Non-destructive and fast splitting method and plug-in for drawings

Abstract

The present application provides a method and plug-in for lossless and rapid splitting of drawings, which relates to the technical field of drawing splitting, including: obtaining K non-standard construction drawings; performing drawing fusion processing to obtain drawings to be split; extracting stage drawings based on predefined construction stages; splitting and reorganizing drawings according to the composition of construction departments; performing construction collaboration feature analysis and storage updates on construction departments to obtain M associated split drawing trees; using drawings to be split to traverse M associated split drawing trees for information proofreading and completion to obtain multiple lossless split drawing trees. This application can solve the technical problem in the prior art that the drawing splitting process is difficult to accurately match the specific needs of each construction stage and department, resulting in omissions or errors in drawing information. By performing multi-dimensional drawing splitting and management according to construction stages and construction departments, it is ensured that the split drawings still contain all necessary information, thereby improving information integrity.

Description

Technical Field

[0001] The present application relates to the technical field of drawing splitting, and in particular to a method and plug-in for non-destructive and rapid drawing splitting. Background Art

[0002] Drawing splitting is the management and splitting of architectural drawings. Architectural drawings usually contain multiple functional areas and complex structural information. During the project execution, the drawings are reasonably split so that different departments (such as design, construction, supervision, etc.) can use and collaborate efficiently. At present, the existing drawing splitting still faces some shortcomings in dealing with highly complex and dynamically changing construction environments, especially when the construction scale is expanded, multiple stages are cross-constructed or parallel, and the efficiency of information transmission and sharing is low when multiple departments collaborate. Although the needs of each stage and department are taken into account, due to the reliance on static, predefined rules, it is impossible to flexibly adjust dynamically, resulting in information isolation, low sharing efficiency and mismatch during the construction process.

[0003] In complex projects, the needs of various stages and departments will not only exist independently, but also have a large number of intersections. The existing drawing splitting often relies on static, predefined rules when dealing with these cross-information, which is difficult to dynamically adjust to cope with project changes and cannot accurately match the specific needs of various construction stages and departments, resulting in two typical problems: incomplete information (omissions) and information overload. Incomplete information means that the drawings obtained by the construction department cannot cover all the required design and operation details, while information overload means that the department obtains too much redundant information that is irrelevant to it, increasing the complexity of work and communication costs.

[0004] In summary, there are technical problems in the prior art that the drawing splitting process relies on static, predefined splitting rules, which makes it difficult to accurately match the specific needs of each construction stage and department, resulting in omissions or errors in drawing information. Summary of the invention

[0005] The purpose of this application is to provide a lossless and fast drawing splitting method and plug-in to solve the technical problem in the prior art that the drawing splitting process relies on static, predefined splitting rules, which makes it difficult to accurately match the specific needs of each construction stage and department, resulting in omissions or errors in drawing information.

[0006] In view of the above problems, this application provides a method and plug-in for non-destructive and rapid splitting of drawings.

[0007] In a first aspect, the present application provides a method for lossless and rapid splitting of drawings, which is implemented by a lossless and rapid splitting plug-in, wherein the method for lossless and rapid splitting of drawings includes: calling local data of a target construction project to obtain K non-standard construction drawings; obtaining drawings to be split by performing drawing fusion processing on the K non-standard construction drawings; extracting stage drawings from the drawings to be split based on predefined construction stages to obtain multiple stage-related drawings, wherein the multiple stage-related drawings have multiple construction stage identifiers; splitting and reorganizing the multiple stage-related drawings according to the construction department composition of the target construction project to obtain M related construction drawing trees of M construction execution departments; performing construction collaboration feature analysis on the M construction execution departments, and storing and updating the M related construction drawing trees based on the analysis results to obtain M related split drawing trees; using the drawings to be split to traverse the M related split drawing trees to proofread and complete information to obtain multiple lossless split drawing trees.

[0008] In the second aspect, the present application also provides a lossless and fast drawing splitting plug-in for executing the lossless and fast drawing splitting method as described in the first aspect, wherein the lossless and fast drawing splitting plug-in includes: a data calling module, the data calling module is used to perform local data calling on the target construction project to obtain K non-standard construction drawings; a fusion processing module, the fusion processing module is used to obtain the drawings to be split by performing drawing fusion processing on the K non-standard construction drawings; a stage extraction module, the stage extraction module is used to extract stage drawings from the drawings to be split based on predefined construction stages, and obtain multiple stage-related drawings, wherein the multiple stage-related drawings It has multiple construction stage identifiers; a splitting and reorganization module, which is used to split and reorganize the drawings associated with the multiple stages according to the construction department composition of the target construction project, and obtain M associated construction drawing trees of M construction execution departments; a storage update module, which is used to analyze the construction coordination characteristics of the M construction execution departments, and perform storage updates of the M associated construction drawing trees based on the analysis results to obtain M associated split drawing trees; an information completion module, which is used to use the drawings to be split to traverse the M associated split drawing trees for information proofreading and completion, and obtain multiple lossless split drawing trees.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By calling local data of the target construction project, K non-standard construction drawings are obtained; by performing drawing fusion processing on the K non-standard construction drawings, drawings to be split are obtained; based on the predefined construction stage, stage drawings are extracted from the drawings to be split to obtain multiple stage-related drawings, wherein the multiple stage-related drawings have multiple construction stage identifiers; the multiple stage-related drawings are split and reorganized according to the construction department composition of the target construction project to obtain M related construction drawing trees of M construction execution departments; construction collaboration feature analysis is performed on the M construction execution departments, and storage and update of the M related construction drawing trees are performed based on the analysis results to obtain M related split drawing trees; the drawings to be split are used to traverse the M related split drawing trees to proofread and complete information to obtain multiple lossless split drawing trees. That is to say, by automatically splitting and reorganizing according to the construction stage and department composition, ensuring that the information accurately matches the needs of each stage and department, the collaborative feature analysis of M related construction drawing trees is performed, the collaboration needs between different departments are identified, and the drawing information is automatically updated to ensure that the drawing information obtained by each department is the most complete. Finally, the drawing tree is traversed and the information is proofread and completed to ensure that the split drawing information is lossless and not lost, thereby improving information integrity.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0013] Figure 1 This is a schematic diagram of the process of non-destructive and rapid splitting method of the drawings in this application;

[0014] Figure 2 This is a schematic diagram of the structure of the lossless quick split plug-in for this application drawing.

[0015] Explanation of the reference numerals: data calling module 11, fusion processing module 12, stage extraction module 13, splitting and reorganization module 14, storage and updating module 15, information completion module 16. DETAILED DESCRIPTION

[0016] This application solves the technical problem in the prior art that the drawing splitting process relies on static, predefined splitting rules, which makes it difficult to accurately match the specific needs of each construction stage and department, resulting in omissions or errors in drawing information by providing a lossless and fast drawing splitting method and plug-in. By automatically splitting and reorganizing according to the construction stage and department composition, it is ensured that the information accurately matches the needs of each stage and department, and collaborative feature analysis is performed on M related construction drawing trees to identify the collaboration needs between different departments. The drawing information is automatically updated to ensure that the drawing information obtained by each department is the most complete. Finally, the drawing tree is traversed and the information is proofread and completed to ensure that the split drawing information is lossless and not lost, thereby improving information integrity.

[0017] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0018] For example, please refer to the attached Figure 1 The present application provides a method for fast lossless splitting of drawings, wherein the method is applied to a plug-in for fast lossless splitting of drawings, and the method specifically comprises the following steps:

[0019] Step 1: Call local data for the target construction project to obtain K non-standard construction drawings.

[0020] Specifically, the data of the target construction project is retrieved and called from the local database or data storage system of the project to obtain K non-standard construction drawings related to the target construction project. Non-standard construction drawings refer to those drawings that do not follow conventional design standards or dimensions and usually require special processing because they contain unique structural or construction requirements. By calling local data, the required drawings can be quickly obtained, reducing the time of searching and waiting.

[0021] Step 2: Obtain the drawings to be split by performing drawing fusion processing on the K non-standard construction drawings.

[0022] Specifically, K non-standard construction drawings are processed and converted into standard formats. N standard construction drawings are randomly extracted from them. The edges of the N standard construction drawings and the remaining KN standard construction drawings are segmented according to the image cutting scale to obtain N drawing information matrices and KN drawing information sequences. Each matrix can clearly describe the structure and characteristics of the corresponding drawing, and each sequence can clearly describe the edge structure and characteristics of the corresponding drawing. The N drawing information matrices are used to traverse the remaining KN drawing information sequences to perform drawing overlap analysis, specifically extracting the drawing edge information and comparing it, splicing and overlapping the drawings, and finally completing the global fusion of the drawings. Each non-standard drawing may represent independent information of different construction stages or specific design parts of the project. The goal of drawing fusion is to seamlessly combine these scattered information and unify them into a drawing to be split. The generated drawing to be split is a complete, comprehensive drawing containing all project design and construction information, integrating data from various construction stages, and including all key elements of the project, such as building structure, equipment, pipeline layout, construction plan, etc. Through drawing fusion, the design and construction information of different construction stages can be integrated to avoid information isolation or omission. The drawings to be split become the unified information carrier of the entire project, including all the design details related to construction. The drawings to be split cover the information of multiple construction stages of the project, including all the design and construction information of the project, and involve each construction stage, which is conducive to the allocation of corresponding drawing information to the construction teams at different stages.

[0023] Step three: extracting stage drawings from the drawings to be split based on the predefined construction stages, and obtaining a plurality of stage-related drawings, wherein the plurality of stage-related drawings have a plurality of construction stage identifiers.

[0024] Specifically, the various stages of construction are predefined according to the construction progress and characteristics of each stage in the project plan. Predefined construction stages refer to the various construction stages that have been determined before the project starts, such as the design stage, construction preparation stage, main construction stage, etc. Each construction stage has its own specific requirements and characteristics, so it is necessary to extract drawings related to each construction stage based on these requirements and characteristics. A unique identifier is configured for each target construction stage to ensure that each target construction stage has a corresponding construction stage identifier.

[0025] Pre-build multiple template matching models, each of which can automatically identify the corresponding areas of each construction stage in the drawings to be split according to geometric features. According to the predefined construction stages, extract the parts corresponding to each stage from the drawings to be split. According to the execution order of the target construction stages, call the corresponding template matching models in turn to search and identify similar areas in the drawings to be split, and obtain the construction area corresponding to each stage. Use spatial index to associate and store each construction stage and the corresponding construction area, and obtain multiple stage-associated drawings, which contain design information highly related to the construction content of the stage and can be used or stored separately.

[0026] The associated drawings of each stage are marked with the construction stage identifier, that is, each associated drawing is assigned an identifier that matches the construction stage, corresponding to the predefined construction stage. Through the identification process, the associated drawings can correspond one-to-one with their corresponding construction stages, ensuring that the drawings of different stages can be clearly distinguished in the subsequent management process. The predefined construction stages and drawing extraction process ensure that the information in the drawings to be split is effectively separated by stage, avoiding confusion or information overload of the drawing content. The drawings of each construction stage are extracted separately and the associated drawings are generated. The identifier is associated with the specific stage to accurately obtain the required drawing information, effectively guide the construction process, and ensure that the tasks of each stage can be smoothly executed.

[0027] Step 4: Split and reorganize the drawings associated with the multiple stages according to the construction department composition of the target construction project, and obtain M associated construction drawing trees of M construction execution departments.

[0028] Specifically, according to the composition of the construction departments of the target construction project, the responsibilities and task scope of each construction department are clarified, the associated drawings of each construction stage are split and reorganized, and the construction drawings of each stage belonging to the same department are segmented and extracted according to the different needs of the construction departments. The split drawing parts are reorganized according to the construction departments to obtain M associated construction drawing trees of M construction execution departments. Each tree represents a construction department, which contains the local construction drawings of the department at different construction stages. The nodes in each drawing tree represent the local construction drawings of the construction department at each construction stage. Each node corresponds to the construction information of a stage, ensuring that the drawings of different stages can be clearly distinguished while still being organized in the drawing structure of the same department. By splitting and reorganizing the drawings according to the composition of the construction departments, each department will only obtain the drawings related to it, avoiding information confusion and misuse. The drawing tree of each department not only includes the drawing information of its current stage, but also covers its work content in multiple construction stages, clearly showing its construction tasks at different stages, which not only improves the management efficiency of the drawings, but also facilitates the overall coordination and allocation of the construction project.

[0029] Step 5: Analyze the construction collaboration characteristics of the M construction execution departments, and based on the analysis results, update the storage of the M associated construction drawing trees to obtain M associated split drawing trees.

[0030] Specifically, the construction tasks and activities of M construction execution departments are analyzed for their collaborative features, and the task intersections of each department in different construction stages, the task nodes that need to be coordinated and collaborated between departments, and the resource sharing and dependency relationships of departments are clarified. Construction collaborative feature analysis refers to the analysis of the collaborative work and interaction characteristics of the construction execution departments during the construction process to identify and understand the collaborative patterns and dependencies between different departments, including the analysis of information sharing, resource allocation, and task coordination between different departments. According to the results of the construction collaborative feature analysis, the M associated construction drawing trees are stored and updated, the node information of the construction tasks is optimized and adjusted to reflect the collaborative relationship and dependency relationship between different departments, and the tasks and data related to collaborative work in the drawing tree are reorganized to ensure that this information can accurately reflect the collaborative needs in construction. The storage update may include adding new nodes, deleting unnecessary nodes, and modifying the relationship between nodes. After the storage update is completed, M associated split drawing trees are generated. Each construction execution department has an associated split drawing tree. Each drawing tree clearly shows the task division, collaborative relationship, and mutual dependence of task nodes of each department during the construction stage, which not only shows the independent construction tasks of the department, but also shows the task information of collaboration with other departments. Through construction collaboration feature analysis, the collaboration nodes between construction execution departments are identified and optimized to ensure seamless integration of tasks among multiple construction departments, which helps reduce construction delays and errors.

[0031] Step 6: Use the drawing to be split to traverse the M associated split drawing trees to check and complete the information, and obtain multiple lossless split drawing trees.

[0032] Specifically, the drawings to be split are used to traverse M associated split drawing trees, and the information in each drawing tree is checked and compared with the drawings to be split to determine whether it is complete, erroneous or missing. The drawings to be split refer to the original drawings that have not yet been split, and contain all the information of the entire construction project. During the traversal process, the information of the drawings to be split is compared and proofread with the information in the M associated split drawing trees to detect whether there are erroneous or missing information in the drawing trees, including checking the design details, dimensions, bill of materials, construction plan and other contents in the drawings to ensure that they are consistent with the information in the drawings to be split. If the information in the associated split drawing tree is found to be erroneous or missing during the proofreading process, the correct information in the drawings to be split is used to complete it to ensure that all nodes in the drawing tree contain complete construction information. After proofreading and completion, it is ensured that the information in the M associated split drawing trees is complete and correct. After completing the information proofreading and completion of all nodes, multiple lossless split drawing trees are generated to accurately reflect the tasks of the construction department and all key information of the construction stage. By traversing M associated split drawing trees, any potential information errors or omissions are detected and supplemented in a timely manner to ensure the accuracy of information, reduce construction risks caused by missing information, and improve the overall efficiency and accuracy of construction management.

[0033] Further, step 2 of this application includes:

[0034] Based on a predefined standard image format, the K non-standard construction drawings are converted into file formats to obtain K standard construction drawings; N standard construction drawings are randomly extracted from the K standard construction drawings, and the N standard construction drawings are used as the starting point for drawing merging, wherein N is a positive integer less than K; information segmentation and collection of the N standard construction drawings are performed according to a predefined image cutting scale to obtain N drawing information matrices; drawing edge information segmentation and collection are performed on the remaining KN standard construction drawings based on the image cutting scale to obtain KN drawing information sequences; drawing overlap analysis is performed by traversing the KN drawing information sequences using the N drawing information matrices, and drawings are iteratively merged based on the analysis results to obtain the drawings to be split.

[0035] Specifically, non-standard construction drawings are converted into standard image formats using standard image formats to ensure that all drawings have a unified format standard in the subsequent processing process, avoiding processing difficulties caused by inconsistent formats. Predefined standard image formats refer to one or more common image file formats, such as PDF, DWG, JPG, etc., which are used as standards in specific fields (such as architecture and engineering) to facilitate sharing and editing between different systems and software. N sheets of K standard construction drawings are randomly extracted as the starting point for drawing merging. Random extraction means selecting some drawings from a set of drawings in no specific order. The drawing merging starting point refers to the drawing used as the starting point in the drawing merging process.

[0036] According to the predefined image cutting scale, N standard construction drawings are segmented and collected. Each drawing is cut into multiple parts according to a specific size or ratio, and then information is collected from each part to form N drawing information matrices. The predefined image cutting scale is set based on the standard size, construction requirements or analysis requirements of the drawing, and refers to the standard size or ratio used in the segmentation and collection process. The drawing information matrix is ​​a data structure used to store information collected from drawing segmentation. Each matrix represents the segmentation information of a single drawing and contains detailed information on each part of the drawing.

[0037] The edge information of the remaining KN standard construction drawings that were not randomly selected is segmented and collected. The edge information of the drawings is segmented and collected with the same image cutting scale as before, and the edge structure and features of the drawings are captured and described to generate KN drawing information sequences, which are similar to the drawing information matrix, but focus on describing the edge structure and features of the corresponding drawings. Use N drawing information matrices to traverse the KN drawing information sequences for drawing information analysis, compare the feature points between each remaining KN standard construction drawing and the N standard construction drawings, and calculate the overlap, and select the first standard construction drawing with the highest overlap with the N standard construction drawings. According to the first standard construction drawing, the second standard construction drawing is reversely called from the KN standard construction drawings, and the first standard construction drawing and the second standard construction drawing are partially fused, and the corresponding parts of the two drawings are merged to obtain N locally fused drawings. The overlapping parts between the drawings are accurately matched, and the fusion results of each round will become the starting point of the next round of merging, until all drawings are fused into a complete drawing after multiple iterations. Iterative fusion is a cyclic process. Each round of fusion is based on the results of the previous round. By gradually merging the overlapping areas, a complete drawing to be split is finally obtained. The accuracy of drawing fusion is ensured through feature point matching and overlap calculation. Multiple rounds of local fusion gradually optimize the drawing merging process, reduce errors and improve efficiency.

[0038] Furthermore, the present application also includes the following steps:

[0039] The key drawing feature points of the N drawing information matrices and KN drawing information sequences are located to obtain N drawing feature point matrices and KN drawing feature point sequences; the first drawing feature point sequence is called from the KN drawing feature point sequences, and the N drawing overlaps of the first drawing feature point sequence and the N drawing feature point matrices are calculated based on feature point matching; the N drawing overlaps are serialized, and the first standard construction drawing with the highest overlap is called from the N standard construction drawings; the second standard construction drawing is called by reverse mapping from the KN standard construction drawings according to the first drawing feature point sequence; the first standard construction drawing and the second standard construction drawing are locally fused to obtain a first locally fused drawing; and so on, N locally fused drawings are obtained by traversing the KN drawing information sequences using the N drawing information matrices to perform drawing overlap analysis; and so on, the drawing merging starting point is updated based on the N locally fused drawings, and multiple rounds of local fusion are performed until the drawings to be split are obtained.

[0040] Specifically, identify specific points or features in N drawing information matrices and KN drawing information sequences, use specific algorithms or techniques to identify and mark key feature points in drawings, such as nodes, intersections, boundary features, specific marks, etc., and obtain N drawing feature point matrices and KN drawing feature point sequences. Each matrix or sequence represents the feature point information of a single drawing and contains the key structure and features of the drawing. Randomly call a feature point sequence (called the first drawing feature point sequence) from the KN drawing feature point sequences, calculate the matching degree between the first drawing feature point sequence and the N drawing feature point matrices, that is, the overlap degree, and measure the overlap of each drawing with other drawings in feature points. The overlap degree calculation is to compare the feature points in the feature point sequence and the feature point matrix, calculate the distance, angle or other related attributes between the feature points, and determine the similarity or matching degree between the two, and obtain the matching degree between the first drawing feature point sequence and each drawing feature point matrix, that is, the overlap degree of N drawings. Sort the overlap of the N drawings in a certain order, such as from high to low, and select the drawing with the highest overlap as the first standard construction drawing, that is, the standard construction drawing with the highest overlap with the feature point sequence of the first drawing that was not randomly selected.

[0041] Using the feature point sequence of the first drawing, reverse mapping is performed from KN standard construction drawings and the second standard construction drawing is called. Reverse mapping is a matching process based on feature point sequence. The feature point sequence of the drawing that is consistent with the feature point sequence of the first drawing is found in the KN standard construction drawings, and the corresponding standard construction drawing is called. The first standard construction drawing and the second standard construction drawing are partially fused to generate the first partially fused drawing. Local fusion refers to splicing and fusing only the overlapping or matching parts of the drawings, specifically extracting edge information and comparing them through feature matching algorithms (such as scale-invariant feature transformation or accelerated robust features), and splicing and fusing the overlapping parts.

[0042] Similarly, N drawing information is used to traverse KN drawing information sequences. For each drawing information matrix, the overlap degree is calculated with each sequence in the KN drawing information sequences. If a match with a high overlap degree is found, the corresponding standard construction drawings are locally fused to create a new locally fused drawing, and N locally fused drawings are obtained. The N locally fused drawings are used as new merging starting points, and multiple rounds of local fusion are performed. Each round of local fusion is based on the current merging starting point. By comparing and fusing the overlapping parts between the drawings, new fused drawings are gradually created. This process is iterative, that is, the fusion result of each round will become the merging starting point of the next round, until all drawings are fused into a complete drawing after multiple iterations, ensuring that the new fusion is optimized based on the previous result. In each fusion process, the edge information of the drawings is extracted and compared, and the splicing and overlap of the drawings are gradually completed until all drawings are globally fused to generate the final complete drawing. The accuracy of the drawing fusion is ensured by feature point matching and overlap calculation. Multiple rounds of local fusion gradually optimize the drawing merging process, reduce errors and improve efficiency.

[0043] Further, step three of this application includes:

[0044] According to the construction phase composition of the target construction project, a unique identifier is configured to obtain the multiple construction phase identifiers of the multiple target construction phases; a plurality of template matching models are pre-constructed, wherein the multiple template matching models are applied to the multiple target construction phases; according to the execution order of the multiple target construction phases, the multiple template matching models are sequentially called to search and identify similar areas in the drawings to be split, to obtain multiple groups of stage construction area drawings; a spatial index is used to associate and store the multiple groups of stage construction area drawings with the multiple target construction phases, to obtain the multiple stage-associated drawings; and the multiple construction phase identifiers are used to perform identification processing on the multiple stage-associated drawings.

[0045] Specifically, determine the construction phase composition of the target construction project, that is, decompose the project into specific construction phases, including the design phase, construction preparation phase, main construction phase, decoration phase, etc. Each construction phase has its specific tasks and goals. A unique identifier is configured for each construction phase, which can be a number, a combination of letters, or any code that can uniquely identify each construction phase. Each identifier is associated with a specific construction phase. Pre-build multiple template matching models, each of which is designed for a specific type of construction phase to ensure that relevant construction documents or data can be accurately identified and matched. Multiple template matching models are applied to multiple target construction phases, which means that in each construction phase, the corresponding template matching model will be used to analyze and process the construction information related to that phase.

[0046] According to the execution order of the target construction phase, the corresponding template matching models are called in sequence to search and identify similar areas in the drawings to be split. Each construction phase has its own specific tasks and goals, so different template matching models are needed to process and analyze the construction information related to that phase. Similar area search and identification refers to using the template matching model to find areas in the drawings to be split that are similar to the template, and obtain multiple sets of stage construction area drawings. Each set of drawings contains information related to a specific construction phase. For example, drawings in the design phase may include building plans and elevations, and drawings in the main construction phase may include structural construction drawings and equipment installation drawings.

[0047] Spatial index is a special data structure used to store and query spatial data, such as maps, architectural drawings, etc. It can help quickly locate and query spatial data and improve the efficiency of data processing. Using spatial index to associate multiple sets of phase construction area drawings with multiple target construction phases means associating each set of construction area drawings with the corresponding construction phase, and using spatial index to store these associated information to obtain multiple phase-associated drawings, which not only contain information related to a specific construction phase, but also are associated with other construction phases in the project. Use multiple construction phase identifiers to identify multiple phase-associated drawings, and apply each construction phase identifier to the corresponding phase-associated drawings to quickly identify and query drawings related to a specific construction phase. By performing template matching, region recognition, spatial indexing and identification processing on the fused drawings, the separation and identification of the drawings of the construction phases are realized. Each stage in the construction project can accurately extract the required drawing area, ensuring that each stage has corresponding drawing support, and through identifier management, it is convenient to track and manage the drawings.

[0048] Furthermore, the present application also includes the following steps:

[0049] Interactively obtain multiple sample design feature sets of multiple sample construction stages, and obtain multiple groups of construction stage templates by performing construction geometric feature analysis on the multiple sample design feature sets; construct a standard matching model based on a similarity measurement algorithm, and use the multiple groups of construction stage templates to update the matching benchmark of the standard matching model to obtain the multiple template matching models.

[0050] Specifically, multiple sample design feature sets of multiple sample construction stages are obtained through interaction, that is, design feature data of different construction stages are collected from actual construction projects, including geometric structures and design information of different construction stages, such as geometric features, material properties, etc. Construction geometry feature analysis is performed on these sample design feature sets to identify and quantify the key geometric features in the design, and extract the key design features of each construction stage. Based on the analysis results, multiple sets of construction stage templates are obtained, which are standardized design templates that include the key design features and standard design requirements of each construction stage and represent the standard design of the construction results.

[0051] Based on the similarity measurement algorithm, a standard matching model is constructed to measure the similarity between the geometric features in the drawings and the templates of the construction stages. By calculating the similarity between the geometric structures in different drawings and the standard templates, the areas of each construction stage are automatically identified on the drawings. The similarity measurement algorithm is an algorithm used to evaluate the similarity between two objects, such as Euclidean distance, cosine similarity, etc. The standard matching model is an algorithm model used to identify and match specific patterns or structures, and is automatically adjusted and optimized according to the characteristics of the input data. The matching benchmark of the standard matching model is updated using multiple sets of construction stage templates, and the standard design of each construction result is used as a reference to optimize the standard matching model to improve its recognition and matching accuracy. Through the analysis of the geometric features of the sample construction stage and the construction of the template matching model, the automatic recognition of the construction stage in the drawings is realized. The introduction of the similarity measurement algorithm enables the model to accurately identify and classify different construction areas based on geometric features, ensure the automatic separation of construction stages and the efficient processing of drawing information, and continuously update the matching benchmark. The model can adapt to the design changes of different projects and maintain a high recognition rate and practicality.

[0052] Furthermore, the present application also includes the following steps:

[0053] According to the execution order of the multiple target construction stages, the first template matching model of the first target construction stage is called from the multiple template matching models; the first template matching model is used to search and identify similar areas in the drawings to be split, and multiple stage construction area drawings are obtained; the spatial index is used to associate and store the multiple stage construction area drawings with the first target construction stage, and the first stage associated drawings are obtained, wherein the first stage associated drawings are identified by the first construction stage identifier; according to the execution order of the multiple target construction stages, the second template matching model of the second target construction stage is called from the multiple template matching models; after the first stage associated drawings are split and deleted from the drawings to be split to obtain the first updated split drawings, the second template matching model is used to search and identify similar areas in the first updated split drawings, and the second stage associated drawings are obtained; and so on, according to the execution order of the multiple target construction stages, the multiple template matching models are called in sequence to search and identify similar areas in the drawings to be split, and the multiple stage associated drawings are obtained.

[0054] Specifically, according to the execution order of the construction stages, the first template matching model corresponding to the first target construction stage is first called, and the first template matching model is used to search and identify similar areas in the drawings to be split, and multiple construction area drawings related to the first construction stage are found. The spatial index is used to associate and store the construction area drawings of multiple stages with the first target construction stage to obtain the first stage associated drawings, that is, all the construction area drawings related to the first target construction stage are stored, and all belong to the first construction stage, and the first stage associated drawings are identified using the first construction stage identifier. According to the execution order of the target construction stages, the second template matching model corresponding to the second target construction stage is called from multiple template matching models.

[0055] The first-stage associated drawings are split and deleted from the drawings to be split to obtain the first updated split drawings. This is because the first-stage associated drawings have been identified and processed and need to be removed from the drawings to be processed to avoid repeated processing or information confusion. Use the second template matching model to search and identify similar areas in the first updated split drawings, find multiple construction area drawings related to the second construction stage and store them in association to obtain the second-stage associated drawings, which include all construction area drawings related to the second stage. According to the execution order of the construction stages, call the template matching models of subsequent construction stages in sequence, perform similar area search and identification on each updated split drawing, and obtain the associated drawings of each construction stage. Multiple rounds of template matching and similar area search ensure that all construction stage areas in the drawings to be split are gradually extracted to generate multiple stage associated drawings.

[0056] Through the template matching model of each construction stage, drawings can be accurately extracted in stages, and the areas of each construction stage can be accurately identified to avoid information mixing or mismatching; after each extraction and deletion of a stage drawing, the drawings to be processed will be automatically updated to ensure the accuracy and efficiency of subsequent template matching. Multiple rounds of processing allow the entire drawing to be gradually split into multiple independent stage areas; the regional drawings of each construction stage can be accurately identified and stored in an associated manner, ensuring that in complex construction projects, drawing information can be efficiently extracted and managed by stage.

[0057] Further, step 4 of this application includes:

[0058] Interactively obtain multiple sample construction feature sets of multiple sample construction departments in the first target construction stage, and construct a first-stage construction identification model based on the multiple sample construction feature sets, wherein the first-stage construction identification model includes multiple drawing feature matching branches corresponding to the multiple sample construction departments; use the first-stage construction identification model to search and identify similar areas in the first-stage associated drawings to obtain multiple groups of construction department-associated drawings; use the multiple groups of construction department-associated drawings to construct M groups of first-layer sub-nodes as M first-layer sub-node combinations; and so on, perform drawing splitting and reorganization of the multiple stage-associated drawings into M multi-layer sub-node combinations; take the M construction execution departments as root nodes, perform drawing intersection analysis on the M multi-layer sub-node combinations, and connect the node branches with the root nodes based on the analysis results to complete the construction of the M associated construction drawing trees.

[0059] Specifically, it interacts with each construction department to obtain multiple sample construction feature sets of multiple sample construction departments in the first target construction stage, including the specific construction information and geometric features of each construction department in this stage. Multiple sample construction feature sets refer to the construction features collected from each construction department in a specific construction stage. The first-stage construction recognition model is constructed based on multiple sample construction feature sets to identify and match specific construction information, including multiple drawing feature matching branches corresponding to multiple sample construction departments. For each construction department, there is a specific branch for identifying and matching construction information related to the department. Drawing feature matching branches refer to different branches in the model, each branch specifically matches drawing features according to the characteristics of a construction department. The first-stage recognition model is used to search and identify similar areas of the first-stage associated drawings, automatically match the features of each construction department, identify the construction areas belonging to different departments in the first-stage associated drawings, and generate multiple groups of construction department associated drawings.

[0060] Using multiple sets of construction department-related drawings, M groups of first-level sub-nodes are constructed to represent the construction area drawings of each construction department in the first phase, forming M first-level sub-node combinations. Similarly, multiple phase-related drawings are split and reorganized to construct M multi-level sub-node combinations, representing the drawing information of each construction department in different construction phases. M construction execution departments are taken as root nodes. Construction execution departments are the basic units in the construction project and are responsible for performing specific construction tasks. Each construction execution department can be regarded as a root node because it is the highest level in the project structure. Drawing intersection analysis is performed on the M multi-level sub-node combinations. Drawing intersection analysis is a technique for determining common elements between different drawings. By analyzing the intersections between different drawings, that is, the overlapping and associated parts, it is determined which parts or features are common in multiple construction phases.

[0061] According to the analysis results, the identified node branches and root nodes are connected to obtain a complete drawing tree, that is, M associated construction drawing trees, which are used to represent different parts or stages of the entire construction project. Each tree corresponds to multiple sets of local construction drawings of a department in multiple construction stages. Through the construction identification model and similar area search, drawings can be divided by construction department, ensuring that each department can obtain relevant drawing information, realizing the refined management of multi-department and multi-stage drawings in complex projects.

[0062] Further, step five of this application includes:

[0063] Interactively obtain M groups of task dependencies of the M construction execution departments in the multiple target construction stages; share data storage of the same-level sub-node data of the M associated construction drawing trees according to the M groups of task dependencies to obtain M associated split drawing trees.

[0064] Specifically, interactive acquisition means obtaining the task dependencies of each department at different construction stages through communication and exchange with the construction execution department, and obtaining M groups of task dependencies of M construction execution departments at multiple target construction stages. Task dependencies refer to the sequence and dependencies between different tasks, including the task sequence, task intersections and collaborative work requirements between departments. Task dependencies clearly define the mutual connection and coordination between different departments during the construction process, ensuring that each department can accurately perform its duties while maintaining close cooperation with other departments. According to the M groups of task dependencies, the data of the sub-nodes at the same level in the M associated construction drawing trees are stored and shared. Through the data sharing of the sub-nodes at the same level, each department can simultaneously access, view and modify the drawing information associated with other departments. The sub-node data at the same level refers to the data contained in different sub-nodes at the same level. Based on data sharing and storage management, M associated split drawing trees are generated. Each drawing tree not only shows the task division of each construction stage, but also reflects the collaborative relationship between departments and the task superposition information of each area. Task superposition information refers to the overlap and interaction of different tasks in the same area. Through task dependencies and same-level data sharing, the associated split drawing tree can accurately present the task division of each stage and the collaborative work between departments, ensuring that the construction tasks of each department are carried out in an orderly manner. It not only displays the task information of each department, but also optimizes the collaboration between departments through task superposition and dependency relationships, thereby improving the efficiency and accuracy of construction management and reducing information asymmetry or task conflicts.

[0065] In summary, the non-destructive and rapid splitting method for drawings provided in this application has the following technical effects:

[0066] By calling local data of the target construction project, K non-standard construction drawings are obtained; by performing drawing fusion processing on the K non-standard construction drawings, drawings to be split are obtained; based on the predefined construction stage, stage drawings are extracted from the drawings to be split to obtain multiple stage-related drawings, wherein the multiple stage-related drawings have multiple construction stage identifiers; the multiple stage-related drawings are split and reorganized according to the construction department composition of the target construction project to obtain M related construction drawing trees of M construction execution departments; construction collaboration feature analysis is performed on the M construction execution departments, and storage and update of the M related construction drawing trees are performed based on the analysis results to obtain M related split drawing trees; the drawings to be split are used to traverse the M related split drawing trees to proofread and complete information to obtain multiple lossless split drawing trees. That is to say, by automatically splitting and reorganizing according to the construction stage and department composition, ensuring that the information accurately matches the needs of each stage and department, the collaborative feature analysis of M related construction drawing trees is performed, the collaboration needs between different departments are identified, and the drawing information is automatically updated to ensure that the drawing information obtained by each department is the most complete. Finally, the drawing tree is traversed and the information is proofread and completed to ensure that the split drawing information is lossless and not lost, thereby improving information integrity.

[0067] Embodiment 2, based on the same inventive concept as the non-destructive and rapid splitting method of drawings in the above embodiment, this application also provides a non-destructive and rapid splitting plug-in for drawings, please refer to the attached Figure 2 The drawing lossless quick split plug-in includes:

[0068] The data calling module 11 is used to call local data of the target construction project to obtain K non-standard construction drawings.

[0069] The fusion processing module 12 is used to obtain drawings to be split by performing drawing fusion processing on the K non-standard construction drawings.

[0070] The phase extraction module 13 is used to extract phase drawings from the drawings to be split based on predefined construction phases, and obtain multiple phase-related drawings, wherein the multiple phase-related drawings have multiple construction phase identifiers.

[0071] The splitting and reorganizing module 14 is used to split and reorganize the drawings associated with the multiple stages according to the construction department composition of the target construction project, and obtain M associated construction drawing trees of M construction execution departments.

[0072] The storage update module 15 is used to perform construction collaboration feature analysis on the M construction execution departments, and based on the analysis results, perform storage update on the M associated construction drawing trees to obtain M associated split drawing trees.

[0073] The information completion module 16 is used to use the drawing to be split to traverse the M associated split drawing trees to perform information proofreading and completion to obtain multiple lossless split drawing trees.

[0074] Furthermore, the fusion processing module 12 in the drawing lossless fast splitting plug-in is also used for:

[0075] Based on a predefined standard image format, the K non-standard construction drawings are converted into file formats to obtain K standard construction drawings; N standard construction drawings are randomly extracted from the K standard construction drawings, and the N standard construction drawings are used as the starting point for drawing merging, wherein N is a positive integer less than K; information segmentation and collection of the N standard construction drawings are performed according to a predefined image cutting scale to obtain N drawing information matrices; drawing edge information segmentation and collection are performed on the remaining KN standard construction drawings based on the image cutting scale to obtain KN drawing information sequences; drawing overlap analysis is performed by traversing the KN drawing information sequences using the N drawing information matrices, and drawings are iteratively merged based on the analysis results to obtain the drawings to be split.

[0076] Furthermore, the fusion processing module 12 in the drawing lossless fast splitting plug-in is also used for:

[0077] The key drawing feature points of the N drawing information matrices and KN drawing information sequences are located to obtain N drawing feature point matrices and KN drawing feature point sequences; the first drawing feature point sequence is called from the KN drawing feature point sequences, and the N drawing overlaps of the first drawing feature point sequence and the N drawing feature point matrices are calculated based on feature point matching; the N drawing overlaps are serialized, and the first standard construction drawing with the highest overlap is called from the N standard construction drawings; the second standard construction drawing is called by reverse mapping from the KN standard construction drawings according to the first drawing feature point sequence; the first standard construction drawing and the second standard construction drawing are locally fused to obtain a first locally fused drawing; and so on, N locally fused drawings are obtained by traversing the KN drawing information sequences using the N drawing information matrices to perform drawing overlap analysis; and so on, the drawing merging starting point is updated based on the N locally fused drawings, and multiple rounds of local fusion are performed until the drawings to be split are obtained.

[0078] Furthermore, the phase extraction module 13 in the drawing lossless fast splitting plug-in is also used for:

[0079] According to the construction phase composition of the target construction project, a unique identifier is configured to obtain the multiple construction phase identifiers of the multiple target construction phases; a plurality of template matching models are pre-constructed, wherein the multiple template matching models are applied to the multiple target construction phases; according to the execution order of the multiple target construction phases, the multiple template matching models are sequentially called to search and identify similar areas in the drawings to be split, to obtain multiple groups of stage construction area drawings; a spatial index is used to associate and store the multiple groups of stage construction area drawings with the multiple target construction phases, to obtain the multiple stage-associated drawings; and the multiple construction phase identifiers are used to perform identification processing on the multiple stage-associated drawings.

[0080] Furthermore, the phase extraction module 13 in the drawing lossless fast splitting plug-in is also used for:

[0081] Interactively obtain multiple sample design feature sets of multiple sample construction stages, and obtain multiple groups of construction stage templates by performing construction geometric feature analysis on the multiple sample design feature sets; construct a standard matching model based on a similarity measurement algorithm, and use the multiple groups of construction stage templates to update the matching benchmark of the standard matching model to obtain the multiple template matching models.

[0082] Furthermore, the phase extraction module 13 in the drawing lossless fast splitting plug-in is also used for:

[0083] According to the execution order of the multiple target construction stages, the first template matching model of the first target construction stage is called from the multiple template matching models; the first template matching model is used to search and identify similar areas in the drawings to be split, and multiple stage construction area drawings are obtained; the spatial index is used to associate and store the multiple stage construction area drawings with the first target construction stage, and the first stage associated drawings are obtained, wherein the first stage associated drawings are identified by the first construction stage identifier; according to the execution order of the multiple target construction stages, the second template matching model of the second target construction stage is called from the multiple template matching models; after the first stage associated drawings are split and deleted from the drawings to be split to obtain the first updated split drawings, the second template matching model is used to search and identify similar areas in the first updated split drawings, and the second stage associated drawings are obtained; and so on, according to the execution order of the multiple target construction stages, the multiple template matching models are called in sequence to search and identify similar areas in the drawings to be split, and the multiple stage associated drawings are obtained.

[0084] Furthermore, the splitting and reorganizing module 14 in the drawing lossless and fast splitting plug-in is also used for:

[0085] Interactively obtain multiple sample construction feature sets of multiple sample construction departments in the first target construction stage, and construct a first-stage construction identification model based on the multiple sample construction feature sets, wherein the first-stage construction identification model includes multiple drawing feature matching branches corresponding to the multiple sample construction departments; use the first-stage construction identification model to search and identify similar areas in the first-stage associated drawings to obtain multiple groups of construction department-associated drawings; use the multiple groups of construction department-associated drawings to construct M groups of first-layer sub-nodes as M first-layer sub-node combinations; and so on, perform drawing splitting and reorganization of the multiple stage-associated drawings into M multi-layer sub-node combinations; take the M construction execution departments as root nodes, perform drawing intersection analysis on the M multi-layer sub-node combinations, and connect the node branches with the root nodes based on the analysis results to complete the construction of the M associated construction drawing trees.

[0086] Furthermore, the storage update module 15 in the drawing lossless fast splitting plug-in is also used for:

[0087] Interactively obtain M groups of task dependencies of the M construction execution departments in the multiple target construction stages; share data storage of the same-level sub-node data of the M associated construction drawing trees according to the M groups of task dependencies to obtain M associated split drawing trees.

[0088] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The non-destructive and rapid splitting method and specific examples in the first embodiment are also applicable to the non-destructive and rapid splitting plug-in of the drawings in this embodiment. Through the above detailed description of the non-destructive and rapid splitting method of drawings, those skilled in the art can clearly know the non-destructive and rapid splitting plug-in of drawings in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the plug-in disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0089] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0090] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A non-destructive and fast drawing splitting method, characterized in that: include: Call local data of the target construction project to obtain K non-standard construction drawings; Obtaining drawings to be split by performing drawing fusion processing on the K non-standard construction drawings; Extracting stage drawings from the drawings to be split based on predefined construction stages to obtain multiple stage-related drawings, wherein the multiple stage-related drawings have multiple construction stage identifiers; According to the construction department composition of the target construction project, the drawings of the multiple phases are split and reorganized to obtain M related construction drawing trees of M construction execution departments; Perform construction collaboration feature analysis on the M construction execution departments, and based on the analysis results, perform storage update on the M associated construction drawing trees to obtain M associated split drawing trees; The to-be-split drawing is used to traverse the M associated split drawing trees to perform information proofreading and completion, so as to obtain multiple lossless split drawing trees; The drawings to be split are obtained by performing drawing fusion processing on the K non-standard construction drawings, including: Convert the K non-standard construction drawings into a standard image format to obtain K standard construction drawings. Randomly extracting N standard construction drawings from the K standard construction drawings, and using the N standard construction drawings as the starting point for drawing merging, wherein N is a positive integer less than K; The information of the N standard construction drawings is segmented and collected using a predefined image cutting scale to obtain N drawing information matrices; Based on the image cutting scale, edge information of the remaining KN standard construction drawings is segmented and collected to obtain KN drawing information sequences; The drawings to be split are obtained by using the N drawing information matrices to traverse the KN drawing information sequences to perform drawing overlap analysis, and iteratively merging the drawings based on the analysis results.

2. The non-destructive and rapid splitting method of drawings according to claim 1, characterized in that: The drawing to be split is obtained by using the N drawing information matrices to traverse the KN drawing information sequences to perform drawing overlap analysis, and iteratively merge the drawings based on the analysis results, including: Positioning key drawing feature points on the N drawing information matrices and KN drawing information sequences to obtain N drawing feature point matrices and KN drawing feature point sequences; Calling a first drawing feature point sequence from the KN drawing feature point sequences, and calculating the overlap between the first drawing feature point sequence and the N drawing feature point matrices based on feature point matching; Serialize the overlap of the N drawings, and call the first standard construction drawing with the highest overlap from the N standard construction drawings; Reverse mapping and calling a second standard construction drawing from the KN standard construction drawings according to the first drawing feature point sequence; Partially integrating the first standard construction drawing and the second standard construction drawing to obtain a first partial integrated drawing; Similarly, by using the N drawing information matrices to traverse the KN drawing information sequences to perform drawing overlap analysis, N local fusion drawings are obtained; By analogy, the drawing merging starting point is updated based on the N locally fused drawings, and multiple rounds of local fusion are performed until the drawing to be split is obtained.

3. The non-destructive and rapid splitting method of drawings according to claim 1, characterized in that: Based on the predefined construction phase, the phase drawings are extracted from the drawings to be split, and multiple phase-related drawings are obtained, including: According to the construction phase composition of the target construction project, a unique identifier is configured to obtain the multiple construction phase identifiers of the multiple target construction phases; Pre-constructing a plurality of template matching models, wherein the plurality of template matching models are applied to the plurality of target construction stages; According to the execution order of the multiple target construction stages, the multiple template matching models are called in sequence to search and identify similar areas in the drawings to be split, so as to obtain multiple groups of stage construction area drawings; Using a spatial index to associate and store the multiple sets of stage construction area drawings with multiple target construction stages, and obtaining the multiple stage-associated drawings; The multiple construction stage identifiers are used to perform identification processing on the multiple stage-related drawings.

4. The non-destructive and rapid splitting method of drawings according to claim 3, characterized in that: Pre-built multiple template matching models, including: Interactively obtaining multiple sample design feature sets of multiple sample construction stages, and obtaining multiple groups of construction stage templates by performing construction geometric feature analysis on the multiple sample design feature sets; A standard matching model is constructed based on a similarity measurement algorithm, and the matching benchmark of the standard matching model is updated using the multiple groups of construction stage templates to obtain the multiple template matching models.

5. The non-destructive and rapid splitting method of drawings according to claim 3, characterized in that: include: According to the execution order of the multiple target construction stages, calling a first template matching model of a first target construction stage from the multiple template matching models; Using the first template matching model to search and identify similar areas in the drawings to be split, to obtain drawings of construction areas in multiple stages; Using a spatial index to associate and store the drawings of the construction areas of the multiple stages with the first target construction stage, to obtain drawings associated with the first stage, wherein the drawings associated with the first stage are identified using a first construction stage identifier; According to the execution order of the multiple target construction stages, calling a second template matching model of a second target construction stage from the multiple template matching models; After deleting the first-stage associated drawings from the drawings to be split to obtain the first updated split drawings, the second template matching model is used to search and identify similar areas in the first updated split drawings to obtain the second-stage associated drawings; By analogy, the multiple template matching models are called in sequence according to the execution order of the multiple target construction stages to search and identify similar areas in the drawings to be split, so as to obtain the drawings associated with the multiple stages.

6. The non-destructive and rapid splitting method of drawings according to claim 5, characterized in that: According to the construction department composition of the target construction project, the drawings associated with the multiple stages are split and reorganized to obtain M associated construction drawing trees, including: Interactively obtain multiple sample construction feature sets of multiple sample construction departments in the first target construction stage, and construct a first-stage construction recognition model based on the multiple sample construction feature sets, wherein the first-stage construction recognition model includes multiple drawing feature matching branches corresponding to the multiple sample construction departments; Using the first-stage construction identification model to search and identify similar areas in the first-stage associated drawings, multiple groups of construction department associated drawings are obtained; Using the multiple groups of construction department-related drawings to construct M groups of first-layer child nodes as M first-layer child node combinations; By analogy, the drawings of the drawings associated with the multiple stages are split and reorganized into M multi-layer sub-node combinations; The M construction execution departments are taken as root nodes, the drawing intersection analysis is performed on the M multi-layer sub-node combinations, and the node branches are connected to the root node based on the analysis results to complete the construction of the M associated construction drawing trees.

7. The non-destructive and rapid splitting method of drawings according to claim 6, characterized in that: Performing construction collaboration feature analysis on the M construction execution departments, and performing storage update of the M associated construction drawing trees based on the analysis results to obtain M associated split drawing trees, including: Interactively obtain M groups of task dependencies of the M construction execution departments in the multiple target construction stages; According to the M groups of task dependencies, data storage and sharing of the same-level sub-node data of the M associated construction drawing trees are performed to obtain M associated split drawing trees.

8. A plug-in for quick non-destructive splitting of drawings, characterized by: The method for implementing the non-destructive and rapid splitting method of drawings in any one of claims 1 to 7, wherein the non-destructive and rapid splitting plug-in comprises: A data calling module, which is used to call local data of a target construction project to obtain K non-standard construction drawings; A fusion processing module, wherein the fusion processing module is used to obtain drawings to be split by performing drawing fusion processing on the K non-standard construction drawings; A phase extraction module, the phase extraction module is used to extract phase drawings from the drawings to be split based on predefined construction phases, and obtain multiple phase-related drawings, wherein the multiple phase-related drawings have multiple construction phase identifiers; A splitting and reorganizing module, the splitting and reorganizing module is used to split and reorganize the drawings associated with the multiple stages according to the construction department composition of the target construction project, and obtain M associated construction drawing trees of M construction execution departments; A storage update module, the storage update module is used to perform construction collaboration feature analysis on the M construction execution departments, and based on the analysis results, perform storage update of the M associated construction drawing trees to obtain M associated split drawing trees; An information completion module is used to use the drawing to be split to traverse the M associated split drawing trees to perform information proofreading and completion to obtain multiple lossless split drawing trees.

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