A tower engineering quantity statistical method and device based on GIM applied to power grid engineering

Abstract

The application provides a tower engineering quantity statistical method and device based on GIM applied to power grid engineering, and relates to the field of power grid construction engineering quantity statistics. The method comprises the following steps: analyzing a GIM file corresponding to the power grid engineering to extract model elements, and disassembling the model elements to form a plurality of rod models; acquiring rod information and node information corresponding to each rod model; judging the rod type of the rod model based on the rod information; performing data calculation based on the node information to obtain rod model length information of the corresponding rod model; determining the quality of the corresponding rod model based on the rod type and the rod model length information; inputting the quality of each rod model into a structure semantic coupling model, and outputting the total quality of the model elements according to the structure semantic coupling model. The application solves the problem that the calculation efficiency is low when only relying on manual experience to perform engineering quantity statistics in the face of large engineering projects, complex structure scenes or multi-specialty collaborative preparation tasks.

Description

Technical Field

[0001] This application relates to the field of power grid construction engineering quantity statistics, and in particular to a method and device for calculating the quantity of pole and tower engineering based on GIM in power grid engineering. Background Technology

[0002] In power grid construction projects, the quantity of work is calculated based on engineering design drawings, construction organization designs or construction plans, and relevant technical and economic documents, in accordance with applicable national standards and the calculation rules and units of measurement specified in this document. This process is referred to as "engineering measurement" in engineering construction. It serves as the basis for project cost estimation at all stages of power grid construction, including feasibility studies and preliminary design.

[0003] Currently, the calculation of quantities for power grid construction projects relies on technical and economic professionals reviewing documents such as two-dimensional drawings and construction plans, and using their experience and technical skills to compile the quantities. This calculation method is relatively cumbersome, requires a high level of expertise, and the results are significantly affected by the professional capabilities of the compilers. It suffers from low calculation efficiency when dealing with large-scale projects, complex structural scenarios, or multi-disciplinary collaborative compilation tasks.

[0004] Therefore, there is an urgent need for a GIM-based method and device for calculating the quantity of pole and tower work in power grid engineering. Summary of the Invention

[0005] This application provides a method and device for calculating the quantity of pole and tower engineering based on GIM in power grid engineering. It solves the problem that relying solely on manual experience to calculate the quantity of engineering work results in low computational efficiency when facing large-scale engineering projects, complex structural scenarios, or multi-disciplinary collaborative compilation tasks.

[0006] The first aspect of this application provides a method for calculating the quantities of poles and towers based on GIM in power grid engineering. The method includes: parsing the GIM file corresponding to the power grid engineering to extract model elements and decomposing the model elements into multiple pole models; obtaining parameter information corresponding to each pole model, including pole information and node information; determining the pole type of the pole model based on the pole information; performing data calculation based on the node information to obtain the pole model length information of the corresponding pole model; determining the mass of the corresponding pole model based on the pole type and pole model length information; inputting the mass of each pole model into a structural semantic coupling model, and outputting the total mass of the model elements according to the structural semantic coupling model.

[0007] Optionally, determining the member type of the member model based on member information specifically includes: obtaining the number of target characters in the member information corresponding to the member model, and determining the member type of the member model based on the number of target characters.

[0008] Optionally, data calculation is performed based on node information to obtain the length information of the corresponding member model. Specifically, this includes: obtaining the node number in the node information and matching the three-dimensional spatial coordinate point information of the corresponding member model based on the node number; and calculating the length information of the member model based on the three-dimensional spatial coordinate point information.

[0009] Optionally, when the member type is angle steel, the mass of the corresponding member model is determined based on the member type and member model length information. Specifically, this includes: obtaining the corresponding member width information and the first member density information based on the member information of the angle steel type; and determining the mass of the corresponding member model through a tolerance correction coefficient based on the member model length information, member width information, and first member density information corresponding to the angle steel type.

[0010] Optionally, when the member type is a constant-diameter steel pipe, the mass of the corresponding member model is determined based on the member type and member model length information. Specifically, this includes: obtaining the corresponding outer radius information, wall thickness information, and second member density information based on the member information of the constant-diameter steel pipe type; and determining the mass of the corresponding member model through a tolerance correction coefficient based on the member model length information, outer radius information, wall thickness information, and second member density information corresponding to the constant-diameter steel pipe type.

[0011] Optionally, when the member type is a tapered steel pipe, the mass of the corresponding member model is determined based on the member type and member model length information. Specifically, this includes: obtaining the diameter information of the first member, the diameter information of the second member, the thickness information, the number of sides information, and the density information of the third member based on the member information corresponding to the tapered steel pipe type, wherein the diameter information of the first member and the diameter information of the second member are the diameter information of different ends of the tapered steel pipe; and determining the mass of the corresponding member model through a tolerance correction coefficient based on the member model length information, the diameter information of the first member, the diameter information of the second member, the thickness information, the number of sides information, and the density information of the third member corresponding to the tapered steel pipe type.

[0012] Optionally, before inputting the mass of each member model into the structural semantic coupling model and outputting the total mass of the model elements based on the structural semantic coupling model, a semantic coupling model needs to be constructed. This includes: obtaining the structural connection information, geometric position information, and functional semantic information corresponding to the member models; constructing a structural topology graph containing multiple member models and their connections based on the structural connection information, and calculating the centrality parameter of each member model in the structural topology graph using a graph traversal algorithm; constructing semantic labels corresponding to multiple member models based on the functional semantic information, and configuring corresponding semantic strength weights for each member model according to the weight matrix corresponding to each semantic label; establishing edge tensor relationships between members based on geometric position information and structural connection information; inputting the centrality parameter, semantic strength weights, and edge tensor relationships as feature vectors into a pre-constructed multidimensional weight fusion function, and calculating coupling weight factors using the multidimensional weight fusion function; and constructing a weight-normalized coupling mapping function based on the coupling weight factors to form the structural semantic coupling model.

[0013] A second aspect of this application provides a GIM-based pole and tower quantity statistics device for use in power grid engineering. The device includes an acquisition module and a processing module, wherein...

[0014] The acquisition module is used to parse the GIM file corresponding to the power grid project to extract model elements and decompose the model elements into multiple member models; it also acquires the parameter information corresponding to each member model, including member information and node information of the member model.

[0015] The processing module is used to determine the member type of the member model based on member information; perform data calculation based on node information to obtain the member model length information of the corresponding member model; determine the mass of the corresponding member model based on member type and member model length information; input the mass of each member model into the structural semantic coupling model, and output the total mass of the model elements according to the structural semantic coupling model.

[0016] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described above.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program, which is executed by a processor using the method described in any of the foregoing descriptions.

[0018] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0019] 1. The GIM file corresponding to the power grid project is parsed to extract model elements, which are then decomposed into multiple member models. Parameter information for each member model is obtained, including member and node information. The member type is determined based on the member information. Data calculations are performed based on the node information to obtain the member model length. The mass of the corresponding member model is determined based on the member type and length. The mass of each member model is input into the structural semantic coupling model, and the total mass of the model elements is output based on the structural semantic coupling model. This process, through parsing the GIM 3D model data, constructs a complete data extraction and calculation workflow. It enables rapid identification of structural component types, extraction of spatial parameters, and mass calculation without relying on 2D drawings or manual identification. After introducing the structural semantic coupling model, weighted calculations can be performed based on the connection relationships, geometric distribution, and functional semantic information of the components in the overall structure, ensuring the structural logic consistency and semantic accuracy of the results. Ultimately, this significantly improves the automation, accuracy, and repeatability of quantity surveying in complex scenarios.

[0020] 2. Obtain the node number from the node information, and match the corresponding three-dimensional spatial coordinate point information of the member model based on the node number; calculate the member model length information based on the three-dimensional spatial coordinate point information, thereby realizing the automatic extraction and accurate modeling of the member's geometric dimensions, avoiding data errors caused by traditional reliance on drawing measurement or manual estimation, improving the standardization and accuracy assurance of length calculation, and providing highly reliable geometric foundation support for subsequent quality estimation and engineering quantity assessment.

[0021] 3. The centrality parameter, semantic strength weight, and edge tensor relationship are used as feature vectors input to a pre-constructed multi-dimensional weight fusion function. The coupling weight factor is calculated through the multi-dimensional weight fusion function. Then, a weight-normalized coupling mapping function is constructed based on the coupling weight factor to form a structural semantic coupling model. This enables the coordinated expression of the importance measurement and semantic role of each member model in the overall structure, ensuring that the structural contribution and semantic value of each component are fully reflected in the quality summary process. This improves the logical completeness and application adaptability of the engineering quantity assessment, especially in scenarios with complex structural topologies and multifunctional components, where it has higher accuracy and robustness. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a method for calculating the quantity of pole and tower engineering based on GIM in power grid engineering, provided in an embodiment of this application.

[0023] Figure 2 This is a schematic diagram of a module for a GIM-based pole and tower quantity statistics device applied in power grid engineering, provided in an embodiment of this application.

[0024] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0025] Explanation of reference numerals in the attached figures: 21. Acquisition module; 22. Processing module; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0027] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0029] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0030] Please refer to Figure 1 The diagram illustrates a flowchart of a GIM-based method for calculating the quantity of pole and tower work in power grid engineering, provided by an embodiment of this application. The flowchart mainly includes the following steps: S101 to S106.

[0031] Step S101: Parse the GIM file corresponding to the power grid project to extract model elements, and decompose the model elements into multiple pole models.

[0032] Specifically, based on the GIM data format specification, the first sixteen bytes are read from the GIM 3D design delivery file to identify the project type identifier. Based on the identification results, the directory structure of multiple models contained in the GIM file, including engineering models, physical models, combined models, and geometric models, is determined. Subsequently, the compressed data in each model directory of the GIM file is decompressed, and a power engineering tree structure is constructed according to the project hierarchy and object numbering, generating an object data dictionary. Model elements are extracted from this object data dictionary, and component objects corresponding to power grid tower structures are identified. Based on the member and node information contained in the object attributes, complex tower models are further decomposed into several member models indexed by start and end node identifiers. Each member model uniquely corresponds to a member description record presented in a specific format in the GIM file. The member description record explicitly includes the node number connected to the member, member specifications, and material properties. This step enables the automatic extraction of structurally complete member data from the GIM file, providing an accurate and traceable data source for subsequent parameter extraction, classification, and quality calculation operations, and establishing a unified expression structure for different types of member models.

[0033] Step S102: Obtain the parameter information corresponding to each member model. The parameter information includes the member information and node information of the member model.

[0034] Specifically, after decomposing the model elements in the GIM file and generating multiple member models, the *.mod files in the geometry model directory of the GIM file are further parsed to obtain the parameter data corresponding to each member model. Member information is obtained by parsing record lines starting with the character "R," which includes connection node numbers, component specifications, component materials, structural direction vectors, or cross-sectional parameters. Node information is obtained by parsing record lines starting with the character "P," which includes node numbers and the spatial coordinates of the nodes in the 3D model. The spatial coordinates are expressed using a unified coordinate system with reference to the model origin. The member and node information of each member model have a clear correspondence in the data structure and can be uniquely matched through node numbers. This allows for the automatic extraction of key structural parameters such as the start and end point numbers, connection topology, geometric specifications, and spatial positioning of components from the model. The extracted parameter information serves as the data basis for subsequent operations such as member type determination, length calculation, and mass estimation, ensuring the integrity and accuracy of the quantity calculation process.

[0035] Step S103: Determine the member type of the member model based on member information.

[0036] Specifically, by analyzing the data structure features contained in the member information, the type category of each member model is identified, thereby enabling the classification and determination of different component forms and providing a type basis for subsequent parameter extraction and mass calculation of various members.

[0037] In one possible implementation, step S103 further includes: obtaining the number of target characters in the bar information corresponding to the bar model, and determining the bar type of the bar model based on the number of target characters.

[0038] Specifically, the component type of each member model is determined by counting the number of target characters in its corresponding member information line, using commas as delimiters to count the number of data fields. When the member information line contains eleven fields, it is identified as an angle steel type, which includes connection node numbers, specifications, materials, and spatial direction vector information for the X and Y limbs. When there are five fields, it is identified as a constant-diameter steel pipe type, containing basic parameters such as node numbers, specifications, and materials. When there are nine fields, it is identified as a tapered steel pipe type, whose member information includes, in addition to basic parameters, the diameter of each end face, wall thickness, and number of sides. This determination process is based on the parsing of the member information format structure. By summarizing and modeling the differences in fields for different component types in the GIM data structure, automatic identification of each member model type is achieved, providing a basic classification basis for subsequent component parameter extraction and quantity calculation.

[0039] Step S104: Perform data calculation based on node information to obtain the length information of the corresponding member model.

[0040] Specifically, by matching the three-dimensional spatial coordinates of the nodes at both ends of each member model, and based on the spatial distance relationship between the coordinate points, the actual length of the member model is calculated using the spatial distance formula. This length serves as the core parameter for subsequent calculations of the component's volume and mass.

[0041] In one possible implementation, step S104 further includes: obtaining the node number in the node information, and matching the three-dimensional spatial coordinate point information of the corresponding rod model based on the node number; and calculating the rod model length information of the rod model based on the three-dimensional spatial coordinate point information.

[0042] Specifically, by parsing the node information records starting with "P" in the GIM file, the three-dimensional coordinate values ​​corresponding to each node number are extracted. Each coordinate value consists of the x, y, and z coordinates relative to the model origin. Then, based on the start and end node numbers listed in the member information, the matching start and end point three-dimensional spatial coordinates are retrieved, and these two points are used as the endpoints of the spatial vector. The spatial distance between the start and end nodes is calculated using the Euclidean distance formula, and this distance is taken as the actual length value of the member model.

[0043] For example, the three-dimensional coordinates of the corresponding nodes are obtained by matching them from the node information. Let the coordinates of the starting point be... The endpoint coordinates are Then the corresponding rod model length information The distance is calculated using the Euclidean spatial distance formula and expressed as follows:

[0044]

[0045] For example, the node information of a certain angle steel member is: P,10,-7750.000000,-400.000000,50500.000000, P,11,-7750.000000,400.000000,50500.000000

[0046] Based on the node number matching, the coordinates of node 10 are: The coordinates of node 11 are Substituting into the above formula, we get:

[0047]

[0048] For example, the node information for a certain equal-diameter steel pipe is: P,1,-13500,-250,49000, P,2,-13500,-250,3600. Their coordinates are respectively... and Substitute into the calculation:

[0049]

[0050] The length information of each rod model can be accurately obtained through the above method. This result can be directly used for subsequent volume and mass derivation calculations, ensuring the data continuity and geometric accuracy of the entire calculation chain.

[0051] Step S105: Determine the mass of the corresponding rod model based on the rod type and rod model length information.

[0052] Specifically, the corresponding calculation model is selected according to the identified bar type. The length of the bar model is combined with its cross-sectional parameters and material density. The volume of the component is estimated by the volume calculation formula, multiplied by the density, and a tolerance correction coefficient is introduced to obtain the actual mass of the component. This achieves unified calculation and standardized processing of the mass of different types of bars.

[0053] In one possible implementation, step S105 further includes: when the member type is angle steel, obtaining the corresponding member width information and the first member density information based on the member information of the angle steel type; and determining the mass of the corresponding member model through a tolerance correction coefficient based on the member model length information, member width information, and first member density information corresponding to the angle steel type.

[0054] Specifically, the geometric parameters of the angle steel member are parsed from its specification field. For example, when the angle steel specification is L63×5, it means that the leg width is 63mm and the leg thickness is 5mm, which are denoted as follows: and Simultaneously, based on the material of the rod, such as Q235 steel, its density is retrieved from the preset material database and set as [the appropriate value]. The unit is kg / mm³. The model length of the rod is known to be... Furthermore, considering the cross-sectional errors present during the manufacturing process, a correction factor is introduced. The formula for calculating the volume of angle steel, based on the simplified rectangular structure formed by two limbs, is as follows:

[0055]

[0056] in, Let be the volume of the rod, and be the width of the rod's leg. Let the thickness of the member be denoted as . Calculate the mass of the angle steel member:

[0057]

[0058] Taking a specific parameter as an example, when the angle steel specification is L63×5, it corresponds to... , The material is Q235 steel, corresponding to The length of the rod is 800mm. Then the calculation yields:

[0059]

[0060]

[0061] The above implementation method can accurately calculate the quality of angle steel based solely on textual GIM structural data without relying on CAD modeling, meeting the technical requirements for automatic quantity statistics and possessing the ability to compensate for engineering errors.

[0062] In one possible implementation, step S105 further includes: when the rod type is a constant diameter steel pipe, obtaining the corresponding outer radius information, wall thickness information, and second rod density information based on the rod information of the constant diameter steel pipe type; and determining the mass of the corresponding rod model through a tolerance correction coefficient based on the rod model length information, outer radius information, wall thickness information, and second rod density information corresponding to the constant diameter steel pipe type.

[0063] Specifically, the outer diameter and wall thickness parameters of the steel pipe are parsed from the rod specification field. If the specification is φ300X8, then the outer diameter is 300mm and the wall thickness is 8mm. The outer radius information is recorded as follows: Wall thickness information is By consulting the density corresponding to Q235 material of the rod, the density information of the second rod is obtained as follows: Given a rod model with a length of... Furthermore, considering dimensional errors during the manufacturing process, a tolerance correction factor is introduced. Perform quality corrections.

[0064] First, calculate the external and internal volumes of the equal-diameter steel pipe, where:

[0065]

[0066]

[0067] The effective volume of the steel pipe is the difference between the two:

[0068]

[0069] The final mass calculation expression is:

[0070]

[0071] Taking a typical example, assuming the steel pipe specification is φ300X8, the corresponding... , , Member length , ,but:

[0072]

[0073]

[0074] This implementation method is based on the typical geometric characteristics of pipes, combined with material properties and spatial dimensions. Through standard volume calculation logic, it accurately obtains the actual mass of components. Different sizes of steel pipes can be batch adapted in the power grid tower model, improving the accuracy and automation of engineering quantity calculation under complex structures.

[0075] In one possible implementation, step S105 further includes: when the rod type is a tapered steel pipe, obtaining the corresponding first rod diameter information, second rod diameter information, thickness information, number of sides information, and third rod density information based on the rod information corresponding to the tapered steel pipe type, wherein the first rod diameter information and the second rod diameter information are the diameter information of different ends of the tapered steel pipe, respectively; and determining the mass of the corresponding rod model through a tolerance correction coefficient based on the rod model length information, first rod diameter information, second rod diameter information, thickness information, number of sides information, and third rod density information corresponding to the tapered steel pipe type.

[0076] Specifically, the diameter information of the first member and the diameter information of the second member are respectively denoted as... and This represents the diameter of the circumcircle of the regular polygons at both ends of the tapered steel pipe; the thickness information is denoted as... , representing the wall thickness of the component section, with the number of sides denoted as . , representing the number of sides of the regular polygon at each end cross-section, and the density information of the third member is denoted as . The unit is kg / mm³, used to reflect the mass density of the material used. Based on the length information of the rod model. First, calculate the outer side lengths of the two end sections:

[0077]

[0078]

[0079] in, , Let be the outer side lengths of the cross-sections at both ends of the tapered steel pipe. The inner side lengths at both ends of the tapered steel pipe are:

[0080]

[0081]

[0082] in , Given the internal side lengths of the cross-sections at both ends of the tapered steel pipe, further calculate the areas of the regular polygons at both ends of the external and internal cross-sections:

[0083]

[0084]

[0085]

[0086]

[0087] in, , The external cross-sections at both ends of the tapered steel pipe. , The internal cross-sections at both ends of the tapered steel tube are shown. The external and internal volumes are calculated using the frustum volume formula:

[0088]

[0089]

[0090] The effective volume of the tapered steel pipe is:

[0091]

[0092] Introducing tolerance correction factor Finally, the mass of the rod model is calculated:

[0093]

[0094] The mass of the rod model. To illustrate with an example, if the diameter information of the first rod... Diameter information of the second member Thickness information Edge count information Third member density information Length information of the rod model Tolerance correction factor Substituting the values ​​into the above formula and calculating each item, the final mass is approximately 194.53 kg. This implementation method, while considering the geometric complexity of the component and manufacturing errors, achieves accurate modeling and efficient estimation of the mass of the tapered steel pipe component, and is suitable for automated quantity statistics of complex cross-section components in power engineering models.

[0095] Step S106: Input the mass of each member model into the structural semantic coupling model, and output the total mass of the model elements according to the structural semantic coupling model.

[0096] Specifically, a structural semantic coupling model is first constructed. This model integrates multi-dimensional information such as structural connections, geometric distribution, and functional semantic labels, and expresses the mutual weights between components through graph structure modeling and feature quantization. After introducing the mass of all member models as input features into this coupling model, the coupling weight factors of each component in the overall structure are calculated using a weighted fusion function, combining the centrality parameter of each member in the structural topology graph, the semantic strength weight assigned to its corresponding semantic label, and the edge tensor relationship between members. Finally, normalization is performed based on these coupling weight factors, and the mass of each member model is combined with its weight factors to output the total mass of the model elements. This achieves dynamic coupling and unified summation of the mass influence of each component in complex structures, significantly improving the accuracy of overall engineering quantity estimation and structural adaptability.

[0097] In one possible implementation, step S106 further includes: acquiring structural connection information, geometric position information, and functional semantic information corresponding to the member models; constructing a structural topology graph containing multiple member models and their connection relationships based on the structural connection information, and calculating the centrality parameter of each member model in the structural topology graph using a graph traversal algorithm; constructing semantic labels corresponding to multiple member models based on the functional semantic information, and configuring corresponding semantic strength weights for each member model according to the weight matrix corresponding to each semantic label; establishing edge tensor relationships between members based on geometric position information and structural connection information; inputting the centrality parameter, semantic strength weights, and edge tensor relationships as feature vectors into a pre-constructed multidimensional weight fusion function, and calculating coupling weight factors using the multidimensional weight fusion function; and constructing a weight-normalized coupling mapping function based on the coupling weight factors to form a structural semantic coupling model.

[0098] Specifically, three types of information are obtained for each member model: structural connection information, geometric position information, and functional semantic information. Structural connection information describes the direct connection relationships between members in the structure, such as shared nodes; geometric position information expresses the position and distribution of members in the three-dimensional coordinate system; and functional semantic information assigns a role label to each member model in the structure, such as "main load-bearing member" or "supporting member".

[0099] A structural topology graph is constructed based on structural connection information, denoted as Graph. ,in This represents the set of nodes formed by all the rod-like models. This represents the connections between nodes. Graph traversal algorithms (such as depth-first search and breadth-first search) are used to calculate the connections between each node. Centrality parameter Centrality can be selected from forms such as degree centrality, betweenness centrality, or proximity centrality. Taking degree centrality as an example:

[0100]

[0101] in Represents nodes The number of connected sides, that is, the number of other members connected to this member; This indicates the total number of members.

[0102] Then, semantic labels are assigned to each link model based on functional semantic information. And it maps the semantic weight matrix through semantic tags. Obtain semantic strength weights ,Right now:

[0103]

[0104] in It is a mapping function from labels to weights. For example, main load-bearing components can be assigned higher weights, while non-load-bearing components can be assigned relatively lower weights.

[0105] Next, based on geometric location information Combined with structural connection information, construct the edge tensor relationships between members. This tensor is used to represent the rod. With rods The relative closeness of positions in geometric space can be defined as the inverse of their Euclidean distance:

[0106]

[0107] in Indicating rods With rods The Euclidean distance between them is such that the closer the distance, the larger the tensor value, indicating a higher coupling strength.

[0108] The centerness parameter of each member semantic strength weights Relationship with edge tensor Together they form the input feature vector, denoted as:

[0109]

[0110] Will Input to a pre-built multidimensional weight fusion function The coupling weight factor of each member model is obtained. :

[0111]

[0112] in These are preset or trained weight coefficients used to adjust the degree of influence of each feature dimension.

[0113] The coupling weight factors of all member models are normalized to obtain the normalized coupling mapping function:

[0114]

[0115] in This represents the total number of links. The final output shows the total mass of all model elements. For the mass of each rod model Its normalized coupling weight factor The weighted summation result:

[0116]

[0117] This method deeply integrates structural, spatial, and semantic features to construct a coupled mapping function for structural correlation modeling of different member masses, thus realizing a refined total mass assessment method driven by multi-source information.

[0118] Please refer to Figure 2 This illustration shows a schematic diagram of a GIM-based pole and tower quantity statistics device for power grid engineering, provided in an embodiment of this application. The device includes an acquisition module 21 and a processing module 22.

[0119] The acquisition module 21 is used to parse the GIM file corresponding to the power grid project to extract model elements and decompose the model elements into multiple member models; and to acquire the parameter information corresponding to each member model, including member information and node information of the member model.

[0120] Processing module 22 is used to determine the member type of the member model based on member information; perform data calculation based on node information to obtain the member model length information of the corresponding member model; determine the mass of the corresponding member model based on member type and member model length information; input the mass of each member model into the structural semantic coupling model, and output the total mass of the model elements according to the structural semantic coupling model.

[0121] In one possible implementation, the processing module 22 is used to determine the type of the rod model based on the rod information, specifically including: obtaining the number of target characters in the rod information corresponding to the rod model, and determining the type of the rod model based on the number of target characters.

[0122] In one possible implementation, the processing module 22 is used to perform data calculations based on node information to obtain the length information of the corresponding member model. Specifically, this includes: obtaining the node number in the node information and matching the three-dimensional spatial coordinate point information of the corresponding member model based on the node number; and calculating the length information of the member model based on the three-dimensional spatial coordinate point information.

[0123] In one possible implementation, the processing module 22 is used to determine the mass of the corresponding rod model based on the rod type and rod model length information when the rod type is angle steel. Specifically, this includes: obtaining the corresponding rod width information and the first rod density information based on the rod information of the angle steel type; and determining the mass of the corresponding rod model through a tolerance correction coefficient based on the rod model length information, rod width information, and the first rod density information corresponding to the angle steel type.

[0124] In one possible implementation, the processing module 22 is used to determine the mass of the corresponding rod model based on the rod type and rod model length information when the rod type is a constant diameter steel pipe. Specifically, this includes: obtaining the corresponding outer radius information, wall thickness information, and second rod density information based on the rod information of the constant diameter steel pipe type; and determining the mass of the corresponding rod model through a tolerance correction coefficient based on the rod model length information, outer radius information, wall thickness information, and second rod density information corresponding to the constant diameter steel pipe type.

[0125] In one possible implementation, the processing module 22 is used to determine the mass of the corresponding rod model based on the rod type and rod model length information when the rod type is a tapered steel pipe. Specifically, this includes: obtaining the diameter information of the first rod, the diameter information of the second rod, the thickness information, the number of sides information, and the density information of the third rod based on the rod information corresponding to the tapered steel pipe type, wherein the diameter information of the first rod and the diameter information of the second rod are the diameter information of different ends of the tapered steel pipe, respectively; and determining the mass of the corresponding rod model through a tolerance correction coefficient based on the rod model length information, the diameter information of the first rod, the diameter information of the second rod, the thickness information, the number of sides information, and the density information of the third rod corresponding to the tapered steel pipe type.

[0126] In one possible implementation, the processing module 22 is used to construct a semantic coupling model before inputting the mass of each member model into the structural semantic coupling model and outputting the total mass of the model elements according to the structural semantic coupling model. Specifically, this includes: obtaining the structural connection information, geometric position information, and functional semantic information corresponding to the member models; constructing a structural topology graph containing multiple member models and their connection relationships based on the structural connection information, and calculating the centrality parameter of each member model in the structural topology graph using a graph traversal algorithm; constructing semantic labels corresponding to multiple member models based on the functional semantic information, and configuring corresponding semantic strength weights for each member model according to the weight matrix corresponding to each semantic label; establishing edge tensor relationships between members based on geometric position information and structural connection information; inputting the centrality parameter, semantic strength weights, and edge tensor relationships as feature vectors into a pre-constructed multidimensional weight fusion function, and calculating coupling weight factors through the multidimensional weight fusion function; and constructing a weight-normalized coupling mapping function based on the coupling weight factors to form a structural semantic coupling model.

[0127] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0128] This application also provides an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0129] The communication bus 302 is used to enable communication between these components.

[0130] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0131] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0132] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0133] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a GIM-based pole and tower quantity statistics application used in power grid engineering.

[0134] exist Figure 3In the illustrated electronic device, the user interface 303 is primarily used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call the application program stored in the memory 305 for GIM-based pole and tower quantity statistics in power grid engineering. When executed by one or more processors 301, the electronic device performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0135] This application also provides a computer-readable storage medium storing instructions. When executed by one or more processors, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0136] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0137] In the various embodiments provided in this application, it should be understood that the disclosed apparatus 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 service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0138] 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.

[0139] Furthermore, the functional units in the various embodiments of this application 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.

[0140] 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 device (CMD). Based on this understanding, the technical solution of this application, 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 memory 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 this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0141] The above description is merely an exemplary embodiment disclosed in this application and should not be construed as limiting the scope of this application. Any equivalent changes and modifications made in accordance with the teachings of this application shall still fall within the scope of this application.

[0142] This application is intended to cover any variations, uses, or adaptations disclosed herein that follow the general principles disclosed herein and include common knowledge or customary technical means in the art that are not described in this application.

Claims

1. A method for calculating the quantity of pole and tower works based on GIM in power grid engineering, characterized in that, The method includes: The GIM file corresponding to the power grid project is parsed to extract model elements, and the model elements are decomposed into multiple pole models; Obtain parameter information corresponding to each of the rod models, wherein the parameter information includes rod information and node information of the rod model; The type of the rod in the rod model is determined based on the rod information; Data calculations are performed based on the node information to obtain the length information of the corresponding rod model. Based on the rod type and the rod model length information, determine the mass of the corresponding rod model; The mass of each of the aforementioned rod models Input a structural semantic coupling model, and output the total mass of the model elements based on the structural semantic coupling model. The total mass of the model elements For the mass of each rod model Coupled with the corresponding normalized mapping function The weighted summation result specifically includes: obtaining the structural connection information, geometric position information, and functional semantic information corresponding to the member model, wherein the functional semantic information is used to assign a role label to each member model in the overall structure; based on the structural connection information, constructing a structural topology graph containing multiple member models and their connection relationships, and calculating the centrality parameter of each member model in the structural topology graph using a graph traversal algorithm; and assigning semantic labels to each member model based on the functional semantic information. And through the semantic tags, a preset semantic weight matrix is ​​mapped. Obtain semantic strength weights Based on the geometric position information and the structural connection information, an edge tensor relationship is established between the member models. This edge tensor relationship represents the relative positional density of different member models in geometric space. The relative positional density is constructed according to the inverse proportionality of the Euclidean distance between different member models. The centrality parameter, the semantic strength weight, and the edge tensor relationship are input as feature vectors to a pre-constructed multidimensional weight fusion function, and the coupling weight factor of each member model is calculated through the multidimensional weight fusion function. ； in, The coupling weight factor is... The multidimensional weight fusion function is... The centrality parameter for each bar model, These are the weight coefficients obtained through preset or training. For rod model With rod model The edge tensor relationship between them, The semantic strength weights are used; a normalized coupling mapping function is constructed based on the aforementioned coupling weight factors: ； in, For the normalized coupling mapping function, This represents the total number of rod models.

2. The method according to claim 1, characterized in that, The step of determining the member type of the member model based on the member information specifically includes: Obtain the number of target characters in the rod information corresponding to the rod model, and determine the rod type of the rod model based on the number of target characters.

3. The method according to claim 1, characterized in that, The step of performing data calculations based on the node information to obtain the length information of the corresponding member model specifically includes: Obtain the node number from the node information, and match the three-dimensional spatial coordinate point information of the corresponding rod model based on the node number; Based on the three-dimensional spatial coordinate point information, the length information of the rod model is calculated.

4. The method according to claim 1, characterized in that, When the member type is angle steel, determining the mass of the corresponding member model based on the member type and the member model length information specifically includes: Based on the information of the members of the angle steel type, obtain the corresponding member width information and the first member density information; Based on the length information, width information, and density information of the member model corresponding to the angle steel type, the mass of the corresponding member model is determined by a tolerance correction coefficient.

5. The method according to claim 1, characterized in that, When the rod type is a constant-diameter steel pipe, determining the mass of the corresponding rod model based on the rod type and the rod model length information specifically includes: Based on the information of the rods of the equal-diameter steel pipe type, obtain the corresponding outer radius information, wall thickness information and second rod density information; Based on the length information, outer radius information, wall thickness information, and second member density information of the member model corresponding to the equal-diameter steel pipe type, the mass of the corresponding member model is determined by the tolerance correction coefficient.

6. The method according to claim 1, characterized in that, When the member type is a tapered steel pipe, determining the mass of the corresponding member model based on the member type and the member model length information specifically includes: Based on the information of the corresponding rods of the tapered steel pipe type, obtain the diameter information of the first rod, the diameter information of the second rod, the thickness information, the number of sides information, and the density information of the third rod. The diameter information of the first rod and the diameter information of the second rod are the diameter information of different ends of the tapered steel pipe, respectively. Based on the length information of the rod model corresponding to the tapered steel pipe type, the diameter information of the first rod, the diameter information of the second rod, the thickness information, the number of sides information, and the density information of the third rod, the mass of the corresponding rod model is determined by the tolerance correction coefficient.

7. A GIM-based pole and tower quantity calculation device applied in power grid engineering, characterized in that, The device includes an acquisition module and a processing module, wherein, The acquisition module is used to parse the GIM file corresponding to the power grid project to extract model elements and decompose the model elements into multiple member models; and to acquire parameter information corresponding to each member model, wherein the parameter information includes member information and node information of the member model; The processing module is used to determine the member type of the member model based on the member information; perform data calculations based on the node information to obtain the member model length information corresponding to the member model; determine the mass of the corresponding member model based on the member type and the member model length information; and set the mass of each member model. Input a structural semantic coupling model, and output the total mass of the model elements based on the structural semantic coupling model. The total mass of the model elements For the mass of each rod model Coupled with the corresponding normalized mapping function The weighted summation result specifically includes: obtaining the structural connection information, geometric position information, and functional semantic information corresponding to the member model, wherein the functional semantic information is used to assign a role label to each member model in the overall structure; based on the structural connection information, constructing a structural topology graph containing multiple member models and their connection relationships, and calculating the centrality parameter of each member model in the structural topology graph using a graph traversal algorithm; and assigning semantic labels to each member model based on the functional semantic information. And through the semantic tags, a preset semantic weight matrix is ​​mapped. Obtain semantic strength weights Based on the geometric position information and the structural connection information, an edge tensor relationship is established between the member models. This edge tensor relationship represents the relative positional density of different member models in geometric space. The relative positional density is constructed according to the inverse proportionality of the Euclidean distance between different member models. The centrality parameter, the semantic strength weight, and the edge tensor relationship are input as feature vectors to a pre-constructed multidimensional weight fusion function, and the coupling weight factor of each member model is calculated through the multidimensional weight fusion function. ； in, The coupling weight factor is... The multidimensional weight fusion function is... The centrality parameter for each bar model, These are the weight coefficients obtained through preset or training. For rod model With rod model The edge tensor relationship between them, The semantic strength weights are used; a normalized coupling mapping function is constructed based on the aforementioned coupling weight factors: ； in, For the normalized coupling mapping function, This represents the total number of rod models.

8. An electronic device, characterized in that, The device includes a processor, a communication bus, a user interface, a network interface, and a memory. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 6.

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