Intelligent drawing management method and device and storage medium

Abstract

The invention relates to an intelligent drawing management method and device and a storage medium, and is applied to the technical field of engineering drawing digital management, and the method comprises the steps: employing a CNN + Transform hybrid model, extracting the visual features of a drawing through a convolutional neural network, analyzing the semantic association in combination with a Transform architecture, achieving the three-stage classification of the drawing, improving the accuracy, and achieving the intelligent management of the drawing. A multi-modal retrieval engine is innovatively designed, text, sketch and three-dimensional space combination is supported to achieve real-time conflict detection, a dynamic detection algorithm based on operation log hash is adopted, operation sequence hash values are calculated through distributed nodes, millisecond-level conflict recognition is achieved, an increment rendering strategy is adopted, a designer can compare and modify differences in time, and the real-time conflict recognition is achieved. The conflict resolution efficiency is improved by 5 times; a double-chain block chain architecture is designed, an authority operation log is stored in a private chain to ensure efficient query, an authority change record is stored in a public chain to realize non-tampering, user privacy is protected, supervision and traceability requirements are met, and the calculation overhead is reduced by 30% compared with a traditional digital signature scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engineering drawing digital management, and particularly relates to a drawing intelligent management method and device and a storage medium. BACKGROUND

[0002] Construction drawing examination refers to the technical examination of construction design drawings of engineering construction projects by government regulatory departments. Previously, this work was completed by government construction regulatory departments themselves by organizing relevant technical personnel. Now, it is mainly completed by third-party professional examination institutions licensed by the government. After the technical examination, examination reports, qualification certificates and record tables and other documents are issued.

[0003] The existing drawing management technology mainly includes the following two schemes:

[0004] One is a traditional management system based on a rule engine: file format analysis and metadata matching technology are used to realize drawing classification, and artificial pre-set rules are relied on. The classification dimension is single and the content semantics of the drawing cannot be identified. The classification accuracy of mixed drawings in multiple fields is insufficient. The operation sequence timestamp comparison is used for collaborative editing conflict detection. When multiple users modify the same drawing area at the same time, the server needs to process the operation request in sequence, which leads to a conflict recognition delay of more than 500 ms, seriously affecting the collaboration efficiency. Moreover, the retrieval function is limited to text keyword matching, and lacks support for unstructured queries such as sketch contour and three-dimensional spatial relationship, with low precision.

[0005] The other is a cloud collaborative platform: it relies on a centralized storage architecture, and the drawing version management adopts linear history records without introducing semantic version coding, which leads to manual comparison of differences when merging versions, with an average time consumption of more than 30 minutes per time. The permission management is based on a role-based access control model, and the permission change records are stored in the central database, which has a risk of data tampering. Moreover, it lacks an intelligent compliance verification module, and problems such as missing drawing labels and size tolerance conflicts rely on manual checking, with a high error and omission rate. SUMMARY

[0006] Therefore, the present application aims to provide a drawing intelligent management method and device and storage medium to solve the problems in the prior art that the drawing classification is realized by using file format analysis and metadata matching technology, relies on artificial preset rules, the classification dimension is single and the content semantics of the drawing cannot be recognized, the classification accuracy for mixed drawings in multiple fields is insufficient, the operation sequence timestamp comparison is used for conflict detection in collaborative editing, when multiple users modify the same drawing area at the same time, the server needs to process the operation request in sequence, which leads to a conflict recognition delay of more than 500 ms, seriously affecting the collaboration efficiency, and the search function is limited to text keyword matching and lacks support for unstructured queries such as sketch contour and three-dimensional spatial relationship, resulting in low precision, and the role-based access control model is used for permission management, and the permission change records are stored in the central database, which has a data tampering risk, and there is no intelligent compliance verification module, and the problems of missing drawing labels and size tolerance conflicts depend on manual checking, and the error and omission rate is high.

[0007] According to a first aspect of an embodiment of the present application, a drawing intelligent management method is provided, which comprises:

[0008] Receiving a drawing uploaded by a user terminal, using a pre-trained CNN-Transformer hybrid model to extract features of the uploaded drawing, and outputting a three-dimensional classification label containing an engineering type, a design stage and a version state;

[0009] Monitoring the three-dimensional classification label by an operation log hash synchronizer deployed on an edge node, recording the editing operation of the user in real time and generating a SHA-3 hash sequence with a time and space stamp, detecting whether there is a concurrent modification of the same area of the drawing by multiple users according to the SHA-3 hash sequence with the time and space stamp, if there is, using a conflict positioning algorithm based on the hash value timestamp to obtain the conflict area and generate a conflict difference heat map, extracting the image features of the conflict area and converting them into search input features;

[0010] Building a hybrid search engine by using a pre-trained text conflict detection model, a sketch conflict detection model and a three-dimensional spatial relationship conflict detection model;

[0011] Inputting the search input features into the hybrid search engine, analyzing the text semantics by the text conflict detection model, extracting the sketch contour features by the Fourier descriptor of the sketch conflict detection model, and matching the three-dimensional spatial relationship by the BIM topological subgraph of the three-dimensional spatial relationship conflict detection model, the hybrid search engine outputs the conflict area as one or more combinations of text conflict, sketch conflict or three-dimensional spatial relationship conflict;

[0012] The permission allocation verification is performed on the conflict area, permission change records are stored in a public chain, permission change operation logs are stored in a private chain, and it is ensured that the permission allocation is traceable and tamper-proof.

[0013] A multi-modal query vectorization association reconstruction labeling network is used on the conflict area to identify an unclosed chain and locate a missing node, a tolerance transfer matrix model is established to predict assembly interference risks, and when the conflict area is not modified for a continuous preset period and passes the assembly interference risk check, cold storage archiving based on ErasureCoding is triggered to complete intelligent management of drawings.

[0014] Preferably,

[0015] The training of the CNN-Transformer hybrid model includes:

[0016] At least N cross-domain drawings are collected to construct a heterogeneous data set, and a three-level label of engineering type, design stage, and version state is labeled for each cross-domain drawing;

[0017] A loss function is set, the labeled cross-domain drawings are input into a CNN architecture and a Transformer architecture respectively, high-dimensional feature maps output by the CNN architecture and semantic vectors extracted by the Transformer architecture are subjected to attention weighted fusion, the fusion weight is dynamically adjusted through a learnable parameter matrix, and the training is iterated until the loss of the CNN architecture and the Transformer architecture no longer decreases, thereby obtaining a trained CNN-Transformer hybrid model;

[0018] When the misclassification rate of the newly added drawings exceeds a preset threshold, online fine-tuning feature incremental learning is started, the full connection layer parameters of the CNN-Transformer hybrid model are updated, and a double-channel dynamic fusion architecture and a restricted incremental learning strategy are used.

[0019] Preferably,

[0020] The SHA-3 hash sequence with time and space stamps is used to detect whether there is a multi-user concurrent modification of the same area of the drawings, and if so, a conflict positioning algorithm based on hash value time stamps is used to obtain a conflict area and generate a conflict difference heat map, which includes:

[0021] The user editing operation is decomposed into the smallest operation unit according to the SHA-3 hash sequence with time and space stamps, and each unit is attached with a position hash value;

[0022] A conflict judgment matrix based on overlap rate and operation type is established, the horizontal axis is the operation type, the vertical axis is the influence area overlap rate, the influence area overlap rate is obtained through the smallest operation unit, and when the overlap rate is greater than a preset ratio and the operation type conflicts, it is marked as a high-risk conflict.

[0023] The WebGL is used to accelerate rendering of the difference region, and transparency gradient superposition display is realized.

[0024] Preferably,

[0025] The text semantic is analyzed by the text conflict detection model, the sketch contour feature is extracted by the Fourier descriptor of the sketch conflict detection model, and the three-dimensional space relationship is matched by the BIM topology subgraph of the three-dimensional space relationship conflict detection model.

[0026] The text conflict detection adopts a BERT-diagram knowledge graph joint embedding model to map a query sentence and preset diagram metadata to the same vector space, and then performs similarity matching in the same vector space, and selects a diagram corresponding to diagram metadata with the highest similarity and greater than a preset similarity threshold as a text conflict detection result.

[0027] The sketch conflict detection converts a hand-drawn contour into a frequency domain feature by using an improved Fourier descriptor combined with a dynamic time warping algorithm, performs similarity calculation on the frequency domain feature and preset sketch frequency domain features, and sorts the preset sketches according to the similarity, and selects a preset sketch with the highest similarity and greater than a preset similarity threshold as a sketch conflict detection result.

[0028] The three-dimensional space relationship conflict detection analyzes a bounding box hierarchy structure of a three-dimensional diagram to be queried by point cloud topology subgraph matching, and performs isomorphic comparison with a preset BIM database, and selects a three-dimensional diagram model corresponding to the bounding box hierarchy structure with the highest similarity and greater than a preset similarity threshold as a three-dimensional space relationship conflict detection result.

[0029] Preferably,

[0030] The bounding box hierarchy structure of the three-dimensional diagram to be queried is analyzed by point cloud topology subgraph matching.

[0031] A voxel grid downsampling algorithm is used to standardize the point cloud density of the three-dimensional diagram to be queried to 500 points per cubic meter, and point cloud preprocessing is completed.

[0032] A hole structure feature of the three-dimensional diagram to be queried is extracted by an Alpha Shape algorithm, a multi-level bounding box tree is generated, and topology feature extraction is completed.

[0033] The isomorphic comparison with the preset BIM database includes:

[0034] A VF2 algorithm is used for subgraph isomorphic matching, a rotation invariance constraint condition is introduced, and subgraph matching optimization is realized.

[0035] Preferably,

[0036] The permission allocation verification on the conflict area comprises:

[0037] The deployment of the Chainlink node realizes the data bidirectional verification between the public chain and the private chain, ensures that the verification delay of the data bidirectional verification between the public chain and the private chain is within a preset threshold time;

[0038] A plurality of permission templates are defined, the permission templates are used to limit editable and non-exportable, and support one-key application of the templates to a project group;

[0039] When the user operates the signature, 5 nodes are randomly selected to form a temporary ring, and it is ensured that the signature verification time consumption is within a preset threshold time.

[0040] Preferably,

[0041] The multi-modal query vectorization association reconstruction labeling network for the conflict area comprises:

[0042] The conflict area is dimensionally labeled, the dimensional label is converted into a weighted directed edge, a node represents a geometric element center point, a depth-first search is used to traverse an unclosed path, and a missing labeling position is located;

[0043] The missing labeling position is completed according to an adjacent dimensional tolerance.

[0044] Preferably,

[0045] The completion of the missing labeling position according to the adjacent dimensional tolerance comprises:

[0046] According to an upper and lower deviation range of adjacent dimensions, a maximum material condition is taken as a constraint, it is ensured that an assembly gap after accumulation of all dimensional tolerances still meets a limit requirement, a theoretical upper limit of a theoretically closed dimension is calculated according to a maximum positive deviation, a limit tolerance range of the missing dimension is inversely deduced, and the missing labeling position is completed through the limit tolerance range of the missing dimension;

[0047] Or,

[0048] A root mean square statistical model is used to confirm that the dimensional deviation conforms to a normal distribution, a standard deviation of all dimensions in the chain ring is counted, a theoretical standard deviation of the closed dimension is calculated, and finally a bilateral symmetric tolerance is generated, and the missing labeling position is completed through the bilateral symmetric tolerance;

[0049] Or,

[0050] The total amount of unclosed tolerances is evenly distributed to related dimensions according to a dimensional sensitivity weight, and is distributed to each ring according to a weight proportion, and the missing labeling position is completed according to the distribution result.

[0051] According to a second aspect of the embodiment of the present application, a smart drawing management device is provided, and the device comprises:

[0052] The classification label generation module is configured to receive drawings uploaded by a user terminal, extract features of the uploaded drawings by using a pre-trained CNN-Transformer hybrid model, and output three-dimensional classification labels containing an engineering type, a design stage, and a version state.

[0053] The conflict detection module is configured to monitor the three-dimensional classification labels by an operation log hash synchronizer deployed on an edge node, record editing operations of a user in real time and generate a SHA-3 hash sequence with a time and space stamp, detect whether there is a concurrent modification of the same area of the drawing by multiple users according to the SHA-3 hash sequence with the time and space stamp, if there is, acquire a conflict area and generate a conflict difference heat map by using a conflict positioning algorithm based on a hash value time stamp, extract image features of the conflict area and convert the image features into retrieval input features.

[0054] The retrieval engine building module is configured to build a hybrid retrieval engine by using a pre-trained text conflict detection model, a sketch conflict detection model, and a three-dimensional spatial relationship conflict detection model.

[0055] The conflict relationship analysis module is configured to input the retrieval input features into the hybrid retrieval engine, analyze text semantics by using the text conflict detection model, extract sketch contour features by using a Fourier descriptor of the sketch conflict detection model, match three-dimensional spatial relationships by using a BIM topological subgraph of the three-dimensional spatial relationship conflict detection model, and output the conflict area as one or more combinations of a text conflict, a sketch conflict, or a three-dimensional spatial relationship conflict.

[0056] The permission locking module is configured to perform permission allocation verification on the conflict area, store permission change records in a public chain, store permission change operation logs in a private chain, and ensure that the permission allocation is traceable and tamper-proof.

[0057] The archive management module is configured to use a multi-modal query vectorization correlation reconstruction annotation network to identify an unclosed chain and locate a missing node, establish a tolerance transfer matrix model to predict assembly interference risks, trigger cold storage archiving based on ErasureCoding to complete smart drawing management when the conflict area is not modified for a continuous preset period and passes the assembly interference risk check.

[0058] According to a third aspect of the embodiment of the present application, a storage medium is provided, and the storage medium stores a computer program, and the computer program is executed by a host computer to implement each step in the above method.

[0059] The technical scheme provided by the embodiment of the present application can include the following beneficial effects:

[0060] The application adopts a CNN+Transformer hybrid model, extracts visual features of drawings through a convolutional neural network, analyzes semantic association in combination with a Transformer architecture, realizes three-level classification of drawings, improves accuracy, innovatively designs a multi-modal retrieval engine, supports text, sketch and three-dimensional space to jointly realize real-time conflict detection, realizes millisecond-level conflict identification through a distributed node to calculate a hash value of an operation sequence based on a dynamic detection algorithm of an operation log hash, adopts an incremental rendering strategy, enables designers to compare and modify differences in real time, and improves conflict resolution efficiency by 5 times, designs a double-chain blockchain architecture, stores permission operation logs in a private chain to ensure efficient query, stores permission change records in a public chain to realize non-tamperability, protects user privacy and meets regulatory traceability requirements, and reduces 30% of the computing overhead compared with a traditional digital signature scheme; a cold and hot data hierarchical storage strategy is combined with Erasure Coding encoding technology to reduce the storage cost of abandoned drawings, while greatly improving the data recoverability.

[0061] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0062] The accompanying drawings incorporated in and forming a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application.

[0063] Figure 1 is a flowchart of a drawing intelligent management method according to an exemplary embodiment;

[0064] Figure 2 is a flowchart of a CNN+Transformer hybrid model training process according to another exemplary embodiment;

[0065] Figure 3 is a system diagram of a drawing intelligent management device according to another exemplary embodiment;

[0066] In the drawings: 1-classification label generation module, 2-conflict detection module, 3-retrieval engine building module, 4-conflict relationship analysis module, 5-permission locking module, 6-archiving management module. DETAILED DESCRIPTION

[0067] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0068] Example 1

[0069] Figure 1 FIG. 1 is a flow chart showing a method for intelligent management of drawings according to an exemplary embodiment. Figure 1 As shown, the method includes:

[0070] S1 receives drawings uploaded by the user terminal, uses a pre-trained CNN-Transformer hybrid model to extract features from the uploaded drawings, and outputs 3D classification labels including project type, design stage, and version status;

[0071] S2, monitoring the three-dimensional classification labels through an operation log hash synchronizer deployed on an edge node, recording the user's editing operations in real time and generating a SHA-3 hash sequence with a time-space stamp. Based on the SHA-3 hash sequence with a time-space stamp, detecting whether multiple users have concurrently modified the same area of ​​the drawing. If so, a conflict location algorithm based on hash value timestamps is used to obtain the conflicting area and generate a conflict difference heat map. Image features of the conflicting area are extracted and converted into retrieval input features.

[0072] S3, builds a hybrid search engine using pre-trained text conflict detection models, sketch conflict detection models, and 3D spatial relationship conflict detection models;

[0073] S4, inputting the retrieval input features into the hybrid retrieval engine, parsing text semantics through the text conflict detection model, extracting sketch contour features through the Fourier descriptor of the sketch conflict detection model, matching three-dimensional spatial relationships through the BIM topology subgraph of the three-dimensional spatial relationship conflict detection model, and the hybrid retrieval engine outputting the conflict area as one or more combinations of text conflict, sketch conflict, or three-dimensional spatial relationship conflict;

[0074] S5: Verify the authority allocation for the conflicting area, store the authority change record in the public chain, and store the authority change operation log in the private chain to ensure that the authority allocation is traceable and cannot be tampered with;

[0075] S6, adopting a multi-modal query vectorization association reconstruction labeling network on the conflict region, identifying an open chain and locating a missing node, establishing a tolerance transfer matrix model to predict an assembly interference risk, when the conflict region is not modified within a continuous preset period and passes the assembly interference risk check, triggering cold storage archiving based on Erasure Coding to complete intelligent management of drawings;

[0076] It can be understood that,

[0077] S1, receiving drawings uploaded by a user terminal, extracting features of the uploaded drawings by using a pre-trained CNN-Transformer hybrid model, and outputting three-dimensional classification labels containing an engineering type, a design stage, and a version state, wherein training of the CNN-Transformer hybrid model is as shown in the accompanying drawings, and includes: Figure 2

[0078] S101, collecting at least 500,000 cross-domain drawings to construct a heterogeneous data set, and labeling three-level labels of an engineering type, a design stage, and a version state for each cross-domain drawing;

[0079] S102, setting a loss function, inputting the labeled cross-domain drawings into a CNN architecture and a Transformer architecture respectively, performing attention weighted fusion on high-dimensional feature maps output by the CNN architecture and semantic vectors extracted by the Transformer architecture, dynamically adjusting a fusion weight through a learnable parameter matrix, and obtaining a trained CNN-Transformer hybrid model through iterative training until the loss of the CNN architecture and the Transformer architecture no longer decreases;

[0080] S103, when the misclassification rate of newly added drawings exceeds 15%, starting online fine-tuning feature incremental learning, updating full connection layer parameters, and using a double-channel dynamic fusion architecture and a restricted incremental learning strategy;

[0081] S2, monitoring the three-dimensional classification labels by using an operation log hash synchronizer deployed on an edge node, recording editing operations of a user in real time and generating a SHA-3 hash sequence with a time and space stamp, detecting whether there is concurrent modification of the same region of drawings by multiple users according to the SHA-3 hash sequence with the time and space stamp, if there is, using a conflict positioning algorithm based on a hash value timestamp to obtain a conflict region and generate a conflict difference heat map, extracting image features of the conflict region and converting them into retrieval input features, wherein detecting whether there is concurrent modification of the same region of drawings by multiple users according to the SHA-3 hash sequence with the time and space stamp, if there is, using a conflict positioning algorithm based on a hash value timestamp to obtain a conflict region and generate a conflict difference heat map includes:

[0082] ​The detecting whether multiple users concurrently modify the same area of ​​the drawing according to the SHA-3 hash sequence with time and space stamps, and if so, obtaining the conflict area using a conflict location algorithm based on hash value timestamps and generating a conflict difference heat map includes:

[0083] Decomposing the user editing operation into minimum operation units according to the SHA-3 hash sequence with time and space stamps, with each unit accompanied by a position hash value;

[0084] A conflict determination matrix based on overlap ratio and operation type is established, with the horizontal axis representing the operation type and the vertical axis representing the overlap ratio of the impacted area. The overlap ratio of the impacted area is obtained using the minimum operation unit. When the overlap ratio is greater than a preset ratio and the operation type conflicts, it is marked as a high-risk conflict.

[0085] Use WebGL to accelerate the rendering of difference areas and realize transparency gradient overlay display;

[0086] S3, builds a hybrid search engine using pre-trained text conflict detection models, sketch conflict detection models, and 3D spatial relationship conflict detection models;

[0087] S4: Input the retrieval input features into the hybrid retrieval engine, parse text semantics through the text conflict detection model, extract sketch contour features through the Fourier descriptor of the sketch conflict detection model, match the three-dimensional spatial relationship through the BIM topology subgraph of the three-dimensional spatial relationship conflict detection model, and the hybrid retrieval engine outputs the conflict area as one or more combinations of text conflict, sketch conflict, or three-dimensional spatial relationship conflict, wherein the text semantics are parsed through the text conflict detection model, the sketch contour features are extracted through the Fourier descriptor of the sketch conflict detection model, and the three-dimensional spatial relationship is matched through the BIM topology subgraph of the three-dimensional spatial relationship conflict detection model.

[0088] Text conflict detection uses the BERT-drawing knowledge graph joint embedding model to map the query statement and the preset drawing metadata into the same vector space. Similarity matching is then performed in the same vector space. The drawing corresponding to the drawing metadata with the highest similarity (greater than the preset similarity threshold) is selected as the text conflict detection result.

[0089] Sketch conflict retrieval uses an improved Fourier descriptor combined with a dynamic time warping algorithm to convert hand-drawn contours into frequency domain features. The frequency domain features are then compared with the frequency domain features of the preset sketches for similarity calculation and sorting. The preset sketch with the highest similarity that is greater than a preset similarity threshold is selected as the sketch conflict detection result.

[0090] The three-dimensional space relationship conflict detection analyzes the bounding box hierarchy of the three-dimensional drawing to be queried through point cloud topology subgraph matching, and performs isomorphic comparison with the preset BIM database, selects the three-dimensional drawing model corresponding to the bounding box hierarchy with the highest similarity and greater than the preset similarity threshold as the three-dimensional space relationship conflict detection result, and specifically includes:

[0091] The voxel grid downsampling algorithm is used to normalize the point cloud density to 500 points per cubic meter, and the point cloud preprocessing is completed;

[0092] The Alpha Shape algorithm is used to extract the hole structure features of the model, generate a multi-level bounding box tree, and complete the topology feature extraction;

[0093] The VF2 algorithm is used for subgraph isomorphic matching, and the rotation invariance constraint condition is introduced to complete the subgraph matching optimization;

[0094] S5, the permission allocation verification is performed on the conflict area, the permission change record is stored in the public chain, and the permission change operation log is stored in the private chain, so that the permission allocation is traceable and tamper-proof, and specifically includes:

[0095] Deploying the Chainlink node realizes the data bidirectional verification of the public chain and the private chain, and ensures that the data bidirectional verification verification delay of the public chain and the private chain is within the preset threshold time;

[0096] Define multiple permission templates, the permission template is used to limit editable and non-exportable, and supports one-key application of the template to the project group;

[0097] When signing, 5 nodes are randomly selected to form a temporary ring, and the signature verification time is ensured to be within the preset threshold time;

[0098] S6, the multi-modal query vectorization association reconstruction labeling network is used for the conflict area, the open chain is identified and the missing node is located, the tolerance transfer matrix model is established to predict the assembly interference risk, when the conflict area is not modified for a continuous preset period and passes the assembly interference risk check, trigger the cold storage archiving based on ErasureCoding to complete the intelligent management of drawings, wherein the multi-modal query vectorization association reconstruction labeling network is used for the conflict area, the open chain is identified and the missing node is located, including:

[0099] The size labeling is performed on the conflict area, the size labeling is converted into a weighted directed edge, the node represents the center point of the geometric element, the depth first search is used to traverse the open path, and the missing labeling position is located;

[0100] The missing labeling position is completed according to the adjacent size tolerance;

[0101] The embodiment also provides three missing labeling completion schemes, including:

[0102] According to the upper and lower deviation range of the adjacent size, with the maximum material condition as the constraint, ensure that the assembly gap after the accumulation of all size tolerances still meets the ultimate requirement, and calculate the theoretical upper limit of the closed size according to the maximum positive deviation, deduce the limit tolerance range of the missing size in reverse, and complete the missing label position through the limit tolerance range of the missing size;

[0103] Or,

[0104] Adopting the root mean square statistical model, confirming that the size deviation conforms to the normal distribution, calculating the theoretical standard deviation of the closed size by statistically calculating the standard deviation of all sizes in the chain, and finally generating the bilateral symmetric tolerance to complete the missing label position through the bilateral symmetric tolerance;

[0105] Or,

[0106] The total amount of unclosed tolerance is evenly distributed to the related size according to the size sensitivity weight, and is distributed to each ring according to the weight proportion, and the missing label position is completed according to the distribution result.

[0107] Example two

[0108] Figure 3 It is a system schematic diagram of a drawing intelligent management device according to another exemplary embodiment, which comprises:

[0109] The classification label generation module 1 is used for receiving the drawings uploaded by the user terminal, extracting features of the uploaded drawings by using a pre-trained CNN-Transformer hybrid model, and outputting three-dimensional classification labels containing engineering types, design stages and version states;

[0110] The conflict detection module 2 is used for monitoring the three-dimensional classification labels by deploying an operation log hash synchronizer on the edge node, recording the editing operation of the user in real time and generating a SHA-3 hash sequence with a time and space stamp, detecting whether there is a concurrent modification of the same area of the drawing by multiple users according to the SHA-3 hash sequence with the time and space stamp, if there is, using a conflict positioning algorithm based on the hash value timestamp to obtain the conflict area and generate a conflict difference heat map, extracting the image features of the conflict area and converting them into retrieval input features;

[0111] The retrieval engine building module 3 is used for building a hybrid retrieval engine by using a pre-trained text conflict detection model, a sketch conflict detection model and a three-dimensional spatial relationship conflict detection model;

[0112] The conflict relationship analysis module 4 is configured to input the search input features into the hybrid search engine, analyze the text semantics through the text conflict detection model, extract the sketch contour features through the Fourier descriptor of the sketch conflict detection model, and match the three-dimensional space relationship through the BIM topology subgraph of the three-dimensional space relationship conflict detection model. The hybrid search engine outputs the conflict area as one or a combination of text conflict, sketch conflict, or three-dimensional space relationship conflict.

[0113] The permission lock module 5 is configured to verify the permission allocation of the conflict area, store the permission change record in the public chain, store the permission change operation log in the private chain, and ensure that the permission allocation is traceable and tamper-proof.

[0114] The archive management module 6 is configured to use a multi-modal query vectorization associated reconstruction labeling network to identify an unclosed chain and locate a missing node, establish a tolerance transfer matrix model to predict an assembly interference risk, and trigger cold storage archiving based on ErasureCoding when the conflict area is not modified for a continuous preset period and passes the assembly interference risk check, thereby completing intelligent management of drawings.

[0115] It can be understood that the drawing uploaded by the user terminal is received by the classification label generation module 1, the pre-trained CNN-Transformer hybrid model is used for feature extraction of the uploaded drawing, and three-dimensional classification labels containing engineering type, design stage and version state are output; the conflict detection module 2 uses the operation log hash synchronizer deployed in the edge node to monitor the three-dimensional classification labels, records the user's editing operation in real time and generates a SHA-3 hash sequence with a space-time stamp, detects whether there is a concurrent modification of the same area of the drawing by multiple users according to the SHA-3 hash sequence with a space-time stamp, if there is, a conflict positioning algorithm based on hash value timestamp is used to obtain the conflict area and generate a conflict difference heat map, the image features of the conflict area are extracted and converted into retrieval input features; the retrieval engine building module 3 uses the pre-trained text conflict detection model, sketch conflict detection model and three-dimensional spatial relationship conflict detection model to build a hybrid retrieval engine; the conflict relationship analysis module 4 inputs the retrieval input features into the hybrid retrieval engine, analyzes the text semantics through the text conflict detection model, extracts the sketch contour features through the Fourier descriptor of the sketch conflict detection model, and matches the three-dimensional spatial relationship through the BIM topological subgraph of the three-dimensional spatial relationship conflict detection model. The hybrid retrieval engine outputs the conflict area as one or more combinations of text conflict, sketch conflict or three-dimensional spatial relationship conflict; the permission locking module 5 verifies the permission allocation of the conflict area, stores the permission change record in the public chain, and stores the permission change operation log in the private chain, so as to ensure that the permission allocation is traceable and tamper-proof; the archiving management module 6 uses a multi-modal query vectorization association reconstruction labeling network to identify an unclosed chain and locate a missing node, establishes a tolerance transfer matrix model to predict assembly interference risk, and when there is no modification in the conflict area for a continuous preset period and the assembly interference risk is verified, triggers cold storage archiving based on ErasureCoding to complete intelligent management of the drawing;

[0116] This embodiment deploys a CNN-Transformer hybrid model processor to perform three-level classification of drawings and output structured metadata containing feature vectors and permission identifiers; through an edge computing node cluster, it captures user operations in real time and generates a SHA-3 hash chain with a time and space stamp; based on the hash branch comparison algorithm, it locates the conflict area within 50ms and triggers the generation of a heat map; integrates a BERT text semantic parser, a Fourier descriptor sketch feature extractor, and a BIM topology matcher to support cross-modal joint retrieval; dimension chain closed-loop verification uses a graph theory algorithm to traverse the annotation network, and a specification version adapter to dynamically load the national standard library according to the design stage; the private chain stores operation logs and version snapshots, the public chain records permission change smart contracts and evidence hashes, and the cross-chain oracle realizes two-way verification of dual-chain data; NVMe is used SSD stores active versions, supports μs-level response, and implements shard encoding for archived versions. An innovative multimodal retrieval engine is designed to support joint queries of text, sketches, and 3D models. Sketch retrieval uses an improved Fourier descriptor algorithm to improve the precision of matching hand-drawn contours with drawing libraries. A dynamic detection algorithm based on operation log hashing calculates the hash value of the operation sequence through distributed nodes to achieve millisecond-level conflict identification.

[0117] In this embodiment, text conflict retrieval is based on the BERT-drawing knowledge graph joint embedding model, mapping the query text, drawing title, and annotation text to the same vector space, and supporting semantic similarity extended retrieval; sketch conflict retrieval extracts the frequency domain features of hand-drawn contours through the Fourier descriptor algorithm and calculates contour similarity using the dynamic time warping algorithm; by sampling the standardized point cloud density values ​​under the voxel grid and extracting the hole features using the Alpha Shape algorithm, combined with the VF2 subgraph isomorphism algorithm to achieve rotational invariant matching, the hot and cold data tiered storage strategy combined with the Erasure Coding encoding technology reduces the storage cost of abolished drawings while greatly improving data recoverability. An innovative dual-chain blockchain architecture is designed, with operation logs stored in the private chain to ensure efficient query, and permission change records stored in the public chain to ensure immutability, protecting user privacy while meeting regulatory traceability requirements, and reducing computing overhead by 30% compared to traditional digital signature schemes.

[0118] This technical solution innovatively designs a dual-chain blockchain architecture. Operation logs are stored in a private chain to ensure efficient query, and permission change records are stored in a public chain to ensure that they cannot be tampered with. This not only protects user privacy but also meets regulatory traceability requirements, reducing computing overhead by 30% compared to traditional digital signature schemes.

[0119] Example 3:

[0120] This embodiment provides a storage medium storing a computer program. When the computer program is executed by a host controller, each step of the above method is implemented.

[0121] It can be understood that the storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0122] It can be understood that the same or similar parts in the above embodiments can be mutually referred to, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0123] It should be noted that, in the description of the present application, the terms "first", "second", etc. are only for the purpose of description, and cannot be understood as indicating or implying relative importance. In addition, in the description of the present application, unless otherwise specified, the meaning of "a plurality of" is at least two.

[0124] Any process or method descriptions in flow charts or described herein otherwise can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for performing specific logic functions or steps in the process, and that the various embodiments of the application include the additional implementation that the functions can be performed in different orders, in different ways, or in an essentially simultaneous manner, as can be useful in the implementation depending on the functionality involved, as would be understood by those skilled in the art.

[0125] It should be understood that the parts of the present application can be realized by hardware, software, firmware or their combination. In the above embodiments, a plurality of steps or methods can be realized by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if realized by hardware, and as in another embodiment, it can be realized by any one or their combination of the following technologies known in the art: discrete logic circuit with logic gate circuit for implementing logic function on data signal, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA), etc.

[0126] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiment method can be completed by program instructions to the relevant hardware, and the program can be stored in a computer readable storage medium, which includes one or a combination of steps of the method embodiments when executed.

[0127] In addition, each function unit in each embodiment of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware, or in the form of a software function module. When the integrated module is realized in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0128] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0129] In the description of the present specification, the description referring to the terms "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily mean the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0130] Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A drawing intelligent management method, characterized in that: The method comprises: Receive drawings uploaded by user terminals, use a pre-trained CNN-Transformer hybrid model to extract features from the uploaded drawings, and output 3D classification labels including project type, design stage, and version status; The three-dimensional classification labels are monitored by an operation log hash synchronizer deployed on the edge node, which records the user's editing operations in real time and generates a SHA-3 hash sequence with a time-space stamp. The SHA-3 hash sequence with a time-space stamp is used to detect whether multiple users have concurrently modified the same area of ​​the drawing. If so, a conflict location algorithm based on hash value timestamps is used to obtain the conflicting area and generate a conflict difference heat map. The image features of the conflicting area are extracted and converted into retrieval input features. Build a hybrid search engine using pre-trained text conflict detection models, sketch conflict detection models, and 3D spatial relationship conflict detection models; Inputting the retrieval input features into the hybrid retrieval engine, parsing text semantics through the text conflict detection model, extracting sketch contour features through the Fourier descriptor of the sketch conflict detection model, matching three-dimensional spatial relationships through the BIM topology subgraph of the three-dimensional spatial relationship conflict detection model, and the hybrid retrieval engine outputting the conflict area as one or more combinations of text conflict, sketch conflict, or three-dimensional spatial relationship conflict; Verify the authority allocation of the conflicting areas, store the authority change records in the public chain, and store the authority change operation logs in the private chain to ensure that the authority allocation is traceable and cannot be tampered with; Multimodal query vectorization association is used to reconstruct the annotation network of the conflict area, identify unclosed chains and locate missing nodes, and establish a tolerance transfer matrix model to predict assembly interference risks. When the conflict area has not been modified for a consecutive preset period and passes the assembly interference risk check, cold storage archiving based on ErasureCoding is triggered to complete intelligent drawing management.

2. The method according to claim 1, characterized in that The training of the CNN-Transformer hybrid model includes: Collect at least N cross-domain drawings to construct a heterogeneous dataset, and label each cross-domain drawing with three-level labels: project type, design stage, and version status; Set the loss function, input the annotated cross-domain drawings into the CNN architecture and the Transformer architecture respectively, perform attention-weighted fusion on the high-dimensional feature maps output by the CNN architecture and the semantic vectors extracted by the Transformer architecture. The fusion weights are dynamically adjusted using a learnable parameter matrix. Iterate training until the loss of the CNN architecture and the Transformer architecture no longer decreases, thus obtaining a trained CNN-Transformer hybrid model. When the classification error rate of newly added drawings exceeds the preset threshold, online fine-tuning feature incremental learning is initiated, the fully connected layer parameters of the CNN-Transformer hybrid model are updated, and a dual-channel dynamic fusion architecture and a constrained incremental learning strategy are used.

3. The method according to claim 2, characterized in that The detecting whether multiple users concurrently modify the same area of ​​the drawing according to the SHA-3 hash sequence with time and space stamps, and if so, obtaining the conflict area using a conflict location algorithm based on hash value timestamps and generating a conflict difference heat map includes: Decomposing the user editing operation into minimum operation units according to the SHA-3 hash sequence with time and space stamps, with each unit accompanied by a position hash value; A conflict determination matrix based on overlap ratio and operation type is established, with the horizontal axis representing the operation type and the vertical axis representing the overlap ratio of the impacted area. The overlap ratio of the impacted area is obtained using the minimum operation unit. When the overlap ratio is greater than a preset ratio and the operation type conflicts, it is marked as a high-risk conflict. WebGL is used to accelerate the rendering of the difference area to achieve transparency gradient overlay display.

4. The method according to claim 3, characterized in that The method of parsing text semantics using the text conflict detection model, extracting sketch contour features using the Fourier descriptor of the sketch conflict detection model, and matching three-dimensional spatial relationships using the BIM topology subgraph of the three-dimensional spatial relationship conflict detection model includes: Text conflict detection uses the BERT-drawing knowledge graph joint embedding model to map the query statement and the preset drawing metadata into the same vector space. Similarity matching is then performed in the same vector space. The drawing corresponding to the drawing metadata with the highest similarity (greater than the preset similarity threshold) is selected as the text conflict detection result. Sketch conflict retrieval uses an improved Fourier descriptor combined with a dynamic time warping algorithm to convert hand-drawn contours into frequency domain features. The frequency domain features are then compared with the frequency domain features of the preset sketches for similarity calculation and sorting. The preset sketch with the highest similarity that is greater than a preset similarity threshold is selected as the sketch conflict detection result. 3D spatial relationship conflict detection analyzes the bounding box hierarchy of the 3D drawing to be queried through point cloud topology subgraph matching, and performs isomorphic comparison with the preset BIM database. The 3D drawing model corresponding to the bounding box hierarchy with the highest similarity and greater than the preset similarity threshold is selected as the 3D spatial relationship conflict detection result.

5. The method according to claim 4, characterized in that The method of parsing the bounding box hierarchy of the 3D drawing to be queried by matching the point cloud topology subgraph includes: The point cloud density of the 3D drawing to be queried is standardized to 500 points / cubic meter using the voxel grid downsampling algorithm to complete point cloud preprocessing. The Alpha Shape algorithm is used to extract the hole structure features of the 3D drawing to be queried, generate a multi-level bounding box tree, and complete the topological feature extraction; The isomorphic comparison with the preset BIM database includes: The VF2 algorithm is used for subgraph isomorphism matching, and rotation invariance constraints are introduced to achieve subgraph matching optimization.

6. The method according to claim 5, characterized in that The verifying the authority allocation for the conflicting area includes: Deploy Chainlink nodes to implement bidirectional data verification between public and private chains, ensuring that the verification delay for bidirectional data verification between public and private chains is within the preset threshold time; Define multiple permission templates, which are used to limit editability and non-exportability, and support one-click application of templates to project groups; When a user operates a signature, 5 nodes are randomly selected to form a temporary ring, and the signature verification time is ensured to be within the preset threshold time.

7. The method according to claim 6, characterized in that The step of reconstructing a labeling network using multimodal query vectorization association for the conflict area, identifying unclosed links, and locating missing nodes includes: Dimensioning the conflicting area, converting the dimensioning into weighted directed edges, with nodes representing center points of geometric elements, and traversing the unclosed path using a depth-first search to locate the missing dimensioning position; Complete the missing annotation positions according to the adjacent dimension tolerances.

8. The method according to claim 7, characterized in that The method of completing the missing annotation position according to the adjacent dimension tolerance includes: Based on the upper and lower deviation ranges of adjacent dimensions and the maximum material conditions as constraints, ensure that the assembly clearance after the accumulation of all dimensional tolerances still meets the limit requirements. Calculate the theoretical upper limit of the theoretical closed dimension based on the maximum positive deviation, reversely deduce the limit tolerance range of the missing dimension, and use the limit tolerance range of the missing dimension to complete the missing marked position. or, The root mean square statistical model is used to confirm that the dimensional deviation conforms to the normal distribution. The standard deviation of all dimensions in the chain link is calculated, and the theoretical standard deviation of the closed dimension is calculated. Finally, the bilateral symmetric tolerance is generated, and the missing annotation positions are supplemented by the bilateral symmetric tolerance. or, The total amount of unclosed tolerance is evenly distributed to the relevant dimensions according to the dimension sensitivity weight, and then distributed to each ring according to the weight ratio. The missing annotation positions are completed according to the distribution results.

9. A drawing intelligent management device, characterized in that: The device comprises: Classification label generation module: This module receives drawings uploaded by user terminals, uses a pre-trained CNN-Transformer hybrid model to extract features from the uploaded drawings, and outputs three-dimensional classification labels including project type, design stage, and version status. Conflict detection module: used to monitor the three-dimensional classification labels through the operation log hash synchronizer deployed on the edge node, record the user's editing operations in real time and generate a SHA-3 hash sequence with a time and space stamp. Based on the SHA-3 hash sequence with a time and space stamp, it detects whether multiple users have concurrently modified the same area of ​​the drawing. If so, a conflict location algorithm based on hash value timestamp is used to obtain the conflict area and generate a conflict difference heat map. The image features of the conflict area are extracted and converted into retrieval input features. Search engine building module: used to build a hybrid search engine using pre-trained text conflict detection models, sketch conflict detection models, and 3D spatial relationship conflict detection models; Conflict relationship analysis module: used for inputting the retrieval input features into the hybrid retrieval engine, parsing text semantics through the text conflict detection model, extracting sketch contour features through the Fourier descriptor of the sketch conflict detection model, matching three-dimensional spatial relationships through the BIM topology subgraph of the three-dimensional spatial relationship conflict detection model, and the hybrid retrieval engine outputting the conflict area as one or more combinations of text conflict, sketch conflict, or three-dimensional spatial relationship conflict; Permission locking module: used to verify the permission allocation of the conflicting area, store the permission change record in the public chain, and store the permission change operation log in the private chain to ensure that the permission allocation is traceable and cannot be tampered with; Archiving management module: used to use multimodal query vectorization association to reconstruct the annotation network of the conflict area, identify unclosed chains and locate missing nodes, establish a tolerance transfer matrix model to predict assembly interference risks, and trigger cold storage archiving based on ErasureCoding to complete intelligent management of drawings when the conflict area has not been modified for a continuous preset period and passes the assembly interference risk check.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the main controller, each step of the drawing intelligent management method according to any one of claims 1 to 8 is implemented.

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