A power transmission line infrastructure construction multi-dimensional intelligent acceptance data checking method, system, device, medium and product

Abstract

The application discloses a power transmission line infrastructure construction multi-dimensional intelligent acceptance data checking method, system, equipment, medium and product, relates to the power transmission construction acceptance technical field, constructs a multi-dimensional integrated acceptance checking system covering space points, component images and physical dimensions, generates a final acceptance report by respectively carrying out point deviation special checking, component feature comparison checking and size risk grading checking and integrating sub-item acceptance results, completely changes the single dimension, scattered checking and short board of the traditional acceptance mode, effectively solves the problem that the existing power transmission line infrastructure construction quality control is not comprehensive, and meanwhile, the application sets professional point deviation fine checking logic, cooperates with component feature accurate comparison and size quantization grading checking mode, gives up inherent disadvantages of traditional manual extensive checking and experiential determination, effectively makes up for defects of insufficient fine degree of construction deviation checking and limited accuracy of acceptance determination.

Description

Technical Field

[0001] This invention relates to the field of power transmission construction and acceptance technology, and in particular to a method, system, equipment, medium and product for multi-dimensional intelligent acceptance data verification of power transmission line infrastructure construction. Background Technology

[0002] Power transmission projects are an important part of the power system, undertaking the core functions of long-distance power transmission and grid interconnection. Among them, the construction of transmission line infrastructure is a key link to ensure the safe and stable operation of power transmission projects.

[0003] With the continuous advancement of the construction of new power systems, the scale of ultra-high voltage and long-span transmission lines is constantly expanding, placing higher demands on the quality and safety management standards of infrastructure construction. The acceptance inspection of transmission line infrastructure construction is a core control measure for ensuring construction quality, identifying safety hazards, and guaranteeing the long-term stable operation of the project. The comprehensiveness and accuracy of the acceptance results directly affect the safety and stability of the transmission project and even the entire power system.

[0004] For a long time, the acceptance of power transmission line infrastructure construction has mainly relied on manual on-site verification, supplemented by random inspections using simple measuring tools. However, the traditional acceptance method is limited by the limitations of manual verification. On the one hand, it lacks a systematic quality verification system, making it difficult to achieve comprehensive control over construction quality; on the other hand, the verification accuracy of key construction deviations is insufficient, failing to provide reliable support for construction quality control and making it difficult to meet the increasingly standardized and refined construction requirements of current power transmission projects. Summary of the Invention

[0005] This invention provides a method, system, equipment, medium, and product for multi-dimensional intelligent acceptance data verification of transmission line infrastructure construction, which solves the technical problems of incomplete coverage of existing quality control of transmission line infrastructure construction, insufficient refinement of construction deviation verification, and limited accuracy of acceptance judgment.

[0006] The first aspect of this invention provides a multi-dimensional intelligent acceptance data verification method for transmission line infrastructure construction, comprising: A standard design database for transmission line infrastructure is constructed, which includes standard spatial location data, standard image data, and standard physical dimension data of standard target acceptance points; Multiple pre-set data acquisition terminals are used to collect construction data at the construction site in real time and perform correlation matching to construct a structured data package of the target to be inspected. The structured data package of the target to be inspected includes the target's spatial location data, image data, and physical size data. The point deviation is verified by comparing the spatial location data to be inspected with the standard spatial location data to obtain the first acceptance report; The component features are compared and verified using the image data to be inspected and the standard image data to obtain a second acceptance report. The physical dimension data to be accepted is compared with the standard physical dimension data to perform a dimension risk classification verification, and a third acceptance report is obtained. Integrate the first acceptance report, the second acceptance report, and the third acceptance report to output the target acceptance report.

[0007] Optionally, the step of verifying the point deviation by comparing the spatial location data to be accepted with the standard spatial location data to obtain a first acceptance report includes: Using the spatial location data to be inspected and the standard spatial location data, the effective absolute deviation of each point to be inspected is calculated in three axes, including the effective absolute deviation of the X-axis, the effective absolute deviation of the Y-axis and the effective absolute deviation of the Z-axis. When any two or three of the three effective absolute deviations are greater than the preset effective deviation threshold, the point to be accepted is marked as a composite deviation point to be accepted. The remaining unmarked points to be inspected will be designated as target points to be inspected. Based on the preset working condition cross-term coefficient table, match the first cross-term coefficient of each composite deviation to be accepted point; Based on the first cross term coefficient and the preset deviation weight coefficient, and combined with the three-axis effective absolute deviation associated with the composite deviation acceptance point, the first spatial deviation value is calculated. By comparing the first spatial deviation value of each composite deviation point to be accepted with the preset weighted deviation threshold, the first spatial acceptance information is obtained; Based on the preset second cross term coefficient and the preset deviation weight coefficient, combined with the effective absolute deviation of the three axes associated with the target acceptance point, the second spatial deviation value is calculated. By comparing the second spatial deviation value of each target acceptance point with the preset weighted deviation threshold, the second spatial acceptance information is obtained. By integrating the first space acceptance information with the second space acceptance information, a first acceptance report is obtained.

[0008] Optionally, the step of performing a size risk classification verification using the physical size data to be accepted and the standard physical size data to obtain a third acceptance report includes: Using the physical dimension data to be accepted and the standard physical dimension data, calculate the absolute dimensional deviation of each component to be accepted. The absolute dimensional deviation includes the target dimensional absolute deviation of multiple measured dimensional items. Using the absolute deviation of the target size, combined with the preset absolute values ​​of the upper and lower tolerances of asymmetry, the preset key weights, and the preset nonlinear penalty index, the target contribution value of each of the measured size items is calculated; Calculate the sum of all the target contribution values ​​to obtain the adaptive nonlinear dimensional deviation index of each of the components to be accepted; Based on the preset quality level range in which the adaptive nonlinear size deviation index is located, size risk classification information is obtained by matching. By integrating all the aforementioned size risk classification information, a third acceptance report is obtained.

[0009] Optionally, the plurality of pre-set data acquisition terminals include a spatial location acquisition terminal, a camera acquisition terminal, and a dimension acquisition terminal. Both the camera acquisition terminal and the dimension acquisition terminal are equipped with object locators. The step of using the plurality of pre-set data acquisition terminals to collect construction data from the construction site in real time and performing correlation matching to construct a structured data package of the target to be accepted includes: The target to be accepted is determined based on the spatial location data collected by the spatial location acquisition terminal; The positioning information of the object locator of the video acquisition terminal and the size acquisition terminal is matched with the spatial location data of the target to be inspected; The data collected by the camera acquisition terminal and the size acquisition terminal, whose matching results show consistent positioning information, are aggregated and processed to obtain a structured data packet of the target to be inspected.

[0010] Optionally, the construction of the transmission line infrastructure standard design database includes: Obtain the infrastructure design drawings and design specifications for power transmission lines; Design parameters are extracted using a standardized transformation model to construct a standard design database; The standardized conversion model includes an optical character recognition model, an image semantic segmentation model, and a natural language processing model. The optical character recognition model, the image semantic segmentation model, and the natural language processing model are used to extract textual information, graphic information, and semantic information from the power transmission line infrastructure design drawings and design specifications, respectively.

[0011] Optionally, it also includes: The target acceptance report is visualized.

[0012] The second aspect of this invention provides a multi-dimensional intelligent acceptance data verification system for transmission line infrastructure construction, comprising: The construction module is used to build a standard design database for transmission line infrastructure, which includes standard spatial location data, standard image data, and standard physical dimension data of standard target acceptance points; The matching module is used to collect construction data from the construction site in real time using multiple pre-set data acquisition terminals and perform correlation matching to construct a structured data package of the target to be accepted. The structured data package of the target to be accepted includes the target's spatial location data to be accepted, image data to be accepted, and physical size data to be accepted. The first verification module is used to verify the point deviation by comparing the spatial location data to be accepted with the standard spatial location data, and to obtain the first acceptance report. The second verification module is used to compare and verify the component features by comparing the image data to be accepted with the standard image data to obtain a second acceptance report. The third verification module is used to perform size risk classification verification by comparing the physical size data to be accepted with the standard physical size data, and to obtain a third acceptance report. The report output module is used to integrate the first acceptance report, the second acceptance report, and the third acceptance report to output the target acceptance report.

[0013] The third aspect of the present invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the multi-dimensional intelligent acceptance data verification method for power transmission line infrastructure construction as described above.

[0014] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the multi-dimensional intelligent acceptance data verification method for transmission line infrastructure construction as described above.

[0015] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the multi-dimensional intelligent acceptance data verification method for transmission line infrastructure construction as described above.

[0016] As can be seen from the above technical solutions, the present invention has the following advantages: This invention provides a multi-dimensional intelligent acceptance data verification method, system, equipment, medium, and product for transmission line infrastructure construction. It constructs a multi-dimensional integrated acceptance verification system covering spatial locations, component images, and physical dimensions. By conducting separate verifications of location deviations, component feature comparisons, and dimensional risk classifications, and integrating the results of each item's acceptance to generate a final acceptance report, it completely changes the shortcomings of traditional acceptance models, which are characterized by single dimensions and fragmented verification. This effectively solves the problem of incomplete coverage in existing transmission line infrastructure construction quality control. Furthermore, this invention specifically sets up a professional, refined verification logic for location deviations, combined with precise comparisons of component features and quantitative dimensional classification verification methods. It abandons the inherent drawbacks of traditional, crude manual verification and experience-based judgment, effectively compensating for the insufficient refinement of construction deviation verification and the limited accuracy of acceptance judgment. Through the coordinated efforts of standardized data benchmarks, structured data matching, and multi-dimensional precise item verification, it achieves comprehensive, refined, and quantitative intelligent acceptance control of transmission line infrastructure construction quality, significantly improving the completeness and accuracy of transmission line infrastructure construction acceptance, and fully meeting the standardized and refined quality control construction needs of modern transmission engineering. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the steps of a multi-dimensional intelligent acceptance data verification method for power transmission line infrastructure construction provided in Embodiment 1 of the present invention. Figure 2 This is a flowchart illustrating the steps of a multi-dimensional intelligent acceptance data verification method for power transmission line infrastructure construction provided in Embodiment 2 of the present invention. Figure 3 This is a structural block diagram of a multi-dimensional intelligent acceptance data verification system for power transmission line infrastructure construction provided in Embodiment 3 of the present invention; Figure 4 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0019] This invention provides a method, system, equipment, medium, and product for verifying multi-dimensional intelligent acceptance data in transmission line infrastructure construction. It addresses the technical problems of incomplete quality control coverage in existing transmission line infrastructure construction, insufficient refinement of construction deviation verification, and limited accuracy of acceptance judgment.

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a multi-dimensional intelligent acceptance data verification method for power transmission line infrastructure construction, as provided in Embodiment 1 of the present invention.

[0022] This invention provides a multi-dimensional intelligent acceptance data verification method for power transmission line infrastructure construction, comprising: Step 101: Construct a standard design database for transmission line infrastructure. The standard design database for transmission line infrastructure includes standard spatial location data, standard image data, and standard physical dimension data of standard target acceptance points.

[0023] In this embodiment of the invention, the complete set of design data corresponding to the infrastructure project of the transmission line to be inspected is first obtained, including line construction drawings, component design parameter files, and supporting design specifications, as the original source of standard data. The obtained original design data is then standardized and structured, and benchmark data corresponding to all the pre-set standard target acceptance points of the project are extracted in batches. Specifically, this includes standard spatial location data of each acceptance point, standard image data of components, and standard physical dimension data of components. The standard spatial location data consists of the standard three-dimensional coordinate benchmarks of each tower, line foundation, and hardware installation point; the standard image data consists of benchmark image data of various transmission components under standard installation forms; and the standard physical dimension data consists of the standard design nominal dimension benchmarks of transmission components. The extracted three types of standard data are then classified, organized, and linked, and uniformly entered and stored in a pre-set database carrier, completing the construction of the transmission line infrastructure standard design database.

[0024] Step 102: Use multiple pre-set data acquisition terminals to collect construction data from the construction site in real time and perform correlation matching to construct a structured data package of the target to be accepted. The structured data package of the target to be accepted includes the target's spatial location data, image data, and physical dimension data.

[0025] In this embodiment of the invention, multiple pre-installed data acquisition terminals are deployed at the construction site of the power transmission line infrastructure. These terminals collect real-time data from the construction site, simultaneously acquiring measured spatial location data, real-scene image data of components, and actual physical dimension data of the components. For the collected multi-source, scattered construction data, a unified association and matching process is performed according to the dimensions of the target to be inspected, filtering and aggregating all measured data belonging to the same target. The aggregated measured data is then standardized, organized, and structured, unifying the data format and data association relationships to construct a structured data package specific to the target to be inspected. This structured data package fully carries the spatial location data, image data, and physical dimension data of the corresponding target to be inspected.

[0026] Step 103: Use the spatial location data to be accepted and the standard spatial location data to verify the point deviation and obtain the first acceptance report.

[0027] In this embodiment of the invention, standard spatial location data matching the current target to be accepted is retrieved from a pre-built standard design database for transmission line infrastructure, and spatial location data to be accepted is extracted from the corresponding structured data package. Using the standard spatial location data as a comparison benchmark, point deviation verification is performed on the two sets of location data to complete the deviation check and status determination for each point to be accepted. All acceptance information obtained from this point deviation verification is summarized and organized, and standardized according to a preset report format to finally generate the first acceptance report.

[0028] Step 104: Compare and verify the component features using the image data to be accepted and the standard image data to obtain the second acceptance report.

[0029] In this embodiment of the invention, standard image data matching the target to be accepted is retrieved from the standard design database for transmission line infrastructure, and image data to be accepted is extracted from the corresponding structured data package. Using the standard image data as a comparison benchmark, component feature comparison and verification are performed on the two sets of image data to check the differences between the actual appearance, installation form, and other features of the components and the design benchmark. All verification information generated from this feature comparison is summarized and organized, and standardized according to a preset report format to finally generate a second acceptance report.

[0030] Step 105: Use the physical dimension data to be accepted and the standard physical dimension data to perform dimension risk classification verification and obtain the third acceptance report.

[0031] In this embodiment of the invention, standard physical dimension data matching the current target to be accepted is retrieved from the standard design database for transmission line infrastructure, and physical dimension data to be accepted is extracted from the corresponding structured data package. Using the standard physical dimension data as a benchmark, the measured physical dimensions of each component to be accepted are compared and verified item by item. Dimensional deviation calculations and dimensional risk classification checks are performed to quantitatively determine the construction quality of the component dimensions. The dimensional verification results and corresponding risk classification information of all components are summarized and organized according to the preset report output specifications, ultimately generating a third acceptance report.

[0032] Step 106: Integrate the first acceptance report, the second acceptance report, and the third acceptance report to output the target acceptance report.

[0033] In this embodiment of the invention, the first acceptance report generated from point deviation verification, the second acceptance report generated from component feature comparison verification, and the third acceptance report generated from dimensional risk classification verification are extracted respectively. Verification data, deviation information, quality judgment results, and risk rating information from these three sub-acceptance reports are collected. Following preset acceptance data integration specifications and report generation standards, the sub-acceptance results of the three different dimensions are systematically summarized, correlated, matched, and uniformly organized to achieve the fusion and linkage of single-dimensional acceptance data, forming complete acceptance information covering all dimensions of the construction quality of the target to be accepted. Finally, a standardized target acceptance report is integrated and output.

[0034] The Transmission Line Infrastructure Standard Design Database stores various standard benchmark data during the design phase of transmission line infrastructure projects, providing a unified and authoritative reference for construction data comparison. Standard target acceptance points are key nodes or components in transmission line infrastructure projects, designated according to design requirements and requiring construction quality acceptance, including tower installation locations, foundation centers, and hardware installation positions. Standard spatial location data are the benchmark coordinate data of the standard target acceptance points in a three-dimensional spatial coordinate system, including standard coordinate values ​​in three directions: line alignment, perpendicular to line alignment, and elevation. Standard image data are benchmark image data of standard target acceptance points in standard installation conditions, recording the standard appearance and installation status of components. Information includes: assembly form, distribution of key components, etc.; standard physical dimension data, which is the nominal design dimension data of the components corresponding to the standard target acceptance points, covering standard values ​​of parameters such as component length, aperture, verticality, and fastening torque; pre-installed data acquisition terminals, which are special equipment pre-deployed at the construction site for collecting various types of construction data, enabling multi-source data acquisition such as spatial location, images, and physical dimensions; construction site, referring to the actual work site of transmission line infrastructure projects, including work areas such as tower installation, line erection, and component processing and installation; construction data, which are various measured data collected during the construction process, used to reflect the actual construction status of the project entity; association matching, which refers to matching multi-source construction data obtained from different acquisition terminals according to the same... The process involves binding and aggregating targets to be inspected, ensuring a one-to-one correspondence between data and inspection points. Targets to be inspected are physical objects on the construction site that require quality inspection, such as single-base towers, individual components, or single installation points. Structured data packages are data packets formed by standardizing and uniformly encapsulating multi-source construction data for the same target. These packages contain three types of structured content: spatial location data, image data, and physical dimension data. Spatial location data refers to the actual three-dimensional coordinates of the target collected on-site, reflecting the true spatial location of the construction point. Image data refers to real-scene images of the target collected on-site, reflecting the structural dimensions. The acceptance criteria include: actual installation form and appearance of the component; physical dimension data to be accepted, which is the measured dimension data of the corresponding component of the target to be accepted collected on the construction site, including parameters such as measured length, hole diameter, verticality, and fastening torque; point deviation verification, which is the process of comparing the actual spatial position data of the target to be accepted with the standard spatial position data, calculating the position deviation, and determining whether the construction status meets the specifications; the first acceptance report, which is the sub-item acceptance report generated after the point deviation verification is completed, recording the deviation calculation results, judgment conclusions, and related original data of each point to be accepted; and component feature comparison verification, which is the process of comparing the real-scene image data of the target to be accepted with the standard image data to identify the differences between the component shape, installation status, and design requirements.The second acceptance report is a sub-item acceptance report generated after the component feature comparison and verification is completed. It records the feature matching results, defect information, and judgment conclusions of each component to be inspected. The dimensional risk classification verification is a verification process that compares the actual physical dimension data of the target to be inspected with the standard physical dimension data, calculates the dimensional deviation, and classifies the risk level according to the severity of the deviation. The third acceptance report is a sub-item acceptance report generated after the dimensional risk classification verification is completed. It records the dimensional deviation calculation results, risk level, and judgment conclusions of each component to be inspected. Integration is a process of associating, summarizing, and merging the data from multiple sub-item acceptance reports to form a comprehensive construction quality information covering all dimensions of the target to be inspected. The target acceptance report is the final acceptance report generated after integrating the first, second, and third acceptance reports. It includes the overall construction quality conclusions of the target to be inspected, details of each sub-item verification, defect risk information, and rectification suggestions.

[0035] This invention first establishes a unified standard design database for power transmission line infrastructure, providing a standardized and unique data comparison benchmark for the entire acceptance process, thus avoiding the problem of inconsistent evaluation benchmarks in traditional acceptance methods. It then utilizes various pre-installed acquisition terminals to collect multi-source data from the field and construct structured data packages, achieving standardized collection and associated management of on-site measured data and ensuring the integrity and standardization of the comparison data sources. Based on this, it sequentially conducts point deviation verification, component feature comparison verification, and dimensional risk classification verification, incorporating the three core verification dimensions of spatial location, component morphology, and physical dimensions into an integrated acceptance process. This effectively compensates for the shortcomings of traditional acceptance models, such as single verification dimensions and incomplete quality control coverage. Simultaneously, each dimension employs specialized verification logic for quantitative judgment, conducting professional verification and result analysis for point offsets, component differences, and dimensional deviations. This changes the previous crude mode of manual, rough verification and experience-based judgment, significantly improving the precision of construction deviation verification and the accuracy of acceptance judgment results. Finally, the three sub-acceptance reports were integrated and summarized to output a complete target acceptance report, realizing the organic integration of multi-dimensional acceptance results and forming a complete acceptance closed loop from benchmark establishment, data collection, sub-item verification to result summary. This not only comprehensively and objectively reflects the overall quality of transmission line infrastructure construction, but also provides detailed and reliable data support for subsequent construction rectification, quality traceability and project operation and maintenance, fully adapting to the refined and standardized quality control needs of transmission engineering.

[0036] Please see Figure 2 , Figure 2 This is a flowchart illustrating the steps of a multi-dimensional intelligent acceptance data verification method for power transmission line infrastructure construction, as provided in Embodiment 2 of the present invention.

[0037] This invention provides a multi-dimensional intelligent acceptance data verification method for power transmission line infrastructure construction, comprising: Step 201: Construct a standard design database for transmission line infrastructure. The standard design database for transmission line infrastructure includes standard spatial location data, standard image data, and standard physical dimension data of standard target acceptance points.

[0038] Further, step 201 may include the following sub-steps: S11. Obtain the infrastructure design drawings and design specifications for the transmission lines.

[0039] In this embodiment of the invention, a complete set of design documents corresponding to the infrastructure project of the transmission line to be inspected is retrieved, including the general layout plan of the project, construction drawings of towers and line foundations, component processing drawings, design change documents, and supporting formal design documents such as design specifications and technical specifications. The collection and integrity verification of the design documents are completed to ensure that the obtained drawings and specifications cover all construction contents of the project to be inspected and are consistent with the actual construction scope and design standards of the project.

[0040] S12. Extract design parameters through a standardized transformation model and construct a standard design database; The standardized conversion model includes an optical character recognition model, an image semantic segmentation model, and a natural language processing model. These models are used to extract textual, graphic, and semantic information from the engineering design drawings and specifications of power transmission lines, respectively.

[0041] In this embodiment of the invention, a pre-built standardized conversion model is invoked to automatically parse and extract parameters from the acquired design drawings and design specifications. The standardized conversion model includes an optical character recognition (OCR) model, an image semantic segmentation model, and a natural language processing (NLP) model. These three models work together to extract and fuse multi-dimensional design information: the OCR model identifies and extracts textual information from the design drawings and specifications, including design parameter annotations, technical specification clauses, and dimensional tolerance descriptions; the image semantic segmentation model parses and extracts graphic information from the design drawings, identifying and segmenting graphic elements such as the location markings of standard target acceptance points, component shape outlines, and key dimension annotations; and the NLP model understands and extracts semantic information from the design specifications, including semantic information from unstructured text such as component installation requirements, spatial location benchmark descriptions, and physical dimension standard definitions. Through the collaborative operation of three types of models, key design parameter information can be accurately identified and extracted from unstructured or semi-structured documents such as design drawings and instructions. Then, the text, graphics, and semantic information scattered in different types of documents are integrated and transformed into unified structured data, forming a machine-readable standard design database. This database contains standard spatial location data, standard image data, and standard physical dimension data of standard target acceptance points, providing a unified and accurate benchmark for the automated comparison and verification of on-site measured data. This avoids ambiguities and errors that may arise from manual interpretation of design documents and ensures the objectivity of acceptance standards.

[0042] Design drawings are construction drawings supporting the infrastructure engineering of transmission lines, including graphic content such as the overall project plan, tower foundation drawings, component fabrication drawings, dimensioning, and installation outlines. Design specifications are textual explanatory documents supporting the infrastructure engineering of transmission lines, including textual and semantic content such as design parameters, technical specifications, component installation requirements, and benchmark definitions. The standardized conversion model is an integrated processing model composed of multiple algorithm models, used to extract design parameters from design drawings and design specifications, and to build a standard design database, enabling the conversion of unstructured documents to structured data. The optical character recognition model is a component of the standardized conversion model, specifically used to recognize and extract various textual information from design drawings and design specifications, such as parameter annotations, technical clauses, and tolerance specifications. The image semantic segmentation model is a component of the standardized conversion model, specifically used to parse and segment various graphic information in design drawings, recognizing graphic elements such as acceptance points, component outlines, and dimensioning. The natural language processing model is a component of the standardized conversion model, specifically used to understand and extract semantic information from design specifications, parsing the textual meaning of component installation rules, location benchmarks, and dimensional standards.

[0043] It is worth mentioning that by incorporating optical character recognition (OCR), image semantic segmentation, and natural language processing (NLP) models into the standardized conversion model, it is understood that power transmission engineering design documents contain various formats such as drawings and text descriptions. By comprehensively utilizing OCR, image semantic segmentation, and NLP models, key parameter information can be accurately identified and extracted from these unstructured or semi-structured documents. Moreover, through multi-model collaboration, information scattered across different types of documents is integrated and transformed into unified structured data, constructing a comprehensive and formatted standard design database, providing a high-quality comparison foundation for intelligent comparison.

[0044] Step 202: Use multiple pre-set data acquisition terminals to collect construction data from the construction site in real time and perform correlation matching to construct a structured data package of the target to be accepted. The structured data package of the target to be accepted includes the target's spatial location data, image data, and physical dimension data.

[0045] In this embodiment of the invention, multiple pre-set data acquisition terminals include a spatial location acquisition terminal, a camera acquisition terminal, and a dimension acquisition terminal. Specifically, the spatial location acquisition terminal employs a camera drone and / or a satellite positioning system, enabling efficient acquisition of macroscopic spatial location data of the target to be inspected in scenarios involving large-scale, high-altitude operations at power transmission line infrastructure construction sites. The camera acquisition terminal employs a fixed camera and / or a mobile camera, capturing high-definition real-scene image data of the target to be inspected. The dimension acquisition terminal employs a laser scanner and / or a CCD camera, accurately acquiring the three-dimensional physical dimension data of the target to be inspected. Simultaneously, both the camera acquisition terminal and the dimension acquisition terminal are equipped with object locators to record their location information during acquisition operations. Through the combined deployment of different types of acquisition terminals, various complex scenarios in power transmission line infrastructure construction can be comprehensively covered, ensuring that construction data from all key acceptance points can be effectively collected.

[0046] Furthermore, the multiple pre-set data acquisition terminals include a spatial location acquisition terminal, a camera acquisition terminal, and a size acquisition terminal. Both the camera acquisition terminal and the size acquisition terminal are equipped with object locators. Step 202 may include the following sub-steps: S21. Determine the target to be accepted based on the spatial location data collected by the spatial location acquisition terminal.

[0047] In this embodiment of the invention, a pre-deployed spatial location acquisition terminal is used to collect data covering the entire construction site and obtain the spatial location data of each target to be inspected. Based on the collected spatial location data and combined with the acceptance point planning information of the power transmission line infrastructure project, each target to be inspected is located and identified, and the unique spatial location information of each target to be inspected is determined, which serves as the benchmark identifier for data association and matching.

[0048] S22. Match the positioning information of the object locator of the video acquisition terminal and the size acquisition terminal with the spatial location data of the target to be accepted.

[0049] In this embodiment of the invention, for the camera acquisition terminal and the size acquisition terminal, during the acquisition operation, the location information corresponding to each acquisition operation is recorded synchronously through the acquisition object locator configured on them; the location information recorded by the acquisition object locator of the camera acquisition terminal and the size acquisition terminal is compared and matched with the spatial location data of the target to be accepted determined in S21, and it is determined whether the acquisition location coincides with the spatial location of the target to be accepted, thereby establishing the association relationship between the image data, physical size data and the corresponding target to be accepted.

[0050] S23. Collect and process the data collected by the camera acquisition terminal and the size acquisition terminal whose matching results are consistent with the positioning information to obtain the structured data package of the target to be accepted.

[0051] In this embodiment of the invention, the data collected by the camera acquisition terminal and the size acquisition terminal whose positioning information overlaps with the spatial location data of the target to be inspected are collected, regulated and structured and packaged; the spatial location data, real scene image data and physical size data corresponding to the same target to be inspected are integrated to construct a structured data package exclusive to the target to be inspected, which includes the target to be inspected spatial location data, the target to be inspected image data and the target to be inspected physical size data.

[0052] Spatial location acquisition terminals are a type of pre-installed data acquisition terminal, mainly used to collect spatial location data of the target to be inspected. Common devices include camera drones and satellite positioning equipment. Camera acquisition terminals are a type of pre-installed data acquisition terminal, mainly used to capture and collect real-scene image data of the target to be inspected. These include fixed cameras and mobile cameras. Size acquisition terminals are a type of pre-installed data acquisition terminal, mainly used to measure and collect physical size data of the target to be inspected. Common devices include laser scanners and CCD cameras. Object locators are positioning devices installed on camera acquisition terminals and size acquisition terminals. They are used to record the position information of the equipment when it is collecting data, and to achieve correlation and matching between image data, size data and the spatial location of the target to be inspected.

[0053] Understandably, this embodiment utilizes multiple types of pre-installed data acquisition terminals to acquire construction site data in real time and constructs a structured data package of the target to be inspected. This transforms the complex construction site situation into a digital model that can be accurately compared with a standard design database, replacing manual on-site observation and recording, thus greatly improving the efficiency and accuracy of data acquisition. Simultaneously, by setting up object locators on camera and dimension acquisition terminals, image data and physical dimension data are bound to spatial location data, and spatial location data is used as a unique identifier for matching. This ensures that all data regarding the same inspection point can be accurately and error-free. This location-matching-based aggregation method avoids data mismatch problems caused by manual recording or time asynchrony, ensuring that the data foundation for subsequent multi-dimensional comparisons is accurate and reliable, directly improving the confidence of the inspection results. Subsequent multi-dimensional comparisons of spatial location, images, and physical dimensions based on this structured data package with the standard design database enable automation and intelligence in the inspection process. This allows for comprehensive and objective detection of construction deviations, significantly improving the comprehensiveness and accuracy of the inspection compared to manual spot checks and subjective judgments.

[0054] It is worth mentioning that this invention sets up object locators in both the camera acquisition terminal and the size acquisition terminal. By binding image data and physical size data with spatial location data through the object locators, and using spatial location data as a unique identifier for matching, it ensures that all data about the same acceptance point can be accurately collected together. Moreover, this location-matching-based collection method avoids data mismatch problems caused by manual recording or time asynchrony, ensuring that the data basis for subsequent multi-dimensional comparison is accurate and reliable, and directly improving the confidence of the acceptance results.

[0055] Furthermore, the spatial location acquisition terminal includes a camera drone and / or a satellite positioning system; the camera acquisition terminal includes a fixed camera and / or a mobile camera; and the size acquisition terminal includes a laser scanner and / or a CCD camera.

[0056] It is worth mentioning that, by including a camera drone and / or a satellite positioning system in the spatial location acquisition terminal, this invention can efficiently acquire macroscopic spatial location data for large-scale, high-altitude power transmission lines; by including a fixed camera and / or a mobile camera in the camera acquisition terminal, fixed or mobile cameras can capture high-definition images for local details; and by including a laser scanner and / or a CCD camera in the dimension acquisition terminal, laser scanners and CCD cameras can accurately acquire three-dimensional dimension information. The combination of different terminals can cover various complex scenarios in power transmission line infrastructure construction, helping to ensure that data from all key acceptance points can be effectively acquired.

[0057] Step 203: Use the spatial location data to be accepted and the standard spatial location data to verify the point deviation and obtain the first acceptance report.

[0058] It should be noted that in the construction and acceptance of transmission line infrastructure, the deviation of spatial locations is a core verification item that directly affects the structural stability and operational safety of the line. Traditional acceptance systems suffer from incomplete quality control coverage. This problem manifests in the location verification stage as most acceptance work involves only sporadic spot checks, failing to incorporate location deviation verification into standardized and systematic routine verification processes. This has resulted in a long-term lack of control over this crucial dimension, further exacerbating the overall incomplete quality coverage. Furthermore, even in the few instances where location verification is conducted, traditional verification methods only perform simple comparisons of deviations in a single direction, using a uniform standard for broad judgment. This approach fails to consider the safety impact weights of different axial deviations based on the operating characteristics of the transmission line, and cannot identify compound deviations formed by the superposition of two or more directions. This broad verification model is prone to misjudgment due to the simplistic judgment criteria, and may overlook hidden compound deviation hazards. Consequently, it has gradually developed inherent defects such as insufficient refinement in location deviation verification and limited accuracy in acceptance judgment. The point deviation verification step set up in this invention first incorporates point verification into the integrated acceptance system, making up for the shortcomings of traditional control in this dimension and improving the problem of incomplete coverage of overall quality control. At the same time, by calculating the effective deviation of the three axes, distinguishing between ordinary points and points with composite deviations and matching the corresponding calculation rules, it realizes the hierarchical and quantitative verification of point deviations. It specifically solves the problems of the traditional point verification method being too crude, unable to identify composite deviations, and having inaccurate judgment results. It makes up for the shortcomings of insufficient refinement of construction deviation verification and low accuracy of acceptance judgment from a detailed dimension.

[0059] Furthermore, step 203 may include the following sub-steps: S31. Using the spatial location data to be inspected and the standard spatial location data, calculate the effective absolute deviation of the three axes for each point to be inspected. The effective absolute deviation of the three axes includes the effective absolute deviation of the X-axis, the effective absolute deviation of the Y-axis, and the effective absolute deviation of the Z-axis.

[0060] In this embodiment of the invention, based on the spatial location data of the target to be inspected and the standard spatial location data of the corresponding standard target inspection points in the standard design database, the three-dimensional coordinate values ​​of the two sets of data in a spatial rectangular coordinate system are extracted, where the X-axis corresponds to the direction of the route, the Y-axis corresponds to the direction perpendicular to the route in the horizontal plane, and the Z-axis corresponds to the vertical elevation direction; by subtracting the three-dimensional coordinates of the target inspection point from the three-dimensional coordinates of the standard target inspection point, the coordinate deviation values ​​of each target inspection point in the X-axis, Y-axis, and Z-axis directions are calculated respectively. Δx , Δy , Δz The absolute values ​​of each deviation are then taken to obtain the effective absolute deviation of the three axes for each point to be inspected, i.e., the effective absolute deviation of the X-axis. |Δx| Y-axis effective absolute deviation |Dy| Z-axis effective absolute deviation |Δz| .

[0061] S32. When any two or three effective absolute deviations within the three axes exceed the preset effective deviation threshold, the point to be accepted is marked as a composite deviation point to be accepted.

[0062] In this embodiment of the invention, based on the voltage level, component type, and safe operation requirements of the transmission line, corresponding effective deviation thresholds are pre-set for each axis. The Y-axis (horizontal plane, perpendicular to the line's direction) has the lowest deviation tolerance and the most stringent threshold setting; the Z-axis (vertical elevation direction) is next; and the X-axis (line's direction) has a relatively lenient deviation tolerance. For each point to be inspected, it is determined whether any two or three of its three-axis effective absolute deviations exceed the corresponding preset effective deviation thresholds. If so, the point to be inspected is marked as a composite deviation inspection point. These inspection points involve the superposition of deviations in multiple directions, and the deviation coupling effect may significantly impact the safe operation of the line.

[0063] S33. Designate the remaining unmarked points to be inspected as target points to be inspected.

[0064] In this embodiment of the invention, the remaining acceptance points that are not marked as composite deviation acceptance points are taken as target acceptance points. Among the three-axis effective absolute deviations of these acceptance points, at most one effective absolute deviation exceeds the preset effective deviation threshold, or all deviations do not exceed the threshold. The deviation coupling effect is not significant, and deviation verification can be performed according to simplified logic.

[0065] S34. Based on the preset working condition cross-term coefficient table, match the first cross-term coefficient of each composite deviation to be accepted point.

[0066] In this embodiment of the invention, the preset operating condition cross-term coefficient table stores the cross-term coefficients corresponding to different transmission line operating conditions. d This coefficient, obtained through regression analysis of historical defect data, is used to capture the combined effects of deviations in different directions, such as the composite deviation caused by tower foundation tilting. For each composite deviation acceptance point, the corresponding first cross-term coefficient is matched from the preset cross-term coefficient table based on its operating condition type (e.g., tower type, geological conditions, line section). d .

[0067]

[0068] S35. Based on the first cross term coefficient and the preset deviation weight coefficient, and combined with the effective absolute deviation of the three axes associated with the composite deviation acceptance point, calculate the first spatial deviation value.

[0069] In this embodiment of the invention, based on a preset deviation weighting coefficient α , β , c (in α This is the X-axis deviation weighting coefficient. β This is the Y-axis deviation weighting coefficient. c This is the Z-axis deviation weighting coefficient, and it is usually set... β > c > α This is to reflect the different impacts of deviations in different directions on safe operation. For example, deviations in the vertical alignment can easily lead to wind-induced discharge, and elevation deviations may affect the safe distance to the ground. This is combined with the effective absolute deviations of the three axes at the acceptance points for composite deviations. Δx , Δy , Δz and the coefficient of the first cross term obtained by matching. d The first spatial deviation value of the composite deviation at the acceptance point is calculated using the weighted spatial deviation formula. D pos .

[0070] ; This formula distinguishes the degree of influence of deviations in different directions by weighting coefficients, and introduces cross terms to capture the coupling effect of deviations, avoiding misjudgments (such as judging points that are slightly out of tolerance in the direction of the line but have little impact on safety as unqualified) or omissions (such as cases where the deviation in the sensitive direction is not out of tolerance but the coupling effect is significant).

[0071] S36. Compare the first spatial deviation value of each composite deviation to be accepted point with the preset weighted deviation threshold to obtain the first spatial acceptance information.

[0072] In this embodiment of the invention, a weighted bias threshold is preset. T pos This threshold is determined by comprehensively considering the safety operation requirements of transmission lines and the influence weights of deviations in different directions. The first spatial deviation value of each composite deviation at the acceptance point is... D pos With respect to the preset weighted deviation threshold T pos Compare: If D pos ≤ T pos If the spatial position deviation of the composite deviation to be inspected is deemed acceptable; D pos > Tpos If the deviation is not met, the corresponding deviation value, deviation direction and working condition information are recorded, and the first space acceptance information is obtained by summarizing them.

[0073] S37. Based on the preset second cross term coefficient and the preset deviation weight coefficient, and combined with the effective absolute deviation of the three axes associated with the target acceptance point, calculate the second spatial deviation value.

[0074] In this embodiment of the invention, for the target acceptance point, since its deviation coupling effect is not significant, the preset second cross-term coefficient is set to 0 (i.e., the cross-term influence is ignored). Based on the preset deviation weight coefficient... α , β , c Combined with the effective absolute deviation of the three axes of the target acceptance point Δx , Δy , Δz The second spatial deviation value of the target acceptance point is calculated using the simplified weighted spatial deviation formula. D pos目标 ; ; This simplified formula can improve the calculation efficiency of non-composite deviation points while ensuring the accuracy of the verification.

[0075] S38. Compare the second spatial deviation value of each target acceptance point with the preset weighted deviation threshold to obtain the second spatial acceptance information.

[0076] In this embodiment of the invention, the second spatial deviation value of each target acceptance point is... D pos目标 With respect to the preset weighted deviation threshold T pos Compare: If D pos目标 ≤ T pos If the spatial position deviation of the target to be inspected is deemed acceptable; D pos目标 > T pos If the deviation is not met, it is deemed unqualified, and the corresponding deviation information is recorded. The results are then summarized to obtain the second space acceptance information.

[0077] S39. Integrate the first space acceptance information with the second space acceptance information to obtain the first acceptance report.

[0078] In this embodiment of the invention, the first spatial acceptance information of the composite deviation test points and the second spatial acceptance information of the target test points are integrated, including the deviation value, deviation direction, qualification judgment result, risk level (such as severe, general, qualified) of each test point. The information is then organized according to the preset acceptance report format to finally generate a first acceptance report containing the spatial position deviation verification results of all test points.

[0079] The effective absolute deviation of the three axes is the comprehensive deviation value obtained by taking the absolute value of the difference between the actual coordinates and the standard coordinates of the point to be inspected in the three-dimensional coordinate system. It is divided into effective absolute deviation of the X-axis, effective absolute deviation of the Y-axis, and effective absolute deviation of the Z-axis. The effective absolute deviation of the X-axis is the absolute value of the difference between the actual coordinates and the standard coordinates of the point to be inspected along the line direction. The effective absolute deviation of the Y-axis is the absolute value of the difference between the actual coordinates and the standard coordinates of the point to be inspected in the horizontal plane perpendicular to the line direction. The effective absolute deviation of the Z-axis is the absolute value of the difference between the actual coordinates and the standard coordinates of the point to be inspected along the vertical elevation direction. The preset effective deviation threshold is the upper limit value of the allowable deviation in each coordinate axis direction, which is set according to the transmission line construction specifications and is used to judge the unilateral deviation. Whether the deviation exceeds the reasonable range; composite deviation acceptance points are acceptance points where any two or three effective absolute deviations in the three axes exceed the preset effective deviation threshold. These points have multiple directional deviations superimposed, and the deviation coupling effect is obvious; target acceptance points are acceptance points where at most one effective absolute deviation in the three axes exceeds the preset effective deviation threshold, or all deviations are within the limit. The deviation coupling effect of these points is negligible; the preset operating condition cross-term coefficient table is a table that is pre-compiled and stored for cross-term coefficients under different operating conditions of different transmission lines. The coefficients are obtained through regression analysis of historical defect data and are used to characterize the joint impact effect of deviations in different directions; the first cross-term coefficient is for composite deviation acceptance points. The cross-term coefficients, matched from the preset cross-term coefficient table based on the operating conditions, are used to calculate the spatial deviation value, including the deviation coupling effect. The preset deviation weight coefficients are pre-set weight coefficients corresponding to the deviations of each coordinate axis direction, assigned according to the degree of influence of different directional deviations on the safe operation of the line. The first spatial deviation value is a comprehensive deviation value calculated by combining the first cross-term coefficient, the preset deviation weight coefficients, and the effective absolute deviations of the three axes of the composite deviation acceptance point, while also considering the deviation weights and deviation coupling effect. The preset weighted deviation threshold is a pre-set upper limit for the comprehensive spatial deviation, based on the deviation weights of each direction and the engineering safety requirements, used to determine whether the comprehensive positional deviation of the point is qualified. The first spatial acceptance information is... The information obtained by comparing the first spatial deviation value of the composite deviation acceptance point with the preset weighted deviation threshold includes the deviation value, the pass / fail judgment result, and relevant supporting data; the preset second cross term coefficient is a cross term coefficient specifically set for the target acceptance point, which is generally set to 0 to simplify deviation calculation and ignore deviation coupling effect; the second spatial deviation value is a comprehensive deviation value calculated by combining the preset second cross term coefficient, the preset deviation weight coefficient, and the three-axis effective absolute deviation of the target acceptance point, which only considers the deviation weight and does not consider the coupling effect; the second spatial acceptance information is the information obtained by comparing the second spatial deviation value of the target acceptance point with the preset weighted deviation threshold, including the deviation value, the pass / fail judgment result, and relevant supporting data.

[0080] Step 204: Compare and verify the component features using the image data to be accepted and the standard image data to obtain the second acceptance report.

[0081] In this embodiment of the invention, firstly, for the target to be inspected, standard image data corresponding to each other is retrieved from a pre-built standard design database for transmission line infrastructure. This data is a reference image of the component in a standard installation state, including the overall outline of the component, the quantity and location distribution of key components (such as bolts, insulators, and hardware), installation morphology characteristics, model identification, and component integrity characteristics. Simultaneously, the image data to be inspected, captured by a pre-set camera acquisition terminal, is extracted from the structured data package of the target to be inspected. This data is real-scene image data after the component installation is completed. Subsequently, the two sets of image data are preprocessed uniformly: histogram equalization and denoising algorithms are used to eliminate interference caused by uneven lighting and shooting noise; perspective transformation algorithms are used to align and correct images from different shooting angles; and the perspective and size of the image to be inspected are normalized and matched with the standard image based on the component outline of the standard image to ensure that the two sets of images have a consistent comparison benchmark. Next, component feature extraction and comparison verification were carried out: On the one hand, the overall contour features and key component contours of the components were extracted using the Canny edge detection algorithm, and the morphological similarity between the component to be accepted and the standard component was calculated using the contour intersection-union ratio (IoU); on the other hand, a pre-trained component feature extraction model was called to extract the depth semantic features of the components in the two sets of images respectively, and the corner points and texture features of the key components were matched to calculate the matching coverage and feature similarity of the key components. During the comparison process, a preset threshold was used as the judgment basis: when the overall contour IoU of the component is ≥0.9 and the key component matching coverage is ≥95% and the depth feature similarity is ≥0.9, the construction status of the component is determined to be consistent with the standard design requirements; if the key component matching coverage is less than 90%, it is identified as a missing component; if the depth feature similarity is less than 0.8 and the contour features match the features of other standard components of other models better, it is identified as an incorrect component model; if the positional deviation of the key component or the shape deviation of the component exceeds the preset threshold after contour alignment (such as the crossarm tilt angle exceeding ±5°, the vertical deviation of the insulator string exceeding ±3°), it is identified as an installation shape deviation. During the verification process, the ID, verification status, defect type, location coordinates of the defect in the image, similarity score, and deviation degree information of each component to be inspected are recorded simultaneously, and images of components with defects are marked. Finally, the verification results, defect information, and judgment criteria of all components to be inspected are summarized and organized, and standardized according to the preset acceptance report format to generate a second acceptance report containing details of the component construction quality verification.

[0082] Step 205: Use the physical dimension data to be accepted and the standard physical dimension data to perform dimension risk classification verification and obtain the third acceptance report.

[0083] It should be noted that physical dimensions are important indicators that determine the load-bearing capacity, assembly matching degree, and long-term operational reliability of various components of transmission lines. They are also an indispensable part of construction quality verification. Traditional acceptance systems have the problem of incomplete quality control coverage. In the case of dimensional verification, most acceptance processes do not include the physical dimensions of various components in a unified and routine verification system. They are often only subject to random sampling or rough inspection, which has resulted in long-term loopholes in the quality control of dimensional dimensions and further amplifies the problem of the lack of overall control scope. Even when dimensional checks are conducted in some scenarios, traditional methods generally employ a simple binary judgment model, only distinguishing whether the dimensions are within the allowable deviation range. This approach fails to incorporate differentiated evaluation rules based on the functional attributes and safety levels of the components, and cannot quantify the corresponding risk levels according to the extent of dimensional deviation. Furthermore, it ignores the differences in safety hazards caused by different degrees of deviation, using the same set of evaluation standards for dimensional deviations of core load-bearing components and ordinary auxiliary components, and does not strengthen the identification of risks caused by severe deviations. This crude verification method leaves dimensional checks at the superficial level, resulting in a severe lack of refinement in construction deviation verification and a lack of quantitative basis for acceptance judgments. Ultimately, this leads to limited accuracy in the judgment results, making it difficult to support subsequent targeted quality rectification and hazard management. The dimensional risk classification and verification step set up in this invention first integrates the verification of the physical dimensions of components into a multi-dimensional integrated acceptance system, making up for the shortcomings of traditional acceptance in this dimension, improving the overall control scope of construction quality, and effectively improving the current situation of incomplete control coverage. At the same time, by configuring exclusive tolerance ranges, key weights, and nonlinear penalty indices for different measured dimensional items, a quantified dimensional deviation index is calculated, and the corresponding quality and risk levels are divided according to the index. This realizes the refined calculation, risk quantification, and classification of dimensional deviations, completely changing the previous crude mode of simple binary judgment. It fully takes into account the different impacts brought about by the importance of components and the deviation range, and makes up for the shortcomings of the traditional method in terms of insufficient refinement and limited accuracy of acceptance judgment from the dimension of dimensional verification. It can also provide clear and reliable data support for differentiated rectification work on site based on risk level.

[0084] Furthermore, step 205 may include the following sub-steps: S41. Using the physical dimension data to be accepted and the standard physical dimension data, calculate the absolute dimensional deviation of each component to be accepted. The absolute dimensional deviation includes the target dimensional absolute deviation of multiple measured dimensional items.

[0085] In this embodiment of the invention, firstly, for the component to be inspected (such as tower foundation, crossarm, connecting bolts, insulator strings, etc.), the physical dimension data to be inspected collected at the construction site is extracted from the structured data package of the target to be inspected, and at the same time, the corresponding standard physical dimension data is retrieved from the standard design database. Both the physical dimension data to be inspected and the standard physical dimension data contain all the key measured dimensions of the component, such as bolt tightening torque, effective length of the component, installation hole diameter, verticality, parallelism, component spacing, etc. For each measured dimension, the algebraic difference between the measured value and the standard value in the physical dimension data to be inspected is calculated based on the nominal value in the standard physical dimension data. ΔL i The absolute value of the difference is then taken to obtain the absolute deviation of the target size for the measured dimension. |ΔL i | .

[0086] S42. Using the absolute deviation of the target size, combined with the preset absolute values ​​of the upper and lower tolerances of asymmetry, the preset critical weights, and the preset nonlinear penalty index, calculate the target contribution value of each measured size item.

[0087] In this embodiment of the invention, for each measured dimension item of each type of component, corresponding asymmetric upper and lower tolerance absolute values, critical weights, and nonlinear penalty exponents are pre-configured: wherein the asymmetric upper and lower tolerance absolute values ​​include the lower tolerance absolute value and the upper tolerance absolute value. T Li For the first i The absolute value of the lower tolerance of the dimensional parameter. T Ui For the first i The absolute value of the upper tolerance of the dimension parameter, and satisfying T Ui ≥ T Li ≥0, supports asymmetric tolerance configuration to adapt to construction specification requirements for different dimensional parameters; v i For the first i The critical weight of each dimension item is 1, and the sum of the critical weights of all measured dimensions is 1. The critical weight is determined according to the degree of influence of the dimension parameter on the safety performance of the component. For example, core safety parameters such as bolt tightening torque and critical stress length of the component have higher weights, while the weight of general appearance dimensions is lower. k i For the first i Nonlinear penalty exponent for term size, k i ≥1, where for critical dimensions strongly correlated with safety (such as bolt tightening torque, dimensions related to component bearing capacity), take ≥1. k i>1, which accelerates the penalty for exceeding the tolerance zone. For general appearance dimensions, it can be taken as... k i =1, apply linear penalty.

[0088] For each measured dimension, calculate its target contribution value: First, determine the absolute deviation of the target dimension. |ΔL i | absolute value of lower tolerance T Li The size relationship, if |ΔL i | ≤ T Li This indicates that the dimensional deviation is within the tolerance zone and its contribution to the deviation index is 0; if |ΔL i | > T Li The calculated deviation exceeding the lower tolerance accounts for a portion of the entire tolerance extension range ( T Ui - T Li The proportion of ), that is Then, adjust the ratio. k i After amplification by exponentiation, multiply by the corresponding key weight. v i The target contribution value of the measured dimension item is obtained. .

[0089] This calculation logic embodies the design of "no penalty within tolerance, and proportional non-linear penalty after exceeding tolerance," adapting to the safety risk characteristics of different size items.

[0090] S43. Calculate the sum of all target contribution values ​​to obtain the adaptive nonlinear dimensional deviation index of each component to be accepted.

[0091] In this embodiment of the invention, the sum of all target contribution values ​​is calculated to obtain the adaptive nonlinear dimensional deviation index of each component to be accepted. In this embodiment of the invention, the target contribution values ​​of all measured dimensional items for the same component to be accepted are summed to obtain the adaptive nonlinear dimensional deviation index of that component. D size Its mathematical expression is: ; in M This represents the total number of measured dimensional parameters for the component. This index is weighted by key parameters. v i Differentiating the degree of safety impact of different size items using a non-linear penalty index. ki This demonstrates a non-linear relationship between the degree of deviation and safety risk: for example, taking the value of critical dimensions... k i When =2, the deviation is 10% (i.e. The penalty contribution of (=0.1) is v i ·(0.1) 2 =0.01 v i And exceeding the tolerance by 100% (i.e. The penalty contribution of (=1.0) is v i ·(1.0) 2 =1.0 v i The values ​​increase exponentially, truly reflecting the accelerated rise in safety risks after critical dimensions deviate from tolerance. Compared to the traditional simple binary judgment of "qualified / unqualified", it can more precisely quantify the severity of dimensional deviations and provide data support for subsequent graded rectification decisions.

[0092] S44. Based on the preset quality level range where the adaptive nonlinear size deviation index is located, size risk classification information is obtained.

[0093] In this embodiment of the invention, a pre-set interval is provided for the corresponding relationship between the adaptive nonlinear dimensional deviation index and the quality risk level, so as to... D size The numerical range is used as the basis for judgment, and the specific mathematical judgment rule is as follows: when D size ≤0.2: The match is rated as "qualified"; the dimensional deviation is within an acceptable range and there is no safety risk. When 0.2 < D size ≤0.5: Matching level is "general risk", indicating a slight deviation, which requires close monitoring in subsequent operations; When 0.5 < D size ≤0.8: Matching is at a "higher risk" level, the deviation is significant, and targeted rectification needs to be arranged; when D size >0.8: Matching is at the "serious risk" level. The deviation has significantly affected the safety performance of the component and work must be stopped immediately for rectification.

[0094] Calculated for each component to be inspected D size The value is matched with its preset range to obtain the corresponding size risk classification information, including the risk level, the severity of the deviation, and the corresponding recommended handling measures. S45. Integrate all size risk classification information to obtain the third acceptance report.

[0095] In this embodiment of the invention, the dimensional verification data of all components to be inspected are summarized, including the adaptive nonlinear dimensional deviation index of each component. D size Details of the deviations for each measured dimension ( ΔL i , |ΔL i | ), target contribution value C i The obtained size risk classification information and handling suggestions are organized according to the preset acceptance report format to generate a third acceptance report containing details of component size construction quality verification. The report clearly marks the risk level of each component, providing detailed data support for subsequent construction rectification, quality traceability and project operation and maintenance.

[0096] It is understandable that this embodiment conducts a comprehensive comparison and verification of construction quality from three key dimensions: location, components, and dimensions, which is far more comprehensive and in-depth than a single-dimensional inspection. At the same time, it transforms qualitative inspection into quantitative deviation calculation and feature matching, and presets error ranges and construction specifications as judgment standards, making the acceptance results more objective and accurate, and helping to eliminate the subjectivity and ambiguity of human experience judgment.

[0097] Dimensional absolute deviation is the absolute value of the difference between the measured physical dimension of a single component to be inspected and the standard physical dimension. It is used to intuitively reflect the degree of deviation of a single dimension and is composed of the target dimension absolute deviations corresponding to multiple measured dimension items. Measured dimension items are the key physical dimension parameters defined when conducting dimensional verification of the component to be inspected, including bolt tightening torque, component length, hole diameter, perpendicularity, parallelism, and component spacing. Target dimension absolute deviation is the absolute value of the difference between the measured dimension corresponding to a single measured dimension item and the standard dimension. Preset asymmetric upper and lower tolerance absolute values ​​are the lower and upper tolerance absolute values ​​pre-set for each measured dimension item. These two values ​​can be unequal to adapt to the construction requirements of asymmetric tolerances in engineering. Preset criticality weights are the weight values ​​pre-assigned to each measured dimension item, with a total weight of 1. The weights are assigned according to the degree of influence of the dimension parameter on the safety performance of the component, with core safety-related dimensions having higher weights. Preset nonlinear penalty index is a pre-assigned weight value for each measured dimension item. The pre-set index parameters for dimensional measurements are greater than or equal to 1. Critical dimensions use values ​​greater than 1 for accelerated penalties for deviations, while regular appearance dimensions use a value of 1 for linear penalties. The target contribution value is a value calculated by combining the absolute deviation of the target dimension, the preset absolute values ​​of asymmetric upper and lower tolerances, the preset criticality weight, and the preset nonlinear penalty index. It is used to characterize the degree of influence of a single measured dimensional deviation on the overall dimensional quality of the component. The adaptive nonlinear dimensional deviation index is a comprehensive index obtained by summing the target contribution values ​​of all measured dimensional items of the same component to be inspected. It can finely quantify the severity of the overall dimensional deviation of the component. The preset quality level interval is a pre-divided numerical interval that corresponds one-to-one with the adaptive nonlinear dimensional deviation index. It is used to classify the dimensional quality level of the component. The dimensional risk classification information is the result obtained by matching the preset quality level interval to which the adaptive nonlinear dimensional deviation index belongs. It includes the component dimensional risk level, the severity of the deviation, and corresponding rectification suggestions.

[0098] Step 206: Integrate the first acceptance report, the second acceptance report, and the third acceptance report to output the target acceptance report.

[0099] In this embodiment of the invention, the first acceptance report generated from point deviation verification, the second acceptance report generated from component feature comparison verification, and the third acceptance report generated from dimensional risk classification verification are extracted respectively. Verification data, deviation information, defect details, risk ratings, and judgment criteria from these three sub-acceptance reports are collected. Using the target to be accepted (such as a single tower, a single line segment, or a single component) as the basic unit, the spatial position deviation results, component morphology feature verification results, and dimensional risk classification results are correlated and matched to achieve the fusion and linkage of multi-dimensional acceptance data, forming complete acceptance information covering all dimensions of the construction quality of the target to be accepted. According to the preset target acceptance report generation specifications, the integrated acceptance information is standardized and structurally packaged. The report includes the overall acceptance conclusion of the target to be accepted, details of each sub-item verification, a list of high-risk / defect points, graded rectification suggestions, and data traceability basis. Finally, a standardized target acceptance report is output, providing complete and detailed integrated acceptance results support for subsequent construction rectification, quality traceability, and engineering operation and maintenance.

[0100] Step 207: Visualize the target acceptance report.

[0101] Visualization is based on the structured acceptance data in the target acceptance report. A three-dimensional visual interactive interface is built to intuitively display the acceptance results such as spatial deviation, component defects, and dimensional risks through color marking, pop-up prompts, and graphic overlays. It also supports the entire process of manual review, annotation, and synchronous updating of the acceptance report.

[0102] In this embodiment of the invention, a three-dimensional visualization interactive interface for the target to be inspected is constructed based on the structured acceptance data in the target acceptance report. Using a standard design three-dimensional model as a base, a three-dimensional model of the measured data of the target to be inspected is overlaid, enabling an intuitive comparison and display of the standard design state and the actual construction state. Specifically, the spatial position deviations, component defect information, and dimensional risk classification results of each inspection point are mapped onto the three-dimensional model and visualized through different colors, highlighted markers, and pop-up annotations. For example, points with deviations exceeding the standard are highlighted in red; locations with missing components, incorrect models, or installation deviations are prominently marked; and components with dimensional deviations of different risk levels are distinguished by different color tones corresponding to "qualified / moderate risk / higher risk / serious risk." Simultaneously, users can view detailed verification data, deviation values, and judgment criteria for any inspection point through interface interaction. This visual interactive interface supports both manual annotation and review functions, allowing experts or supervisors to manually intervene in edge cases, complex working conditions, or questionable judgment results found in the automated comparison. Users can directly add text notes, mark review opinions, correct defect judgment results, or adjust risk levels on the 3D model. The system automatically updates the corresponding content of the target acceptance report based on the manual review results, completing a closed-loop correction. This step intuitively displays multi-dimensional comparison results through a 3D model, enabling acceptance personnel to quickly and clearly understand complex construction deviations. At the same time, the manual review mechanism retains human experience and final decision-making power, fully leveraging the efficiency of automated comparison while effectively avoiding the risk of machine misjudgment in complex scenarios. This makes the transmission line infrastructure acceptance process more rigorous and reliable, and provides intuitive and easy-to-use visual support for subsequent construction rectification and operation and maintenance management.

[0103] In some specific embodiments, the target acceptance report includes a comparison result list, a deviation distribution chart, pass rate statistics, and rectification suggestions for non-conformities. This report not only includes the pass / fail judgment conclusions for each target to be accepted, but also intuitively and clearly displays the specific comparison results and deviation statistical characteristics of each sub-item verification through structured lists, visual deviation distribution charts, and overall pass rate statistics. This facilitates project managers to quickly grasp the overall status of project quality and identify key issues. At the same time, the rectification suggestions generated in the report for non-conformities are based on the intelligent analysis results of multi-dimensional acceptance data, providing clear and specific implementation directions for subsequent construction rectification and effectively enhancing the practical value of the acceptance work.

[0104] Subsequently, all non-conformity information is extracted from the target acceptance report, including deviation location, type, degree, risk level, and corresponding rectification suggestions. This information is then transformed into standardized, directly executable construction adjustment instructions. These instructions include rectification objectives, requirements, adjustment parameters, priority levels, and completion deadlines. The generated construction adjustment instructions are fed back in real-time to the construction equipment or personnel terminals at the construction site: if connected to automated construction equipment, the equipment's automatic correction process is triggered, directly executing deviation adjustments; if connected to personnel terminals, rectification tasks are pushed out in the form of work orders, clearly specifying rectification requirements and operational guidelines to guide on-site personnel in completing targeted rectifications.

[0105] Understandably, this embodiment transforms the rectification suggestions in the target acceptance report into directly executable construction adjustment instructions and feeds them back to the site in real time. This reduces intermediate steps such as manual communication and order dispatch, enabling construction deviations to be responded to and rectified immediately, preventing the problems from escalating. At the same time, the instructions go directly to the on-site construction equipment or personnel, which can not only enable rapid correction of deviations through automated equipment, but also provide clear guidance for manual rectification, effectively ensuring construction progress and project quality, and forming a closed-loop management process from acceptance verification to on-site rectification.

[0106] In some specific embodiments, the pre-built standard design database for transmission line infrastructure, along with the structured data packages (including spatial location data, image data, and physical dimension data) corresponding to each target to be inspected, undergoes data format and integrity verification to ensure no data is missing and the format is consistent. This data is then uploaded to a cloud server for storage via an encrypted transmission channel. Simultaneously, a multi-dimensional index is established for the stored standard design database and structured data packages. Index fields include project number, target ID, inspection time, voltage level, tower type, geological conditions, and other information, supporting rapid retrieval by project, inspection point, time, and operating condition. Cloud storage prevents the loss of critical engineering data due to local equipment failure or other reasons, ensuring the long-term security of engineering data. After the index is established, project managers, supervisors, owners, and other authorized parties can conveniently query and trace the design standard data, original construction data, and inspection results of any inspection point anytime, anywhere through the cloud platform, providing complete and reliable digital asset support for subsequent project audits, operation and maintenance management, and line modifications.

[0107] In some specific embodiments, firstly, acceptance data from historical transmission line infrastructure projects of the same type, voltage level, and similar operating conditions are collected. This includes standard design parameters, actual construction data, sub-item verification results, quality defect records, risk classification information, etc., to construct a historical acceptance dataset. Based on this historical acceptance dataset, a quality trend analysis model is constructed. The model pre-sets an engineering quality evaluation index system, including key indicators such as spatial position deviation pass rate, component feature matching rate, dimensional deviation index distribution, and defect occurrence rate. Subsequently, the target acceptance report data of the current project (including sub-item verification results, pass rate statistics, defect information, risk classification data, etc.) is input into the quality trend analysis model. A horizontal comparative analysis is performed with the acceptance data of similar historical projects to calculate the relative level of the current project in each quality indicator and generate a construction quality level assessment report. By comparing the current project acceptance results with historical data, we can objectively assess its relative position in the quality level of similar projects, identify the quality advantages and weaknesses in the current construction process, and provide data support for construction companies to improve quality, formulate more scientific construction plans and acceptance standards. This will help improve the overall quality and safety management level of power transmission line infrastructure projects from a macro perspective.

[0108] This invention has the following advantages: 1. By automatically extracting parameters from design drawings and design specifications through a standardized conversion model, a machine-readable standard design database is constructed, providing a unified and accurate benchmark for subsequent automated comparison, avoiding ambiguity and errors that may arise from manual interpretation of design documents, and ensuring the objectivity of the acceptance criteria.

[0109] 2. By using data acquisition terminals to acquire on-site data in real time and constructing structured data packages, the complex construction site conditions are transformed into digital models that can be accurately compared with standard design databases, replacing manual on-site observation and recording, and greatly improving the efficiency and accuracy of data acquisition.

[0110] 3. By comparing the construction data package with the standard database in multiple dimensions such as spatial location, image, and physical dimensions, the acceptance process is automated and intelligent. It can comprehensively and objectively identify construction deviations, significantly improving the comprehensiveness and accuracy of the acceptance process compared to manual spot checks and subjective judgments.

[0111] 4. By automatically generating acceptance reports, the acceptance results are documented and standardized, making them easier to retain, trace, and manage.

[0112] Please see Figure 3 , Figure 3 This is a structural block diagram of a multi-dimensional intelligent acceptance data verification system for power transmission line infrastructure construction provided in Embodiment 3 of the present invention.

[0113] This invention provides a multi-dimensional intelligent acceptance data verification system for transmission line infrastructure construction, comprising: Module 301 is used to build a standard design database for transmission line infrastructure. The standard design database for transmission line infrastructure includes standard spatial location data, standard image data, and standard physical dimension data of standard target acceptance points. The matching module 302 is used to collect construction data from the construction site in real time using multiple pre-set data acquisition terminals and perform correlation matching to construct a structured data package of the target to be accepted. The structured data package of the target to be accepted includes the target's spatial location data to be accepted, image data to be accepted, and physical size data to be accepted. The first verification module 303 is used to verify the point deviation by comparing the spatial location data to be accepted with the standard spatial location data, and to obtain the first acceptance report. The second verification module 304 is used to compare and verify the component features by comparing the image data to be accepted with the standard image data, and to obtain the second acceptance report. The third verification module 305 is used to perform size risk classification verification by comparing the physical size data to be accepted with the standard physical size data, and to obtain the third acceptance report. The report output module 306 is used to integrate the first acceptance report, the second acceptance report, and the third acceptance report to output the target acceptance report.

[0114] Since the above is a system corresponding to a multi-dimensional intelligent acceptance data verification method for transmission line infrastructure construction, its implementation principle is the same as that of a multi-dimensional intelligent acceptance data verification method for transmission line infrastructure construction. For the sake of convenience and brevity, those skilled in the art can clearly understand that the specific working process of the system and modules described above can be referred to the corresponding process in the aforementioned method embodiments, and will not be repeated here.

[0115] Please see Figure 4 , Figure 4 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0116] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402. The memory 401 stores a computer program. When the computer program is executed by the processor 402, the processor 402 executes the multi-dimensional intelligent acceptance data verification method for power transmission line infrastructure construction as described in the above embodiment.

[0117] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When these codes are run by a computing device, the computing device causes the device to perform the various steps in the multi-dimensional intelligent acceptance data verification method for transmission line infrastructure construction described above.

[0118] Embodiment 5 of the present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the multi-dimensional intelligent acceptance data verification method for transmission line infrastructure construction as described in the above embodiments.

[0119] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the multi-dimensional intelligent acceptance data verification method for transmission line infrastructure construction as described in the above embodiments.

[0120] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0124] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0125] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for verifying multi-dimensional intelligent acceptance data of transmission line infrastructure construction, characterized in that, include: A standard design database for transmission line infrastructure is constructed, which includes standard spatial location data, standard image data, and standard physical dimension data of standard target acceptance points; Multiple pre-set data acquisition terminals are used to collect construction data at the construction site in real time and perform correlation matching to construct a structured data package of the target to be inspected. The structured data package of the target to be inspected includes the target's spatial location data, image data, and physical size data. The point deviation is verified by comparing the spatial location data to be inspected with the standard spatial location data to obtain the first acceptance report; The component features are compared and verified using the image data to be inspected and the standard image data to obtain a second acceptance report. The physical dimension data to be accepted is compared with the standard physical dimension data to perform a dimension risk classification verification, and a third acceptance report is obtained. Integrate the first acceptance report, the second acceptance report, and the third acceptance report to output the target acceptance report.

2. The method for verifying multi-dimensional intelligent acceptance data of transmission line infrastructure construction according to claim 1, characterized in that, The step of verifying the point deviation by comparing the spatial location data to be inspected with the standard spatial location data to obtain a first acceptance report includes: Using the spatial location data to be inspected and the standard spatial location data, the effective absolute deviation of each point to be inspected is calculated in three axes, including the effective absolute deviation of the X-axis, the effective absolute deviation of the Y-axis and the effective absolute deviation of the Z-axis. When any two or three of the three effective absolute deviations are greater than the preset effective deviation threshold, the point to be accepted is marked as a composite deviation point to be accepted. The remaining unmarked points to be inspected will be designated as target points to be inspected. Based on the preset working condition cross-term coefficient table, match the first cross-term coefficient of each composite deviation to be accepted point; Based on the first cross term coefficient and the preset deviation weight coefficient, and combined with the three-axis effective absolute deviation associated with the composite deviation acceptance point, the first spatial deviation value is calculated. By comparing the first spatial deviation value of each composite deviation point to be accepted with the preset weighted deviation threshold, the first spatial acceptance information is obtained; Based on the preset second cross term coefficient and the preset deviation weight coefficient, combined with the effective absolute deviation of the three axes associated with the target acceptance point, the second spatial deviation value is calculated. By comparing the second spatial deviation value of each target acceptance point with the preset weighted deviation threshold, the second spatial acceptance information is obtained. By integrating the first space acceptance information with the second space acceptance information, a first acceptance report is obtained.

3. The method for verifying multi-dimensional intelligent acceptance data of transmission line infrastructure construction according to claim 1, characterized in that, The step involves comparing the physical dimension data to be accepted with the standard physical dimension data to perform a dimension risk classification verification, resulting in a third acceptance report, which includes: Using the physical dimension data to be accepted and the standard physical dimension data, calculate the absolute dimensional deviation of each component to be accepted. The absolute dimensional deviation includes the target dimensional absolute deviation of multiple measured dimensional items. Using the absolute deviation of the target size, combined with the preset absolute values ​​of the upper and lower tolerances of asymmetry, the preset key weights, and the preset nonlinear penalty index, the target contribution value of each of the measured size items is calculated; Calculate the sum of all the target contribution values ​​to obtain the adaptive nonlinear dimensional deviation index of each of the components to be accepted; Based on the preset quality level range in which the adaptive nonlinear size deviation index is located, size risk classification information is obtained by matching. By integrating all the aforementioned size risk classification information, a third acceptance report is obtained.

4. The method for verifying multi-dimensional intelligent acceptance data of transmission line infrastructure construction according to claim 1, characterized in that, The multiple pre-set data acquisition terminals include spatial location acquisition terminals, camera acquisition terminals, and dimension acquisition terminals. Both the camera acquisition terminals and the dimension acquisition terminals are equipped with object locators. The process involves using these multiple pre-set data acquisition terminals to collect construction data from the construction site in real time and performing correlation matching to construct a structured data package of the target to be accepted, including: The target to be accepted is determined based on the spatial location data collected by the spatial location acquisition terminal; The positioning information of the object locator of the camera acquisition terminal and the size acquisition terminal is matched with the spatial location data of the target to be inspected; The data collected by the camera acquisition terminal and the size acquisition terminal, whose matching results show consistent positioning information, are aggregated and processed to obtain a structured data packet of the target to be inspected.

5. The method for verifying multi-dimensional intelligent acceptance data of transmission line infrastructure construction according to claim 1, characterized in that, The construction of the transmission line infrastructure standard design database includes: Obtain the infrastructure design drawings and design specifications for power transmission lines; Design parameters are extracted using a standardized transformation model to construct a standard design database; The standardized conversion model includes an optical character recognition model, an image semantic segmentation model, and a natural language processing model. The optical character recognition model, the image semantic segmentation model, and the natural language processing model are used to extract textual information, graphic information, and semantic information from the power transmission line infrastructure design drawings and design specifications, respectively.

6. The method for verifying multi-dimensional intelligent acceptance data of transmission line infrastructure construction according to any one of claims 1-5, characterized in that, Also includes: The target acceptance report is visualized.

7. A multi-dimensional intelligent acceptance data verification system for transmission line infrastructure construction, characterized in that, include: The construction module is used to build a standard design database for transmission line infrastructure, which includes standard spatial location data, standard image data, and standard physical dimension data of standard target acceptance points; The matching module is used to collect construction data from the construction site in real time using multiple pre-set data acquisition terminals and perform correlation matching to construct a structured data package of the target to be accepted. The structured data package of the target to be accepted includes the target's spatial location data to be accepted, image data to be accepted, and physical size data to be accepted. The first verification module is used to verify the point deviation by comparing the spatial location data to be accepted with the standard spatial location data, and to obtain the first acceptance report. The second verification module is used to compare and verify the component features by comparing the image data to be accepted with the standard image data to obtain a second acceptance report. The third verification module is used to perform size risk classification verification by comparing the physical size data to be accepted with the standard physical size data, and to obtain a third acceptance report. The report output module is used to integrate the first acceptance report, the second acceptance report, and the third acceptance report to output the target acceptance report.

8. An electronic device, characterized in that, The system includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the multi-dimensional intelligent acceptance data verification method for transmission line infrastructure construction as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the multi-dimensional intelligent acceptance data verification method for transmission line infrastructure construction as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the multi-dimensional intelligent acceptance data verification method for transmission line infrastructure construction as described in any one of claims 1-6.

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