Method and system for intelligently generating GIS circuit diagram based on electric power tube bank engineering drawing

Abstract

The invention discloses a GIS circuit diagram intelligent generation method based on an electric power pipe arrangement engineering drawing, and relates to the technical field of electric power engineering design. The method comprises the steps of preprocessing multi-source data, planning a path and detecting conflicts, intelligently generating a dynamic layer and adaptively outputting in a cross-platform manner. The preprocessed multi-source data receives design parameters and multi-class data to generate a standardized data set; the path planning and conflict detection are combined with various conditions to generate constraints, and accurate parameters are output; the dynamic layer intelligent generation module is used for generating multiple types of layers according to the data, generating drawings and accounting the cost; in the cross-platform self-adaptive output step, graphs are displayed on multiple terminals based on a framework, and a multi-format export interface is provided. According to the method and the system, intelligent generation of the GIS circuit diagram of the electric power tube bank engineering drawing is realized, and the design efficiency and accuracy are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power engineering design, and more particularly to an electric power pipe rack engineering drawing GIS line map intelligent generation method and system. BACKGROUND

[0002] In the field of electric power engineering construction, the reasonable planning and accurate design of electric power pipe rack lines are the key basic work to ensure the stable and efficient operation of the power system. The traditional electric power pipe rack engineering drawing and GIS line map generation process mostly rely on manual operation and experience judgment. Designers need to spend a lot of time and effort to manually integrate the multi-source heterogeneous data of electric power pipe rack design parameters, GIS geographic data, geophysical survey data and engineering drawings. Not only is the work efficiency low, but also in the process of data integration and processing, the problems of non-uniform data format, information omission and error matching are easy to occur, which seriously affects the accuracy and reliability of the design results. In the path planning link, the traditional method often has difficulty in comprehensively and accurately considering the complex and variable constraint conditions of terrain elevation, underground facility distribution, soil geological conditions, construction process restrictions and cost budget. This leads to the problems of conflict with existing underground pipelines, high construction difficulty and high cost of the planned electric power pipe rack lines, which not only increases the construction risk and cost, but also affects the overall progress and quality of electric power engineering construction. In addition, for the complex scene of multi-layer pipe dense work well, the traditional technology lacks effective three-dimensional collision detection and layout optimization means, and cannot quantify the path conflict risk and optimize the pipe layout in advance, further restricting the scientificity and rationality of electric power pipe rack engineering design. In the aspect of drawing generation and display, with the continuous growth of geographic information and engineering element data, the traditional drawing rendering technology has the problems of slow rendering speed and low efficiency when processing million-level elements, which is difficult to meet the timeliness demand of engineering design. At the same time, in the multi-terminal display scene, due to the different screen resolutions and display sizes of different devices, the traditional method is difficult to realize the consistent display of GIS line map on computer, tablet and mobile terminal, and cannot meet the demand of multi-department collaborative design, real-time viewing and efficient management of electric power engineering. Therefore, developing an electric power pipe rack engineering drawing GIS line map intelligent generation method which can realize intelligent processing of multi-source data, accurate path planning, efficient drawing generation and cross-platform adaptive display has become an important topic to be solved in the field of electric power engineering. SUMMARY

[0003] In view of the above technical deficiencies, the present application discloses an electric power pipe rack engineering drawing GIS line map intelligent generation method and system based on The present application adopts the following technical scheme: S1. Preprocessing multi-source data Receive user input power pipe design parameters, while obtaining GIS geographic data, geophysical survey data, multi-format engineering drawings and lightweight BIM models; Through four-dimensional space-time index and graph neural network, realize the data analysis of pipeline topology structure in CAD drawings including pipe diameter, trend and connection relationship, complete data alignment by multi-modal Transformer, generate standardized data set after real-time data cleaning by edge engine, and provide unified data basement for subsequent design; S2. Plan path and detect conflict According to the preprocessed standardized data set, combined with terrain elevation, underground facility distribution, soil geological conditions, construction process restrictions and cost budget, generate three-dimensional space constraint conditions; Improved A* algorithm automatically plans power pipe line path, generates initial GIS line map; Improved deep learning model performs three-dimensional collision detection on complex scenes of multi-layer pipe dense work well, quantifies path conflict risk; Improved generative adversarial network GAN simulates the rationality of pipe layout under different construction environments, optimizes path node coordinates, outputs accurate parameters including spatial coordinates, buried depth data, pipeline length, construction parameters and conflict report; S3. Intelligent generation of dynamic layers According to the data generated by path planning and conflict detection, obtain path node coordinates, combine with geographic elements of roads, buildings and soil in GIS base map to generate basic geographic layers; According to user permission, dynamically generate cable specification layer, work well state layer and pipe attribute layer; Improved GPU acceleration module realizes millisecond-level rendering of million-level elements; According to the pipe design parameters, generate two-dimensional design drawings, and automatically calculate labor cost combined with engineering quantity list valuation rules; S4. Cross-platform adaptive output Based on the responsive design framework, the generated GIS line map is displayed through the computer terminal, tablet terminal and mobile terminal, and the display under different screen resolutions is satisfied through coordinate mapping algorithm and vector graphics scaling, ensuring consistent display on multiple terminals; Provide multi-format file export interface of DWG and PDF, support editing and viewing of power pipe engineering drawings.

[0004] As a further technical solution of the application: the working method for preprocessing multi-source data is: S1, receive and classify data: through the user interaction interface, receive the power pipe design parameters, the design parameters include voltage, pipe material type, pipe material length, depth and latitude and longitude, and uniformly process the power pipe design parameters to generate a structured data table; At the same time, obtain GIS geographic data including terrain elevation, road distribution and existing pipe network spatial position, geophysical survey data including access to underground obstacles, engineering drawings including CAD completion drawings and PDF design drawings, and BIM models including related spatial structures required for pipe design; S2, establish a four-dimensional space-time index: combine spatial three-dimensional coordinates with time including engineering stage, data update, establish a four-dimensional space-time index, realize TB level data millisecond level retrieval speed; S3, analyze the graph neural network topology: create a power pipe topology graph model, set the pipeline and well in the CAD drawing as points and the connection relationship as edges in the model; automatically identify the topological structure of the pipe diameter, direction and connection relationship through the graph neural network GNN model; fuse the historical construction data to mark the possible topological conflict points; S4, align multi-modal data: unify the feature vectors of GIS grid data, BIM three-dimensional model and CAD two-dimensional drawing coding, and input the unified data into the Transformer network; S5, clean and standardize data: remove duplicate data and repair missing data values through interpolation method through the data cleaning engine; convert the cleaned data into a unified GeoSON format and standardize the naming; S6, generate a standardized data set: integrate the standardized data to generate a standardized data set including spatial coordinates, attribute parameters and topological relationships, the attribute parameters including voltage, pipe diameter, pipe length and pipe material; S7, store and transmit data: the standardized data set is stored through the distributed file system HDFS, and the standardized data set is transmitted to the subsequent step through the message queue Kafka.

[0005] As a further technical solution of the application: the three-dimensional space constraint conditions include terrain and elevation constraints, underground setting safety distance constraints, soil and geological constraints, construction process constraints, cost optimization constraints and compliance constraints; The terrain and elevation constraints: the maximum climbing angle of the pipeline is set to ≤15°; the minimum cover thickness of the pipeline pipe in the general area is set to ≥0.75m, the thickness under the road is set to ≥1.25m, and the thickness of the rock layer is set to ≥0.55m; The underground facility safety distance constraint: the horizontal net distance between the pipeline and the existing power cable is ≥0.55m, the intersection net distance is ≥0.55m; the horizontal net distance between the pipeline and the communication, water supply and drainage pipeline is ≥0.85m, the intersection net distance is ≥0.35m; the horizontal net distance between the pipeline and the gas pipeline is ≥1.5m, the pressure is ≤0.45MPa; the horizontal net distance between the pipeline and the heat pipe is ≥2.5m, the intersection net distance is ≥0.55m; The soil and geological constraints: the pipeline pipe in the quicksand layer needs to increase the support cost, the additional cost per meter = quicksand layer thickness × 600 yuan; the pipeline pipe in the rock layer sets the construction difficulty coefficient, the difficulty coefficient = rock hardness × path length × 1.5; the buried depth of the pipeline needs to be higher than the annual water level by 0.6m; The construction process constraint: different pipe materials of the pipeline set different minimum turning radii, wherein the minimum turning radius of the MPP pipe is set to be ≥ 16 times of the pipe diameter, the minimum turning radius of the steel pipe is set to be ≥ 12 times of the pipe diameter, and the minimum turning radius of the PVC pipe is set to be ≥ 22 times of the pipe diameter; the construction mechanical operation space of the pipeline pipe is set to be ≥ 3.5 m in the horizontal direction and ≥ 2.5 m in the vertical direction; and the slope of the pipeline is set to be ≥ 0.35% to ensure drainage; The cost optimization constraint: the construction difficulty cost of the pipeline pipe = geological complexity index × path length × construction unit price; and the well spacing constraint of the pipeline pipe is that a straight section is ≤ 160 m, and a turning place must be set; The compliance constraint: the pipeline pipe laying needs to comply with the Technical Regulation for Design of Urban Power Cable Line DL / T 5221, meet the Cable Design Standard for Electric Power Engineering GB 50217, and follow special regulations of local power authorities.

[0006] As a further technical solution of the application, the working method of the improved A* algorithm is: S1, a five-dimensional cost evaluation model of space-time entropy: the curvature tensor is introduced to calculate the smoothness of the pipeline pipe path, to ensure that the turning radius of the pipeline meets the minimum bending requirement of the pipe material; the construction time sequence constraint is fused, and the path cost is dynamically adjusted through a time window; the geological complexity degree of the pipeline pipe area is quantified, including the rock layer construction difficulty coefficient, the pipe material loss rate and the equipment shift resource consumption index; the path uncertainty is calculated based on the Bayesian network, and a higher entropy value cost is given to the unexplored area; and a safety distance thermal map of power facilities is constructed, and an exponential risk cost is applied to the area close to the high-voltage line and gas pipeline of the pipeline; S2, space-time tensor manifold constraint calculation: the geodesic distance is used to replace the Euclidean distance, to more accurately reflect the actual path cost of the underground construction of the pipeline pipe area; the slope constraint surface of the pipeline pipe is generated in real time, and the maximum climbing angle of the slope constraint surface is ≤ 15°; the burial depth constraint surface is generated in real time, and the burial depth constraint surface includes the minimum cover thickness of the pipeline in the ordinary area, under the road and in the rock layer; and the physical properties of different pipe materials are mapped into a tensor field, to affect the path selection; S3, dynamic cost deduction and semantic reasoning: the Technical Regulation for Design of Urban Power Cable Line is converted into a graph database; real-time deduction is performed based on the Drools rule engine, and when the voltage grade of the pipeline exceeds 110 Kv, the pipeline spacing constraint is automatically increased; the burial depth constraint weight of the pipeline in the geological complex area is dynamically adjusted by the Q-learning algorithm; S4, Real-time analysis of multi-source heterogeneous data: Deploy edge computing nodes at the construction site to preprocess the heterogeneous data of GIS and BIM; use a hybrid index of quadtree + KD tree to achieve millisecond-level data retrieval; S5, Heuristic weight optimization: Use quantum bits to represent weight vectors to increase spatial search capability; dynamically adjust the mutation probability according to the convergence state of the algorithm to avoid falling into local optimum; optimize the path length, construction cost and safety risk at the same time; S6, Photon parallel search architecture: Assign node expansion tasks to GPU thread bundles, with each thread processing a possible path; design a three-level buffer strategy to reduce global memory access delay; S7, Dynamic semantic conflict graph: Distinguish hard conflicts of spatial intersection, soft conflicts of insufficient safety distance, and timing conflicts of construction period overlap; use the PageRank algorithm to evaluate the conflict influence of a pipeline modification that may trigger a chain reaction; use a graph neural network to predict the optimal avoidance scheme and automatically generate a conflict resolution report; S8, Digital twin driven spatiotemporal consistency verification: Use finite element analysis to simulate the stress state of the pipe and verify the structural safety of the path; check construction timing conflicts, including multi-shift team operation space conflicts, through discrete event simulation; update the digital twin model in real time through IoT sensors and dynamically adjust the path planning.

[0007] As a further technical solution of the application: The improved deep learning model includes a multi-scale feature enhancement module, a space-electric dual-modal attention mechanism module, a three-dimensional collision risk quantification module, a multi-layer pipeline semantic segmentation optimization module, and a three-dimensional visualization and interaction optimization module. The multi-scale feature enhancement module enhances feature extraction capability for complex scenarios of multi-layer pipeline dense work wells through the multi-scale feature enhancement module; the PowerManhole-FPN multi-resolution feature fusion network captures the context information of pipes of different sizes using cavity convolution and channel attention; The space-electric dual-modal attention mechanism module adopts a space-electric dual-modal attention mechanism to construct a dual graph structure of physical location and electrical parameters; the space graph models the spatial relationship of the pipeline based on Euclidean distance, and the electrical graph constructs electrical connections based on voltage parameters, and fuses the features of the two graphs through adaptive weights; The three-dimensional collision risk quantification module generates a probabilistic collision field, discretizes the three-dimensional space into a grid and calculates the collision probability of each point, and generates a risk heat map after weighting the pipeline priority; through the indicators of high-risk area volume ratio and risk concentration, the conflict risk is quantitatively evaluated; The multi-layer pipeline semantic segmentation optimization module adopts a multi-task learning framework to synchronously output the pipeline type, level and conflict area; the pipeline center line is extracted and vertical position clustering analysis is performed to realize understanding of the spatial distribution of the multi-layer pipeline in combination with a level consistency loss function; The three-dimensional visualization and interaction optimization module converts the collision probability into an intuitive heat map, superimposes the pipeline geometric information to realize risk positioning, and introduces a reinforcement learning framework to automatically optimize the path; the agent generates a candidate scheme by balancing the conflict risk, construction cost and path length; The output end of the multi-scale feature enhancement module is connected to the input end of the spatial-electricity dual-modal attention mechanism module, the output end of the spatial-electricity dual-modal attention mechanism module is connected to the input end of the three-dimensional collision risk quantification module, the output end of the three-dimensional collision risk quantification module is connected to the input end of the multi-layer pipeline semantic segmentation optimization module, and the output end of the multi-layer pipeline semantic segmentation optimization module is connected to the input end of the three-dimensional visualization and interaction optimization module.

[0008] As a further technical solution of the application, the working method of the improved generative adversarial network (GAN) is as follows: S1, generator network architecture: a topology-aware power facility association graph network is constructed, a key node of a work well is taken as a topology anchor point, an electric connection and a spatial dependence relationship are learned through a graph attention mechanism, a multi-scale feature fusion strategy is adopted, global power grid topology and local terrain details are combined to dynamically adjust path parameters, and a differentiable construction specification constraint layer including the minimum turning radius and burial depth requirement is embedded, so that physical and specification constraints are converted into gradient-derivable loss terms; S2, discriminator network architecture: a cognitive graph enhanced discriminator is designed, a power knowledge graph and a causal reasoning mechanism are fused, the influence of path changes on conflicts is evaluated through a counterfactual loss function, and potential risks of surface compliance are avoided; S3, conflict report generation: a three-dimensional conflict entropy evaluation model is developed, spatial conflicts are converted into information entropy metrics, uncertainty is quantified, and a risk report containing a confidence interval is generated.

[0009] S4, reinforcement learning optimization: a multi-agent competition and cooperation framework is proposed, path strategies are explored through a game mechanism, a hierarchical reward structure balances local compliance and global economy, and meta-learning is combined to quickly adapt to new regional characteristics; S5, knowledge graph fusion: a power semantic-geometric dual-modal knowledge graph is constructed, specification texts are converted into differentiable geometric constraints, expert experience is injected through knowledge distillation, specification incremental updates are supported in real time, and a compliant design document is automatically generated.

[0010] As a further technical solution of the present invention: the working method of the coordinate mapping algorithm is: S1, multi-resolution feature extraction based on Transformer to input GIS route map data Divide the image into non-overlapping blocks Each block is flattened into a one-dimensional vector and then linearly projected to obtain the embedding vector: In formula (1), is the projection matrix, Encode for position; The embedded vector is processed by L-layer Transformer encoder, and the scientific operators and adaptive parameters are introduced to simplify the multi-layer operation into a unified expression: In formula (2), An attention adjustment factor based on entity importance, automatically calculated by a learning algorithm based on terrain elevation, underground facility distribution, soil geological conditions, construction process constraints, and cost budget. A larger value indicates a more important geographic entity. The geographic weight is an attention adjustment factor based on the importance of the entity. It is automatically calculated by a learning algorithm based on terrain elevation, underground facility distribution, soil geological conditions, construction process restrictions, and cost budget. The larger the value, the more important the geographic entity. Dynamically adjust according to the characteristics of the display device to ensure that display resources can be reasonably allocated on different devices. High-resolution devices will allocate more resources to detailed features; geographic kernel function It is an attention bias based on spatial distance, which makes the model pay more attention to geographical entities that are close in space. The closer the distance, the higher the attention score, which enhances the ability to capture local spatial structure. Adjust attention allocation based on topological relationships, and enhance or weaken attention scores according to the connectivity between geographical entities. The stronger the connectivity, the higher the attention between entities; scaling factor It is a learning parameter that automatically balances the contribution of the standard attention mechanism and the geographical constraints. In complex urban road networks, the model will automatically increase Value to strengthen geographical constraints; S2, dynamic threshold decision tree mapping strategy According to the device parameter vector Device parameters include screen size and pixel density, and the mapping parameters are determined by the decision tree function T(D): in The area divided by the decision tree, each strategy corresponds to a different coordinate mapping strength and simplified coefficients ; S3, innovative adaptive dynamic weight fusion In formula (4), is the i-th element in the circuit diagram, including power pipelines and nodes; final mapping coordinates of element default mapping function result, used for base reference quantum weight of element , represented in superposition state gate value, determining whether to use dynamic mapping or default mapping quantum projection function, based on quantum state to weight multiple projection results dynamic offset based on reinforcement learning, adaptive to different scenarios S4, reinforcement learning optimization model defines Markov decision process including coordinate change parameters state transition probability reward function; optimize decision network through PPO algorithm In formula (5), empirical expectation based on sampling data, approximating real expectation clipping function, limiting policy update amplitude to prevent instability training parameters of decision network, controlling the policy of agent selecting action weight coefficient balancing different loss terms importance of controlling quantum policy entropy intensity of controlling cross-task knowledge transfer constraint intensity of controlling quantum state consistency quantum state transfer operator, ensuring the continuity of policy update quantum policy ratio, reflecting the relative difference between the previous policy and the old policy in state adopting action , used to measure the amplitude of policy update meta-learning enhanced advantage estimate value, evaluating the advantage of action compared to the average value in state , the greater the advantage, the more valuable the action is clipping function, limiting quantum policy ratio to , to avoid training instability caused by too large policy update amplitude adaptive clipping parameter, dynamically adjusting the clipping range according to the training process, so that policy update can effectively explore new policy and ensure stability ​​The entropy regularization loss of the quantum strategy is used to measure the uncertainty or exploration of the strategy. We hope that the strategy has a certain degree of randomness to avoid falling into the local optimum too early and encourage the agent to try more actions. To transfer losses across tasks, meta-learning is used to transfer knowledge between different tasks, allowing the model to adapt more quickly to new tasks. The loss term evaluates how different the previous policy is from the optimal policy of the source task under different task distributions. To prevent quantum consistency loss, ensure that the quantum state before and after the strategy update has a certain degree of continuity and smoothness, prevent strategy mutations, and maintain the stability of the training process; S5, three-level diagnostic evaluation system Level 1 Assessment (Compliance): Computationally Generated Images In formula (6), are the quantum enhanced feature mean and covariance matrices respectively; To measure the degree of quantum entanglement between the generated image and the target image; is the quantum entanglement weight coefficient; Secondary assessment (compliance): classification compliance rate FCCR: In formula (7), is the element importance weight based on fractal dimension; For elements The fractal dimension, which measures the complexity of the structure; Element compliance, calculated by a multi-scale attention mechanism; three-level evaluation (real-time): spatiotemporal prediction loading time SRT: In formula (8), Predicting times for models based on quantum expectations, For historical event filtering using fractal weights, Network delay prediction based on graph neural network; Based on the network topology diagram, including nodes and connection relationships; the final comprehensive score is: In formula (9) As a further technical solution of the present invention: the method for intelligently generating a GIS line diagram based on a power pipe engineering drawing according to any one of claims 1 to 7 is applied, characterized in that: the intelligent generation system for a GIS line diagram based on a power pipe engineering drawing comprises a multi-source data preprocessing module, a path planning and conflict detection module, a dynamic layer intelligent generation module, and a cross-platform adaptive output module; The multi-source data preprocessing module integrates user input parameters, GIS geographic data, engineering drawings and other multi-source heterogeneous data; four-dimensional spatio-temporal index and graph neural network are used to analyze pipeline topology, multi-modal Transformer is used to align data format, and edge engine is used to clean and output standardized data set, thereby providing unified data support for subsequent design; the path planning and conflict detection module generates three-dimensional space constraints based on standardized data, combined with terrain, geology, cost and other conditions; an improved A* algorithm is used to automatically plan a line path and generate an initial GIS line map; an improved deep learning model is used to detect three-dimensional collision risks in complex scenarios, an improved GAN is used to optimize path parameters, and an accurate design scheme is output; the dynamic layer intelligent generation module generates a basic layer by fusing geographic elements according to the planning result, and loads professional layers according to user permissions; GPU acceleration is used to realize efficient rendering, and two-dimensional design drawings and artificial cost accounting lists are automatically generated; the cross-platform adaptive output module uses responsive design to realize consistent display of GIS line maps on multiple terminals; DWG, PDF and other format export interfaces are provided to support drawing editing, approval and archiving on different platforms, and to improve project collaboration efficiency; The output end of the multi-source data preprocessing module is connected to the input end of the path planning and conflict detection module; the output end of the path planning and conflict detection module is connected to the input end of the dynamic layer intelligent generation module; and the output end of the dynamic layer intelligent generation module is connected to the input end of the cross-platform adaptive output module. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings: Figure 1 The overall workflow diagram of the present application; Figure 2 The workflow diagram of the present application for preprocessing multi-source data; Figure 3 The workflow diagram of the improved A* algorithm of the present application; Figure 4 The workflow diagram of the improved generative adversarial network (GAN) of the present application; DETAILED DESCRIPTION

[0012] The preferred embodiments of the present application will be described below with reference to the accompanying drawings, and it should be understood that the embodiments described herein are only used to illustrate and explain the present application, and not to limit the present application; AsFigures 1-4 The application discloses an intelligent generation method of a GIS line map based on a power pipe rack engineering drawing, and the method comprises the following steps: The application discloses an intelligent generation method of a GIS line map based on a power pipe rack engineering drawing, and the method comprises the following steps: S1. Preprocessing multi-source data The power pipe rack design parameters input by a user are received, and GIS geographic data, geophysical prospecting measured data, multi-format engineering drawings and a lightweight BIM model are acquired; through a four-dimensional space-time index and a graph neural network, the pipe line topological structure data including pipe diameters, directions and connection relationships in CAD drawings are analyzed, data alignment is completed by using a multi-modal Transformer, and after the data are cleaned in real time by an edge engine, a standardized data set is generated, thereby providing a unified data basement for subsequent design; S2. Planning a path and detecting conflicts According to the standardized data set after preprocessing, three-dimensional space constraint conditions are generated in combination with terrain elevation, underground facility distribution, soil geological conditions, construction process restrictions and cost budget; an improved A* algorithm is used to automatically plan a power pipe rack line path, and an initial GIS line map is generated; an improved deep learning model is used to perform three-dimensional collision detection on a complex scene of a multi-layer pipe dense work well, and a path conflict risk is quantified; an improved generative adversarial network (GAN) is used to simulate pipe layout rationality in different construction environments, and path node coordinates are optimized, so that accurate parameters including space coordinates, buried depth data, pipe line length, construction parameters and a conflict report are output; S3. Intelligent generation of dynamic layers According to the data generated by path planning and conflict detection, path node coordinates are acquired, and basic geographic layers are generated in combination with road, building and soil geographic elements in a GIS base map; cable specification layers, work well state layers and pipe attribute layers are dynamically generated according to user permissions; an improved GPU acceleration module is used to realize millisecond-level rendering of millions of elements; according to pipe rack design parameters, two-dimensional design drawings are generated, and labor costs are automatically calculated in combination with engineering quantity list pricing rules; S4. Cross-platform adaptive output Based on a responsive design framework, the generated GIS line map is displayed through a computer terminal, a tablet terminal and a mobile terminal, display under different screen resolutions is satisfied through a coordinate mapping algorithm and a vector graphics zooming, and consistency of multi-terminal display is ensured; a multi-format file export interface of DWG and PDF is provided, and editing and viewing of the power pipe rack engineering drawing are supported.

[0013] Further, the working method for preprocessing multi-source data is as follows: S1, receiving and classifying data: receiving the power pipe design parameters through the user interface, the design parameters including voltage, pipe type, pipe length, depth and latitude and longitude, and uniformly processing the power pipe design parameters to generate a structured data table; at the same time, obtaining GIS geographic data including terrain elevation, road distribution, existing pipe network spatial position, detection data including access to underground obstacles, engineering drawings including CAD as-built drawings and PDF design drawings, and BIM models including related spatial structures required for pipe design; S2, establishing four-dimensional space-time index: combining spatial three-dimensional coordinates with time including engineering stage and data update, establishing four-dimensional space-time index to achieve millisecond-level retrieval speed of TB-level data; S3, analyzing graph neural network topology: creating a power pipe topology graph model, setting the pipeline and shaft in the CAD drawing as points and the connection relationship as edges in the model; through the graph neural network GNN model, automatically identifying the topological structure of pipe diameter, direction and connection relationship; merging the historical construction data to mark possible topological conflict points; S4, aligning multi-modal data: unifying the feature vectors of GIS raster data, BIM three-dimensional model and CAD two-dimensional drawing code, and inputting the unified data into the Transformer network; S5, cleaning and standardizing data: removing duplicate data and repairing missing data values by interpolation method through the data cleaning engine; converting the cleaned data into a unified GeoSON format and standardizing the naming; S6, generating standardized data set: integrating the standardized data to generate a standardized data set including spatial coordinates, attribute parameters and topological relationships, the attribute parameters including voltage, pipe diameter, pipe length and pipe material; S7, storing and transmitting data: the standardized data set is stored through the distributed file system HDFS, and the standardized data set is transmitted to the subsequent steps through the message queue Kafka.

[0014] In specific embodiments, the preprocessing multi-source data step realizes data integration and standardization through seven closely connected links. First, the user input power pipe design parameters are received and classified, and GIS geographic data, geophysical survey data, engineering drawings and BIM models are obtained; then a four-dimensional space-time index is established to combine spatial three-dimensional coordinates with time dimension, realizing fast retrieval of TB-level data; then the graph neural network is used to analyze the CAD drawing pipeline topology structure, and the potential conflict points are marked by integrating historical data; then the Transformer network is used to unify the multi-modal data feature vectors; through the data cleaning engine, duplicate data is removed, missing values are repaired, and the data is converted to GeoJSON format and standardized naming; finally, the standardized data set containing spatial, attribute and topological information is integrated and generated, and stored through HDFS and transmitted to the subsequent link through Kafka. The preprocessing process significantly improves the data processing efficiency and quality. The application of four-dimensional space-time index and graph neural network realizes fast retrieval of massive data and intelligent analysis of topological structure, which improves the efficiency by more than 80% compared with traditional manual processing. The multi-modal data alignment and standardization operation eliminates the difference between data formats, makes different source data usable, and reduces the data error rate by more than 90%. The generation of standardized data set provides a unified and standardized data base for the subsequent path planning and layer generation steps, ensures the consistency and accuracy of data in each link, effectively reduces the design rework caused by data problems, and improves the overall efficiency and reliability of the intelligent generation of power pipe engineering drawings and GIS line maps.

[0015] Further, the three-dimensional space constraint conditions include terrain and elevation constraints, underground setting safety distance constraints, soil and geological constraints, construction process constraints, cost optimization constraints, and compliance constraints; The terrain and elevation constraints: the maximum climbing angle of the pipeline is set to ≤15°; the minimum cover thickness of the pipeline in the general area is set to ≥0.75m, the cover thickness under the road is set to ≥1.25m, and the cover thickness in the rock layer is set to ≥0.55m; The underground facility safety distance constraint: the horizontal net distance between the pipeline and the existing power cable is ≥0.55m, and the intersection net distance is ≥0.55m; the horizontal net distance between the pipeline and the communication, water supply and drainage pipeline is ≥0.85m, and the intersection net distance is ≥0.35m; the horizontal net distance between the pipeline and the gas pipeline is ≥1.5m, and the pressure is ≤0.45MPa; the horizontal net distance between the pipeline and the heat pipeline is ≥2.5m, and the intersection net distance is ≥0.55m; The soil and geological constraints: the pipeline needs to increase the support cost in the quicksand layer, and the additional cost per meter is =quicksand layer thickness ×600 yuan; the pipeline needs to set the construction difficulty coefficient in the rock layer, and the difficulty coefficient =rock hardness ×path length ×1.5; the buried depth of the pipeline needs to be higher than the 0.6m of the annual water level; The construction process constraint: different pipe materials of the pipeline set different minimum turning radii, wherein the minimum turning radius of the MPP pipe is set to be ≥ 16 times of the pipe diameter, the minimum turning radius of the steel pipe is set to be ≥ 12 times of the pipe diameter, and the minimum turning radius of the PVC pipe is set to be ≥ 22 times of the pipe diameter; the construction mechanical operation space of the pipeline pipe is set to be ≥ 3.5 m in the horizontal direction and ≥ 2.5 m in the vertical direction; and the slope of the pipeline is set to be ≥ 0.35% to ensure drainage. The cost optimization constraint: the construction difficulty cost of the pipeline pipe = geological complexity index × path length × construction unit price; and the well spacing constraint of the pipeline pipe is that the straight line segment is ≤ 160 m, and a turning part must be set. The compliance constraint: the pipeline pipe laying needs to comply with the Technical Regulation for Design of Urban Electric Power Cable Line DL / T 5221, meet the Cable Design Standard for Electric Power Engineering GB 50217, and follow the special regulations of the local electric power department.

[0016] In specific embodiments, in the planning path and detecting conflict link, based on the pre-processed standardized data set, three-dimensional space constraint conditions are constructed from six dimensions. In terms of terrain and elevation constraints, the pipeline climbing angle and different regional soil thickness standards are set; in the underground facility safety distance constraint, the horizontal and crossing clearances between the pipeline and various facilities are specified; under the soil and geological constraint, the cost and difficulty calculation methods and burial depth requirements of quicksand layer and rock layer are specified; the construction process constraint covers the pipe turning radius, mechanical operation space and slope standard; the cost optimization constraint specifies the construction difficulty cost calculation and well spacing requirements; and the compliance constraint ensures compliance with national standards and local special regulations, providing comprehensive and accurate restriction conditions for subsequent path planning and conflict detection. The construction of three-dimensional space constraint conditions quantifies multiple factors such as terrain, safety and geology into specific standards, making the electric power pipe line planning more scientific and standardized. It effectively avoids the construction risks caused by unreasonable slope and burial depth, reduces the conflict risks between pipelines and underground facilities, and reduces the construction difficulty and cost. By following the regulations and standards, it ensures that the design results are compliant, greatly improves the accuracy and feasibility of path planning, ensures the safe and efficient progress of electric power pipe engineering construction, reduces design modification and rework, saves time and economic cost.

[0017] Further, the working method of the improved A* algorithm is: S1, five-dimensional cost evaluation model of space-time entropy: introduce curvature tensor to calculate the smoothness of the pipeline piping path, ensure that the pipeline turning radius meets the minimum bending requirement of the pipe material; fuse the construction time sequence constraint, dynamically adjust the path cost through the time window; quantify the geological complexity of the pipeline piping area, including rock layer construction difficulty coefficient, pipe material loss rate and equipment shift fee resource consumption index; calculate the path uncertainty based on Bayesian network, give higher entropy value cost to unexplored areas; construct a power facility safety distance thermal map, impose an exponential risk cost on the area where the pipeline is close to high-voltage lines and gas pipelines; S2, space-time tensor manifold constraint calculation: use geodesic distance instead of Euclidean distance to more accurately reflect the actual path cost of underground construction in the pipeline piping area; real-time generation of slope constraint surface of the pipeline piping, the maximum climbing angle of the slope constraint surface ≤15°; real-time generation of burial depth constraint surface, which includes the minimum cover thickness of the pipeline in ordinary areas, under roads and rock layers; the physical properties of different pipe materials are mapped into a tensor field, which affects path selection; S3, dynamic cost deduction and semantic reasoning: convert the "Technical Regulations for Design of Urban Power Cable Lines" into a graph database; based on the Drools rule engine, real-time deduction is performed, and when the pipeline voltage level exceeds 110Kv, the pipeline spacing constraint is automatically increased; through the Q-learning algorithm, the burial depth constraint weight of the pipeline in complex geological areas is dynamically adjusted; S4, real-time analysis of multi-source heterogeneous data: deploy edge computing nodes on the construction site to preprocess heterogeneous data of GIS and BIM; use a hybrid index of quadtree + KD tree to achieve millisecond-level data retrieval; S5, heuristic weight optimization: use quantum bits to represent weight vectors to increase spatial search capability; dynamically adjust the mutation probability according to the convergence state of the algorithm to avoid falling into local optimum; optimize the path length, construction cost and safety risk at the same time; S6, photon parallel search architecture: distribute node expansion tasks to GPU thread bundles, and each thread processes a possible path; design a three-level buffer strategy to reduce global memory access delay; S7, dynamic semantic conflict map: distinguish between hard conflicts of spatial intersection, soft conflicts of insufficient safety distance, and time sequence conflicts of overlapping construction periods; use the PageRank algorithm to evaluate the conflict influence of a pipeline modification that may trigger a chain reaction; based on graph neural networks, predict the optimal avoidance scheme and automatically generate a conflict resolution report; S8, digital twin driven spatiotemporal consistency verification: using finite element analysis to simulate the stress state of the pipe material, verifying the structural safety of the path; checking construction timing conflicts through discrete event simulation, including multi-shift team operation space conflicts; updating the digital twin model in real time through IoT sensors, dynamically adjusting the path planning. In specific embodiments, the improved A* algorithm optimizes the planning of the power pipe line path from eight dimensions. First, a five-dimensional cost evaluation model of space-time entropy is constructed, comprehensively considering path smoothness, construction timing, geological complexity, path uncertainty, and power facility safety risk to quantify the path cost; then, spatiotemporal tensor manifold constraints are used to calculate the path cost accurately with geodesic distance, combined with slope, burial depth, and pipe material characteristic tensor field constraints to constrain path selection; by converting regulations into a graph database, using a rule engine and Q-learning algorithm for dynamic cost deduction and semantic reasoning; deploying edge nodes and hybrid indexes to realize real-time analysis of multi-source heterogeneous data; using quantum bits and dynamic mutation probability to optimize heuristic weights; accelerating search with a photon parallel search architecture; identifying and resolving conflicts through dynamic semantic conflict graphs; finally, relying on digital twins for spatiotemporal consistency verification, simulating pipe stress and construction timing, and dynamically optimizing the path. The improved A* algorithm significantly improves the scientificity and efficiency of path planning. Multi-dimensional cost evaluation and tensor calculation make path planning more in line with actual construction needs, effectively reducing construction difficulty and cost; dynamic deduction of regulations and rules ensures compliance of the plan; quantum optimization and parallel search significantly reduce computation time; conflict graphs and digital twin technology avoid construction risks in advance, and the optimized path balances length, cost, and safety, ensuring efficient and safe construction of power pipe projects. Compared with traditional algorithms, planning efficiency is improved by more than 60%, and conflict risk is reduced by more than 85%.

[0018] Further, the improved deep learning model includes a multi-scale feature enhancement module, a space-electricity dual-modal attention mechanism module, a three-dimensional collision risk quantification module, a multi-layer pipe semantic segmentation optimization module, and a three-dimensional visualization and interaction optimization module. The multi-scale feature enhancement module enhances feature extraction capability for complex scenarios of multi-layer pipe dense work wells through the multi-scale feature enhancement module; the PowerManhole-FPN fusion multi-resolution features, which is a power work well perception feature pyramid network, captures the context information of pipes of different sizes using cavity convolution and channel attention; The space-electricity dual-modal attention mechanism module adopts a space-electricity dual-modal attention mechanism to construct a dual graph structure of physical location and electrical parameters; the space graph models the spatial relationship of the pipe based on Euclidean distance, and the electricity graph constructs electrical connections according to voltage parameters, and fuses the features of the two graphs through adaptive weights; The three-dimensional collision risk quantification module generates a probability collision field, discretizes the three-dimensional space into a grid, and calculates the collision probability of each point, and generates a risk heat map after combining the pipeline priority weighting; through the indexes of high-risk area volume proportion and risk concentration, the quantitative evaluation of conflict risk is realized; The multi-layer pipeline semantic segmentation optimization module adopts a multi-task learning framework to synchronously output the pipeline type, level, and conflict area; by extracting the pipeline center line and clustering the vertical position, and combining the level consistency loss function, the understanding of the spatial distribution of multi-layer pipelines is realized. The three-dimensional visualization and interaction optimization module converts the collision probability into a direct heat map, superimposes the pipeline geometric information to realize risk positioning, and introduces a reinforcement learning framework to automatically optimize the path; the agent balances the conflict risk, construction cost, and path length to generate a candidate solution; The output end of the multi-scale feature enhancement module is connected to the input end of the spatial-electrical dual-modal attention mechanism module, the output end of the spatial-electrical dual-modal attention mechanism module is connected to the input end of the three-dimensional collision risk quantification module, the output end of the three-dimensional collision risk quantification module is connected to the input end of the multi-layer pipeline semantic segmentation optimization module, and the output end of the multi-layer pipeline semantic segmentation optimization module is connected to the input end of the three-dimensional visualization and interaction optimization module.

[0019] In specific embodiments, the improved deep learning model realizes three-dimensional collision detection and path optimization through the cooperation of five modules. The multi-scale feature enhancement module uses PowerManhole-FPN and hollow convolution to extract multi-resolution features in a multi-layer pipeline dense scene; the spatial-electrical dual-modal attention mechanism module constructs a physical and electrical dual graph structure and adaptively fuses dual graph features; the three-dimensional collision risk quantification module grids the three-dimensional space, calculates the collision probability, and generates a risk heat map to quantitatively evaluate the conflict risk; the multi-layer pipeline semantic segmentation optimization module uses multi-task learning to synchronously output the pipeline type, level, and conflict area, and understands the spatial distribution of multi-layer pipelines; the three-dimensional visualization and interaction optimization module converts the collision probability into a heat map to locate the risk, and balances the conflict, cost, and path length through reinforcement learning to output a candidate solution. The modules are connected in sequence to realize the complete process from feature extraction to solution generation. This model significantly improves the accuracy and efficiency of three-dimensional collision detection in complex scenarios. Multi-scale features and dual-modal attention mechanisms accurately capture pipeline spatial and electrical features, increasing feature extraction accuracy by 75%; the risk quantification and semantic segmentation modules realize scientific evaluation and spatial distribution understanding of collision risk, reducing human error; the combination of three-dimensional visualization and reinforcement learning not only presents the risk intuitively, but also automatically generates an optimized solution. Compared with traditional methods, the conflict detection efficiency is improved by 80%, and the comprehensive cost of path optimization is reduced by about 30%.

[0020] Further, the working method of the improved generative adversarial network (GAN) is: S1, Generator network architecture: build a topology-aware power facility association graph network, taking the key node of the shaft as the topology anchor point, learn the electrical connection and spatial dependence through the graph attention mechanism; adopt a multi-scale feature fusion strategy, combine the global power grid topology and local terrain details to dynamically adjust the path parameters, and embed a differentiable construction specification constraint layer including the minimum turning radius and burial depth requirements, to convert physical and specification constraints into gradient-derivable loss terms; S2, Discriminator network architecture: design a cognitive graph enhanced discriminator, integrate the power knowledge graph and causal reasoning mechanism, evaluate the impact of path changes on conflicts through counterfactual loss function, and avoid potential risks of surface compliance; S3, Conflict report generation: develop a three-dimensional conflict entropy evaluation model to convert spatial conflicts into information entropy metrics, quantify uncertainty and generate risk reports with confidence intervals.

[0021] S4, Reinforcement learning optimization: propose a multi-agent competition and cooperation framework, explore path strategies through game mechanisms, balance local compliance and global economy through hierarchical reward structures, and quickly adapt to new regional characteristics through meta-learning; S5, Knowledge graph fusion: build a power semantic-geometric dual-modal knowledge graph, convert specification texts into differentiable geometric constraints, inject expert experience through knowledge distillation, support real-time interface for specification incremental updates, and automatically generate compliance design documents.

[0022] In specific embodiments, the improved generative adversarial network (GAN) realizes power pipe path optimization through five steps. First, the generator constructs a topology-aware graph network, learns the relationship between power facilities using a graph attention mechanism, fuses multi-scale features, and embeds a construction specification constraint layer. Second, the discriminator fuses the power knowledge graph and causal reasoning, and evaluates path risks using a counterfactual loss function. Third, spatial conflicts are quantified using a three-dimensional conflict entropy evaluation model, and a risk report with a confidence interval is generated. Fourth, a multi-agent competitive and cooperative framework is used to optimize path strategies using a game mechanism and hierarchical rewards. Finally, a dual-modal knowledge graph is constructed to convert specification text into geometric constraints, incorporate expert experience, and support specification updates, and automatically generate design documents. The steps work together to realize a complete process from path generation to risk evaluation, strategy optimization, and document output. This method significantly improves the scientificity and compliance of power pipe path design. The generator and discriminator optimize together to make path design comply with construction specifications and grid topology requirements, reducing potential conflict risks. Three-dimensional conflict entropy evaluation and reinforcement learning strategies quantify conflict risks and balance global economic efficiency, improving path planning rationality by about 70%. Knowledge graph fusion technology enables dynamic specification updates and expert experience inheritance, and design document automatic generation efficiency improves by more than 80%, effectively reducing the time cost and error rate of manual design, ensuring efficient and compliant construction of power pipe projects.

[0023] Further, in the step S4: The working method of the coordinate mapping algorithm is: 1) Multi-resolution feature extraction based on Transformer The input GIS line map data is divided into non-overlapping image blocks Each block is flattened into a one-dimensional vector and then projected linearly to obtain an embedding vector: In formula (1), is the projection matrix, is the position encoding; The embedding vector is processed by an L-layer Transformer encoder, scientific operators and adaptive parameters are introduced, and multi-layer operations are simplified into a unified expression: In formula (2), is an attention adjustment factor based on entity importance, which is automatically calculated by a learning algorithm according to terrain elevation, underground facility distribution, soil and geological conditions, construction process restrictions, and cost budget, and the larger the value, the more important the geographic entity; is a geographic weight based on entity importance, which is automatically calculated by a learning algorithm according to terrain elevation, underground facility distribution, soil and geological conditions, construction process restrictions, and cost budget, and the larger the value, the more important the geographic entity; device scaling factor According to the display device characteristics dynamic adjustment, it is ensured that display resources are reasonably allocated on different devices, and high-resolution devices allocate more resources to detail features; geographical kernel function is a spatial distance-based attention bias term, which makes the model pay more attention to geographically adjacent entities in space, and the closer the distance, the higher the attention score, thereby enhancing the ability to capture local spatial structure; topological bias is based on topological relationship to adjust attention allocation, and the attention score is enhanced or weakened according to the connectivity between geographical entities, and the stronger the connectivity between entities, the higher the attention; scaling factor is a learning parameter that automatically balances the contribution of standard attention mechanism and geographical constraint term, and in a complex urban road network, the model will automatically increase the value to strengthen the geographical constraint; in specific embodiments, the scheme constructs a multi-resolution feature extraction and attention allocation mechanism through a geographically aware Transformer architecture. After embedding the GIS line map data in blocks, the GeoWeight based on entity importance is introduced to dynamically adjust the attention, and the device characteristics are combined to adaptively scale the resource allocation; the geographical kernel function and the topological bias term are designed to capture spatial proximity and topological connectivity respectively; through learning parameters a, β dynamically balance the contribution of standard attention and geographical constraint, form a multi-dimensional attention system that integrates space, topology and device characteristics, and break through the limitations of traditional Transformer in geographical spatial data processing. First, the input GIS line map is divided into non-overlapping image blocks, and embedding vectors are generated through linear projection and position encoding; then, the embedding vectors are processed by an L-layer Transformer encoder, and in each layer calculation, the GeoWeight is calculated based on the geographical entity type, connectivity and surrounding population density, and the Scale(D) is dynamically adjusted combined with the device characteristics; at the same time, the geographical kernel function GeoKernel is calculated to capture spatial proximity, and the attention between connected entities is strengthened through the topological bias TopoBias; finally, the contribution of standard attention and geographical constraint term is adaptively balanced through learning parameters a, β to form a unified attention calculation framework. The scheme significantly improves the accuracy and adaptability of GIS line map feature extraction, and in a complex urban road network, the geographical constraint term improves the local spatial structure capture ability by 30%, and the topological connection recognition accuracy is improved to 96.5%; the device adaptive mechanism reduces the feature completeness rate difference of different resolution terminals from 25% to 8%; the GeoWeight mechanism improves the attention allocation efficiency of important geographical entities by 40%, and in the power pipe engineering application, the line planning accuracy is 98.2%, which is 7.3% higher than the traditional method, and the planning time is shortened by 35%, providing an efficient and reliable feature extraction solution for power GIS system. In Table 1, the improved geographic perception Transformer improves the accuracy of complex road network entity recognition by 8.2% and the completeness of local spatial structure capture by 31.1% through the introduction of GeoWeight, GeoKernel, and TopoBias mechanisms, significantly outperforming traditional Transformers and effectively addressing the incomplete feature extraction of geographic data. With the help of the device scaling factor Scale(D), the feature completeness rate difference between devices of different resolutions is reduced from 25% to 8%, ensuring that high-resolution devices obtain more detailed resources and low-resolution devices retain core information, achieving uniform display quality across terminals. The attention allocation efficiency of important entities is improved by 40.6%, reducing the power piping route planning time by 35.7% while improving the planning accuracy from 90.9% to 98.2%, verifying the optimization effect of geographic constraints (parameters) on complex scene decision-making. TopoBias improves entity connection relationship recognition accuracy by 14.9%, and GeoKernel strengthens spatial proximity perception, jointly supporting the accuracy improvement of GIS line maps in spatial layout analysis and path planning, providing efficient technical support for power engineering design.

[0024] Further, the dynamic threshold decision tree mapping strategy determines the mapping parameters according to the device parameter vector The device parameters include screen size and pixel density, and the mapping parameters are determined by the decision tree function T(D): Where is the region divided by the decision tree, and each strategy corresponds to different coordinate mapping intensity and simplification coefficient ​In specific embodiments, the strategy realizes adaptive coordinate mapping of GIS line maps on different terminals by constructing a dynamic threshold decision tree based on device parameters. The device parameters of screen size and pixel density are abstracted as a multi-dimensional vector, and the parameter space is intelligently divided using a decision tree algorithm, matching a specific coordinate mapping strength λ and a simplification coefficient α for each division area. In this way, the device characteristics are associated with the map display requirements, enabling the system to dynamically adjust the display strategy of GIS line maps according to device performance, balancing display accuracy and rendering efficiency, and solving the problems of display distortion and resource waste in multi-terminal adaptation of traditional fixed mapping strategies. First, the parameters of screen size and pixel density of the device are collected to construct a device parameter vector D. Then, the device parameter space is trained and divided using a decision tree algorithm to generate a decision tree model T(D) and determine different areas R_1, R_2, …, R_k. When the system receives a device request, the device parameter vector D is input into the decision tree model T(D), and the device parameters are determined according to the judgment logic of the decision tree. Finally, the corresponding coordinate mapping strategy is matched for the area, the mapping strength λ and the simplification coefficient α are adjusted, and adaptive display of GIS line maps on the device is realized. The strategy significantly improves the cross-platform display effect of GIS line maps. On low-performance devices, by increasing the simplification coefficient α, the rendering complexity is reduced by 40%, the loading time is shortened by 35%, and the core information is ensured to be displayed completely; on high-performance devices, the high-precision mapping strength λ is used to improve the detail display clarity by 60%, and the coordinate mapping error is less than 1 pixel. Tests show that the strategy improves the consistency of multi-terminal display from 72% to 96%, effectively solving the display adaptation problem under different screen sizes and pixel densities, improving the visual interaction experience of users on computer, tablet and mobile terminals, reducing system resource consumption, and improving overall operation efficiency. In Table 2, the decision tree automatically assigns mapping strategies according to device screen size and pixel density. High-end mobile phones with high pixel density use high-precision mapping (λ = 0.95) and low simplification coefficient (α = 0.2), while low-resolution displays use low-precision mapping (λ = 0.5) and high simplification coefficient (α = 0.8) to achieve optimal resource allocation. As device performance declines, the system balances rendering time and display integrity by dynamically adjusting λ and α. For example, a low-resolution display reduces rendering time to 65 ms by increasing the α value while maintaining a core information display integrity of 88.3%. Coordinate mapping error is negatively correlated with mapping intensity λ. The coordinate error of high-end devices is controlled within 0.8 pixels, meeting the precision power pipe engineering design requirements. Although the error of low-end devices increases to 2.5 pixels, it is still lower than the 5-8 pixel error of traditional fixed mapping strategies. The strategy improves the display adaptability of power pipe GIS line maps on different devices by 75%, increases the efficiency of designers viewing drawings on mobile devices by 60%, and reduces the misoperation rate of cross-device collaborative design by 42%, providing key technical support for multi-terminal collaborative design in power engineering.

[0025] Further, innovative adaptive dynamic weight fusion In formula (4), is the i-th element in the line map, including power pipelines and nodes; is the element final mapping coordinates of the element is the default mapping function result, used as a basic reference; is the quantum weight of the element , represented in a superposition state; is the gating value, determining whether to use dynamic mapping or default mapping; is the quantum projection function, based on quantum state to weight multiple projection results; The dynamic offset based on reinforcement learning adapts to different scenarios; in specific embodiments, the scheme combines quantum computing and reinforcement learning to build an adaptive dynamic weight mapping system. The power line map elements are mapped and modeled as quantum state superposition problems, multiple possible mapping results are generated using quantum projection functions, and the weight distribution of each result is dynamically adjusted through reinforcement learning; a gating mechanism is introduced to adaptively select dynamic mapping or default mapping path, balancing accuracy and efficiency. In this way, the system optimizes the mapping strategy in real time according to different scene characteristics (such as terrain complexity and device performance), breaks through the adaptability limitations of traditional fixed mapping algorithms in complex environments, and realizes high-precision and intelligent coordinate mapping of power GIS line maps. First, construct a quantum state representation φ_i for each element v_i in the line map, generate multiple candidate mapping coordinates through a quantum projection function; at the same time, based on the reinforcement learning framework, build a state space (including terrain complexity, device parameters, and element importance features) and an action space (weight distribution of each projection result), and train the strategy network with the minimum mapping error as the objective function. In runtime, the system senses the scene characteristics s_t in real time, generates RLBias(v_i, s_t) through the trained strategy network, and dynamically adjusts the weight of each projection result; the gating mechanism g_i determines whether to use dynamic mapping or default mapping path based on the complexity of the previous scene and the constraints of computing resources, and finally generates the optimal mapping coordinates of the element v_i. The scheme significantly improves the mapping accuracy and adaptability of power line maps, reducing the mapping error in complex terrain areas by 65% compared to traditional methods, to within 0.25 meters; the dynamic response capability is improved by 300%, and the mapping strategy adjustment is completed within 50ms; in resource-constrained scenarios, the gating mechanism intelligently switches the mapping path, reducing computing consumption by 40% while maintaining mapping accuracy above 92%. In actual power pipe engineering applications, the technology improves the matching degree of design drawings and actual site from 83% to 97%, and improves the efficiency of cross-regional collaborative design by 55%, providing key technical support for the digital transformation of power engineering. In Table 3, the innovative fusion method of full dynamic adjustment reduces the mapping error of complex terrain areas from 0.72 meters to 0.25 meters through quantum projection and dynamic offset of reinforcement learning, reducing by 65.3% compared with the traditional method, effectively solving the coordinate distortion problem in complex terrain areas. The dynamic response time is shortened from 180ms to 50ms, increasing by 72.2%, quickly adapting to scene changes (such as switching terminal devices and loading new geographic data), and ensuring real-time interaction experience. Combined with the gating mechanism, the mapping path is intelligently selected, reducing the calculation resource consumption by 40%, maintaining high-precision mapping on low-performance devices, and balancing efficiency and accuracy. The mapping accuracy rate is improved from 83% to 97% in different scenarios, and the adaptation success rate is increased by 36.8% in extreme scenarios (such as high altitude and dense underground pipe network areas), significantly enhancing the system robustness. In the power pipe engineering collaborative design, the matching degree between drawings and actual scenes is improved by 20%, reducing the amount of manual calibration, and helping to improve the cross-regional design efficiency by 55%, verifying the technical advantages of the method in practical application.

[0026] Further, the reinforcement learning optimization model defines a Markov decision process, including various terminal display quality indicators, as the action space, including coordinate change parameters, as the state transition probability; as the reward function; the decision network is optimized by PPO algorithm : In formula (5), is the experience expectation based on sampling data, which approximates the true expectation; is a clipping function that limits the policy update amplitude to prevent instability; is the training parameter of the decision network, which controls the policy of the agent selecting actions; is the weight coefficient for balancing different loss terms, is the importance of controlling quantum policy entropy, is the strength of controlling cross-task knowledge transfer, is the constraint degree of controlling quantum state consistency; is the quantum state transition operator, which ensures the continuity of policy update; is the quantum policy ratio, reflecting the relative difference between the previous policy and the old policy in state adopting action , used to measure the amplitude of policy update; is the advantage estimate value enhanced by meta-learning, evaluating the advantage degree of adopting action compared to the average value in state , the greater the advantage, the more valuable the action is; is a clipping function that limits the quantum policy ratio to The strategy update range is limited to avoid excessive strategy update range, thereby avoiding unstable training; The adaptive clipping parameter is used to dynamically adjust the clipping range during training, so that the strategy update can effectively explore new strategies and ensure stability; The quantum entropy regularization loss is used to measure the uncertainty or exploration of the strategy. It is desirable for the strategy to have a certain randomness to avoid falling into a local optimum too early and to encourage the agent to try more actions; The cross-task transfer loss is used to transfer knowledge between different tasks through meta-learning, so that the model can adapt more quickly in new tasks. The loss term evaluates the difference between the previous strategy and the optimal strategy of the source task under different task distributions; The quantum consistency loss ensures that the quantum state before and after the strategy update has a certain continuity and smoothness, preventing strategy mutation and maintaining the stability of the training process. In specific embodiments, the scheme is based on Markov decision process and quantum meta-learning proximal policy optimization (Q-META-PPO) to build an intelligent decision system in a dynamic environment. By defining a state space containing terminal display quality indicators, a coordinate change parameter action space, and modeling the decision process with state transition probabilities and reward functions, the PPO algorithm is introduced to optimize the decision network, and the quantum policy ratio, meta-learning advantage estimation, and adaptive clipping mechanism are used to constrain strategy updates, balancing exploration and utilization. Through quantum state consistency, cross-task transfer, and entropy regularization loss, the strategy stability and generalization ability are ensured, and dynamic optimization of GIS line maps in multi-terminal display is achieved. First, a Markov decision process model is built, and terminal resolution and rendering quality indicators are included in the state space S, and coordinate scaling and projection transformation operations are defined as the action space A. The reward function R is set to positively encourage actions with high display accuracy and low resource consumption. Second, the PPO algorithm is used to train the decision network π_θ, the experience expectation is calculated by sampling data, the quantum policy ratio r_t^q(θ) is used to measure the difference in strategy update, and the update range is limited by the clipping function. The adaptive clipping parameter ϵ_t^ad is dynamically adjusted to balance strategy exploration and stability. At the same time, the quantum entropy regularization, cross-task transfer, and quantum state consistency losses are adjusted by the weight coefficients λ_1, λ_2, and λ_3 to optimize the strategy network parameters θ, so that the model can adapt to different terminal display requirements. The scheme significantly improves the intelligent optimization capability of GIS line map multi-terminal display. In complex scenarios, the display accuracy is improved by 42% compared to traditional methods, and the terminal adaptation success rate is increased from 78% to 96%. The stability of strategy update is enhanced, and the model collapse rate during training is reduced by 85%. The cross-task transfer capability shortens the new terminal adaptation time by 60% and reduces the repeated training cost. The entropy regularization promotes strategy exploration, and the probability of finding better display strategies is increased by 55%, effectively solving the problem of uneven multi-terminal display quality and low adaptation efficiency, and providing efficient and stable visualization support for power pipe engineering design. In Table 4, the improved Q-META-PPO reduces the display pixel error from 3.2 to 0.9 and improves the terminal adaptation success rate from 75% to 96% through quantum state consistency constraints and meta-learning advantage estimation, effectively solving the multi-terminal display distortion problem. The adaptive cropping parameters and quantum state transfer operator reduce the training collapse frequency by 93.8%, with only 0.5 unstable cases per 100 training rounds, ensuring continuous model optimization. The cross-task transfer loss and meta-learning mechanism shortens the new terminal adaptation time by 73.3%, from 45 minutes to 12 minutes, significantly improving the multi-device deployment efficiency. Quantum policy entropy regularization doubles the policy exploration efficiency, increasing the new policy discovery rate from 35% to 70%, helping the model find better solutions in complex display scenarios. The optimization of cross-task transfer loss reduces the model error between different terminal tasks by 71.4%, achieving efficient knowledge reuse, and providing core technical support for multi-terminal adaptive display of power pipe GIS line maps.

[0027] Further, the three-level diagnostic evaluation system includes a first-level evaluation (compliance): calculating the generated image In formula (6), are the quantum-enhanced feature mean and covariance matrix, respectively; is the quantum entanglement degree between the generated image and the target image; is the quantum entanglement weight coefficient; a second-level evaluation (compliance): the typing compliance rate FCCR: In formula (7), is the element importance weight based on fractal dimension; is the fractal dimension of element , which measures the structural complexity; is the element compliance degree, calculated by the multi-scale attention mechanism; a third-level evaluation (real-time): space-time prediction loading time SRT: In formula (8), is the model prediction time based on quantum expectation, is the historical event filtering using fractal weight, is the network delay prediction based on graph neural network; is based on the network topology graph, including node and connection relationships; and the final comprehensive score: In formula (9) In specific implementation, the three-level diagnosis evaluation system constructs a multi-dimensional and high-precision power GIS line map quality evaluation framework through deep integration of quantum computing, fractal geometry and graph neural network. The first-level evaluation introduces quantum enhanced features and entanglement mechanism to measure the difference between the generated image and the target image from the essential feature level; the second-level evaluation calculates the importance weight of elements based on fractal dimension and evaluates compliance combined with multi-scale attention mechanism; the third-level evaluation integrates quantum prediction, fractal filtering and graph neural network to realize the loading time prediction in time and space dimensions. Through dynamic weight fusion of the evaluation results of the three levels, a comprehensive and real-time quality scoring system is formed to provide reliable quality assurance for power engineering design. First, in the first-level evaluation, the quantum enhanced features of the generated image and the target image are extracted, the feature mean, covariance matrix and quantum entanglement degree are calculated, and the image quality difference is quantified by QFID index. Second, the second-level evaluation calculates the structural complexity of each element as the importance weight using fractal dimension algorithm, evaluates the element compliance degree combined with multi-scale attention mechanism, and obtains the fractal compliance rate FCCR. Then, the third-level evaluation respectively predicts the time through quantum expectation model, filters historical events through fractal weight, and processes network topology and delay through graph neural network, and comprehensively obtains the space-time prediction loading time SRT. Finally, according to the evaluation results of the three levels, the final comprehensive score is calculated through dynamic weight, and the overall quality evaluation of power GIS line map is realized. The evaluation system significantly improves the quality evaluation precision and efficiency of power GIS line map. The QFID index of the first-level evaluation improves the image quality discrimination accuracy by 28% compared with the traditional FID index, which can more accurately capture the topological feature difference of power facilities; the FCCR index of the second-level evaluation reduces the compliance detection false positive rate by 45%, especially in complex terrain areas; the SRT prediction error of the third-level evaluation is controlled within 5%, which is improved by 60% compared with the traditional method, effectively supporting real-time decision-making. The comprehensive scoring mechanism makes the overall evaluation accuracy reach 96.8%, which is improved by 23 percentage points compared with the traditional single-dimensional evaluation, providing a scientific and reliable quality assurance tool for the design, approval and operation of power pipe engineering. In Table 5, the QFID value is reduced by 33.5% through quantum enhancement features and entanglement mechanism, and the feature difference detection time is shortened by 61.4%, indicating that quantum computing can more accurately and efficiently capture the essence of image differences, especially in the feature alignment of power topology structure. The fractal dimension weight mechanism increases the FCCR value by 15.6% and reduces the complex element omission rate by 66.9%, indicating that the compliance evaluation of high-complexity power facilities (such as urban underground pipe networks) is more comprehensive, avoiding the key element omission caused by weight averaging in traditional methods. The combination of quantum expectation and graph neural network reduces the SRT prediction error by 61.6% and shortens the real-time response time to 150ms, meeting the real-time collaborative design requirements of power engineering, and is more suitable for dynamic network environment than traditional rule-based prediction methods. After the fusion of three-level evaluation, the overall evaluation accuracy is improved by 23.8%, and the single evaluation time is reduced by 82.2%, and the evaluation generalization ability for extreme scenarios (such as mountainous power grids and high-density cable groups) is significantly enhanced, verifying the technical leadership of the quantum fractal fusion method in power GIS quality control. The system quantitatively evaluates multiple dimensions, increasing the one-time pass rate of design schemes from 72% to 91%, and shortening the approval cycle of typical projects by 50%, providing a scientific and efficient quality assurance tool for smart grid construction, and promoting the digital and intelligent transformation of power engineering management.

[0028] An intelligent generation system of GIS line diagram based on power pipe engineering drawings, characterized in that: the intelligent generation method of GIS line diagram based on power pipe engineering drawings according to any one of claims 1-8 is applied, characterized in that: comprising a multi-source data fusion preprocessing module, an intelligent path planning and conflict detection module, a dynamic layer intelligent generation module, and a cross-platform adaptive output module; The multi-source data fusion preprocessing module comprises a four-dimensional spatiotemporal index and graph neural network module, a multi-modal Transformer module, and an edge engine and intelligent service platform; the multi-source data fusion preprocessing module receives multi-source data of power pipe design parameters, GIS geographic data, lightweight BIM models, and multi-format CAD files, and performs analysis, alignment, cleaning, and standardization processing to provide high-quality data for subsequent processes; The intelligent path planning and conflict detection module comprises a three-dimensional space constraint construction submodule, an improved A* algorithm path planning submodule, an improved deep learning collision detection submodule, and an improved GAN layout optimization submodule; the intelligent path planning and conflict detection module constructs three-dimensional space constraints, plans power pipe line paths, performs three-dimensional collision detection and layout rationality simulation, and outputs optimized path node coordinates; The dynamic layer intelligent generation module comprises a geographic element automatic matching module, a user permission dynamic loading module, a layer interaction function module and an improved GPU acceleration module; the dynamic layer intelligent generation module generates a basic geographic layer according to path node coordinates, loads a business layer based on user permission, supports layer interaction operation and efficient rendering; The cross-platform adaptive output module comprises a responsive design framework module, a coordinate mapping algorithm module and a multi-format file export interface module; the cross-platform adaptive output module realizes adaptive display of a GIS line map on a computer terminal, a tablet terminal and a mobile terminal based on a responsive design framework, and provides a multi-format file export interface; In the GIS line map intelligent generation system based on power pipe engineering drawings, the multi-source data fusion preprocessing module analyzes, aligns and cleans inputted various data, generates a standardized spatial coordinate data set and transmits the data set to the intelligent path planning and collision detection module; the intelligent path planning and collision detection module plans a path in combination with the three-dimensional space constraint condition, detects collision and optimizes layout, transmits path node coordinates and collision results to the dynamic layer intelligent generation module; the dynamic layer intelligent generation module automatically matches geographic elements to generate a basic layer, loads a business layer and efficiently renders; the cross-platform adaptive output module realizes adaptive display of a line map on multiple terminals based on responsive design and coordinate mapping algorithm, and provides multi-format file export; the modules are closely cooperated to form a complete working chain through data transmission, instruction interaction and feedback optimization.

[0029] In specific embodiments, the GIS line diagram intelligent generation system significantly improves the intelligent and efficient level of power pipe rack engineering design through the cooperation of four modules. The multi-source data fusion preprocessing module uses four-dimensional spatiotemporal index and graph neural network to realize TB-level CAD data millisecond-level analysis, combines multi-modal Transformer and edge engine to complete high-precision alignment and cleaning of cross-source data, and the data standardization rate reaches 99.2%, the redundancy elimination efficiency is improved by 75%, and the basic error is reduced to below 0.1 meters, laying a high-reliability data foundation for subsequent processes; the intelligent path planning and conflict detection module constructs a dynamic three-dimensional constraint system, integrates a five-dimensional cost evaluation model and quantum state superposition weight, and the path planning accuracy rate reaches 98.5% and the time consumption is shortened by 65%, the improved deep learning and quantum point cloud technology makes the collision detection accuracy reach nanometer level and the conflict avoidance success rate is ≥99.8%, and the design cycle is shortened by an average of 42%; the dynamic layer intelligent generation module automatically matches geographic features based on path nodes, and the layer generation efficiency is improved by 80% and the matching accuracy is 97.6%, the quantum spatiotemporal permission matrix and brain-computer interface realize permission second-level adjustment, and the abuse prediction accuracy is 85%, the improved GPU acceleration makes the rendering time of million-level elements <200ms, and the interaction delay <50ms; the cross-platform adaptive output module uses a responsive framework and a dynamic threshold decision tree to make the multi-terminal display completeness difference <8%, the coordinate error <1.5 pixels, the low-resolution device loading time is shortened by 35%, the multi-format export efficiency is improved by 60%, and the collaborative misoperation rate is reduced by 42%. The whole process automation of the system improves the overall efficiency by 70% and reduces the whole life cycle cost by 15%-20%, and breaks through the efficiency and accuracy bottleneck of the traditional design mode, providing core technical support for the digital transformation of power engineering.

[0030] Although the specific embodiments of the present application are described above, those skilled in the art should understand that these specific embodiments are only illustrative, and those skilled in the art can make various omissions, substitutions and changes to the details of the above-mentioned methods and systems without departing from the principles and essence of the present application; for example, combining the above method steps, so as to perform substantially the same function in substantially the same manner to achieve substantially the same result, which is within the scope of the present application; therefore, the scope of the present application is only limited by the appended claims.

Claims

1. An intelligent generation method based on power tube engineering drawing GIS line drawing, characterized by: The method comprises the following steps: S1. Preprocessing multi-source data Receive user input power pipe design parameters, while obtaining GIS geographic data, geophysical survey data, multi-format engineering drawings and lightweight BIM models; through four-dimensional space-time index and graph neural network, realize the data analysis of pipeline topological structure including pipe diameter, trend and connection relationship in CAD drawings, complete data alignment by multi-modal Transformer, generate standardized data set after real-time data cleaning by edge engine, and provide unified data basement for subsequent design; S2. Plan path and detect conflict According to the preprocessed standardized data set, combined with terrain elevation, underground facility distribution, soil geological condition, construction process limitation and cost budget, generate three-dimensional space constraint condition; improve the A* algorithm to automatically plan the power pipe line path, generate the initial GIS line map; the improved deep learning model performs three-dimensional collision detection on the complex scene of multi-layer pipe dense work well, and quantifies the path conflict risk; the improved generative adversarial network GAN simulates the rationality of pipe layout under different construction environments, optimizes the path node coordinates, and outputs accurate parameters including space coordinates, buried depth data, pipe length, construction parameters and conflict report; S3. Dynamic layer intelligent generation According to the data generated by path planning and conflict detection, obtain the path node coordinates, combine the geographic elements of road, building and soil in GIS base map to generate basic geographic layer; dynamically generate cable specification layer, work well state layer and pipe attribute layer according to user permission; the improved GPU acceleration module realizes the millisecond-level rendering of million-level elements; according to the pipe design parameters, generate two-dimensional design drawing, and automatically calculate the labor cost according to the engineering quantity list valuation rules; S4. Cross-platform adaptive output Based on the responsive design framework, the generated GIS line map is displayed through computer, tablet and mobile terminal, and the display is consistent on different platforms through coordinate mapping algorithm and vector graphics scaling to meet the display of different screen resolutions; provide multi-format file export interface of DWG and PDF, support editing and viewing of power pipe engineering drawings.

2. The method for generating an intelligent power tube engineering drawing GIS line map according to claim 1, characterized in that: The working method for preprocessing multi-source data is: S1, receiving and classifying data: through the user interface, receive the power pipe design parameters, the design parameters include voltage, pipe material type, pipe material length, depth and latitude and longitude, and uniformly process the power pipe design parameters to generate a structured data table; at the same time, obtain GIS geographic data including terrain elevation, road distribution and existing pipe network spatial position, geophysical survey data including access to underground obstacles, engineering drawings including CAD completion drawings and PDF design drawings, and BIM models including related spatial structures required for pipe design; S2, establish four-dimensional space-time index: combine spatial three-dimensional coordinates with time including engineering stage and data update to establish four-dimensional space-time index, realize millisecond-level retrieval speed of TB-level data; S3, analyze the graph neural network topology: create a power pipe topology graph model, set the pipelines and shafts in the CAD drawing as points and the connection relationship as edges in the model; automatically identify the topological structure of the pipe diameter, direction and connection relationship through the graph neural network GNN model; mark the possible topological conflict points by fusing the historical construction data; S4, align multi-modal data: unify the feature vectors of GIS grid data, BIM three-dimensional model and CAD two-dimensional drawing coding, and input the unified data into the Transformer network; S5, clean and standardize data: remove duplicate data and repair missing data values through interpolation method by data cleaning engine; convert the cleaned data into unified GeoSON format and standardize the naming; S6, generate standardized data set: integrate the standardized data to generate a standardized data set including spatial coordinates, attribute parameters and topological relationships, the attribute parameters including voltage, pipe diameter, pipe length and pipe material; S7, store and transmit data: the standardized data set is stored through the distributed file system HDFS, and the standardized data set is transmitted to the subsequent steps through the message queue Kafka.

3. The method according to claim 1, characterized in that: The three-dimensional space constraints include terrain and elevation constraints, underground setting safety distance constraints, soil and geological constraints, construction process constraints, cost optimization constraints and compliance constraints; The terrain and elevation constraints: the maximum climbing angle of the pipeline is set to ≤15°; the minimum cover thickness of the pipeline is set to ≥0.75m in the general area, ≥1.25m under the road and ≥0.55m in the rock layer; The underground facility safety distance constraint: the horizontal net distance between the pipeline and the existing power cable is ≥0.55m, and the intersection net distance is ≥0.55m; The horizontal net distance between the pipeline and the communication, water supply and drainage pipeline is ≥0.85m, and the intersection net distance is ≥0.35m; the horizontal net distance between the pipeline and the gas pipeline is ≥1.5m, and the pressure is ≤0.45MPa; the horizontal net distance between the pipeline and the heat pipeline is ≥2.5m, and the intersection net distance is ≥0.55m; The soil and geological constraints: the pipeline needs to increase support cost in the quicksand layer, the additional cost per meter = quicksand layer thickness × 600 yuan; the pipeline needs to set a construction difficulty coefficient in the rock layer, the difficulty coefficient = rock hardness × path length × 1.5; the buried depth of the pipeline needs to be higher than the 0.6m of the perennial water level; The construction process constraint: different minimum turning radii are set for different pipe materials of the pipeline, the minimum turning radius of MPP pipe is set to ≥16 times the pipe diameter, the minimum turning radius of steel pipe is set to ≥12 times the pipe diameter; the minimum turning radius of PVC pipe is set to ≥22 times the pipe diameter; the construction mechanical operation space of the pipeline is set to ≥3.5m horizontally and ≥2.5m vertically; the slope of the pipeline is set to ≥0.35% to ensure drainage; The cost optimization constraint: the construction difficulty cost of the pipeline is = geological complexity index × path length × construction unit price; the well spacing constraint of the pipeline is that the straight line segment is ≤160m, and a turn must be set. The compliance constraint: the pipeline conduit laying needs to comply with the "Urban Power Cable Line Design Technology Regulation" DL / T5221, meet the "Power Engineering Cable Design Standard" GB 50217, and follow the special provisions of the local power authority.

4. The method for intelligent generation of a power tube engineering drawing GIS line drawing according to claim 1, characterized in that: The working method of the improved A* algorithm is: S1, space-time entropy five-dimensional cost evaluation model: introduce curvature tensor to calculate the smoothness of the pipeline conduit path, ensure that the pipeline turning radius meets the minimum bending requirement of the pipe material; fuse the construction time sequence constraint, dynamically adjust the path cost through the time window; quantify the geological complexity of the pipeline conduit area, including rock layer construction difficulty coefficient, pipe material loss rate and equipment shift cost resource consumption index; calculate the path uncertainty based on Bayesian network, give higher entropy value cost to unexplored areas; construct a power facility safety distance thermal map, impose an exponential risk cost on the area where the pipeline is close to high-voltage lines and gas pipelines; S2, space-time tensor manifold constraint calculation: use geodesic distance instead of Euclidean distance to more accurately reflect the actual path cost of underground construction in the pipeline conduit area; generate the slope constraint surface of the pipeline conduit in real time, and the maximum climbing angle of the slope constraint surface is ≤15°; generate the burial depth constraint surface in real time, which includes the minimum cover thickness of the pipeline in ordinary areas, under roads and rock layers; the physical properties of different pipe materials are mapped into a tensor field, which affects path selection; S3, dynamic cost deduction and semantic reasoning: convert the "Urban Power Cable Line Design Technology Regulation" into a graph database; based on the Drools rule engine, real-time deduction is carried out, and when the pipeline voltage level exceeds 110Kv, the pipeline spacing constraint is automatically increased; Through the Q-learning algorithm, the burial depth constraint weight of the pipeline in the complex geological area is dynamically adjusted; S4, real-time analysis of multi-source heterogeneous data: deploy edge computing nodes at the construction site to preprocess heterogeneous data of GIS and BIM; use a hybrid index of quadtree+KD tree to achieve millisecond-level data retrieval; S5, heuristic weight optimization: use quantum bits to represent weight vectors to increase spatial search capability; dynamically adjust the mutation probability according to the convergence state of the algorithm to avoid falling into local optimum; at the same time, optimize the path length, construction cost and safety risk; S6, photon parallel search architecture: expand the node task assignment to GPU thread bundles, and each thread processes a possible path; design a three-level buffer strategy to reduce global memory access delay; S7, dynamic semantic conflict map: distinguish between hard conflicts of spatial intersection, soft conflicts of insufficient safety distance, and time sequence conflicts of overlapping construction periods; use the PageRank algorithm to evaluate the conflict influence of a pipeline modification that may trigger a chain reaction; Based on the graph neural network, the optimal avoidance scheme is predicted, and a conflict resolution report is automatically generated. S8, digital twin driven spatio-temporal consistency verification: using finite element analysis to simulate the stress state of the pipe material, verifying the structural safety of the path; checking construction timing conflicts through discrete event simulation, including multi-shift operation space conflicts; Real-time updating of the digital twin model through IoT sensors, dynamic adjustment of path planning.

5. The method for intelligent generation of a power tube engineering drawing GIS line map according to claim 1, characterized in that: The improved deep learning model includes a multi-scale feature enhancement module, a space-electricity dual-modal attention mechanism module, a three-dimensional collision risk quantification module, a multi-layer pipeline semantic segmentation optimization module, and a three-dimensional visualization and interaction optimization module; The multi-scale feature enhancement module enhances feature extraction capability for complex scenes of multi-layer pipeline dense work wells through a multi-scale feature enhancement module; the PowerManhole-FPN fusion multi-resolution feature perceives the features of the power work well, and uses cavity convolution and channel attention to capture the context information of different size pipelines; The space-electricity dual-modal attention mechanism module adopts a space-electricity dual-modal attention mechanism to construct a dual graph structure of physical location and electrical parameters; the space graph models the pipeline space relationship based on Euclidean distance, and the electrical graph constructs electrical connection according to voltage parameters, and fuses the features of the two graphs through adaptive weight; The three-dimensional collision risk quantification module generates a probability collision field, discretizes the three-dimensional space into a grid and calculates the collision probability of each point, and generates a risk heat map after weighting the pipeline priority; through the indexes of high-risk area volume ratio and risk concentration degree, the quantitative evaluation of conflict risk is realized; The multi-layer pipeline semantic segmentation optimization module adopts a multi-task learning framework to synchronously output the pipeline type, level and conflict area; by extracting the pipeline center line and clustering analysis of the vertical position, combined with the level consistency loss function, the understanding of the spatial distribution of multi-layer pipelines is realized; The three-dimensional visualization and interaction optimization module converts the collision probability into an intuitive heat map, superimposes the pipeline geometric information to realize risk positioning, and introduces a reinforcement learning framework to automatically optimize the path; the agent generates a candidate solution by balancing the conflict risk, construction cost and path length; The output end of the multi-scale feature enhancement module is connected to the input end of the space-electricity dual-modal attention mechanism module, the output end of the space-electricity dual-modal attention mechanism module is connected to the input end of the three-dimensional collision risk quantification module, the output end of the three-dimensional collision risk quantification module is connected to the input end of the multi-layer pipeline semantic segmentation optimization module, and the output end of the multi-layer pipeline semantic segmentation optimization module is connected to the input end of the three-dimensional visualization and interaction optimization module.

6. The method for intelligent generation of a power tube engineering drawing GIS line map according to claim 1, characterized in that: The working method of the improved generative adversarial network GAN is: S1, generator network architecture: construct a topological perception power facility correlation graph network, take the work well key node as the topological anchor point, learn the electrical connection and spatial dependence relationship through graph attention mechanism; adopt a multi-scale feature fusion strategy, combine the global power grid topology and local terrain details to dynamically adjust the path parameters, and embed a differentiable construction specification constraint layer including the minimum turning radius and burial depth requirements, convert physical and specification constraints into gradient-derivable loss terms; S2, discriminator network architecture: design a cognitive graph enhanced discriminator, integrate power knowledge graph and causal reasoning mechanism, evaluate the impact of path changes on conflicts through counterfactual loss function, and avoid potential risks of surface compliance; S3, conflict report generation: develop a three-dimensional conflict entropy evaluation model to convert spatial conflicts into information entropy metrics, quantify uncertainty and generate risk reports with confidence intervals; S4, reinforcement learning optimization: propose a multi-agent competition and cooperation framework to explore path strategies through game mechanisms, balance local compliance and global economy with hierarchical reward structures, and quickly adapt to new regional characteristics with meta-learning; S5, knowledge graph fusion: build a power semantic-geometric dual-mode knowledge graph to convert standard texts into differentiable geometric constraints, inject expert experience through knowledge distillation, support real-time interface for standard incremental updates, and automatically generate compliance design documents.

7. The method according to claim 1, characterized in that: The working method of the coordinate mapping algorithm is: S1, multi-resolution feature extraction based on Transformer divides the input GIS line map data into non-overlapping image blocks Each block is flattened into a one-dimensional vector and then projected linearly to obtain an embedding vector: In formula (1), is a projection matrix, is a position encoding;​ The embedding vector is processed by the L-layer Transformer encoder, and the operators and adaptive parameters of the scientific system are introduced, and the multi-layer operation is simplified into a unified expression: In formula (2), is an attention adjustment factor based on entity importance, which is automatically calculated by a learning algorithm according to terrain elevation, underground facility distribution, soil geological conditions, construction process restrictions and cost budget, and the larger the value represents the more important the geographic entity is; is an attention adjustment factor based on entity importance, which is automatically calculated by a learning algorithm according to terrain elevation, underground facility distribution, soil geological conditions, construction process restrictions and cost budget, and the larger the value represents the more important the geographic entity is; device scaling factor According to the display device characteristics, it is dynamically adjusted to ensure that the display resources can be reasonably allocated on different devices, and high-resolution devices will allocate more resources to detailed features; geographic kernel function is an attention bias item based on spatial distance, which makes the model pay more attention to the geographic entities adjacent in space, and the closer the distance, the higher the attention score, which enhances the ability to capture local spatial structure; topological bias Based on the topological relationship, the attention allocation is adjusted, and the attention score is enhanced or weakened according to the connectivity between geographic entities, and the stronger the connectivity between entities, the higher the attention. scaling factor is a learning parameter, which automatically balances the contribution of the standard attention mechanism and the geographic constraint term. In complex urban road networks, the model will automatically increase the value of to strengthen the geographic constraint; S2, dynamic threshold decision tree mapping strategy According to the device parameter vector The device parameters include screen size and pixel density, and the mapping parameters are determined by a decision tree function T(D): Wherein is the region of the decision tree division, and each strategy corresponds to different coordinate mapping strength and the simplified coefficient ; S3, innovative adaptive dynamic weight fusion In formula (4), is the i-th element in the line graph, including power pipelines and nodes; is the final mapping coordinates of the element , is the default mapping function result, used as the base reference; is the quantum weight of the element , represented by a superposition state; is the gate value, which determines whether to use dynamic mapping or default mapping; is the quantum projection function, which weights multiple projection results based on quantum states; Dynamic offset based on reinforcement learning, adaptive to different scenarios; S4, the reinforcement learning optimization model defines a Markov decision process, including the terminal display quality indicators of each terminal, is an action space, including coordinate change parameters, is a state transition probability; is a reward function; the decision network is optimized by the PPO algorithm : In formula (5), is the empirical expectation based on sampled data, which approximates the true expectation; is a clipping function that limits the policy update amplitude to prevent instability; is the training parameter of the decision network, which controls the policy of the agent selecting actions; is the weight coefficient for balancing different loss terms, is the importance of controlling quantum policy entropy, is the strength of controlling cross-task knowledge transfer, controls the consistency of the quantum state; is a quantum state transfer operator that ensures the continuity of policy updates; is the quantum policy ratio, reflecting the relative difference between the previous policy and the old policy in the state taking action , used to measure the amplitude of policy updates; is the advantage estimate enhanced by meta-learning, which evaluates the advantage of taking action in state compared to the average value; the greater the advantage, the more valuable the action is; is a clipping function that limits the quantum policy ratio to the range , avoiding excessive policy update amplitude leading to unstable training; is an adaptive clipping parameter that dynamically adjusts the clipping range during training, allowing the policy to effectively explore new strategies while ensuring stability; is the entropy regularization loss of the quantum policy, used to measure the uncertainty or exploration of the policy; it is desirable for the policy to have some randomness to avoid premature convergence to local optima and encourage the agent to try more actions; is the cross-task transfer loss, which uses meta-learning to transfer knowledge between different tasks, allowing the model to adapt more quickly to new tasks; the loss term evaluates the difference between the previous policy and the optimal policy of the source task under different task distributions; is the quantum consistency loss, which ensures that the quantum state before and after policy update has certain continuity and smoothness, preventing policy mutation and maintaining the stability of the training process; S5, three-level diagnosis evaluation system Primary evaluation (compliance): calculate the generated image In formula (6), Quantum-enhanced feature mean and covariance matrix, respectively; To measure the degree of quantum entanglement between the generated image and the target image; Quantum entanglement weight coefficient; Secondary evaluation (compliance): typing compliance rate FCCR: In formula (7), Fractal dimension-based element importance weight; Fractal dimension of element , measure the structural complexity; Element compliance degree, calculated by multi-scale attention mechanism; Third evaluation (real-time): space-time prediction loading time SRT: In formula (8), Model prediction time based on quantum expectation, History event filtering using fractal weight, Network delay prediction based on graph neural network; Based on network topology graph, containing node and connection relationship; Final comprehensive score: In formula (9) .

8. An intelligent generation system based on power tube engineering drawing GIS line drawing, characterized in that: The application discloses an intelligent generation method for a GIS line map based on power pipe engineering drawings, and a system thereof. The multi-source data preprocessing module integrates user input parameters, GIS geographic data, engineering drawings and other multi-source heterogeneous data; four-dimensional space-time indexes and graph neural networks are used to analyze pipeline topology, multi-modal Transformers are used to align data formats, and edge engines are used to clean up and output standardized data sets, thereby providing unified data support for subsequent design; the path planning and conflict detection module generates three-dimensional space constraints based on the standardized data in combination with terrain, geology, cost and other conditions; an improved A* algorithm is used to automatically plan a line path and generate an initial GIS line map; an improved deep learning model is used to detect three-dimensional collision risks in complex scenes, an improved GAN is used to optimize path parameters, and an accurate design scheme is output; the dynamic layer intelligent generation module generates basic layers by fusing geographic elements according to the planning results and loading professional layers according to user permissions; GPU acceleration is used to realize efficient rendering, and two-dimensional design drawings and artificial cost accounting lists are automatically generated; the cross-platform adaptive output module adopts responsive design to realize consistent display of the GIS line map on multiple terminals; DWG, PDF and other format export interfaces are provided to support drawing editing, approval and archiving on different platforms, and project collaboration efficiency is improved; The output end of the multi-source data preprocessing module is connected to the input end of the path planning and conflict detection module; the output end of the path planning and conflict detection module is connected to the input end of the dynamic layer intelligent generation module; and the output end of the dynamic layer intelligent generation module is connected to the input end of the cross-platform adaptive output module.

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