-1.

[0235] ‌Overall Evaluation Index (EQM): EQM = α × QS + β × ES, where α, β are weights (e.g. α = 0.6, β = 0.4), reflecting the quality-first strategy.

[0236] ‌Edge Computing and Data Upload: ‌Local Real-Time Computing: Edge nodes are equipped with TensorRT (NVIDIA's deep learning inference framework) to accelerate index calculation. For example, QS calculation delay <50ms, meeting real-time requirements.

[0237] ‌Data compression and encryption‌: Data is compressed using the Zstandard (ZSTD) algorithm and encrypted through the TLS (Transport Layer Security) protocol before uploading to the cloud. For example, 1MB of raw data is compressed to 200KB, reducing upload bandwidth usage by 80%.

[0238] ‌Cache and offline resume‌: In weak network environments, data is temporarily stored on the local SSD (Solid State Drive), and automatically resumes when the network is restored. For example, cache capacity 512GB, supporting offline storage of 72 hours of data.

[0239] Based on energy efficiency-quality evaluation index, a hierarchical reinforcement learning model is trained, the upper layer optimizes load prediction parameters, the lower layer adjusts mechanical motion trajectory, iteratively updates global load distribution map, forms a closed loop construction management link.

[0240] ‌Hierarchical reinforcement learning model (Hierarchical Reinforcement Learning Model)‌ is divided into upper-level policy (Upper-Level Policy) and lower-level policy (Lower-Level Policy). The upper layer is responsible for global optimization (such as load prediction model parameter optimization), and the lower layer is responsible for local control (such as mechanical arm motion trajectory adjustment), through collaborative iteration to realize closed loop optimization.

[0241] ‌Hierarchical model architecture design‌: ‌Upper-level policy (Upper-Level)‌: ‌State space (State Space)‌: Contains global load distribution map, historical EQM index, weather forecast data. For example, the state vector is composed of 1000 load grid values (each grid 1m×1m), QS / ES sequence in the past 24 hours, and future 6-hour wind speed prediction.

[0242] ‌Action space (Action Space)‌: Adjust the hyperparameters (Hyperparameters) of the multi-modal fusion network (deep neural network in step 1), such as learning rate (Learning Rate), kernel count (Kernel Count). For example, action space = [learning rate: 0.001~0.01, kernel: 16~64].

[0243] ‌Reward Function: Improvement Rate based on EQM. For example, current EQM=0.8, optimized EQM=0.85 → Reward Value +5.

[0244] ‌Lower-Level: ‌State Space: Mechanical real-time position & orientation, sensor readings (deformation, inclination), current task progress. For example, State Vector = [X Coordinate, Y Coordinate, Clamping Force, Deformation Value, Remaining Task Number].

[0245] ‌Action Space: Adjust mechanical motion parameters such as speed, acceleration, path smoothing. For example, Action Space = [Speed: 0.1~0.5 m / s, Acceleration: 0.05~0.2 m / s 2 ].

[0246] ‌Reward Function: Combine local EQM (such as single task quality score) with energy efficiency. For example, Task A1's QS=0.9 and energy consumption 0.5 kWh / m 2 → Reward Value +3.

[0247] ‌Model Training and Iterative Optimization: ‌Upper-Level Training Process: Use PPO (Proximal Policy Optimization) algorithm, update policy every 24 hours: ‌Data Sampling: Load past 7 days of global load data and EQM records from the cloud.

[0248] ‌Policy Update: Calculate Advantage Function to update network parameters, constrain policy change amplitude (KL Divergence ≤0.05).

[0249] ‌Parameter Issuance: Synchronize optimized hyperparameters to the multi-modal fusion network to generate a new load prediction model.

[0250] ‌Lower-Level Training Process: Use DQN (Deep Q-Network) algorithm, update policy every 1 hour: ‌Experience Replay: Store mechanical execution records (state, action, reward), randomly extract batches (Batch) to train the network.

[0251] ‌Target Network: Delayed update target Q-network (Update Interval = 1000 steps) to stabilize the training process.

[0252] ‌Action Exploration: Add ε-greedy policy (ε = 0.1 → 10% probability of random action) to avoid local optima.

[0253] ‌Global Load Distribution Map Update: ‌Data Fusion and Interpolation: Based on real-time construction data (deformation, inclination) and the optimized prediction model, use Kriging Interpolation to update the load map. For example, if the measured deformation in a certain area is 1.0 mm and the predicted value is 0.9 mm, the correction factor is 0.95, and the updated value is 0.905 mm.

[0254] ‌Map Visualization and Verification: Display the updated load distribution in the form of a heat map on the digital twin platform and compare it with the finite element simulation results. For example, a maximum load deviation of ≤5% is considered to pass the verification.

[0255] ‌Closed Loop Link Triggering Conditions: When the EQM index improves by <1% for 3 consecutive iterations or the load prediction error is >10%, trigger the manual intervention process (such as adjusting the reinforcement learning model structure or resetting the training data).

[0256] ‌Key Technology Examples and Parameter Descriptions: ‌CAN Bus (Controller Area Network): A communication protocol for vehicles and industrial machinery that supports high-reliability data transmission.

[0257] ‌EtherCAT: Real-time industrial Ethernet protocol for synchronous communication in multi-axis motion control systems.

[0258] ‌PID Control (Proportional-Integral-Derivative): Closed-loop control algorithm that adjusts system output through proportional, integral, and derivative components.

[0259] ‌PPO (Proximal Policy Optimization): A reinforcement learning algorithm based on policy gradient that improves stability by constraining the magnitude of policy updates.

[0260] ‌KL Divergence (Kullback-Leibler Divergence): Measure of the difference between two probability distributions used to constrain the magnitude of policy updates.

[0261] Construction machinery performs high-precision operations according to the instruction set, and the edge node analyzes installation errors and energy consumption data in real time. The hierarchical reinforcement learning model realizes dynamic updating of the load atlas and continuous improvement of the construction strategy through double iteration of upper-layer optimization of load prediction parameters and lower-layer adjustment of mechanical trajectory, constructs a complete closed loop of "perception-decision-execution-optimization", and enables the construction management to have self-learning ability. Through continuous data feedback and model iteration, the quality and disaster resistance of power station construction are continuously improved.

[0262] It can be seen that, according to the terrain elevation data, meteorological historical data and component mechanical parameters of the planning area of the photovoltaic power station, a dynamic load intensity heat map is output; based on the dynamic load intensity heat map, an anti-wind pressure optimized construction zoning scheme is generated; according to the construction zoning scheme, a conflict-free construction resource allocation matrix is output; based on the construction resource allocation matrix, an adaptive particle swarm optimization algorithm is used to dynamically correct the construction parameters, and an anti-deformation construction instruction set is generated; according to the anti-deformation construction instruction set, the construction machinery is driven to execute the operation, and the energy efficiency feedback data is analyzed synchronously through the edge computing node, and the global load distribution atlas is iteratively updated using the reinforcement learning model, forming a closed loop construction management link, so that the balance of construction precision and efficiency in complex environments can be realized by fusing multi-source data, optimizing zoning planning, intelligently scheduling equipment and closed loop quality control.

[0263] Another embodiment of the present application provides a photovoltaic power station distributed construction management system based on load analysis, referring to Figure 3 , the system can include: The fusion module 301 is used for fusing satellite remote sensing images and ground sensor time series data through a multi-modal fusion deep neural network according to terrain elevation data, meteorological historical data and component mechanical parameters of the planning area of the photovoltaic power station, and outputting a dynamic load intensity heat map; The division module 302 is used for dividing the construction sub-area based on the dynamic load intensity heat map, dynamically adjusting the clustering center according to the load extreme point and the topographic mutation feature, and generating an anti-wind pressure optimized construction zoning scheme by using a gradient constrained spectral clustering algorithm; The generation module 303 is used for generating an equipment scheduling path by using a space-time conflict resolution algorithm according to the construction zoning scheme and combining the real-time state data of the construction equipment, outputting a conflict-free construction resource allocation matrix through a dynamic time window constraint and a load balancing objective function; The correction module 304 is used for simulating the construction process through a digital twin model based on the construction resource allocation matrix, collecting real-time support frame deformation data and component inclination data, dynamically correcting the construction parameters by using an adaptive particle swarm optimization algorithm, and generating an anti-deformation construction instruction set; The updating module 305 is configured to drive the construction machinery to perform operations according to the anti-deformation construction instruction set, synchronously analyze energy efficiency feedback data through the edge computing node, iteratively update the global load distribution atlas by using the reinforcement learning model, and form a closed-loop construction management link.

[0264] It can be seen that the dynamic load intensity heat map is output according to the terrain elevation data, meteorological historical data and component mechanical parameters of the photovoltaic power station planning area; the anti-wind pressure optimized construction zoning scheme is generated based on the dynamic load intensity heat map; the conflict-free construction resource allocation matrix is output according to the construction zoning scheme; the anti-deformation construction instruction set is generated by dynamically correcting the construction parameters based on the construction resource allocation matrix and using the adaptive particle swarm optimization algorithm; the construction machinery is driven to perform operations according to the anti-deformation construction instruction set, and the energy efficiency feedback data is synchronously analyzed through the edge computing node; the global load distribution atlas is iteratively updated by using the reinforcement learning model, and a closed-loop construction management link is formed, so that the balance between construction precision and efficiency in a complex environment can be realized by fusing multi-source data, optimizing zoning planning, intelligently scheduling equipment and closed-loop quality control.

[0265] The embodiment of the present application also provides a storage medium, wherein the storage medium stores a computer program, and the computer program is set to execute the steps in any of the method embodiments.

[0266] Specifically, in the present embodiment, the above-mentioned storage medium can be set to store a computer program for executing the following steps: S201, according to the terrain elevation data, meteorological historical data and component mechanical parameters of the photovoltaic power station planning area, the dynamic load intensity heat map is output by fusing satellite remote sensing images and ground sensor time series data through a multi-modal fusion deep neural network; S202, based on the dynamic load intensity heat map, the construction sub-area is divided by using a gradient-constrained spectral clustering algorithm, the clustering center is dynamically adjusted according to the load extreme point and the terrain mutation feature, and the anti-wind pressure optimized construction zoning scheme is generated; S203, according to the construction zoning scheme, the equipment scheduling path is generated by using a space-time conflict resolution algorithm combined with real-time state data of the construction equipment, the algorithm outputs a conflict-free construction resource allocation matrix by using a dynamic time window constraint and a load balancing objective function; S204, based on the construction resource allocation matrix, the construction process is simulated through a digital twin model, the support deformation data and the component inclination data are collected in real time, the anti-deformation construction instruction set is generated by dynamically correcting the construction parameters through an adaptive particle swarm optimization algorithm; S205, driving the construction machinery to perform operations according to the anti-deformation construction instruction set, synchronously analyzing the energy efficiency feedback data through the edge computing node, iteratively updating the global load distribution atlas by using the reinforcement learning model, and forming a closed-loop construction management link.

[0267] It can be seen that, according to the terrain elevation data, meteorological historical data and component mechanical parameters of the photovoltaic power station planning area, a dynamic load intensity heat map is output; based on the dynamic load intensity heat map, an anti-wind pressure optimized construction zoning scheme is generated; according to the construction zoning scheme, a conflict-free construction resource allocation matrix is output; based on the construction resource allocation matrix, the adaptive particle swarm optimization algorithm is used to dynamically correct the construction parameters, and an anti-deformation construction instruction set is generated; according to the anti-deformation construction instruction set, the construction machinery is driven to perform operations, and the energy efficiency feedback data is synchronously analyzed through the edge computing node, and the global load distribution atlas is iteratively updated by using the reinforcement learning model, and a closed-loop construction management link is formed, so that the balance between construction precision and efficiency in complex environments can be realized by fusing multi-source data, optimizing zoning planning, intelligently scheduling equipment and closed-loop quality control.

[0268] The embodiment of the present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any of the above method embodiments.

[0269] Specifically, the above electronic device can further comprise a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0270] Specifically, in the present embodiment, the processor can be configured to execute the following steps by the computer program: S201, according to the terrain elevation data, meteorological historical data and component mechanical parameters of the photovoltaic power station planning area, fusing satellite remote sensing images and ground sensor time series data by a multi-modal fusion deep neural network, and outputting a dynamic load intensity heat map; S202, based on the dynamic load intensity heat map, using a gradient-constrained spectral clustering algorithm to divide a construction sub-area, dynamically adjusting the clustering center according to the load extreme point and the terrain mutation feature, and generating an anti-wind pressure optimized construction zoning scheme; S203, according to the construction zoning scheme, combining the real-time state data of the construction equipment, and using a space-time conflict resolution algorithm to generate an equipment scheduling path, the algorithm outputs a conflict-free construction resource allocation matrix by a dynamic time window constraint and a load balancing objective function; S204, based on the construction resource allocation matrix, simulate the construction process through the digital twin model, collect the support deformation data and the component inclination angle data in real time, dynamically correct the construction parameters by using the adaptive particle swarm optimization algorithm, and generate the anti-deformation construction instruction set; S205, according to the anti-deformation construction instruction set, drive the construction machinery to perform operation, synchronously analyze the energy efficiency feedback data through the edge computing node, iteratively update the global load distribution atlas by using the reinforcement learning model, and form a closed-loop construction management link.

[0271] It can be seen that, according to the terrain elevation data, meteorological historical data and component mechanical parameters of the photovoltaic power station planning area, the dynamic load intensity thermal map is output; based on the dynamic load intensity thermal map, the anti-wind pressure optimized construction partition scheme is generated; according to the construction partition scheme, the conflict-free construction resource allocation matrix is output; based on the construction resource allocation matrix, the adaptive particle swarm optimization algorithm is used to dynamically correct the construction parameters, and the anti-deformation construction instruction set is generated; according to the anti-deformation construction instruction set, the construction machinery is driven to perform operation, and the energy efficiency feedback data is analyzed synchronously through the edge computing node, and the global load distribution atlas is iteratively updated by using the reinforcement learning model, and a closed-loop construction management link is formed, so that the balance of construction precision and efficiency under complex environment can be realized by fusing multi-source data, optimizing partition planning, intelligently scheduling equipment and closed-loop quality control.

[0272] The above describes the structure, features and effects of the present application in detail according to the embodiments shown in the drawings. The above description is only the preferred embodiment of the present application, but the present application is not limited by the drawings shown. Any changes or modifications made in accordance with the concept of the present application, or equivalent embodiments with equivalent changes, shall be within the scope of the present application.

Claims

1. A distributed construction management method for photovoltaic power stations based on load analysis, characterized in that: The method comprises: Based on the terrain elevation data, meteorological historical data, and component mechanical parameters of the PV power station planning area, a multimodal fusion deep neural network is used to integrate satellite remote sensing images and ground sensor time series data to output a dynamic load intensity heat map. Based on the dynamic load intensity heat map, a gradient-constrained spectral clustering algorithm is used to divide the construction sub-areas, and the cluster centers are dynamically adjusted according to the load extreme points and terrain mutation characteristics to generate a construction zoning scheme optimized for wind pressure resistance; Based on the construction zoning scheme and in combination with the real-time status data of construction equipment, a time-space conflict resolution algorithm is used to generate equipment scheduling paths. The algorithm outputs a conflict-free construction resource allocation matrix through dynamic time window constraints and load balancing objective functions; Based on the construction resource allocation matrix, the construction process is simulated through a digital twin model, support deformation data and component inclination data are collected in real time, and an adaptive particle swarm optimization algorithm is used to dynamically correct construction parameters to generate an anti-deformation construction instruction set; The construction machinery is driven to perform operations according to the anti-deformation construction instruction set, and the energy efficiency feedback data is analyzed simultaneously through the edge computing node. The reinforcement learning model is used to iteratively update the global load distribution map to form a closed-loop construction management link.

2. The method according to claim 1, characterized in that Based on the terrain elevation data, meteorological historical data and component mechanical parameters of the photovoltaic power station planning area, a multimodal fusion deep neural network is used to fuse satellite remote sensing images and ground sensor time series data to output a dynamic load intensity heat map, including: Based on satellite remote sensing images and terrain elevation data, a terrain-component mapping grid is generated through a geometric registration algorithm, and the spatial grid is output after eliminating cloud interference; The real-time wind speed, temperature and humidity data collected by spatial grids and ground sensors are input into the spatiotemporal interpolation model, and combined with historical meteorological data to generate a three-dimensional dynamic meteorological field; The three-dimensional dynamic meteorological field and component mechanical parameters are input into the multimodal fusion network. The terrain texture features are extracted through residual convolution, and the meteorological time series pattern is extracted through bidirectional LSTM. After fusion, the initial load prediction map is output; The initial load prediction map is subjected to optical flow-guided spatiotemporal noise filtering, and the intensity distribution is optimized according to the correlation of loads in adjacent grids to generate a dynamically updated load intensity heat map.

3. The method according to claim 2, characterized in that The method uses a gradient-constrained spectral clustering algorithm based on the dynamic load intensity heat map to divide the construction sub-areas, dynamically adjusts the cluster centers according to the load extreme points and terrain mutation characteristics, and generates a construction zoning scheme optimized for wind pressure resistance, including: The gradient field is calculated based on the dynamic load heat map, and the area where the load change rate exceeds the threshold is extracted as the gradient mutation boundary; A regional similarity matrix is ​​constructed based on the gradient mutation boundary, and an adaptive Gaussian kernel function is used to quantify the load correlation strength of adjacent regions; The regional similarity matrix is ​​input into the spectral clustering algorithm, and the initial cluster center position is constrained by the terrain elevation mutation point to perform preliminary construction sub-area division; Dynamically adjust cluster centers based on real-time wind speed forecast data to shift high-load areas toward low wind pressure and optimize construction sub-area boundaries; Finite element wind pressure simulation is performed on the optimized sub-areas to verify the construction zoning scheme that meets the critical wind pressure resistance threshold.

4. The method according to claim 3, characterized in that The equipment scheduling path is generated based on the construction zoning scheme and the real-time status data of the construction equipment using a spatiotemporal conflict resolution algorithm. The algorithm outputs a conflict-free construction resource allocation matrix through dynamic time window constraints and a load balancing objective function, including: Converting the construction zoning plan into a task node graph with time constraints, wherein the nodes of the task node graph include equipment type requirements, operation duration, and safe load thresholds; Build a device capability-location matrix based on device GPS positioning data and energy consumption logs, and calculate the time and space costs of moving the device to each node; Based on the task node graph and the device capability-location matrix, a dynamic time window is used to detect device path conflicts, and an initial path sequence is generated through priority scheduling. Taking load balancing as the objective function, a mixed integer programming solver is used to optimize the initial path sequence, outputting the device-task allocation matrix and the conflict-free scheduling path.

5. The method according to claim 4, characterized in that Based on the construction resource allocation matrix, the construction process is simulated through the digital twin model, the bracket deformation data and component inclination data are collected in real time, and the construction parameters are dynamically corrected using the adaptive particle swarm optimization algorithm to generate an anti-deformation construction instruction set, including: Map the construction resource allocation matrix to the digital twin model to drive virtual construction machinery to perform operations according to the scheduled path; Collect bracket deformation sensor data and component inclination data in real time to generate a joint deformation-inclination distribution map with millimeter-level accuracy; According to the deformation-inclination joint distribution graph, the deformation peak value, inclination uniformity and progress deviation are defined as the multi-objective fitness function and input into the adaptive particle swarm optimization algorithm; Dynamically adjust the particle swarm search direction based on real-time deformation data to solve the optimal mechanical control parameters under the constraints of construction specifications; The optimal mechanical control parameters are encoded into an anti-deformation construction instruction set, and the integrity and traceability of the instructions are ensured through blockchain signatures.

6. The method according to claim 5, characterized in that The construction machinery is driven to perform operations according to the anti-deformation construction instruction set, and energy efficiency feedback data is analyzed simultaneously through edge computing nodes. The global load distribution map is iteratively updated using a reinforcement learning model to form a closed-loop construction management link, including: Parse the anti-deformation construction instruction set into mechanical control signals to drive the hydraulic system and robotic arm to perform high-precision construction operations; The edge computing nodes collect component installation errors and mechanical energy consumption data in real time to generate energy efficiency-quality evaluation indicators; A hierarchical reinforcement learning model is trained based on energy efficiency-quality evaluation indicators. The upper layer optimizes load prediction parameters, the lower layer adjusts the mechanical motion trajectory, and iteratively updates the global load distribution map to form a closed-loop construction management link.

7. A distributed construction management system for photovoltaic power stations based on load analysis, characterized in that: The system comprises: The fusion module is used to output a dynamic load intensity heat map based on the terrain elevation data, meteorological historical data, and component mechanical parameters of the PV power station planning area, through a multimodal fusion deep neural network, integrating satellite remote sensing images and ground sensor time series data; A partitioning module is used to divide the construction sub-areas based on the dynamic load intensity heat map using a gradient-constrained spectral clustering algorithm, dynamically adjust the cluster centers according to load extreme points and terrain mutation characteristics, and generate a construction zoning scheme optimized for wind pressure resistance; A generation module is used to generate an equipment scheduling path based on the construction zoning scheme and the real-time status data of the construction equipment using a spatiotemporal conflict resolution algorithm. The algorithm outputs a conflict-free construction resource allocation matrix through dynamic time window constraints and a load balancing objective function; A correction module is used to simulate the construction process through a digital twin model based on the construction resource allocation matrix, collect support deformation data and component inclination data in real time, dynamically correct construction parameters using an adaptive particle swarm optimization algorithm, and generate an anti-deformation construction instruction set; The update module is used to drive the construction machinery to perform operations according to the anti-deformation construction instruction set, simultaneously analyze the energy efficiency feedback data through the edge computing node, and use the reinforcement learning model to iteratively update the global load distribution map to form a closed-loop construction management link.

8. The system according to claim 7, characterized in that The fusion module is specifically used to: Based on satellite remote sensing images and terrain elevation data, a terrain-component mapping grid is generated through a geometric registration algorithm, and the spatial grid is output after eliminating cloud interference; The real-time wind speed, temperature and humidity data collected by spatial grids and ground sensors are input into the spatiotemporal interpolation model, and combined with historical meteorological data to generate a three-dimensional dynamic meteorological field; The three-dimensional dynamic meteorological field and component mechanical parameters are input into the multimodal fusion network. The terrain texture features are extracted through residual convolution, and the meteorological time series pattern is extracted through bidirectional LSTM. After fusion, the initial load prediction map is output; The initial load prediction map is subjected to optical flow-guided spatiotemporal noise filtering, and the intensity distribution is optimized according to the correlation of loads in adjacent grids to generate a dynamically updated load intensity heat map.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 6 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 6.

Images