Three-dimensional engineering quantity calculation method and system based on multi-modal data, medium and product

Abstract

The invention discloses a three-dimensional engineering quantity calculation method and system based on multi-modal data, a medium and a product, and relates to the field of engineering data computation.The method comprises the steps that two-dimensional engineering drawing data are received, converted into three-dimensional grid data and imported into a 3DMax platform, and a three-dimensional component model is generated; extracting spatial positioning data and component attribute data of each component in the three-dimensional component model, and calculating a spatial projection overlap ratio; classifying the components greater than a preset coincidence threshold value into the same space association group, and generating a plurality of component space groups; calculating and generating a component parameter table; the component codes serve as index keys, the engineering quantity calculation rules are written into associated fields of a component parameter table, and an engineering quantity calculation matrix is generated; identifying an updated target component code; and re-calculating the geometric parameters corresponding to the target component codes based on the 3DMax platform, and updating the engineering quantity data according to the geometric parameters and the calculation rules in the engineering quantity calculation matrix. By implementing the method, the engineering quantity statistical efficiency can be improved.

Description

Technical Field

[0001] This application relates to the field of engineering data calculation, and particularly to a three-dimensional engineering quantity calculation method, system, medium and product based on multi-modal data. Background Art

[0002] With the rapid development of the construction engineering industry, the accurate calculation of project cost is of great significance for project cost control and management efficiency. The accurate calculation of engineering quantity is the basis of project cost, and the types of components involved in construction engineering are numerous and the structures are complex, which puts higher requirements on the accuracy and efficiency of engineering quantity calculation.

[0003] In related technologies, engineering quantity calculation mainly uses two-dimensional CAD software or three-dimensional modeling software. In the two-dimensional CAD method, engineers need to manually measure dimension parameters such as length and width in the drawings and calculate according to the engineering quantity calculation specifications; in the three-dimensional modeling method, engineers use modeling software such as 3D Max to build a three-dimensional model, use the built-in measurement tools of the software to obtain parameters such as area and volume, and then combine tabular data to conduct engineering quantity statistics.

[0004] However, when engineering design changes occur, the three-dimensional model data and the engineering quantity list data are not synchronized in a timely manner, and related technologies often require a large amount of calculation and verification work, resulting in low engineering quantity statistics efficiency. Summary of the Invention

[0005] This application provides a three-dimensional engineering quantity calculation method, system, medium and product based on multi-modal data, which is used to improve the efficiency of engineering quantity statistics.

[0006] In a first aspect, this application provides a three-dimensional engineering quantity calculation method based on [not specified in the original, might be a mistake], which is applied to a data processing system. The method includes: receiving multiple two-dimensional engineering drawing data and engineering quantity list data, converting the two-dimensional engineering drawing data into three-dimensional grid data and importing it into the 3DMax platform to generate a three-dimensional component model; extracting the spatial positioning data and component attribute data of each component in the three-dimensional component model, and calculating the spatial projection coincidence degree of each component; classifying the components with a spatial projection coincidence degree greater than a preset coincidence threshold into the same spatial association group to generate multiple component spatial groups; calculating the area parameters and volume parameters of each component in the component spatial group based on the 3DMax platform to generate a component parameter table; using the component code as the index key, writing the engineering quantity calculation rules in the engineering quantity list data into the associated fields of the component parameter table to generate an engineering quantity calculation matrix; when it is detected that the data in the two-dimensional engineering drawing is updated, identifying the updated target component code; recalculating the geometric parameters corresponding to the target component code based on the 3DMax platform, and updating the engineering quantity data according to the geometric parameters and the calculation rules in the engineering quantity calculation matrix.

[0007] In the above embodiments, the data processing system receives two-dimensional engineering drawings and bill of quantities data, converts them into three-dimensional grid data, generates three-dimensional component models on the 3DMax platform, calculates the spatial projection coincidence degree based on the spatial positioning data and component attributes, groups the components with high coincidence degree into the same spatial group, and calculates geometric parameters to generate a component parameter table; associates the bill of quantities calculation rules through component coding, can quickly identify target components and automatically update their bill of quantities data when the drawings are updated, avoiding manual repeated calculations, and improving the automation degree and calculation efficiency of bill of quantities statistics.

[0008] Combined with some embodiments of the first aspect, in some embodiments, the steps of receiving multiple two-dimensional engineering drawing data and bill of quantities data, converting the two-dimensional engineering drawing data into three-dimensional grid data and importing it into the 3DMax platform to generate three-dimensional component models specifically include: receiving multiple two-dimensional engineering drawing data and bill of quantities data; extracting the layer data and component reference point coordinates in the two-dimensional engineering drawing data to determine the plane grid nodes; calculating the spatial coordinates of the plane grid nodes based on the layer annotation height information and reference point coordinates; organizing the spatial coordinates into a three-dimensional grid data set according to the layer attributes and importing it into the 3DMax platform; generating three-dimensional component models based on the spatial coordinates and layer attributes of the three-dimensional grid data set.

[0009] In the above embodiments, the data processing system determines the plane grid nodes by extracting the layer data and reference point coordinates of the two-dimensional drawings, calculates the spatial coordinates in combination with the layer annotation height information, organizes the coordinate data into a three-dimensional grid data set according to the layer attributes and imports it into the 3DMax platform; realizes the automatic conversion from two-dimensional drawings to three-dimensional models, ensures the accuracy of spatial geometric information, simplifies the modeling process, and improves the generation efficiency of three-dimensional component models.

[0010] Combined with some embodiments of the first aspect, in some embodiments, the steps of identifying the updated target component code when it is detected that the two-dimensional engineering drawing has data updates specifically include: obtaining the version identification information of the two-dimensional engineering drawing to generate a drawing version data set; performing version difference comparison according to the drawing version data set to obtain the updated data identification; when there is an updated data identification, positioning the updated layer based on the updated data identification to obtain the layer difference data; extracting the component code information from the layer difference data to generate a target component code table.

[0011] In the above embodiments, the data processing system obtains the drawing version identification information to generate a version data set, obtains the updated data identification through version difference comparison, locates the updated layer to obtain the difference data, and extracts the component code from it to generate a target component code table; realizes the automatic identification and difference positioning of engineering drawing version changes, avoids the cumbersome work of manual checking, and improves the accuracy and efficiency of component identification after drawing updates.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, before the steps of extracting the spatial positioning data and component attribute data of each component in the three-dimensional component model and calculating the spatial projection coincidence degree of each component, the method further includes: constructing a multi-level component type index tree based on the three-dimensional component model; classifying components with the same component attributes into the same component type node, and assigning a unique component coding identifier to each component type node to obtain a component spatial index; extracting the geometric feature parameters of each component type node to generate a component feature library; the geometric feature parameters include component contour feature points and spatial topological relationships.

[0013] In the above embodiments, the data processing system constructs a multi-level component type index tree based on the three-dimensional component model, classifies components with the same attributes and assigns unique codes, extracts geometric feature parameters to generate a feature library; establishes a classification index system for components, facilitating the quick retrieval and identification of components, and improving the accuracy of spatial relationship analysis.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the step of constructing a multi-level component type index tree based on the three-dimensional component model specifically includes: extracting the material attribute, function attribute, and spatial attribute of each component to generate a component attribute vector; using a clustering algorithm to perform clustering analysis on the component attribute vector to determine the component type hierarchical relationship; constructing a tree-shaped index structure based on the component type hierarchical relationship, associating component type nodes with subordinate relationships through parent-child node relationships, and assigning a weight coefficient to each component type node to obtain a multi-level component type index tree.

[0015] In the above embodiments, the data processing system extracts the material, function, and spatial attributes of components to generate an attribute vector, uses a clustering algorithm to analyze and determine the hierarchical relationship, constructs a tree-shaped index structure and assigns weight coefficients; realizes the automatic classification and hierarchical organization of component types, improves the accuracy and flexibility of component type indexing, and facilitates the management and maintenance of component relationships.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of writing the engineering quantity calculation rules in the bill of quantities data into the associated fields of the component parameter table with the component code as the index key to generate an engineering quantity calculation matrix, the method further includes: obtaining the engineering quantity difference of each component between two consecutive engineering drawing versions, and calculating the engineering quantity change percentage; when the engineering quantity change percentage exceeds a predetermined change threshold, extracting the three-dimensional geometric data of the corresponding target component in different versions; projecting the three-dimensional geometric data of the target component onto the same coordinate system according to the version identifier to generate spatial positioning marks for the different parts; calculating the area parameter, volume parameter, and calculation rule number corresponding to the engineering quantity change according to the spatial positioning marks to generate an engineering quantity change traceability data table.

[0017] In the above embodiments, the data processing system calculates the difference in engineering quantity and the percentage change between consecutive versions, analyzes the changes exceeding the threshold, generates spatial positioning marks through three-dimensional projection, and calculates relevant parameters to generate change traceability data; realizes the automatic tracking and analysis of engineering quantity changes, improves the accuracy of change management, and facilitates the monitoring and verification of engineering quantity changes.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of calculating the area parameter, volume parameter, and calculation rule number corresponding to the engineering quantity change based on the spatial positioning mark and generating the engineering quantity change traceability data table, the method further includes: based on the spatial positioning mark in the engineering quantity change traceability data table, locating the grid coordinates of each different part on the 3DMax platform; according to the grid coordinates, determining the associated components in the component space group that have a coincidence relationship with the different parts; performing an orthogonal matrix operation on the engineering quantity calculation rules of the associated components and the parameters of the different parts to obtain the engineering quantity association influence coefficient; when the engineering quantity association influence coefficient is greater than the preset linkage threshold, synchronously updating the engineering quantity data of the associated components to the engineering quantity change traceability data table.

[0019] In the above embodiments, the data processing system locates the grid coordinates based on the spatial positioning mark, determines the associated components with a coincidence relationship, calculates the influence coefficient through orthogonal matrix operation, and realizes the linkage update of the engineering quantity data; establishes an associated conduction mechanism for engineering quantity changes between components, improves the accuracy and integrity of engineering quantity update, and avoids omissions and errors.

[0020] In a second aspect, an embodiment of the present application provides a data processing system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, and the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the data processing system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions, which, when the computer program product runs on a data processing system, causes the data processing system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, which, when the instructions run on a data processing system, cause the data processing system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] Understandably, the data processing system provided in the second aspect above, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Since the technical solution of receiving two-dimensional engineering drawings and bill of quantities data and converting them into three-dimensional models, combining spatial positioning and attribute data to calculate the coincidence degree for grouping, and associating engineering quantity calculation rules through component coding is adopted, the automatic association and synchronous update of engineering drawings, three-dimensional models, and engineering quantity data are realized, effectively solving the problems in the prior art of manual measurement of parameters, manual calculation of engineering quantities, and repeated data verification. Furthermore, the automation, accuracy, and efficiency of engineering quantity statistics are significantly improved. It can automatically identify changed components and update relevant data when the drawings are updated, avoiding manual repetitive labor and ensuring data consistency.

[0025] 2. Since the technical solution of constructing a multi-level component type index tree, classifying and coding components with the same attributes, and extracting geometric feature parameters to establish a feature library is adopted, the systematic management and rapid retrieval of components are realized, effectively solving the problems in the prior art of chaotic component classification, difficult retrieval, and incomplete feature extraction. Furthermore, the standardization of component management, the efficiency of retrieval, and the comprehensiveness of feature analysis are realized. By establishing a structured index system, the accuracy of spatial relationship analysis is improved, making the engineering quantity calculation more accurate.

[0026] 3. Since the technical solution of calculating the difference and percentage change of engineering quantities, performing spatial positioning and parameter analysis on changes exceeding the threshold, and generating change traceability data is adopted, the automatic monitoring and traceability analysis of engineering quantity changes are realized, effectively solving the problems in the prior art of difficult timely discovery of engineering quantity changes, difficult traceability of change reasons, and difficult determination of the impact scope. Furthermore, the timeliness, traceability, and accuracy of change management are realized. The system can accurately locate the change position and analyze the change impact, facilitating project management. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flowchart of a three-dimensional engineering quantity calculation method based on multi-modal data in an embodiment of the present application; Figure 2 is another flowchart of a three-dimensional engineering quantity calculation method based on multi-modal data in an embodiment of the present application; Figure 3 is a schematic structural diagram of an entity device of a data processing system in an embodiment of the present application. Detailed implementation manners

[0028] The terms used in the following embodiments of this application are only for the purpose of describing specific embodiments, and are not intended to limit this application. As used in the specification of this application, the singular forms "a", "an", "above-mentioned", "the", and "this" are also intended to include the plural forms, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are only for descriptive purposes, and cannot be understood as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of this application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of this application are introduced below.

[0031] In a large commercial complex project, the architectural design team needs to frequently modify the architectural plan to meet the changing needs of the owner. The design changes involve the adjustment of the spatial layout of multiple floors, including the optimization of the area of shops and the re-planning of public areas. These changes result in changes in the positions and dimensions of a large number of components, thereby affecting the calculation of the engineering quantity. The traditional manual calculation method requires re-measuring the dimensions of each changed component and re-calculating the engineering quantity, which not only involves a huge workload but also is prone to omissions and errors. Especially when the change of a certain component affects adjacent components, it is often difficult for engineers to quickly identify all the affected components, resulting in inaccurate calculation of the engineering quantity and affecting cost estimation and material procurement.

[0032] In the related art, the update of the engineering quantity after the change of the engineering drawing can be achieved by adopting the methods of manual verification and an independent software system. However, this method requires a large amount of manual intervention and is prone to problems such as data inconsistency and omission. The scenario of using the three-dimensional engineering quantity calculation method based on multi-modal data in the related art is introduced below.

[0033] The design team of a residential project uses existing CAD software and engineering quantity calculation software to handle design changes. When the residential unit type is adjusted, the designer first modifies the drawing in CAD, then manually marks the scope of change, and then enters the change information into the engineering quantity calculation software. Because the two software systems are independent of each other, data transmission requires manual intervention, which is prone to information faults. For example, when the position of a wall changes, the engineering quantity updates of the connected components such as doors, windows, and pipelines are easily overlooked. At the same time, the existing software cannot automatically identify the spatial association between components, resulting in the inability to detect the chain change effect in a timely manner. This decentralized data processing method is not only inefficient, but also difficult to ensure data consistency and accuracy.

[0034] By adopting the three-dimensional engineering quantity calculation method based on multimodal data in the embodiment of the present application, the three-dimensional model and spatial index system are constructed to realize the automatic identification of component association relationships and the linkage update of engineering quantities, which not only improves the data processing efficiency but also ensures the accuracy of the calculation results. The following introduces the scenario of using the three-dimensional engineering quantity calculation method based on multimodal data in the present application.

[0035] In an office building renovation project, the data processing system of this solution was applied to realize the linkage update of design changes and engineering quantities. When the designer adjusted the layout of the partition wall on a certain floor, the system automatically converted the two-dimensional drawings into a three-dimensional model and established the spatial index relationship of the components. Through the spatial projection coincidence analysis, the system automatically identified the doors, windows, ceilings, floors and other components related to the changed partition walls, and classified them into the same spatial association group. When calculating the engineering quantity, the system automatically updates the area and volume parameters of the associated components according to the preset calculation rules, and generates a complete change traceability record; this intelligent processing method greatly improves the accuracy and efficiency of engineering quantity calculation.

[0036] It can be seen that the three-dimensional engineering quantity calculation method based on multimodal data in the embodiment of the present application can not only realize the rapid update of engineering quantities, but also effectively solve the problems of difficult component association identification and poor data consistency, thereby realizing the intelligent management of engineering drawing changes.

[0037] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of a three-dimensional engineering quantity calculation method based on multimodal data in an embodiment of the present application.

[0038] S101, receiving a plurality of two-dimensional engineering drawing data and bill of quantities data, converting the two-dimensional engineering drawing data into three-dimensional mesh data and importing the data into a 3DMax platform to generate a three-dimensional component model.

[0039] Among them, the two-dimensional engineering drawing data represents the architectural engineering design drawings stored in CAD format, including geometric information and annotation information of multiple layers such as floor plans, elevation views, and sectional views; the bill of quantities data refers to the data set of the calculation rules and measurement units compiled according to the construction project bill of quantities valuation specification; the three-dimensional mesh data is used to represent the spatial geometric model composed of multiple mesh units, and each mesh unit contains vertex coordinates and topological relationships; the three-dimensional component model represents the digital model of the building components with complete geometric shapes and material properties generated on the 3DMax platform.

[0040] The data processing system executes this step after receiving the project-related data submitted by the user. Specifically, the data processing system first reads multiple two-dimensional engineering drawing files, parses the layer structure, primitive data, and annotation information therein, and at the same time loads the calculation rules and measurement standards in the bill of quantities database. Then, based on the reference point coordinate system in the drawings, the data processing system maps the two-dimensional geometric data to the three-dimensional space and constructs a spatial mesh model composed of triangular mesh units. Next, the data processing system calls the API interface of the 3DMax platform, imports the mesh data and performs optimization processing, including operations such as mesh smoothing and boundary repair. Finally, according to the geometric characteristics and material properties of the components, the data processing system converts the mesh model into a complete three-dimensional component model.

[0041] In some embodiments, the data processing system first converts the primitive data in the two-dimensional CAD drawings into geometric entities in the three-dimensional space. Specifically, the system stretches the planar contour along the height direction through a scanning algorithm, and applies Bezier surface interpolation calculation to generate a smooth three-dimensional surface during this process. For complex components, the system adopts a feature decomposition strategy, decomposes the components into a combination of basic geometric bodies, and uses Boolean operations (such as union, intersection, difference) to combine these basic bodies into a complete three-dimensional model. At the same time, based on the material information and construction practices, the system assigns corresponding physical properties and rendering parameters to the model. For example, for a cylindrical concrete column, the system first extracts the circular cross-section of its planar contour, determines the column height, then generates a cylinder through scanning, and then considers attribute information such as the steel bar protection layer and concrete strength.

[0042] In some embodiments, two-dimensional to three-dimensional data conversion and model construction can be achieved in various ways: Optionally, the data processing system can adopt a method based on contour extraction. First, identify the closed contour lines in the two-dimensional drawing, calculate the sequence of feature points of the contour, generate a three-dimensional scan body of the contour according to the marked height information, and then generate a complete component model through Boolean operations. Optionally, the data processing system can also adopt a method based on feature recognition, identify the standard component symbols in the drawing through a deep learning algorithm, match the preset parametric model library, and automatically generate the corresponding three-dimensional component model according to the identified position and dimension parameters. It can be understood that other data conversion and model construction methods can also be used to implement the conversion process from two-dimensional engineering drawings to three-dimensional component models, which are not limited here.

[0043] S102. Extract the spatial positioning data and component attribute data of each component in the three-dimensional component model, and calculate the spatial projection coincidence degree of each component.

[0044] Among them, the spatial positioning data represents the position, direction, and dimension information of the component in the three-dimensional coordinate system, including the center point coordinates, rotation angle, and bounding box parameters; the component attribute data refers to various parameters describing the characteristics of the component, including information such as material type, component category, and functional attributes; the spatial projection coincidence degree is used to represent the intersection degree of different components in three-dimensional space, and is calculated by methods such as the projection area ratio or volume intersection ratio.

[0045] The data processing system executes this step after generating the three-dimensional component model. Specifically, the data processing system first traverses each component in the three-dimensional component model, extracts its boundary vertex coordinates, normal vector, and transformation matrix, and constructs a spatial positioning data structure. Then, the data processing system reads the attribute data such as the material information, component type identifier, and function mark of the component, and establishes a component feature vector. Next, the data processing system calculates the spatial relationship between any two components, including the projected overlapping area, volume intersection, etc., and performs normalization processing to obtain the standardized spatial projection coincidence degree. Finally, the data processing system generates a spatial relationship matrix between components for subsequent grouping analysis.

[0046] It should be noted that the data processing system uses the projection integral method to calculate the spatial relationship between components. First, calculate the projected area of the component in the three main axis directions respectively, and then quantify the coincidence degree by the ratio of the overlapping area to the total projected area. The system uses the Riemann integral to calculate the projected area of irregular shapes and processes the projection of inclined components through coordinate transformation. For continuously changing curved surface components, the system adopts the numerical integration method, discretizes the curved surface into tiny plane elements for cumulative calculation. For example, for two intersecting pipes, the system will calculate the projected overlapping area on the horizontal plane and the vertical plane, and comprehensively obtain the spatial interference degree.

[0047] In some embodiments, the calculation of the spatial projection coincidence degree can be achieved in various ways: Optionally, the data processing system can perform orthographic projection on the components from multiple directions based on the ray projection method, calculate the overlapping area ratio of the projection contours, and obtain the comprehensive coincidence degree through weighted averaging; Optionally, the data processing system can also adopt the voxelization method, convert the components into voxel representations, and determine the coincidence degree by calculating the intersection volume ratio of the voxel occupied spaces. It can be understood that other spatial relationship analysis methods can also be used to implement the coincidence degree calculation between components, which is not limited here.

[0048] S103. Classify the components with a spatial projection coincidence degree greater than the preset coincidence threshold into the same spatial association group to generate multiple component spatial groups.

[0049] Among them, the spatial projection coincidence degree represents the overlapping degree of two or more components in spatial positions, calculated through the projection area ratio or the volume intersection ratio; the preset coincidence threshold refers to the critical value for determining the existence of a spatial association relationship between components, determined by engineering experience; the spatial association group represents a set of components with a close relationship in spatial positions; the component spatial group is used to represent the classification set of components with similar spatial characteristics and topological relationships.

[0050] The data processing system executes this step after completing the calculation of the component spatial projection coincidence degree. Specifically, the data processing system first obtains the spatial projection coincidence degree data matrix of all components and sets the coincidence degree threshold (usually between 0.3 and 0.7). Then, the data processing system uses the clustering analysis method, with the spatial projection coincidence degree as the distance metric, to divide the components with a coincidence degree greater than the threshold into the same spatial association group. Next, the data processing system optimizes each spatial association group, including operations such as removing isolated components, merging similar groups, and adjusting group boundaries. Finally, the data processing system assigns a unique identifier to each component spatial group and establishes the mapping relationship between the components and the groups.

[0051] S104. Calculate the area parameters and volume parameters of each component within the component spatial group based on the 3DMax platform to generate a component parameter table.

[0052] Among them, the area parameters represent two-dimensional geometric features such as the surface area and projection area of the component; the volume parameters refer to three-dimensional geometric features such as the volume and net volume of the component; the component parameter table is a data structure for storing various geometric parameters and calculation results of the components, including fields such as component identifiers, parameter types, and parameter values.

[0053] The data processing system executes this step after completing the division of the component space group. Specifically, the data processing system first calls the geometric calculation engine of the 3DMax platform to perform parametric analysis on the components within each space group. Then, the data processing system calculates the surface area of the components, including the total surface area, visible surface area, contact area, etc.; at the same time, it calculates the volume parameters of the components, including the total volume, net volume, filled volume, etc. Next, the data processing system performs unit conversion and precision processing on the calculation results to ensure compliance with the engineering measurement standards. Finally, the data processing system organizes all the parameters into a structured component parameter table and establishes the association relationships between the parameters.

[0054] In some embodiments, the calculation of component parameters can be achieved in various ways: Optionally, the data processing system can adopt the grid division method to divide the component surface into triangular grids, and obtain accurate area parameters by accumulating the grid areas and considering surface corrections; Optionally, the data processing system can also use the analytic geometry method to directly calculate geometric parameters through the mathematical model of the component. It can be understood that other geometric calculation methods can also be used to obtain component parameters, which are not limited here.

[0055] S105. Using the component code as the index key, write the engineering quantity calculation rules in the bill of quantities data into the associated field of the component parameter table to generate an engineering quantity calculation matrix.

[0056] Among them, the component code represents the unique identifier of the component, including information such as component type, spatial location, version, etc.; the engineering quantity calculation rule refers to the calculation methods and measurement units of the engineering quantities of various components defined in the specification; the associated field is used to store the mapping relationship between component parameters and calculation rules; the engineering quantity calculation matrix represents a two-dimensional data structure containing all component calculation rules and parameters.

[0057] The data processing system executes this step after generating the component parameter table. Specifically, the data processing system first parses the bill of quantities data and extracts the calculation rules and measurement unit definitions of various components. Then, the data processing system establishes a mapping table between the component code and the calculation rules to ensure that each component can find the corresponding calculation method. Next, the data processing system writes the calculation rules into the associated field of the component parameter table in a structured form to establish the correspondence between parameters and rules. Finally, the data processing system generates a complete engineering quantity calculation matrix, including all necessary calculation parameters and rules.

[0058] It should be noted that the data processing system is constructed with a multi-dimensional matrix data structure. The rows of the matrix represent different components, and the columns represent calculation parameters and rules. The system first establishes a parameter mapping relationship, establishing a correspondence between the geometric features of the components (such as length, area, volume) and the variables in the engineering quantity calculation rules. Then, through matrix operations, the geometric parameters are substituted into the calculation formula to obtain standardized engineering quantity values. The system also considers the association relationships between components and processes the engineering quantity allocation in the overlapping areas by introducing a weight coefficient matrix. For example, for the painting engineering quantity of a wall, the system will consider deducting the area of door and window openings and at the same time include the developed area of the perimeter of the door and window casing multiplied by the width.

[0059] In some embodiments, the association of calculation rules can be achieved in various ways: Optionally, the data processing system can adopt the rule engine method to convert the calculation rules into an executable rule set and achieve dynamic binding of parameters and calculation methods through rule matching; Optionally, the data processing system can also use the template matching method to pre-define the calculation rule templates for common components and determine the applicable rules through pattern matching. It can be understood that other rule association methods can also be used to implement the embedding of calculation rules, which are not limited here.

[0060] S106. When it is detected that the two-dimensional engineering drawing has data updates, identify the updated target component code.

[0061] Among them, data update refers to the modification, adjustment or version change of the two-dimensional engineering drawing; the target component code refers to the unique identifier of the component where the update occurs; the two-dimensional engineering drawing refers to the design drawing in CAD format, which contains multiple layers and primitive information.

[0062] The data processing system continuously monitors the changes in the drawing data during operation. Specifically, the data processing system first establishes a monitoring mechanism for the drawing file, regularly checking the modification timestamp and content hash value of the file. Then, when it detects a drawing change, the data processing system compares the drawing data of the new and old versions, identifies the changed layers and primitives. Next, the data processing system locates the affected components according to the change position and attribute information and extracts their component codes. Finally, the data processing system generates a list of the codes of the updated components to prepare for subsequent parameter updates.

[0063] In some embodiments, the monitoring of drawing updates can be achieved in various ways: Optionally, the data processing system can adopt the file system monitoring method to capture the modification behavior of the drawing through file system events and trigger the update detection process in real time; Optionally, the data processing system can also use the version comparison method to regularly obtain the version information of the drawing and identify the updated content through difference analysis. It can be understood that other monitoring methods can also be used to achieve the detection of drawing updates, which are not limited here.

[0064] S107. Recalculate the geometric parameters corresponding to the target component code based on the 3DMax platform, and update the engineering quantity data according to the geometric parameters and the calculation rules in the engineering quantity calculation matrix.

[0065] Among them, the geometric parameters represent geometric features such as the spatial dimensions, area, and volume of the component; the calculation rules refer to the measurement methods and calculation formulas defined in the bill of quantities; the engineering quantity data represents the actual measurement results of the component, including engineering quantity indicators such as quantity, area, and volume.

[0066] The data processing system executes this step after identifying the updated component. Specifically, the data processing system first calls the API of the 3DMax platform to reload the updated component model. Then, the data processing system calculates the latest geometric parameters of the updated component, including spatial dimensions, surface area, volume, etc. Next, the data processing system extracts the corresponding calculation rules from the engineering quantity calculation matrix and substitutes the new geometric parameters into the rules for calculation. Finally, the data processing system updates the relevant records in the engineering quantity database to ensure data consistency.

[0067] In some embodiments, the updated calculation of the engineering quantity can be implemented in various ways: Optionally, the data processing system can adopt an incremental calculation method to only calculate the changed parameters and quickly update the engineering quantity data through difference analysis; Optionally, the data processing system can also use a complete recalculation method to recalculate all relevant parameters to ensure data accuracy. It can be understood that other calculation methods can also be used to achieve the dynamic update of the engineering quantity, which is not limited here.

[0068] In the above embodiments, the component association analysis method based on spatial coincidence degree is mainly introduced. In practical applications, different feature extraction algorithms and classification methods can also be selected according to the project characteristics to further improve the adaptability of the system. The following supplements the scenario of this embodiment.

[0069] An optimized version of this solution is adopted in the construction process of a frame-type high-rise building project. The system can not only handle conventional design changes, but also establish a multi-level component classification system according to the material properties and functional characteristics of the components. When the cross-sectional size of a column on a certain floor needs to be adjusted, the system quickly locates all relevant components through a tree-shaped index and uses orthogonal matrix operations to evaluate the impact of the change. For engineering quantity changes exceeding the preset threshold, the system automatically generates a warning prompt and provides a visual display of the change impact range; it not only improves the efficiency of change processing, but also provides strong support for project cost control.

[0070] After combining the above scenarios, the following provides a more specific process description of the method provided in this embodiment. Please refer to Figure 2, which is another schematic flowchart of the three-dimensional engineering quantity calculation method based on multi-modal data in the embodiments of the present application.

[0071] S201. Receive multiple two-dimensional engineering drawing data and bill of quantities data, convert the two-dimensional engineering drawing data into three-dimensional grid data, and import it into the 3DMax platform to generate a three-dimensional component model.

[0072] Referring to step S101, the data processing system constructs a three-dimensional component model on the 3DMax platform.

[0073] In some embodiments, the data processing system will perform the construction of layers and attributes, that is, the data processing system will receive multiple two-dimensional engineering drawing data and bill of quantities data; extract the layer data and component reference point coordinates in the two-dimensional engineering drawing data, and determine the plane grid nodes; based on the layer annotation height information and reference point coordinates, calculate the spatial coordinates of the plane grid nodes; organize the spatial coordinates into a three-dimensional grid data set according to the layer attributes, and import it into the 3DMax platform; based on the spatial coordinates and layer attributes of the three-dimensional grid data set, generate a three-dimensional component model.

[0074] Among them, the two-dimensional engineering drawing data represents the engineering design drawing information stored in the CAD format; the bill of quantities data refers to the calculation rules and unit definitions compiled according to the engineering measurement specifications; the layer data represents the hierarchical information of different types of components in the engineering drawing; the component reference point coordinates are used to represent the positioning points of the components in the plane coordinate system; the plane grid nodes refer to the discrete sampling points in the two-dimensional space; the spatial coordinates represent the position information in the three-dimensional space; the three-dimensional grid data set is used to represent the spatial geometric model composed of multiple grid units; the three-dimensional component model refers to the complete digital model of building components in the 3DMax platform.

[0075] The data processing system executes this data conversion process after receiving the project initialization instruction. Specifically, the data processing system first reads the engineering drawing file in the CAD format and the bill of quantities database, and parses the layer structure and annotation information of the drawing. Then, the data processing system extracts the reference point coordinates of the components in each layer and establishes an array of regular plane grid sampling points. Next, the data processing system combines the annotation height information in the layer to map the plane grid nodes to the three-dimensional space and calculates the spatial coordinate values of each node. After that, the data processing system organizes the spatial coordinate points with the same layer attributes into a structured three-dimensional grid data set and imports the data into the 3DMax platform through an interface. Finally, in the 3DMax platform, the data processing system generates a complete three-dimensional component model based on the imported grid data and layer attribute information through grid reconstruction and feature extraction.

[0076] It should be noted that the data processing system adopts an adaptive grid meshing algorithm. First, triangular meshing is performed on the two-dimensional graph, and the Delaunay criterion is used to ensure the grid quality. Then, a three-dimensional grid is constructed based on the elevation information, and the surface grid is generated through node interpolation and boundary constraints. The system uses a grid optimization algorithm to adjust the node distribution, including boundary preservation, feature protection, and grid smoothing. For curved surface components, the system adopts the NURBS parameterization method for accurate modeling. For example, for a special-shaped roof, the system will first generate a plane control grid, then generate a curved surface grid through elevation control point interpolation, and finally perform grid optimization to ensure geometric accuracy.

[0077] In some embodiments, the conversion process from two-dimensional data to a three-dimensional model can be achieved in various ways: Optionally, the data processing system can adopt a feature line-based method. First, identify the feature line segments and key points in the drawing, construct the topological structure of the plane contour, then perform contour scanning and lofting based on the height information to generate an initial three-dimensional grid model, and finally obtain an accurate component model through grid optimization and smoothing processing; Optionally, the data processing system can also adopt a voxel-based method. First, discretize the plane graph into a regular grid, assign height attributes to each grid cell to construct a voxel model, then reconstruct the surface grid through algorithms such as marching cube, and finally perform feature-preserving grid refinement and optimization. It can be understood that other three-dimensional reconstruction methods can also be used to achieve the spatial expression of engineering drawings, which are not limited here.

[0078] S202. Based on the three-dimensional component model, construct a multi-level component type index tree.

[0079] Among them, the multi-level component type index tree represents a hierarchical data structure used to describe the subordinate relationship and classification system between different types of components; the component type refers to the classification of components with similar functions, materials, or structural characteristics; the index node represents the data unit in the tree structure, including node attributes, parent-child relationships, and index information.

[0080] The data processing system executes this step after generating the three-dimensional component model. Specifically, the data processing system first analyzes the attribute characteristics of all components in the three-dimensional component model, including information such as the functional category, material attribute, and structural characteristics of the components. Then, the data processing system establishes a multi-level classification standard, defining the classification hierarchy of the components from coarse to fine, for example, dividing them into levels such as the main structure, sub-projects, major component categories, and specific types. Next, the data processing system creates a tree-shaped data structure, constructs the hierarchical relationship from the root node to the leaf node, and each node contains the classification information and index data at this level. Finally, the data processing system maps all components to the corresponding type nodes to complete the construction of the index tree.

[0081] It should be noted that the data processing system uses a hierarchical clustering algorithm to construct a classification system. First, a similarity matrix between components is calculated based on multiple dimensions such as material characteristics, functional attributes, and geometric features. Then, through a bottom-up aggregation process, categories with high similarity are gradually merged to form a hierarchical tree structure. The system uses the information gain criterion to optimize the selection of split points to ensure the rationality of classification. At the same time, the inheritance relationship and constraint conditions between categories are maintained to support dynamic updates and queries. For example, the system will first classify concrete components by structural function (beams, columns, slabs, etc.), then further divide them by strength grade, and finally classify them by specific specifications and models.

[0082] In some embodiments, the construction of a multi-level component type index tree can be achieved in multiple ways: Optionally, the data processing system can adopt a bottom-up aggregation method. First, feature extraction and similarity calculation are performed on the underlying components, and similar component types are gradually merged through a hierarchical clustering algorithm to form a multi-level type tree structure. At the same time, the index relationship and access path of each layer of nodes are established; Optionally, the data processing system can also adopt a top-down decomposition method. Based on a predefined engineering classification system, starting from the top-level category, it is gradually subdivided, and components are assigned to appropriate type nodes through rule matching to establish a complete index system. It can be understood that other classification index methods can also be used to achieve the multi-level organization of component types, which is not limited here.

[0083] In some embodiments, the data processing system will perform relationship extraction on each component, that is, the data processing system will extract the material attributes, functional attributes, and spatial attributes of each component to generate a component attribute vector; a clustering algorithm is used to perform clustering analysis on the component attribute vector to determine the hierarchical relationship of component types; based on the hierarchical relationship of component types, a tree-shaped index structure is constructed, and the component type nodes with a subordinate relationship are associated through the parent-child node relationship, and a weight coefficient is assigned to each component type node to obtain a multi-level component type index tree.

[0084] Among them, the component attribute vector represents a multi-dimensional data structure describing component characteristics; the clustering algorithm refers to a mathematical model used for data classification; the hierarchical relationship of component types represents the subordinate relationship between different types of components; the tree-shaped index structure is used to represent a data organization method with a hierarchical relationship; the parent-child node relationship refers to the connection relationship between nodes in the tree structure; the weight coefficient is used to represent the importance of a node in the classification system.

[0085] The data processing system executes the classification index construction process after obtaining the complete attribute data of the components. Specifically, the data processing system first analyzes the multi-dimensional attribute characteristics of each component, including information such as material type, functional use, and spatial location, and converts these characteristics into standardized numerical vectors. Then, the data processing system uses a hierarchical clustering algorithm and, based on the similarity measure of the attribute vectors, gradually merges component types with similar characteristics to form a multi-level classification system. Next, the data processing system constructs a tree-shaped data structure according to the clustering results, establishes connections between type nodes according to the subordinate relationship, and assigns weight values according to the number of components and importance contained in the nodes. Finally, the data processing system optimizes the balance and retrieval efficiency of the tree structure to generate a complete multi-level component type index tree.

[0086] In some embodiments, the type classification and index construction of components can be implemented in various ways: Optionally, the data processing system can adopt a method based on fuzzy clustering, use fuzzy membership degrees to describe the association degree between components and types, and determine the optimal type division through iterative optimization, while considering the influence of attribute weights, to construct a flexible classification system; Optionally, the data processing system can also adopt a method based on deep learning, train a deep neural network to automatically learn the feature representation and type relationship of components, and achieve automatic classification of components through multi-level feature extraction and classification. It can be understood that other classification methods can also be used to implement the organization and management of component types, which are not limited here.

[0087] S203. Classify components with the same component attributes into the same component type node, and assign a unique component coding identifier to each component type node to obtain a component space index.

[0088] Among them, component attributes represent characteristic information such as the function, material, and structural type of the component; a component type node refers to a data unit in the index tree that represents a specific type of component; a component coding identifier represents a coding system used to uniquely identify a component type, including type information and hierarchical relationships; a component space index is used to represent the positioning information of a component in a three-dimensional space and a type system.

[0089] The data processing system executes this step after constructing a multi-level component type index tree. Specifically, the data processing system first analyzes the attribute data of each component, including information such as the functional attributes, material characteristics, and construction types of the components. Then, the data processing system aggregates components with the same or similar attribute characteristics into the same type node through an attribute matching algorithm. Next, the data processing system generates a unique coding identifier for each component type node, which includes hierarchical information, type information, serial numbers, etc., to ensure the uniqueness and scalability of the coding. Finally, the data processing system establishes a mapping relationship between component instances and type nodes to form a complete component space index structure.

[0090] It should be noted that the data processing system adopts a hierarchical coding scheme to generate unique identifiers. The coding structure includes multiple fields such as project area code, component type code, spatial location code, and serial number. The system compresses and encodes this information through bit operations to ensure the uniqueness and scalability of the coding. At the same time, a coding index tree is established to support fast retrieval and relational queries. During the coding generation process, the system considers the inheritance relationship and version changes of components, and ensures the similarity of the codes of related components through prefix coding. For example, the possible code for a frame column on a certain floor may be "A01-C-0304-001", where A01 represents the building unit, C represents the column type, 0304 represents the floor and location, and 001 is the serial number.

[0091] In some embodiments, the type classification and coding assignment of components can be implemented in various ways: Optionally, the data processing system can adopt a method based on fuzzy clustering to construct a feature vector of component attributes, calculate the attribute similarity matrix, determine the type attribution of components through the fuzzy clustering algorithm, generate a hierarchical coding system according to the clustering results, and establish the correspondence between components and codes; Optionally, the data processing system can also adopt a method based on rule reasoning, predefined a set of decision rules for component classification, analyze and match component attributes through a rule engine, automatically determine the type attribution of components, and assign unique identifiers according to the preset coding rules. It can be understood that other classification methods and coding strategies can also be adopted to implement the type management of components, which is not limited here.

[0092] S204. Extract the geometric feature parameters of each component type node to generate a component feature library.

[0093] Among them, the geometric feature parameters represent numerical indicators describing the shape and size of components, including basic parameters such as length, area, and volume, and advanced features such as shape factors and curvatures; the component feature library refers to a database storing the geometric features of various components, which is used to support component recognition and parameter calculation.

[0094] The data processing system executes this step after completing the component type classification and coding. Specifically, the data processing system first traverses all component type nodes and extracts representative instances of each type of component. Then, the data processing system calculates the basic geometric parameters of the components, including peripheral dimensions, surface area, volume, etc., and at the same time extracts advanced geometric features, such as cross-section features, surface features, and topological features. Next, the data processing system performs standardization processing and data cleaning on the feature parameters to ensure the consistency and comparability of the parameters. Finally, the data processing system organizes and stores all feature parameters according to component types to establish a structured component feature library.

[0095] It should be noted that the data processing system extracts component features through differential geometry calculations. First, the principal curvature and Gaussian curvature of the component surface are calculated to characterize local shape features. Then, integral invariants are used to describe global shape features, including volume moment, moment of inertia, and shape factor, etc. The system uses discrete differential operators to calculate the normal vector field and curvature distribution of the surface, and identifies key geometric features through feature point detection. For complex components, the system uses multi-scale analysis methods to extract feature parameters at different precision levels. For example, for a special-shaped column, the system will calculate parameters such as the second moment of its cross-section, torsional stiffness, and surface curvature distribution for subsequent engineering quantity calculation and component identification.

[0096] In some embodiments, the extraction and storage of geometric features can be achieved in various ways: Optionally, the data processing system can adopt a method based on geometric analysis to extract the surface features, boundary features, and topological features of components through differential geometry calculations, combine shape descriptors to generate complete feature vectors, and establish a hierarchical storage structure for feature parameters; Optionally, the data processing system can also adopt a method based on deep learning to automatically learn the geometric feature representation of components through a convolutional neural network, extract multi-scale feature descriptors, and construct a vector-based feature index library. It can be understood that other feature extraction methods can also be used to represent and manage component features, which are not limited here.

[0097] S205. Extract the spatial positioning data and component attribute data of each component in the 3D component model, and calculate the spatial projection coincidence degree of each component.

[0098] Referring to step S102, the data processing system will calculate the spatial projection coincidence degree of each component.

[0099] S206. Classify the components with a spatial projection coincidence degree greater than the preset coincidence threshold into the same spatial association group to generate multiple component spatial groups.

[0100] Referring to step S103, the data processing system will perform component grouping.

[0101] S207. Calculate the area parameters and volume parameters of each component in the component spatial group based on the 3DMax platform to generate a component parameter table.

[0102] Referring to step S104, the data processing system will calculate the component parameter table.

[0103] S208. Using the component code as the index key, write the engineering quantity calculation rules in the bill of quantities data into the associated fields of the component parameter table to generate an engineering quantity calculation matrix.

[0104] Referring to step S105, the data processing system will calculate the engineering quantity calculation matrix.

[0105] S209. Obtain the engineering quantity difference of each component between two consecutive engineering drawing versions, and calculate the percentage change in engineering quantity.

[0106] Among them, the engineering quantity difference represents the numerical change in the engineering quantity calculation result of the same component between consecutive versions; the engineering drawing version refers to the revised status of the design drawing at different times, and each version has a unique identifier and timestamp; the percentage change in engineering quantity is used to represent the relative amplitude of the engineering quantity change, and is calculated by the ratio of the difference to the reference value.

[0107] The data processing system executes this step after obtaining the engineering drawing data of two consecutive versions. Specifically, the data processing system first extracts the engineering drawing data of adjacent versions and the corresponding engineering quantity calculation results from the version management system. Then, the data processing system matches the same component in the two versions according to the component code and calculates the absolute difference of its engineering quantity index. Next, the data processing system takes the engineering quantity of the earlier version as the reference value, calculates the ratio of the change amount to the reference value, and obtains the standardized percentage change. Finally, the data processing system organizes the change data of all components into a structured statistical table, including information such as component code, reference value, change value, and percentage change.

[0108] It should be noted that the data processing system can establish a mathematical model based on the change in engineering quantity. The system first constructs a time-series state vector to record the engineering quantity parameters of the component in different versions. Describes the parameter change law through the state transition matrix and calculates the difference between adjacent states. The system uses the weighted average method to process the comprehensive differences of multiple measurement indicators and identifies abnormal changes through standard deviation analysis. During the calculation process, the system considers the conversion of different measurement units and accuracy requirements to ensure the accuracy of the difference calculation. For example, when the wall component changes, the system will calculate the change amounts of multiple engineering quantity indicators such as masonry area, plastering area, and painting area respectively.

[0109] In some embodiments, the calculation and analysis of the change in engineering quantity can be implemented in various ways: Optionally, the data processing system can adopt a method based on statistical analysis, identify the components with significant changes by calculating the coefficient of variation and standard deviation of each component, and at the same time consider the measurement unit and accuracy requirements of the engineering quantity to ensure the accuracy and reliability of the change calculation; Optionally, the data processing system can also adopt a method based on time series, establish a trend model of the change in engineering quantity, identify abnormal change points through moving average and difference analysis, and track the time-series characteristics of the change in combination with version information. It can be understood that other statistical methods can also be used to measure and evaluate the change in engineering quantity, which is not limited here.

[0110] S210. When the percentage change in engineering quantity exceeds the predetermined change threshold, extract the 3D geometric data of the corresponding target component in different versions.

[0111] Among them, the predetermined change threshold represents the standard of the engineering quantity change ratio for triggering in-depth analysis; the target component refers to the component whose engineering quantity change exceeds the threshold; the three-dimensional geometric data is used to represent the spatial shape, position, and dimension information of the component.

[0112] The data processing system executes this step after calculating the percentage change in the engineering quantity. Specifically, the data processing system first compares the percentage change of each component with the preset change threshold to determine the target components that need to be focused on. Then, the data processing system retrieves the complete three-dimensional model data of these target components in different versions from the version library, including vertex coordinates, boundary surfaces, topological relationships, and other information. Next, the data processing system preprocesses the extracted geometric data, including operations such as coordinate system unification, precision calibration, and data cleaning. Finally, the data processing system organizes and stores the processed geometric data by version to prepare for subsequent difference analysis.

[0113] In some embodiments, the screening of target components and the extraction of geometric data can be achieved in multiple ways: Optionally, the data processing system can adopt a multi-level threshold judgment method, set different levels of change thresholds, perform hierarchical screening according to the importance and sensitivity of the engineering quantity, and perform priority sorting and classification processing on the components that exceed the threshold; Optionally, the data processing system can also adopt an association analysis method, consider the spatial relationship and functional dependence between components, and include the components related to the significantly changed components in the analysis scope to ensure the complete tracking of the change impact. It can be understood that other screening methods can also be used to determine the target components and obtain the data, which are not limited here.

[0114] S211. Project the three-dimensional geometric data of the target component onto the same coordinate system according to the version identifier to generate spatial positioning marks for the different parts.

[0115] Among them, the version identifier represents the unique mark of the drawing revision in different periods; the same coordinate system refers to the unified spatial reference system for comparative analysis; the different parts refer to the specific positions where the component changes between different versions; the spatial positioning marks are used to accurately locate and describe the changed area.

[0116] The data processing system executes this step after obtaining the three-dimensional geometric data of the target component. Specifically, the data processing system first establishes a unified spatial coordinate reference system, determines the coordinate origin and the main axis direction. Then, the data processing system projects the component models of different versions into this unified coordinate system through coordinate transformation, maintaining the relative position relationship of the components. Next, the data processing system identifies the geometric differences between the components in different versions through spatial overlay analysis, including shape changes, position offsets, etc. Finally, the data processing system generates spatial marks at the different positions, recording the position coordinates and change characteristics of the changed area.

[0117] It should be noted that the data processing system evaluates the impact of component changes using the principle of orthogonal transformation. The geometric parameters and calculation rules of the components are represented as orthogonal basis vectors, and the influence coefficient is obtained by calculating the inner product of the vectors. The system constructs a feature vector matrix that includes the spatial position, shape features, and calculation rule parameters of the components, and extracts the main influencing factors through SVD decomposition. Transformation and projection operations are performed in the orthogonal space to ensure the orthogonality and completeness of the calculation results. For example, when the cross-sectional dimensions of a certain beam change, the system will evaluate the force changes and the impact on the engineering quantity of the columns, walls, and other components connected to it.

[0118] In some embodiments, the positioning and marking of geometric differences can be achieved in various ways: Optionally, the data processing system can adopt a grid comparison method, discretize the component model into regular grids, identify the changed areas by comparing the occupancy status of the grid cells, and determine the boundaries and ranges of the continuously changed areas in combination with the spatial clustering algorithm; Optionally, the data processing system can also adopt a feature matching method, extract the local geometric features of the components, determine the changed parts through the analysis of the feature correspondence relationship, and establish the feature mapping relationship before and after the change. It can be understood that other spatial analysis methods can also be used to achieve the precise positioning of component changes, which is not limited here.

[0119] S212. According to the spatial positioning mark, calculate the area parameter, volume parameter, and calculation rule number corresponding to the engineering quantity change, and generate an engineering quantity change traceability data table.

[0120] Among them, the area parameter represents the surface area change amount of the changed part; the volume parameter refers to the volume change amount of the changed part; the calculation rule number is used to identify the specific rule adopted for engineering quantity calculation; the engineering quantity change traceability data table refers to a structured data set that records the reasons and impacts of engineering quantity changes.

[0121] The data processing system executes this step after obtaining the spatial positioning mark of the different part. Specifically, the data processing system first extracts the geometric range of the changed area based on the spatial positioning mark and calculates the area change and volume change of the area. Then, the data processing system looks up the corresponding calculation rule number from the engineering quantity calculation matrix to determine the calculation basis for the engineering quantity change. Next, the data processing system associates information such as the change parameters, calculation rules, and spatial positions to establish a complete traceability chain for the engineering quantity change. Finally, the data processing system generates a change traceability data table containing all relevant information to support subsequent change analysis and management.

[0122] It should be noted that the data processing system stores change information in a graph structure. The system constructs a change event graph, where nodes represent component states and edges represent change operations. The propagation path of changes is traced through the depth-first search algorithm to construct a complete change chain. The system also maintains a dependency matrix of change operations to record the associated impacts between components. Information such as the timestamp of the change, the operation type, and the scope of influence is stored in a data table to support multi-dimensional traceability queries. For example, for a design change, the system will record a complete information chain including the reason for the change of the starting component, the change process of the affected components, and the engineering quantity calculation results at each stage.

[0123] In some embodiments, the traceability analysis of engineering quantity changes can be achieved in various ways: Optionally, the data processing system can adopt a parameter correlation analysis method to establish a mathematical relationship model between geometric parameters, calculation rules, and engineering quantities, and determine the main influencing factors of changes through parameter sensitivity analysis to construct a complete change impact chain; Optionally, the data processing system can also adopt a graph structure analysis method to represent the change process as a directed graph, trace the propagation path and scope of influence of changes through path analysis, and establish a multi-dimensional traceability system. It can be understood that other analysis methods can also be used to achieve the tracking and traceability of engineering quantity changes, which are not limited here.

[0124] In some embodiments, the data processing system updates the engineering quantity change traceability data table, that is, the data processing system locates the grid coordinates of each different part in the 3DMax platform based on the spatial positioning marks in the engineering quantity change traceability data table; according to the grid coordinates, the associated components with overlapping relationships with the different parts are determined from the component space group; the orthogonal matrix operation is performed on the engineering quantity calculation rules of the associated components and the parameters of the different parts to obtain the engineering quantity association impact coefficient; when the engineering quantity association impact coefficient is greater than the preset linkage threshold, the engineering quantity data of the associated components is synchronously updated to the engineering quantity change traceability data table.

[0125] Among them, the engineering quantity change traceability data table refers to a structured data set that records engineering quantity change information; the spatial positioning mark represents the spatial coordinate information of the change location; the grid coordinate refers to the discrete point position in three-dimensional space; the associated component represents the component that has a spatial association with the changed part; the engineering quantity association impact coefficient is used to represent the degree of association of engineering quantity changes between components; the linkage threshold refers to the critical value for triggering associated updates.

[0126] The data processing system executes the correlation analysis process after detecting a change in the engineering quantity. Specifically, the data processing system first reads the spatial positioning markers from the change traceability data table and converts the markers into the grid coordinate system of the 3DMax platform. Then, the data processing system searches for components in the component space group that have spatial overlap with the changed position and establishes a set of potential associated components. Next, the data processing system performs an orthogonal matrix operation on the engineering quantity calculation rules of the associated components and the geometric parameters of the changed part to evaluate the propagation impact of the engineering quantity change. Finally, the data processing system determines the associated components that need to be synchronously updated based on the calculated impact coefficient and updates their engineering quantity data to the traceability data table.

[0127] In some embodiments, the correlation analysis and synchronous update of the engineering quantity can be achieved in various ways: Optionally, the data processing system can adopt a graph theory-based method to represent the spatial relationship between components as a weighted graph structure, analyze the propagation path of the engineering quantity change through the shortest path algorithm, evaluate the impact degree received by each node, and determine the associated range that needs to be updated; Optionally, the data processing system can also adopt a tensor decomposition-based method to construct a multi-dimensional engineering quantity correlation tensor, extract key influencing factors through tensor decomposition, and establish an accurate propagation model. It can be understood that other correlation analysis methods can also be used to achieve the collaborative update of the engineering quantity, which is not limited here.

[0128] S213. When detecting data updates in the two-dimensional engineering drawing, identify the updated target component code.

[0129] Referring to step S105, the data processing system will identify the updated target component code.

[0130] In some embodiments, the data processing system will perform component update confirmation based on the representation, that is, the data processing system will obtain the version identification information of the two-dimensional engineering drawing and generate a drawing version data set; perform version difference comparison according to the drawing version data set to obtain the updated data identification; when there is an updated data identification, locate the updated layer based on the updated data identification to obtain the layer difference data; extract the component code information from the layer difference data to generate a target component code table.

[0131] Among them, the version identification information represents the unique identifier of different revision states of the engineering drawing; the drawing version data set refers to a structured data set containing information of multiple version drawings; the updated data identification is used to represent the data position where changes occur between versions; the layer difference data represents the change information of the layer content between different versions; the target component code table refers to a set of unique identifiers of the components that need to be updated.

[0132] The data processing system executes the version analysis process when it detects an update to an engineering drawing. Specifically, the data processing system first extracts the identification information of all versions from the engineering drawing management system, including version numbers, timestamps, and revision notes, to build a complete version data set. Then, the data processing system performs a difference comparison on adjacent versions of the drawing data, and determines the location of the updated data by comparing the content hash value and the file feature code. Next, the data processing system locates the specific layer that has changed, extracts the addition, deletion, and modification information of the layer, and generates a detailed difference data record. Finally, the data processing system parses the affected component information from the difference data, extracts its unique code, and forms a target component code table that needs to be updated.

[0133] In some embodiments, version difference analysis and component location can be achieved in a variety of ways: Optionally, the data processing system can use a tree structure comparison-based method to organize the drawing content into a hierarchical tree structure, accurately locate the changed nodes through node traversal and attribute comparison of the tree structure, and then extract the relevant component information along the node path to establish a mapping relationship of version differences; Optionally, the data processing system can also use a feature matching-based method to extract the local feature descriptor of the drawing, identify the area with content changes through feature matching and similarity calculation, and locate the relevant components in combination with the spatial index. It is understandable that other difference analysis methods can also be used to achieve change tracking of drawing versions, which is not limited here.

[0134] S214. Recalculate the geometric parameters corresponding to the target component code based on the 3DMax platform, and update the engineering quantity data according to the geometric parameters and the calculation rules in the engineering quantity calculation matrix.

[0135] Referring to step S106 , the data processing system updates the engineering quantity data.

[0136] In the embodiment of the present application, due to the use of a component identification method based on three-dimensional space conversion and a component classification system of a multi-level index tree, and the introduction of a method for calculating the association of engineering quantities using spatial projection coincidence analysis and orthogonal matrix operations, it is possible to complete the conversion of two-dimensional drawings to three-dimensional models, the establishment of component association relationships, and the linkage update of engineering quantities, effectively solving the problems of low manual identification efficiency, difficulty in determining associated components, and untimely data updates in traditional technologies, thereby realizing intelligent management of engineering quantities during changes in engineering drawings, significantly improving data processing efficiency and accuracy, and at the same time ensuring data consistency and traceability through a change traceability mechanism, which facilitates the refined management of engineering projects.

[0137] The data processing system in the embodiment of the present invention is described below from the perspective of hardware processing. Figure 3, which is a schematic structural diagram of an entity device in the data processing system according to an embodiment of the present application.

[0138] It should be noted that Figure 3 The structure of the data processing system shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0139] As Figure 3 shown, the data processing system includes a CPU 301, which can perform various appropriate actions and processes according to the program stored in the ROM 302 or the program loaded from the storage section 308 into the RAM 303, such as executing the method described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. The I / O interface 305 is also connected to the bus 304.

[0140] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read from it can be installed into the storage section 308 as needed.

[0141] Specifically, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the CPU 301, various functions defined in the present invention are executed.

[0142] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings.

[0143] Specifically, the data processing system of this embodiment includes a processor and a memory, and a computer program is stored on the memory. When the computer program is executed by the processor, the three-dimensional engineering quantity calculation method based on multi-modal data provided in the above-mentioned embodiment is implemented.

[0144] On the other hand, the present invention also provides a computer-readable storage medium, which may be included in the data processing system described in the above-mentioned embodiment; or it may exist separately without being assembled into the data processing system. The above storage medium carries one or more computer programs. When the one or more computer programs are executed by a processor of the data processing system, the data processing system is enabled to implement the three-dimensional engineering quantity calculation method based on multi-modal data provided in the above-mentioned embodiment.

[0145] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the various embodiments of the present application.

[0146] As used in the above embodiments, depending on the context, the term "when..." may be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" may be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

Claims

1. A 3D engineering quantity calculation method based on, characterized in that, Applied to a data processing system, the method includes: Receiving a plurality of two-dimensional engineering drawing data and bill of quantities data, converting the two-dimensional engineering drawing data into three-dimensional mesh data and importing it into the 3DMax platform to generate three-dimensional component models; Extracting the spatial positioning data and component attribute data of each component in the three-dimensional component model, and calculating the spatial projection coincidence degree of each component; Classifying the components with a spatial projection coincidence degree greater than a preset coincidence threshold into the same spatial association group to generate a plurality of component spatial groups; Calculating the area parameters and volume parameters of each component in the component spatial group based on the 3DMax platform to generate a component parameter table; Writing the engineering quantity calculation rules in the bill of quantities data into the associated fields of the component parameter table with the component code as the index key to generate an engineering quantity calculation matrix; When it is detected that the data of the two-dimensional engineering drawing is updated, identifying the target component code of the update; Recalculating the geometric parameters corresponding to the target component code based on the 3DMax platform, and updating the engineering quantity data according to the geometric parameters and the calculation rules in the engineering quantity calculation matrix.

2. The method according to claim 1, wherein The step of receiving a plurality of two-dimensional engineering drawing data and bill of quantities data, converting the two-dimensional engineering drawing data into three-dimensional mesh data and importing it into the 3DMax platform to generate three-dimensional component models specifically includes: Receiving a plurality of two-dimensional engineering drawing data and bill of quantities data; Extracting the layer data and component reference point coordinates in the two-dimensional engineering drawing data to determine the plane grid nodes; Calculating the spatial coordinates of the plane grid nodes based on the layer annotation height information and reference point coordinates; Organizing the spatial coordinates into a three-dimensional mesh data set according to the layer attributes and importing it into the 3DMax platform; Generating three-dimensional component models based on the spatial coordinates and layer attributes of the three-dimensional mesh data set.

3. The method according to claim 1, characterized in that, The step of, when it is detected that the data of the two-dimensional engineering drawing is updated, identifying the target component code of the update specifically includes: Obtaining the version identification information of the two-dimensional engineering drawing to generate a drawing version data set; Performing version difference comparison according to the drawing version data set to obtain an update data identification; When there is the update data identification, positioning the updated layer based on the update data identification to obtain layer difference data; Extracting component code information from the layer difference data to generate a target component code table.

4. The method according to claim 1, characterized in that Before the step of extracting the spatial positioning data and component attribute data of each component in the three-dimensional component model and calculating the spatial projection coincidence degree of each component, the method further includes: Constructing a multi-level component type index tree based on the three-dimensional component model; Classifying the components with the same component attributes into the same component type node, and assigning a unique component code identifier to each component type node to obtain a component spatial index; Extracting the geometric feature parameters of each component type node to generate a component feature library; the geometric feature parameters include component contour feature points and spatial topological relationships.

5. The method according to claim 4, characterized in that, The step of constructing a multi-level component type index tree based on the three-dimensional component model specifically includes: Extract the material attributes, functional attributes, and spatial attributes of each component to generate a component attribute vector; Use a clustering algorithm to perform clustering analysis on the component attribute vectors to determine the hierarchical relationship of component types; Based on the hierarchical relationship of component types, construct a tree-shaped index structure, associate the component type nodes with subordinate relationships through parent-child node relationships, and assign weight coefficients to each component type node to obtain a multi-level component type index tree.

6. The method according to claim 1, wherein After the step of using the component code as the index key and writing the engineering quantity calculation rules in the bill of quantities data into the associated fields of the component parameter table to generate an engineering quantity calculation matrix, the method further includes: Obtain the engineering quantity differences of each component between two consecutive engineering drawing versions, and calculate the engineering quantity change percentage; When the engineering quantity change percentage exceeds a predetermined change threshold, extract the 3D geometric data of the corresponding target component in different versions; Project the 3D geometric data of the target component onto the same coordinate system according to the version identifier to generate spatial positioning marks for the different parts; According to the spatial positioning marks, calculate the area parameters, volume parameters, and calculation rule numbers corresponding to the engineering quantity change to generate an engineering quantity change traceability data table.

7. The method according to claim 6, wherein After the step of calculating the area parameters, volume parameters, and calculation rule numbers corresponding to the engineering quantity change according to the spatial positioning marks to generate an engineering quantity change traceability data table, the method further includes: Based on the spatial positioning marks in the engineering quantity change traceability data table, locate the grid coordinates of each different part in the 3DMax platform; According to the grid coordinates, determine the associated components that have a coincidence relationship with the different parts from the component space group; Perform an orthogonal matrix operation on the engineering quantity calculation rules of the associated components and the parameters of the different parts to obtain an engineering quantity association influence coefficient; When the engineering quantity association influence coefficient is greater than a preset linkage threshold, synchronously update the engineering quantity data of the associated components to the engineering quantity change traceability data table.

8. A data processing system, characterized in that, The data processing system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the data processing system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instructions run on the data processing system, cause the data processing system to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the data processing system, cause the data processing system to execute the method according to any one of claims 1-7.

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