A power transmission line engineering acceptance method and system based on big data

Abstract

The application provides a power transmission line engineering acceptance method and system based on big data, which comprises the following steps: internally marking the dependency relationship, execution condition and constraint relationship between each acceptance item in the three-dimensional space coordinate to generate a corresponding acceptance task baseline library; analyzing the acceptance item dependency directed acyclic graph based on the depth-first search algorithm to assign an acceptance task subset meeting the prerequisite dependency condition and having the closest physical distance to each portable edge acceptance device; performing multi-modal data acquisition and local feature extraction processing on the acceptance task subset by the portable edge acceptance device to perform global consistency reasoning on all generated local feature vectors and generate a corresponding global acceptance state matrix; converting the global acceptance state matrix into a corresponding chain data structure according to the execution sequence of the acceptance items, and generating a corresponding engineering acceptance report according to the target data contained in the chain data structure. The application can greatly improve the acceptance efficiency.

Description

Technical Field

[0001] This invention relates to the field of power transmission engineering technology, and in particular to a method and system for the acceptance of power transmission line projects based on big data. Background Technology

[0002] Acceptance testing of power transmission line projects is a crucial step in ensuring the quality of power grid construction and safe operation. In recent years, big data acceptance technology based on UAV lidar and AI intelligent analysis has been widely used in projects at voltage levels from 110kV to 1000kV, achieving a 4-6 fold increase in acceptance efficiency and significantly reducing the safety risks of high-altitude operations. However, existing big data acceptance systems generally adopt a "cloud-based centralized processing" architecture, which heavily relies on stable network connections and high-performance cloud servers, resulting in significant limitations in engineering scenarios with insufficient network coverage.

[0003] Currently available technical solutions only offer partial offline functionality and cannot meet the full-process on-site acceptance requirements. Some systems support offline review of pre-generated 3D models in the cloud, but cannot process raw point cloud and image data collected on-site in real time. Some systems deploy lightweight image recognition models at the edge, which can only complete preliminary defect screening; core steps such as point cloud denoising and registration, 3D reconstruction, and geometric deviation calculation still need to be performed by uploading data back to the cloud. Other systems only have local data storage capabilities, and acceptance data needs to be uploaded and processed in batches after network recovery, making it impossible to generate acceptance conclusions on-site. Approximately 40% of my country's transmission lines are located in mountainous and remote areas without network coverage. These deficiencies make it difficult to implement digital acceptance technology in these areas, and acceptance personnel still need to use traditional methods of paper records and post-event data entry, which not only results in high data error rates and poor traceability but also significantly extends the acceptance cycle.

[0004] In summary, current technologies have not yet overcome the technical bottleneck of "cloud dependence." No publicly available solution can achieve a complete acceptance process—from point cloud data preprocessing, 3D reconstruction, automatic GIM model comparison, AI-based intelligent defect identification, to standardized acceptance report generation—in a network-free environment, relying solely on portable edge devices. This technological gap severely restricts the widespread adoption of digital acceptance technology for transmission lines, making seamless data integration between the construction and operation / maintenance phases impossible. Therefore, there is an urgent need to develop a method and system for accepting transmission line projects that supports offline, end-to-end operations. Summary of the Invention

[0005] Based on this, the purpose of this invention is to provide a method and system for the acceptance of power transmission line projects based on big data, so as to solve the problem that the existing technology cannot achieve the complete acceptance of power transmission line projects independently by relying solely on portable edge devices in a network-free environment.

[0006] The first aspect of the present invention proposes: A method for acceptance testing of power transmission line projects based on big data, wherein the method includes: Several acceptance items and corresponding physical space constraint topologies of the transmission line project are preloaded, and the acceptance items are mapped to corresponding three-dimensional spatial coordinates according to the physical space constraint topologies. The dependencies, execution conditions and constraint relationships between each acceptance item are marked inside the three-dimensional spatial coordinates to generate the corresponding acceptance task baseline library. Based on the baseline library of acceptance tasks, a corresponding directed acyclic graph of acceptance item dependencies is constructed, and the directed acyclic graph of acceptance item dependencies is parsed based on the depth-first search algorithm, so as to allocate a subset of acceptance tasks that meet the prerequisite dependency conditions and are physically closest to each portable edge acceptance device. The portable edge acceptance device performs multimodal data acquisition and local feature extraction processing on the subset of acceptance tasks, so as to perform global consistency inference on all the generated local feature vectors and generate the corresponding global acceptance state matrix. The global acceptance status matrix is ​​converted into a corresponding chained data structure according to the execution order of the acceptance items, and the corresponding project acceptance report is generated based on the target data contained in the chained data structure.

[0007] The beneficial effects of this invention are as follows: This technical solution effectively solves the pain point of existing technologies being unable to independently complete the complete acceptance of transmission line projects using only portable edge devices in a network-free environment. By preloading acceptance items and physical space constraint topology and mapping them to three-dimensional spatial coordinates, an acceptance task baseline library containing dependencies, execution conditions, and constraint relationships is generated. This achieves spatialized and structured pre-configuration of acceptance tasks, ensuring that task data is complete and usable and the acceptance process is compliant even without a network. Based on the task allocation mechanism of directed acyclic graphs of acceptance item dependencies and depth-first search algorithm, the system prioritizes the allocation of the closest physical subset of tasks while satisfying pre-dependency conditions, significantly reducing equipment movement costs and improving the efficiency of multi-device collaborative acceptance. By generating a global acceptance state matrix through multimodal data acquisition, local feature extraction, and global consistency inference at the edge, real-time fusion and state synchronization of distributed data can be achieved without cloud computing, improving the accuracy and reliability of acceptance results. Finally, the acceptance state is automatically converted into a chain-like data structure and an engineering acceptance report is generated, achieving automation and traceability of the entire acceptance process and significantly improving the efficiency and quality of transmission line project acceptance.

[0008] Furthermore, the step of annotating the dependencies, execution conditions, and constraints between each acceptance item within the three-dimensional spatial coordinates to generate a corresponding acceptance task baseline library includes: The physical space constraint topology is discretized into an unstructured mesh composed of tetrahedral elements, and each acceptance item is mapped to a target tetrahedral element containing its physical location. The vertex coordinates, volume, normal vector, and adjacent element connection relationship of the target tetrahedral element are extracted as the spatial constraint features of the current acceptance item. Using the key structural nodes of the transmission line project as the root node, a spatial constraint graph is constructed with tetrahedral elements as nodes and common surfaces between elements as edges. Starting from the root node, the constraint relationships are propagated along the edges of the spatial constraint graph to generate the dependencies, execution relationships, and constraint relationships between each of the acceptance items. Based on the dependencies, execution conditions, and constraints, a corresponding set of conditions is constructed, and the acceptance task baseline library is generated accordingly based on the set of conditions.

[0009] Furthermore, the step of generating the acceptance task baseline library based on the condition set includes: The common surface area, the angle between the common surface normal vectors, and the volume ratio of the two adjacent tetrahedral elements corresponding to each edge in the spatial constraint graph are detected to calculate the constraint transfer coefficient of each edge. The constraint transfer coefficients are then multiplied to obtain a transfer weight value that matches the condition set. Based on the set of conditions, a conditional dependency adjacency matrix with all acceptance items as nodes is constructed according to the transmitted weight values, and a normalized Laplace transform is performed on the conditional dependency adjacency matrix to generate a spatially constrained Laplace matrix. The eigenvectors corresponding to the first few smallest non-zero eigenvalues ​​of the spatial constraint Laplacian matrix are calculated, and the projection values ​​of each acceptance item onto the eigenvectors are used as the constraint eigenvectors of the current acceptance item. A corresponding constraint set is constructed based on the constraint eigenvectors, and the constraint set and the condition set are integrated into the acceptance task baseline library.

[0010] Furthermore, the step of parsing the directed acyclic graph of the acceptance item dependencies based on the depth-first search algorithm to allocate the subset of acceptance tasks that satisfy the pre-dependency conditions and are physically closest to each portable edge acceptance device includes: The acceptance item depends on each node in the directed acyclic graph to perform spatial clustering to generate several node clusters, and calculates the three-dimensional spatial centroid of each node cluster to assign each node cluster to the portable edge acceptance device closest to its centroid. Each of the portable edge acceptance devices is assigned a corresponding local depth-first search stack to detect the central node in the corresponding node cluster and to calculate the three-dimensional Euclidean distance between the central node and the three-dimensional centroid. If the detected three-dimensional spatial Euclidean distance is greater than or equal to a preset distance threshold, the central node is added to the global set of nodes to be assigned, and a corresponding subset of acceptance tasks is assigned to the portable edge acceptance device according to the global set of nodes to be assigned.

[0011] Furthermore, the step of allocating a corresponding subset of acceptance tasks to the portable edge acceptance device based on the global set of nodes to be allocated includes: Calculate the point connectivity of each central node in the global set of nodes to be assigned in the directed acyclic graph dependent on the acceptance item, and use the central node with a point connectivity of 1 as the split point. The global set of nodes to be allocated is divided into several dependency chain segments according to the segmentation point, and the starting node coordinates and ending node coordinates of each dependency chain segment are detected. The corresponding top node coordinates of each portable edge acceptance device in the local depth-first search stack are also detected. The target three-dimensional Euclidean distance between the starting node coordinates, the ending node coordinates, and the stack top node coordinates is calculated, and a corresponding subset of acceptance tasks is assigned to each of the portable edge acceptance devices according to the magnitude of the target three-dimensional Euclidean distance.

[0012] Furthermore, the step of performing multimodal data acquisition and local feature extraction processing on the subset of acceptance tasks using the portable edge acceptance device, and then performing global consistency inference on all generated local feature vectors to generate the corresponding global acceptance state matrix includes: The portable edge acceptance device detects the dependency chain of the corresponding acceptance task subset, and assigns corresponding feature extraction operator weights to each acceptance item according to the execution order of each acceptance item in the dependency chain. The portable edge acceptance device collects images, point clouds, and temperature and humidity data, and collects local feature vectors of the corresponding dimensions according to the weights of the feature extraction operators, and generates corresponding feature metadata for each local feature vector. Based on the edge connection relationship of the directed acyclic graph that the acceptance items depend on, the local feature vectors and feature metadata of each portable edge acceptance device are matrixed to generate the global acceptance state matrix.

[0013] Furthermore, the step of converting the global acceptance status matrix into a corresponding chained data structure according to the execution order of the acceptance items includes: Each row of the global acceptance status matrix is ​​mapped to a status block, wherein the status block contains a block header and a block body; According to the execution order of the directed acyclic graph that the acceptance items depend on, a hash operation is performed on the data contained in the block header to generate a first hash chain, and a second hash operation is performed on the data contained in the block body to generate a second hash chain. The first hash chain and the second hash chain are associated and stored to generate a chained data structure with hash values.

[0014] The second aspect of the present invention proposes: A big data-based power transmission line engineering acceptance system, wherein the system includes: The mapping module is used to preload several acceptance items of the transmission line project and the corresponding physical space constraint topology, and to map the several acceptance items to the corresponding three-dimensional space coordinates according to the physical space constraint topology. The module also marks the dependencies, execution conditions and constraint relationships between the acceptance items in the three-dimensional space coordinates to generate the corresponding acceptance task baseline library. The construction module is used to construct the corresponding directed acyclic graph of acceptance item dependencies based on the acceptance task baseline library, and parse the directed acyclic graph of acceptance item dependencies based on the depth-first search algorithm, so as to allocate the subset of acceptance tasks that meet the prerequisite dependency conditions and are physically closest to each portable edge acceptance device. The processing module is used to perform multimodal data acquisition and local feature extraction processing on the subset of acceptance tasks through the portable edge acceptance device, so as to perform global consistency inference on all the generated local feature vectors and generate the corresponding global acceptance state matrix. The generation module is used to convert the global acceptance status matrix into a corresponding chain data structure according to the execution order of the acceptance items, and generate a corresponding project acceptance report based on the target data contained in the chain data structure.

[0015] Furthermore, the mapping module is specifically used for: The physical space constraint topology is discretized into an unstructured mesh composed of tetrahedral elements, and each acceptance item is mapped to a target tetrahedral element containing its physical location. The vertex coordinates, volume, normal vector, and adjacent element connection relationship of the target tetrahedral element are extracted as the spatial constraint features of the current acceptance item. Using the key structural nodes of the transmission line project as the root node, a spatial constraint graph is constructed with tetrahedral elements as nodes and common surfaces between elements as edges. Starting from the root node, the constraint relationships are propagated along the edges of the spatial constraint graph to generate the dependencies, execution relationships, and constraint relationships between each of the acceptance items. Based on the dependencies, execution conditions, and constraints, a corresponding set of conditions is constructed, and the acceptance task baseline library is generated accordingly based on the set of conditions.

[0016] Furthermore, the mapping module is specifically used for: The common surface area, the angle between the common surface normal vectors, and the volume ratio of the two adjacent tetrahedral elements corresponding to each edge in the spatial constraint graph are detected to calculate the constraint transfer coefficient of each edge. The constraint transfer coefficients are then multiplied to obtain a transfer weight value that matches the condition set. Based on the set of conditions, a conditional dependency adjacency matrix with all acceptance items as nodes is constructed according to the transmitted weight values, and a normalized Laplace transform is performed on the conditional dependency adjacency matrix to generate a spatially constrained Laplace matrix. The eigenvectors corresponding to the first few smallest non-zero eigenvalues ​​of the spatial constraint Laplacian matrix are calculated, and the projection values ​​of each acceptance item onto the eigenvectors are used as the constraint eigenvectors of the current acceptance item. A corresponding constraint set is constructed based on the constraint eigenvectors, and the constraint set and the condition set are integrated into the acceptance task baseline library.

[0017] Furthermore, the building module is specifically used for: The acceptance item depends on each node in the directed acyclic graph to perform spatial clustering to generate several node clusters, and calculates the three-dimensional spatial centroid of each node cluster to assign each node cluster to the portable edge acceptance device closest to its centroid. Each of the portable edge acceptance devices is assigned a corresponding local depth-first search stack to detect the central node in the corresponding node cluster and to calculate the three-dimensional Euclidean distance between the central node and the three-dimensional centroid. If the detected three-dimensional spatial Euclidean distance is greater than or equal to a preset distance threshold, the central node is added to the global set of nodes to be assigned, and a corresponding subset of acceptance tasks is assigned to the portable edge acceptance device according to the global set of nodes to be assigned.

[0018] Furthermore, the building module is specifically used for: Calculate the point connectivity of each central node in the global set of nodes to be assigned in the directed acyclic graph dependent on the acceptance item, and use the central node with a point connectivity of 1 as the split point. The global set of nodes to be allocated is divided into several dependency chain segments according to the segmentation point, and the starting node coordinates and ending node coordinates of each dependency chain segment are detected. The corresponding top node coordinates of each portable edge acceptance device in the local depth-first search stack are also detected. The target three-dimensional Euclidean distance between the starting node coordinates, the ending node coordinates, and the stack top node coordinates is calculated, and a corresponding subset of acceptance tasks is assigned to each of the portable edge acceptance devices according to the magnitude of the target three-dimensional Euclidean distance.

[0019] Furthermore, the processing module is specifically used for: The portable edge acceptance device detects the dependency chain of the corresponding acceptance task subset, and assigns corresponding feature extraction operator weights to each acceptance item according to the execution order of each acceptance item in the dependency chain. The portable edge acceptance device collects images, point clouds, and temperature and humidity data, and collects local feature vectors of the corresponding dimensions according to the weights of the feature extraction operators, and generates corresponding feature metadata for each local feature vector. Based on the edge connection relationship of the directed acyclic graph that the acceptance items depend on, the local feature vectors and feature metadata of each portable edge acceptance device are matrixed to generate the global acceptance state matrix.

[0020] Furthermore, the generation module is specifically used for: Each row of the global acceptance status matrix is ​​mapped to a status block, wherein the status block contains a block header and a block body; According to the execution order of the directed acyclic graph that the acceptance items depend on, a hash operation is performed on the data contained in the block header to generate a first hash chain, and a second hash operation is performed on the data contained in the block body to generate a second hash chain. The first hash chain and the second hash chain are associated and stored to generate a chained data structure with hash values.

[0021] The third aspect of the present invention proposes: A computer includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the big data-based power transmission line engineering acceptance method described above.

[0022] The fourth aspect of the present invention proposes: A readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the big data-based acceptance method for power transmission line projects as described above.

[0023] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0024] Figure 1 A flowchart of the big data-based acceptance method for power transmission line projects provided in the first embodiment of the present invention; Figure 2 The structural block diagram of the big data-based power transmission line engineering acceptance system provided in the third embodiment of the present invention is shown.

[0025] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0026] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0027] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0029] Please see Figure 1 The diagram illustrates a big data-based acceptance method for power transmission line projects provided in the first embodiment of this invention. This method generates a global acceptance state matrix through multimodal data acquisition at the edge, local feature extraction, and global consistency inference. It achieves real-time fusion and state synchronization of distributed data without cloud computing, improving the accuracy and reliability of the acceptance results. Finally, it automatically converts the acceptance state into a chain-like data structure and generates an engineering acceptance report, realizing automation and traceability of the entire acceptance process and significantly improving the efficiency and quality of power transmission line project acceptance.

[0030] Specifically, this embodiment provides: A method for acceptance testing of power transmission line projects based on big data, wherein the method includes: Step S10: Preload several acceptance items of the transmission line project and the corresponding physical space constraint topology, and map the several acceptance items to the corresponding three-dimensional space coordinates according to the physical space constraint topology. Mark the dependency relationship, execution condition and constraint relationship between each acceptance item inside the three-dimensional space coordinates to generate the corresponding acceptance task baseline library. It should be noted that the acceptance items for transmission line projects number in the hundreds, covering multiple disciplines such as foundation engineering, tower engineering, line stringing, and grounding engineering. Furthermore, there are strict business dependencies between these acceptance items (e.g., the foundation pouring quality must be inspected before tower erection can be inspected). Additionally, all acceptance items correspond to specific physical spatial locations, constrained by terrain and structural space. Traditional acceptance methods rely solely on paper specifications listing acceptance items, failing to link these items to physical space and dependencies. This leads to a chaotic acceptance process, prone to omissions and violations. This step first preloads the transmission line acceptance items that conform to national and industry standards, and simultaneously imports the project's three-dimensional physical space constraint topology (including terrain, tower locations, conductor routes, foundation coordinates, and other full-space information). Each acceptance item is then precisely mapped to its corresponding three-dimensional spatial coordinates, achieving a one-to-one binding between "acceptance item and physical location." Next, the business dependencies, execution conditions (e.g., tower erection can only proceed when concrete strength reaches 100% of the design value), and spatial constraints (e.g., sag acceptance of adjacent towers must be conducted under the same temperature conditions) between each acceptance item are marked in three-dimensional space. Finally, a standardized acceptance task baseline library is generated. This baseline library clarifies the location, sequence, and constraints of all acceptance items from both spatial and business dimensions, providing a unified benchmark for subsequent task allocation and process control.

[0031] Step S20: Construct the corresponding directed acyclic graph of acceptance item dependencies based on the acceptance task baseline library, and parse the directed acyclic graph of acceptance item dependencies based on the depth-first search algorithm, so as to allocate the subset of acceptance tasks that meet the prerequisite dependency conditions and are physically closest to each portable edge acceptance device. It's important to note that power transmission line projects typically span tens or even hundreds of kilometers, with acceptance personnel scattered across different work sites. Traditional manual task allocation often only considers the number of tasks, neglecting the dependencies and physical distances between acceptance items. This leads to acceptance personnel frequently traveling between different areas, resulting in extremely low efficiency and even violations such as proceeding with subsequent acceptances before completing prerequisite items. This step constructs a directed acyclic graph (DAG) of acceptance item dependencies based on the dependencies in the baseline acceptance task library. Each node in the graph represents an acceptance item, and directed edges represent prerequisite dependencies between acceptance items, ensuring that subsequent nodes can only be started after prerequisite nodes are completed. Then, the DAG is parsed using a depth-first search algorithm, combined with the real-time locations of each portable edge acceptance device, to assign each device a subset of acceptance tasks that meet the prerequisite dependency conditions and are physically closest. This allocation method ensures that the acceptance process strictly follows the business dependency order, preventing violations, while minimizing the travel distance of acceptance personnel, achieving optimal task allocation and significantly improving on-site acceptance efficiency.

[0032] Step S30: Perform multimodal data acquisition and local feature extraction processing on the subset of acceptance tasks using the portable edge acceptance device, so as to perform global consistency inference on all generated local feature vectors and generate the corresponding global acceptance state matrix. It's important to note that in traditional acceptance testing, data collected by different personnel often lacks uniformity in format and standards, and is scattered across personal devices, making real-time sharing and global verification impossible. This can easily lead to duplicate acceptance or missed acceptance of the same item by multiple personnel. This step utilizes portable edge acceptance devices (such as handheld terminals equipped with LiDAR, high-definition cameras, and sensors) to collect multimodal data on-site, including high-definition images of acceptance items, 3D point clouds, environmental temperature and humidity, and concrete strength data. Simultaneously, local feature extraction is performed at the edge, converting the raw data into standardized local feature vectors, significantly reducing data transmission volume. Finally, the local feature vectors from all edge devices are uploaded to the cloud for global consistency inference. This verifies whether there are conflicts in the data collected by different devices, whether the dependencies of acceptance items are met, and whether the acceptance standards are consistent, ultimately generating a global acceptance status matrix covering all acceptance items. This matrix clearly presents the acceptance status of each item (not started, in progress, passed, failed), acceptance data, acceptance personnel, and time, achieving global visualization and consistent control of the acceptance process.

[0033] Step S40: Convert the global acceptance status matrix into a corresponding chain data structure according to the execution order of the acceptance items, and generate a corresponding project acceptance report based on the target data contained in the chain data structure.

[0034] It should be noted that traditional acceptance reports are mostly paper or electronic documents, which are easily tampered with, and intermediate data in the acceptance process cannot be traced, failing to meet the requirements of the lifelong responsibility system for project quality. This step converts the global acceptance status matrix into a chain data structure according to the execution order of the acceptance items. The acceptance data and status of each acceptance item are treated as nodes in the chain structure, and a hash algorithm ensures that the node data is tamper-proof. Based on this chain data structure, a standardized project acceptance report is automatically generated, including an acceptance overview, details of acceptance items, rectification records of non-conformities, signatures of acceptance personnel, and on-site data attachments. This report is not only complete in content and standardized in format, but all data is traceable to the corresponding acceptance personnel, time, and original data collection, and it also has anti-tampering characteristics, fully complying with the compliance requirements of project acceptance.

[0035] Second Embodiment Furthermore, the step of annotating the dependencies, execution conditions, and constraints between each acceptance item within the three-dimensional spatial coordinates to generate a corresponding acceptance task baseline library includes: The physical space constraint topology is discretized into an unstructured mesh composed of tetrahedral elements, and each acceptance item is mapped to a target tetrahedral element containing its physical location. The vertex coordinates, volume, normal vector, and adjacent element connection relationship of the target tetrahedral element are extracted as the spatial constraint features of the current acceptance item. Using the key structural nodes of the transmission line project as the root node, a spatial constraint graph is constructed with tetrahedral elements as nodes and common surfaces between elements as edges. Starting from the root node, the constraint relationships are propagated along the edges of the spatial constraint graph to generate the dependencies, execution relationships, and constraint relationships between each of the acceptance items. Based on the dependencies, execution conditions, and constraints, a corresponding set of conditions is constructed, and the acceptance task baseline library is generated accordingly based on the set of conditions.

[0036] It is important to note that transmission lines traverse complex and varied terrains, including irregular landforms such as mountains, hills, and valleys. The structures of towers, foundations, and conductors also possess complex spatial forms. Traditional structured meshes (such as square meshes) cannot accurately fit these irregular spatial structures, leading to inaccurate descriptions of the spatial constraints of acceptance items. This step uses tetrahedral elements to discretize the physical spatial constraint topology. Tetrahedral elements are the simplest polyhedra in three-dimensional space, capable of flexibly fitting arbitrarily complex terrains and structures to generate unstructured meshes. Each acceptance item is then mapped to a target tetrahedral element containing its physical location. For example, the acceptance item for the foundation of a tower is mapped to a tetrahedral element containing the coordinates of that foundation, and the sag acceptance item for a conductor is mapped to a tetrahedral element containing the conductor. Simultaneously, the vertex coordinates, volume, normal vector, and connectivity with adjacent elements of the target tetrahedral element are extracted as spatial constraint features for that acceptance item. These features comprehensively describe the spatial location of the acceptance item, the terrain conditions, and the relationship with adjacent structures, providing an accurate spatial foundation for subsequent propagation of spatial constraint relationships.

[0037] Key structural nodes in transmission line projects (such as starting towers, ending towers, and long-span towers) form the backbone of the entire project, and all acceptance constraints revolve around these key nodes. This step uses these key structural nodes as root nodes to construct a spatial constraint diagram: each node in the diagram corresponds to a tetrahedral element, the common faces between elements correspond to edges in the diagram, and the weight of the edge represents the spatial association strength between two elements. Then, starting from the root node, the constraint relationships are propagated along the edges of the spatial constraint diagram, gradually spreading the acceptance constraints of the root node (such as tower erection can only proceed after foundation acceptance is passed) to all adjacent tetrahedral elements. Combined with the engineering acceptance specifications, the business dependencies (such as conductor erection can only proceed after tower erection acceptance is passed), execution relationships (such as all acceptance items for the same tower must be executed consecutively), and spatial constraint relationships (such as the sag acceptance of two adjacent conductor spans must be conducted under the same weather conditions) between various acceptance items are automatically generated. This automatic propagation method is more efficient and accurate than manually sorting out constraint relationships, covering all acceptance item constraints and avoiding omissions.

[0038] This step integrates the dependencies, execution conditions, and spatial constraints of all previously generated acceptance items to construct a standardized set of conditions. Each acceptance item corresponds to a subset of conditions, clearly defining all prerequisites that must be met before the acceptance item can be initiated. The condition set is then bound to the basic information, spatial coordinates, and acceptance criteria of the acceptance items, ultimately generating a complete acceptance task baseline library. This baseline library not only contains the basic information of all acceptance items but also fully defines all constraints between them. It serves as the sole benchmark for subsequent task allocation, process control, and result verification, ensuring that the entire acceptance process strictly adheres to engineering specifications and spatial constraints.

[0039] Furthermore, the step of generating the acceptance task baseline library based on the condition set includes: The common surface area, the angle between the common surface normal vectors, and the volume ratio of the two adjacent tetrahedral elements corresponding to each edge in the spatial constraint graph are detected to calculate the constraint transfer coefficient of each edge. The constraint transfer coefficients are then multiplied to obtain a transfer weight value that matches the condition set. Based on the set of conditions, a conditional dependency adjacency matrix with all acceptance items as nodes is constructed according to the transmitted weight values, and a normalized Laplace transform is performed on the conditional dependency adjacency matrix to generate a spatially constrained Laplace matrix. The eigenvectors corresponding to the first few smallest non-zero eigenvalues ​​of the spatial constraint Laplacian matrix are calculated, and the projection values ​​of each acceptance item onto the eigenvectors are used as the constraint eigenvectors of the current acceptance item. A corresponding constraint set is constructed based on the constraint eigenvectors, and the constraint set and the condition set are integrated into the acceptance task baseline library.

[0040] It's important to note that traditional constraint relationships only qualitatively describe the existence of dependencies, failing to quantify the strength of these dependencies. This makes it difficult to distinguish between critical and secondary constraints during task allocation. This step calculates the constraint transfer coefficient for each edge based on the geometric characteristics of adjacent tetrahedral cells in the spatial constraint graph: a larger common face area indicates a stronger spatial relationship between the two cells, resulting in a larger constraint transfer coefficient; a smaller angle between the common face normals indicates a more consistent spatial orientation between the two cells, also resulting in a larger constraint transfer coefficient; and a volume ratio closer to 1 indicates comparable spatial scales between the two cells, further increasing the constraint transfer coefficient. By weighting these three geometric characteristics, the constraint transfer coefficient for each edge is obtained, quantifying the constraint influence between two adjacent acceptance items. Then, all constraint transfer coefficients along the path from the root node to the target acceptance item are multiplied to obtain the transfer weight value for that acceptance item. A larger weight value indicates a stronger constraint from the root node, resulting in a higher priority during the acceptance process. This quantification method transforms the originally qualitative constraint relationships into quantitative weight values, providing a precise quantitative basis for subsequent task allocation and priority ranking.

[0041] This step uses all acceptance items as nodes and the transitive weights as elements of the adjacency matrix to construct a conditional dependency adjacency matrix. The element A[i][j] in the matrix represents the transitive weight of the constraint on acceptance item j by acceptance item i. Then, a normalized Laplace transform is performed on this adjacency matrix to generate a spatial constraint Laplace matrix. The Laplace matrix is ​​a core tool in graph theory for describing graph structure features. It effectively captures the global relationships between nodes in the graph. Normalization eliminates the influence of differences in node degree, allowing the matrix to more accurately reflect the constraint relationships between acceptance items. The spatial constraint Laplace matrix transforms physical and business constraints in three-dimensional space into a mathematical matrix form, providing a foundation for subsequent constraint feature extraction.

[0042] The eigenvectors corresponding to the smallest non-zero eigenvalues ​​of the spatial constraint Laplacian matrix are called the Fourier basis of the graph, reflecting the global clustering characteristics of the nodes in the graph. This step calculates the eigenvectors corresponding to the first k smallest non-zero eigenvalues ​​of the spatial constraint Laplacian matrix. The projection values ​​of each acceptance item onto these eigenvectors are combined to form the constraint feature vector for that acceptance item. This vector fully characterizes the position and constraint characteristics of the acceptance item in the entire spatial constraint graph. Then, the acceptance items are clustered based on the constraint feature vectors, grouping those with similar constraint characteristics into one class to construct an optimized constraint set. Finally, the optimized constraint set is integrated with the original condition set to generate the final acceptance task baseline library. The optimized baseline library not only contains qualitative constraint relationships but also quantitative constraint feature vectors, providing a more accurate basis for subsequent task allocation. This ensures that task allocation considers not only physical distance but also the constraint characteristics of the acceptance items, further improving the rationality of task allocation.

[0043] Furthermore, the step of parsing the directed acyclic graph of the acceptance item dependencies based on the depth-first search algorithm to allocate the subset of acceptance tasks that satisfy the pre-dependency conditions and are physically closest to each portable edge acceptance device includes: The acceptance item depends on each node in the directed acyclic graph to perform spatial clustering to generate several node clusters, and calculates the three-dimensional spatial centroid of each node cluster to assign each node cluster to the portable edge acceptance device closest to its centroid. Each of the portable edge acceptance devices is assigned a corresponding local depth-first search stack to detect the central node in the corresponding node cluster and to calculate the three-dimensional Euclidean distance between the central node and the three-dimensional centroid. If the detected three-dimensional spatial Euclidean distance is greater than or equal to a preset distance threshold, the central node is added to the global set of nodes to be assigned, and a corresponding subset of acceptance tasks is assigned to the portable edge acceptance device according to the global set of nodes to be assigned.

[0044] It should be noted that the acceptance items for transmission line projects are linearly distributed along the line, but clustered at locations such as towers and foundations. Spatial clustering can effectively identify this clustering characteristic. This step uses K-means++ or DBSCAN spatial clustering algorithms to divide the nodes in the dependent directed acyclic graph into several spatial clusters based on the three-dimensional spatial coordinates of the acceptance items. The acceptance items within each cluster are physically concentrated, and the distance between clusters is relatively large. Then, the three-dimensional spatial centroid of each cluster is calculated, and each cluster is assigned to the portable edge acceptance device closest to its centroid. This initial allocation method ensures that the acceptance area handled by each device is relatively concentrated, eliminating the need for acceptance personnel to work across large areas, significantly reducing travel costs and improving acceptance efficiency.

[0045] This step assigns a local depth-first search stack to each portable edge acceptance device to manage acceptance tasks within the clusters it is responsible for. Within each cluster, the node whose constraint feature vector is closest to the cluster centroid is identified as the central node. This node is typically the core acceptance item within the cluster (e.g., pole erection acceptance item). The Euclidean distance between the central node and the cluster's 3D centroid is then calculated. This distance reflects the rationality of the central node's position within the cluster. If the central node is close to the centroid, the cluster division is reasonable, and tasks within the cluster can be assigned using local depth-first search. If the distance is far, the cluster division is unreasonable, and some tasks may need to be assigned across clusters.

[0046] When the distance between the central node and the cluster centroid exceeds a preset threshold, it indicates that the central node and its associated acceptance items are too far from the area of ​​responsibility of the current device. Forcibly assigning them to the current device would result in excessive travel distances for acceptance personnel, leading to inefficiency. In this case, the central node is added to the global set of nodes to be assigned, and the cloud scheduling center performs global unified allocation based on the real-time location, task load, and completion progress of all portable edge acceptance devices. This "local allocation + global balancing" task allocation mode ensures efficient local execution of most tasks while resolving the issues of unreasonable initial clustering and uneven load distribution through global scheduling, achieving overall optimal task allocation.

[0047] Furthermore, the step of allocating a corresponding subset of acceptance tasks to the portable edge acceptance device based on the global set of nodes to be allocated includes: Calculate the point connectivity of each central node in the global set of nodes to be assigned in the directed acyclic graph dependent on the acceptance item, and use the central node with a point connectivity of 1 as the split point. The global set of nodes to be allocated is divided into several dependency chain segments according to the segmentation point, and the starting node coordinates and ending node coordinates of each dependency chain segment are detected. The corresponding top node coordinates of each portable edge acceptance device in the local depth-first search stack are also detected. The target three-dimensional Euclidean distance between the starting node coordinates, the ending node coordinates, and the stack top node coordinates is calculated, and a corresponding subset of acceptance tasks is assigned to each of the portable edge acceptance devices according to the magnitude of the target three-dimensional Euclidean distance.

[0048] It's important to note that vertex connectivity is a graph theory metric describing the importance of nodes. Nodes with a connectivity of 1 are "bridge nodes" in a dependency chain; removing such a node breaks the dependency chain into two independent parts. This step calculates the connectivity of each central node in the global set of nodes to be assigned, using nodes with a connectivity of 1 as split points. These split points represent independently splittable locations within the dependency chain. By using these split points, the originally continuous dependency chain can be divided into several independent dependency chain segments. Each segment contains acceptance items with strong dependencies, requiring continuous execution by the same device, while the dependencies between segments are weaker and can be assigned to different devices. This segmentation method ensures that the dependencies within each dependency chain segment are not broken while also enabling flexible allocation of global tasks.

[0049] This step divides the global set of nodes to be assigned into several independent dependency chains based on the identified split points. Each chain has a clearly defined start and end node. Simultaneously, it acquires the coordinates of the top node in the local depth-first search stack of each portable edge acceptance device in real time. The top node represents the location of the acceptance item currently being executed or about to be executed by the device, which is its current operating position. By obtaining this coordinate information, the distance between each dependency chain and the current operating position of each device can be calculated, providing a quantitative basis for subsequent allocation.

[0050] This step calculates the three-dimensional Euclidean distances between the start and end nodes of each dependency chain segment and the top node of each device stack, taking the minimum value as the target distance between the dependency chain segment and the corresponding device. Then, each dependency chain segment is assigned to the portable edge acceptance device with the smallest target distance. This assignment method allows devices to directly proceed to the nearest dependency chain segment after completing the task at the top of the stack, eliminating the need for back-and-forth travel and further optimizing the movement path of acceptance personnel, thus improving overall acceptance efficiency. Simultaneously, because the dependencies within each dependency chain segment are complete and assigned to the same device for execution, it ensures that the dependencies during the acceptance process are not broken, preventing unauthorized operations.

[0051] Furthermore, the step of performing multimodal data acquisition and local feature extraction processing on the subset of acceptance tasks using the portable edge acceptance device, and then performing global consistency inference on all generated local feature vectors to generate the corresponding global acceptance state matrix includes: The portable edge acceptance device detects the dependency chain of the corresponding acceptance task subset, and assigns corresponding feature extraction operator weights to each acceptance item according to the execution order of each acceptance item in the dependency chain. The portable edge acceptance device collects images, point clouds, and temperature and humidity data, and collects local feature vectors of the corresponding dimensions according to the weights of the feature extraction operators, and generates corresponding feature metadata for each local feature vector. Based on the edge connection relationship of the directed acyclic graph that the acceptance items depend on, the local feature vectors and feature metadata of each portable edge acceptance device are matrixed to generate the global acceptance state matrix.

[0052] It's important to note that different acceptance items have varying degrees of importance in project quality. For example, the acceptance of foundation rebar spacing is far more important than the acceptance of foundation appearance. Furthermore, the quality of upstream acceptance items in the dependency chain directly affects the results of downstream acceptance items. Therefore, different feature extraction weights need to be assigned to different acceptance items. This step first detects the dependency chain of the current equipment acceptance task subset. Based on the importance level of the acceptance item and its position in the dependency chain, a corresponding feature extraction operator weight is assigned to each acceptance item: the higher the importance and the upstream of the dependency chain, the greater the weight, and the higher the accuracy requirement for feature extraction; conversely, the lower the importance and the lower the accuracy requirement for feature extraction. This weighted approach allows for the rational allocation of computing resources from edge devices, improving overall processing efficiency while ensuring the accuracy of feature extraction for key acceptance items.

[0053] The quality characteristics of different acceptance items for power transmission lines need to be characterized using different modal data: image data is suitable for detecting appearance defects (such as concrete cracks and galvanized layer peeling), 3D point cloud data is suitable for detecting geometric dimensions (such as foundation dimensions, tower tilt, and conductor sag), and temperature and humidity data are used to record the environmental conditions during acceptance (such as the ambient temperature for concrete strength testing and sag acceptance). This step uses a portable edge acceptance device with a high-definition camera, LiDAR, and environmental sensors to simultaneously collect these three types of multimodal data; then, based on the previously assigned feature extraction operator weights, features are extracted from the data of different modalities to generate standardized local feature vectors; simultaneously, corresponding feature metadata is generated for each local feature vector, including acceptance item number, collection time, collection personnel, equipment number, environmental parameters, etc., ensuring that each feature vector is traceable to the corresponding collection process. This multimodal weighted feature extraction method comprehensively covers the quality characteristics of acceptance items while ensuring the standardization and traceability of feature data.

[0054] This step constructs a global acceptance status matrix, with acceptance items as rows and feature dimensions, acceptance status, and metadata as columns. Based on the edge connections of the directed acyclic graph (DAG) that the acceptance items depend on, the local feature vectors and feature metadata uploaded by each edge device are filled into the corresponding positions in the matrix. Simultaneously, global consistency inference is performed to verify the following: first, dependency verification, ensuring that when the status of a downstream acceptance item is "passed," the status of all its upstream acceptance items is also "passed"; second, data consistency verification, ensuring that there are no conflicts between multi-source data for the same acceptance item; and third, standard consistency verification, ensuring that feature extraction and judgment for all acceptance items follow a unified acceptance standard. Any items that fail verification are marked as abnormal and reported to the corresponding acceptance personnel for review. The final generated global acceptance status matrix completely and accurately presents the acceptance status and quality data of all acceptance items, achieving global visualization and consistency control of the acceptance process.

[0055] Furthermore, the step of converting the global acceptance status matrix into a corresponding chained data structure according to the execution order of the acceptance items includes: Each row of the global acceptance status matrix is ​​mapped to a status block, wherein the status block contains a block header and a block body; According to the execution order of the directed acyclic graph that the acceptance items depend on, a hash operation is performed on the data contained in the block header to generate a first hash chain, and a second hash operation is performed on the data contained in the block body to generate a second hash chain. The first hash chain and the second hash chain are associated and stored to generate a chained data structure with hash values.

[0056] It's important to note that each status block consists of two parts: a block header and a block body. The block header stores the metadata for the acceptance item, including the item number, execution order, hash of the preceding acceptance item, acceptance time, and acceptance personnel number, representing the execution order and relationships of the acceptance items. The block body stores the core acceptance data for the item, including local feature vectors, feature metadata, acceptance conclusions, and descriptions of non-conforming items, representing the quality status of the acceptance item. This block-based storage method encapsulates the information of each acceptance item into an independent unit, laying the foundation for subsequent hash chain construction.

[0057] This step strictly follows the execution order of the directed acyclic graph (DAG) that the acceptance items depend on. A SHA-256 hash operation is performed on the block header data of each state block, embedding the hash value of the previous block into the block header of the next block, forming the first hash chain. The order of the first hash chain is completely consistent with the execution order of the acceptance items; any tampering with the acceptance execution order will cause the hash chain to break, making it easily detectable. Simultaneously, a second hash operation is performed on the block body data of each state block, generating a second hash chain. The second hash chain protects the core acceptance data of each acceptance item; any tampering with the acceptance data will cause a change in the corresponding block body hash value, thus being detected. This dual hash chain structure provides double protection for both the acceptance execution order and the acceptance data, ensuring the immutability of the entire acceptance process.

[0058] This step links the first and second hash chains through acceptance item numbers. The block header hash and block body hash of each acceptance item correspond one-to-one, forming a unique digital fingerprint for that item. Then, all blocks are chained together in execution order to generate the final chained data structure. This chained data structure has three core characteristics: first, immutability—any alteration to the acceptance order or acceptance data will break the hash chain; second, traceability—the hash chain allows tracing the execution process and original data of each acceptance item; and third, integrity—the integrity of the hash chain ensures that no data from any acceptance item has been omitted or tampered with. The engineering acceptance report generated based on this chained data structure is not only complete and formatted correctly, but also possesses legal traceability and tamper-proof characteristics, fully meeting the compliance requirements of the lifelong responsibility system for engineering quality and providing a reliable digital basis for the quality control of transmission line projects.

[0059] Please see Figure 2 The third embodiment of the present invention provides: A big data-based power transmission line engineering acceptance system, wherein the system includes: The mapping module is used to preload several acceptance items of the transmission line project and the corresponding physical space constraint topology, and to map the several acceptance items to the corresponding three-dimensional space coordinates according to the physical space constraint topology. The module also marks the dependencies, execution conditions and constraint relationships between the acceptance items in the three-dimensional space coordinates to generate the corresponding acceptance task baseline library. The construction module is used to construct the corresponding directed acyclic graph of acceptance item dependencies based on the acceptance task baseline library, and parse the directed acyclic graph of acceptance item dependencies based on the depth-first search algorithm, so as to allocate the subset of acceptance tasks that meet the prerequisite dependency conditions and are physically closest to each portable edge acceptance device. The processing module is used to perform multimodal data acquisition and local feature extraction processing on the subset of acceptance tasks through the portable edge acceptance device, so as to perform global consistency inference on all the generated local feature vectors and generate the corresponding global acceptance state matrix. The generation module is used to convert the global acceptance status matrix into a corresponding chain data structure according to the execution order of the acceptance items, and generate a corresponding project acceptance report based on the target data contained in the chain data structure.

[0060] Furthermore, the mapping module is specifically used for: The physical space constraint topology is discretized into an unstructured mesh composed of tetrahedral elements, and each acceptance item is mapped to a target tetrahedral element containing its physical location. The vertex coordinates, volume, normal vector, and adjacent element connection relationship of the target tetrahedral element are extracted as the spatial constraint features of the current acceptance item. Using the key structural nodes of the transmission line project as the root node, a spatial constraint graph is constructed with tetrahedral elements as nodes and common surfaces between elements as edges. Starting from the root node, the constraint relationships are propagated along the edges of the spatial constraint graph to generate the dependencies, execution relationships, and constraint relationships between each of the acceptance items. Based on the dependencies, execution conditions, and constraints, a corresponding set of conditions is constructed, and the acceptance task baseline library is generated accordingly based on the set of conditions.

[0061] Furthermore, the mapping module is specifically used for: The common surface area, the angle between the common surface normal vectors, and the volume ratio of the two adjacent tetrahedral elements corresponding to each edge in the spatial constraint graph are detected to calculate the constraint transfer coefficient of each edge. The constraint transfer coefficients are then multiplied to obtain a transfer weight value that matches the condition set. Based on the set of conditions, a conditional dependency adjacency matrix with all acceptance items as nodes is constructed according to the transmitted weight values, and a normalized Laplace transform is performed on the conditional dependency adjacency matrix to generate a spatially constrained Laplace matrix. The eigenvectors corresponding to the first few smallest non-zero eigenvalues ​​of the spatial constraint Laplacian matrix are calculated, and the projection values ​​of each acceptance item onto the eigenvectors are used as the constraint eigenvectors of the current acceptance item. A corresponding constraint set is constructed based on the constraint eigenvectors, and the constraint set and the condition set are integrated into the acceptance task baseline library.

[0062] Furthermore, the building module is specifically used for: The acceptance item depends on each node in the directed acyclic graph to perform spatial clustering to generate several node clusters, and calculates the three-dimensional spatial centroid of each node cluster to assign each node cluster to the portable edge acceptance device closest to its centroid. Each of the portable edge acceptance devices is assigned a corresponding local depth-first search stack to detect the central node in the corresponding node cluster and to calculate the three-dimensional Euclidean distance between the central node and the three-dimensional centroid. If the detected three-dimensional spatial Euclidean distance is greater than or equal to a preset distance threshold, the central node is added to the global set of nodes to be assigned, and a corresponding subset of acceptance tasks is assigned to the portable edge acceptance device according to the global set of nodes to be assigned.

[0063] Furthermore, the building module is specifically used for: Calculate the point connectivity of each central node in the global set of nodes to be assigned in the directed acyclic graph dependent on the acceptance item, and use the central node with a point connectivity of 1 as the split point. The global set of nodes to be allocated is divided into several dependency chain segments according to the segmentation point, and the starting node coordinates and ending node coordinates of each dependency chain segment are detected. The corresponding top node coordinates of each portable edge acceptance device in the local depth-first search stack are also detected. The target three-dimensional Euclidean distance between the starting node coordinates, the ending node coordinates, and the stack top node coordinates is calculated, and a corresponding subset of acceptance tasks is assigned to each of the portable edge acceptance devices according to the magnitude of the target three-dimensional Euclidean distance.

[0064] Furthermore, the processing module is specifically used for: The portable edge acceptance device detects the dependency chain of the corresponding acceptance task subset, and assigns corresponding feature extraction operator weights to each acceptance item according to the execution order of each acceptance item in the dependency chain. The portable edge acceptance device collects images, point clouds, and temperature and humidity data, and collects local feature vectors of the corresponding dimensions according to the weights of the feature extraction operators, and generates corresponding feature metadata for each local feature vector. Based on the edge connection relationship of the directed acyclic graph that the acceptance items depend on, the local feature vectors and feature metadata of each portable edge acceptance device are matrixed to generate the global acceptance state matrix.

[0065] Furthermore, the generation module is specifically used for: Each row of the global acceptance status matrix is ​​mapped to a status block, wherein the status block contains a block header and a block body; According to the execution order of the directed acyclic graph that the acceptance items depend on, a hash operation is performed on the data contained in the block header to generate a first hash chain, and a second hash operation is performed on the data contained in the block body to generate a second hash chain. The first hash chain and the second hash chain are associated and stored to generate a chained data structure with hash values.

[0066] The fourth embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the big data-based power transmission line engineering acceptance method described above.

[0067] The fifth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the big data-based power transmission line engineering acceptance method as described above.

[0068] In summary, the big data-based transmission line engineering acceptance method and system provided in the above embodiments of the present invention generates a global acceptance status matrix through multimodal data acquisition at the edge, local feature extraction, and global consistency inference. This achieves real-time fusion and status synchronization of distributed data without cloud computing, improving the accuracy and reliability of the acceptance results. Finally, the acceptance status is automatically converted into a chain-like data structure and an engineering acceptance report is generated, realizing automation and traceability of the entire acceptance process and significantly improving the efficiency and quality of transmission line engineering acceptance.

[0069] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0070] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0071] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0072] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0073] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0074] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for acceptance testing of power transmission line projects based on big data, characterized in that, The method includes: Several acceptance items and corresponding physical space constraint topologies of the transmission line project are preloaded, and the acceptance items are mapped to corresponding three-dimensional spatial coordinates according to the physical space constraint topologies. The dependencies, execution conditions and constraint relationships between each acceptance item are marked inside the three-dimensional spatial coordinates to generate the corresponding acceptance task baseline library. Based on the baseline library of acceptance tasks, a corresponding directed acyclic graph of acceptance item dependencies is constructed, and the directed acyclic graph of acceptance item dependencies is parsed based on the depth-first search algorithm, so as to allocate a subset of acceptance tasks that meet the prerequisite dependency conditions and are physically closest to each portable edge acceptance device. The portable edge acceptance device performs multimodal data acquisition and local feature extraction processing on the subset of acceptance tasks, so as to perform global consistency inference on all the generated local feature vectors and generate the corresponding global acceptance state matrix. The global acceptance status matrix is ​​converted into a corresponding chained data structure according to the execution order of the acceptance items, and the corresponding project acceptance report is generated based on the target data contained in the chained data structure.

2. The method for acceptance of power transmission line projects based on big data according to claim 1, characterized in that, The step of annotating the dependencies, execution conditions, and constraints between each acceptance item within the three-dimensional spatial coordinates to generate a corresponding acceptance task baseline library includes: The physical space constraint topology is discretized into an unstructured mesh composed of tetrahedral elements, and each acceptance item is mapped to a target tetrahedral element containing its physical location. The vertex coordinates, volume, normal vector, and adjacent element connection relationship of the target tetrahedral element are extracted as the spatial constraint features of the current acceptance item. Using the key structural nodes of the transmission line project as the root node, a spatial constraint graph is constructed with tetrahedral elements as nodes and common surfaces between elements as edges. Starting from the root node, the constraint relationships are propagated along the edges of the spatial constraint graph to generate the dependencies, execution relationships, and constraint relationships between each of the acceptance items. Based on the dependencies, execution conditions, and constraints, a corresponding set of conditions is constructed, and the acceptance task baseline library is generated accordingly based on the set of conditions.

3. The method for acceptance of power transmission line projects based on big data according to claim 2, characterized in that, The step of generating the acceptance task baseline library according to the set of conditions includes: The common surface area, the angle between the common surface normal vectors, and the volume ratio of the two adjacent tetrahedral elements corresponding to each edge in the spatial constraint graph are detected to calculate the constraint transfer coefficient of each edge. The constraint transfer coefficients are then multiplied to obtain a transfer weight value that matches the condition set. Based on the set of conditions, a conditional dependency adjacency matrix with all acceptance items as nodes is constructed according to the transmitted weight values, and a normalized Laplace transform is performed on the conditional dependency adjacency matrix to generate a spatially constrained Laplace matrix. The eigenvectors corresponding to the first few smallest non-zero eigenvalues ​​of the spatial constraint Laplacian matrix are calculated, and the projection values ​​of each acceptance item onto the eigenvectors are used as the constraint eigenvectors of the current acceptance item. A corresponding constraint set is constructed based on the constraint eigenvectors, and the constraint set and the condition set are integrated into the acceptance task baseline library.

4. The method for acceptance of power transmission line projects based on big data according to claim 1, characterized in that, The step of parsing the directed acyclic graph of acceptance item dependencies using a depth-first search algorithm to allocate the subset of acceptance tasks that satisfy the pre-dependency conditions and are physically closest to each portable edge acceptance device includes: The acceptance item depends on each node in the directed acyclic graph to perform spatial clustering to generate several node clusters, and calculates the three-dimensional spatial centroid of each node cluster to assign each node cluster to the portable edge acceptance device closest to its centroid. Each of the portable edge acceptance devices is assigned a corresponding local depth-first search stack to detect the central node in the corresponding node cluster and to calculate the three-dimensional Euclidean distance between the central node and the three-dimensional centroid. If the detected three-dimensional spatial Euclidean distance is greater than or equal to a preset distance threshold, the central node is added to the global set of nodes to be assigned, and a corresponding subset of acceptance tasks is assigned to the portable edge acceptance device according to the global set of nodes to be assigned.

5. The method for acceptance of power transmission line projects based on big data according to claim 4, characterized in that, The step of allocating a corresponding subset of acceptance tasks to the portable edge acceptance device based on the global set of nodes to be assigned includes: Calculate the point connectivity of each central node in the global set of nodes to be assigned in the directed acyclic graph dependent on the acceptance item, and use the central node with a point connectivity of 1 as the split point. The global set of nodes to be allocated is divided into several dependency chain segments according to the segmentation point, and the starting node coordinates and ending node coordinates of each dependency chain segment are detected. The corresponding top node coordinates of each portable edge acceptance device in the local depth-first search stack are also detected. The target three-dimensional Euclidean distance between the starting node coordinates, the ending node coordinates, and the stack top node coordinates is calculated, and a corresponding subset of acceptance tasks is assigned to each of the portable edge acceptance devices according to the magnitude of the target three-dimensional Euclidean distance.

6. The method for acceptance of power transmission line projects based on big data according to claim 1, characterized in that, The step of performing multimodal data acquisition and local feature extraction processing on the subset of acceptance tasks using the portable edge acceptance device, and then performing global consistency inference on all generated local feature vectors to generate the corresponding global acceptance state matrix includes: The portable edge acceptance device detects the dependency chain of the corresponding acceptance task subset, and assigns corresponding feature extraction operator weights to each acceptance item according to the execution order of each acceptance item in the dependency chain. The portable edge acceptance device collects images, point clouds, and temperature and humidity data, and collects local feature vectors of the corresponding dimensions according to the weights of the feature extraction operators, and generates corresponding feature metadata for each local feature vector. Based on the edge connection relationship of the directed acyclic graph that the acceptance items depend on, the local feature vectors and feature metadata of each portable edge acceptance device are matrixed to generate the global acceptance state matrix.

7. The method for acceptance of power transmission line projects based on big data according to claim 1, characterized in that, The step of converting the global acceptance status matrix into a corresponding chained data structure according to the execution order of the acceptance items includes: Each row of the global acceptance status matrix is ​​mapped to a status block, wherein the status block contains a block header and a block body; According to the execution order of the directed acyclic graph that the acceptance items depend on, a hash operation is performed on the data contained in the block header to generate a first hash chain, and a second hash operation is performed on the data contained in the block body to generate a second hash chain. The first hash chain and the second hash chain are associated and stored to generate a chained data structure with hash values.

8. A big data-based power transmission line engineering acceptance system, characterized in that, The system includes: The mapping module is used to preload several acceptance items of the transmission line project and the corresponding physical space constraint topology, and to map the several acceptance items to the corresponding three-dimensional space coordinates according to the physical space constraint topology. The module also marks the dependencies, execution conditions and constraint relationships between the acceptance items in the three-dimensional space coordinates to generate the corresponding acceptance task baseline library. The construction module is used to construct the corresponding directed acyclic graph of acceptance item dependencies based on the acceptance task baseline library, and parse the directed acyclic graph of acceptance item dependencies based on the depth-first search algorithm, so as to allocate the subset of acceptance tasks that meet the prerequisite dependency conditions and are physically closest to each portable edge acceptance device. The processing module is used to perform multimodal data acquisition and local feature extraction processing on the subset of acceptance tasks through the portable edge acceptance device, so as to perform global consistency inference on all the generated local feature vectors and generate the corresponding global acceptance state matrix. The generation module is used to convert the global acceptance status matrix into a corresponding chain data structure according to the execution order of the acceptance items, and generate a corresponding project acceptance report based on the target data contained in the chain data structure.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the big data-based acceptance method for power transmission line projects as described in any one of claims 1 to 7.

10. A readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the big data-based acceptance method for power transmission line projects as described in any one of claims 1 to 7.

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