Construction drawing and cost drawing fusion method, device and system, and storage medium

Abstract

The application relates to a construction graph and cost graph fusion method, device and system and a storage medium. First, a collaborative embedding method is proposed. An initial semantic embedding is generated by combining a TF-IKGF algorithm and a FastText model, and then a graph convolution network is used to fuse the topological structure of the graph to generate a node vector representation with deep semantics and structural context. Subsequently, a two-stage unsupervised entity alignment strategy of cluster coarse screening and intra-cluster fine matching is adopted. The matching range is narrowed through K-Means clustering, and then accurate matching is carried out based on cosine similarity in the cluster to efficiently identify and align entity pairs. Finally, an automatic workflow containing artificial verification is used to integrate construction knowledge into the cost system according to business logic to complete the deep fusion of the knowledge graph. The application can automatically break through the knowledge link between construction and cost, construct a unified knowledge graph with clear logic and serve technical and economic integration analysis, and significantly improve the intelligent application level of knowledge in the building field.

Description

Technical Field

[0001] This invention relates to the field of building information technology and knowledge management, specifically to a method, apparatus, system, and storage medium for integrating construction drawings and cost estimates. Background Technology

[0002] With the deep application of information technology in the architecture, engineering, and construction (AEC) industry, effectively integrating the massive amounts of information throughout the entire project lifecycle has become crucial for improving the intelligence level of engineering management. In recent years, knowledge graph technology, due to its powerful semantic association and knowledge reasoning capabilities, has been widely used in constructing knowledge bases in the construction field, and scholars both domestically and internationally have achieved certain research results in this area. For example, Chen et al. focused on solving the alignment challenges of knowledge from different sources. They proposed a semi-supervised entity alignment method for heterogeneous architectural knowledge graphs, effectively improving the alignment accuracy between different data sources by introducing a small amount of manual annotation to guide model learning. Other research focuses on automatically constructing knowledge graphs from specific documents. For instance, Zhao et al. extracted key entities and event relationships from construction dispute texts to construct a knowledge graph aimed at providing intelligent decision support for contract and legal issues in projects. Domestically, related research has also made significant progress. For example, Wang et al. began exploring cross-domain knowledge graph entity alignment methods based on graph neural networks, providing new ideas for breaking down domain barriers by learning the representation of entities under different knowledge systems. Furthermore, Liu et al. focused on constructing knowledge graphs for specific scenarios such as construction safety. They systematically analyzed the causes, impacts, and preventative measures of safety risks, forming a professional knowledge base capable of assisting in risk early warning. These studies have promoted the structuring of knowledge in the construction field and successfully formed numerous professional and vertical knowledge systems, such as a construction knowledge system centered on construction techniques, technical specifications, and quality control, and a cost knowledge system centered on bill of quantities pricing specifications, quota items, and cost composition. The construction knowledge graph and cost knowledge graph processed in this application are typical examples of such professional knowledge bases with well-developed multi-level indicator hierarchical structures.

[0003] However, most existing research focuses on single-dimensional knowledge modeling, resulting in a long-standing separation between the two core knowledge systems of construction and cost estimation. Specifically, a knowledge graph describing construction techniques follows technical processes and causal relationships internally; while a knowledge graph describing engineering costs is organized according to pricing standards and cost structure. There is a natural "semantic gap" between the two in terms of conceptual granularity, descriptive methods, and organizational structure. For example, a specific "high-strength bolt connection process" in construction knowledge might be abstracted into a sub-item under the "steel structure fabrication and installation" quota in cost estimation knowledge, associated with corresponding labor, material, and machinery costs. This asymmetric, many-to-many complex mapping relationship makes traditional rule-based or surface text matching-based association methods unsustainable. Furthermore, existing research, when performing entity alignment, rarely considers the contextual information of knowledge nodes within their graph topology; that is, the complete semantics of a node is determined by its own attributes and neighboring nodes. Ignoring structural information and relying solely on text similarity for judgment easily leads to ambiguity and mismatches in alignment.

[0004] In summary, although research on architectural knowledge graphs in a single domain has reached a considerable scale, overcoming the semantic and structural barriers between different knowledge systems to achieve efficient, accurate, automated alignment and deep integration remains a weak link and a critical technical bottleneck that urgently needs to be overcome. Project managers and technicians still rely on personal experience to conduct time-consuming and laborious manual comparisons between the two knowledge systems. The problem of information silos severely restricts the realization of higher-level applications such as integrated cost and technology analysis and intelligent decision-making. Therefore, there is an urgent need at this stage for a novel knowledge graph fusion method that can simultaneously understand the deep semantics of nodes and the graph topology, in order to automatically and intelligently connect the knowledge links between construction and cost estimation, and construct a unified architectural engineering knowledge model. Summary of the Invention

[0005] To address the problems existing in the prior art, the purpose of this invention is to provide a method, apparatus, system, and storage medium for integrating construction drawings and cost estimates, which can automatically and intelligently connect the knowledge links between construction and cost estimates, and construct a unified knowledge model for building engineering.

[0006] The specific objectives are as follows: I. Constructing Domain-Adaptive Initial Semantic Embeddings: Based on the FastText pre-trained model, and combining TF (Term Frequency) and IKGF (Inverse Knowledge Graph Frequency) statistical features, a weighted initial semantic embedding is generated for each entity node. This aims to dynamically capture the core semantics of architectural engineering terminology and differentiate the importance of different entities within their respective knowledge systems.

[0007] II. Achieving Deep Node Representations with Integrated Topological Structures: A multi-layer graph convolutional network (GCN) with a normalized directed adjacency matrix as input is designed and applied. The goal is to effectively integrate the contextual structure information of nodes into their initial semantic embeddings through the neighborhood aggregation mechanism of graph neural networks, thereby generating a final node representation that combines deep semantics and topological relevance, improving the robustness and expressive power of vectors.

[0008] III. Proposing an efficient unsupervised hierarchical alignment strategy: To balance the efficiency and accuracy of entity alignment, this invention aims to adopt a hierarchical strategy of "coarse screening by clustering and fine matching within clusters." First, the K-Means clustering algorithm is used to quickly and unsupervisedly partition all final node embeddings of the two graphs to effectively reduce the search space for potential matches. Second, within each cluster, precise matching is performed using high-threshold cosine similarity calculation to ensure high confidence in the alignment results.

[0009] IV. Establish a Knowledge Integration Workflow: Design an automated process to integrate validated aligned entity pairs in the graph database. This goal is not only to connect nodes, but more importantly, to restructure the knowledge system based on the business logic that construction knowledge belongs to cost knowledge. By creating new hierarchical relationships and removing redundant nodes, a clearly structured integrated construction-cost knowledge graph is ultimately generated.

[0010] To achieve the above objectives, the present invention adopts the following technical solution: The method for integrating construction drawings and cost estimates includes the following steps: Based on a pre-trained word vector model and combined with statistical features representing the weights of domain terms, an initial semantic embedding vector is generated for each entity node in the construction drawing and cost drawing. Using a graph neural network, combined with the initial semantic embedding vector and the respective topological structures of the construction drawing and the cost drawing, a final node embedding vector that integrates structural information is learned and generated. To perform entity alignment, firstly, all final node embedding vectors are clustered using an unsupervised clustering algorithm. Then, within each cluster, alignment entity pairs from different graphs are identified based on similarity calculation. Based on aligned entity pairs, construction drawings and cost drawings are integrated in the graph database to establish new hierarchical relationships and generate an integrated knowledge graph.

[0011] Furthermore, the statistical characteristics representing the weight of domain terms are a combination of word frequency and inverse knowledge graph frequency.

[0012] Furthermore, graph neural networks are multi-layer graph convolutional networks.

[0013] Furthermore, the steps for generating the final node embedding vector include: taking the normalized adjacency matrix as input and applying a non-linear activation function after each convolutional layer.

[0014] Furthermore, the entity pair alignment step also includes a preprocessing sub-step: identifying entities with the same name in the construction drawings and cost drawings, and replacing their respective final node embedding vectors with a fusion vector, which is the arithmetic mean of the final node embedding vectors of entities with the same name.

[0015] Furthermore, the unsupervised clustering algorithm is the K-Means algorithm, and the similarity calculation within clusters is based on cosine similarity.

[0016] Furthermore, the fusion of construction drawings and cost drawings in the graph database includes: creating a new node that inherits the attributes of entity nodes in the construction drawings; establishing a hierarchical relationship between the new node and the corresponding entity node in the cost drawings; and deleting the original entity nodes and their associated relationships in the construction drawings.

[0017] The device for integrating construction drawings and cost estimates includes: The initial semantic embedding vector generation module is used to generate an initial semantic embedding vector for each entity node in the construction drawing and cost drawing based on a pre-trained word vector model and combined with statistical features representing the weights of domain terms. The terminal node embedding vector generation module is used to learn and generate a terminal node embedding vector that integrates structural information by using a graph neural network, combining the initial semantic embedding vector and the topological structure of the construction drawing and the cost drawing. The entity alignment module is used to first cluster all the final node embedding vectors using an unsupervised clustering algorithm, and then identify aligned entity pairs from different graphs within each cluster based on similarity calculation. The fusion module is used to merge construction drawings and cost drawings in a graph database based on aligned entity pairs, and establish new hierarchical relationships to generate an integrated knowledge graph.

[0018] The system for integrating construction drawings and cost estimates includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned integration method.

[0019] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned fusion method.

[0020] In summary, the present invention has the following advantages: I. A collaborative embedding method for heterogeneous semantics in the construction-cost domain is proposed: To address the challenge of knowledge alignment between construction knowledge descriptions (such as processes and procedures) and cost knowledge descriptions (such as bills of quantities), this invention applies the TF-IKGF algorithm to quantify the weights of domain terms and incorporates graph convolutional networks (GCNs) to integrate the topological information of the graph. This method can effectively capture and quantify the entity connotations under two different discourse systems, generating robust entity vector representations that are highly adapted to the domain.

[0021] II. Design an entity alignment strategy that adapts to many-to-one mapping relationships in the architectural field: To address the complex mapping issue where multiple specific knowledge points on the construction side may correspond to a single quota item on the cost side, this invention proposes a two-stage alignment method: "clustering for coarse screening and intra-cluster fine matching." This method efficiently aggregates potentially related construction and cost entities into the same matching space through unsupervised clustering, and then performs precise matching through intra-cluster similarity calculation. This strategy not only significantly improves the efficiency and accuracy of entity alignment in large-scale, highly complex building knowledge graphs, but also provides a clear candidate set for subsequent manual review.

[0022] Third, an automated knowledge fusion workflow driven by engineering business logic was constructed: This invention not only provides an alignment algorithm, but more importantly, establishes an end-to-end automated workflow from alignment to fusion. The fusion operation in this process is not a simple node merging, but rather, based on the core engineering logic that "construction knowledge is the concretization and supplement to cost items," it reconstructs the knowledge system in a graph database, thereby generating a new knowledge graph that is logically clear, adds value to knowledge, and can directly serve the integrated technical and economic analysis of construction schemes. Attached Figure Description

[0023] Figure 1 A flowchart illustrating a method for integrating construction drawings and cost estimates according to an embodiment of the present invention; Figure 2 A logical framework diagram of a system for integrating construction drawings and cost estimates, provided in one embodiment of the present invention; Figure 3 A schematic diagram illustrating the node representation learning process of the method and system for fusing construction drawings and cost estimates according to an embodiment of the present invention; Figure 4 A schematic diagram illustrating the TF-IKGF algorithm principle of a method and system for fusing construction drawings and cost estimates according to an embodiment of the present invention; Figure 5 A schematic diagram of graph convolutional network (GCN) neighborhood aggregation of a method and system for fusing construction drawings and cost estimates according to an embodiment of the present invention; Figure 6A flowchart illustrating the two-stage entity alignment strategy of the method and system for fusing construction drawings and cost estimates, as provided in an embodiment of the present invention. Figure 7 This is a schematic diagram illustrating the knowledge fusion process of a method and system for fusing construction drawings and cost estimates, provided in an embodiment of the present invention.

[0024] Figure 8 This is a structural block diagram of a device for integrating construction drawings and cost estimates, provided in one embodiment of the present invention.

[0025] Figure 9 This is a structural block diagram of a system for integrating construction drawings and cost estimates, provided in one embodiment of the present invention. Detailed Implementation

[0026] The present invention will now be described in further detail.

[0027] This invention proposes a method, apparatus, system, and storage medium for integrating construction drawings and cost estimates, aiming to provide a solution to the problems of fragmented knowledge, disconnect between construction technology and cost quota information, and low efficiency of knowledge utilization in the current construction engineering field. The overall research framework for this task is as follows: Figure 2 As shown. The specific implementation of this invention will elaborate on the entire process from the collection and processing of heterogeneous data sources, to the initial construction of two independent domain knowledge graphs, and finally to the high-quality fusion of the two graphs through an innovative entity alignment method that integrates deep learning and domain knowledge. The following will combine... Figure 1 and Figure 2 A detailed explanation will be provided.

[0028] Example 1: This embodiment provides a method for integrating construction drawings and cost estimates.

[0029] I. Data Preparation and Construction of Multi-level Indicator Knowledge Graph like Figure 1 As shown, step S100 in this embodiment involves constructing two independent, high-quality domain knowledge graphs: a construction engineering knowledge graph (hereinafter referred to as the "construction graph") and a construction engineering cost knowledge graph (hereinafter referred to as the "cost graph"). This process encompasses data collection, structured preprocessing, and the design and instantiation of the graph schema based on a multi-level indicator system.

[0030] 1. Data Collection and Sources The data in this embodiment mainly comes from two core knowledge systems in the field of construction engineering: Construction Knowledge Domain: This embodiment processes source data on construction knowledge, including information on construction techniques and operating procedures. The data is primarily collected from authoritative building construction codes, construction method standards, technical guidelines, and professional textbooks. This data describes the "how-to" question, encompassing specific construction techniques, operating procedures, key quality control points, material requirements, and machinery selection, among other technical knowledge.

[0031] Cost Knowledge Domain: This embodiment processes cost knowledge source data containing information such as quota items and quantity calculation rules. The data is primarily collected from national or local construction engineering budget quotas, bill of quantities pricing specifications, and related cost composition documents. This data describes the question of "how much money is spent," including economic knowledge such as the division of engineering projects, work content, quantity calculation rules, and pricing units.

[0032] The data collected in this embodiment are of various forms, mainly semi-structured text, which needs to be preprocessed before it can be used for map construction.

[0033] 2. Establishment of a multi-level indicator system and data structuring In order to systematically express complex architectural engineering knowledge, this embodiment introduces a multi-level index system to structure the collected data, which lays a solid foundation for subsequent map construction.

[0034] The multi-level indicator system of construction drawings: Referring to the construction knowledge source data table used in this invention, the knowledge in the construction drawings is organized into a hierarchical structure. For example, the top-level node can be "concrete engineering," under which there can be sub-nodes such as "formwork engineering," "reinforcement engineering," and "concrete preparation and pouring." Each sub-node can be further subdivided, such as "formwork engineering," which includes more specific knowledge nodes such as "formwork design," "formwork installation and removal," and "various types of formwork (such as wall formwork, beam formwork)." This hierarchical indicator system allows complex construction knowledge to be clearly expressed in a "general-specific" structure. In the source data, the "node data" table defines these entities, and the "relationship data" table connects these hierarchical and related entities through relationships such as "contains," forming a set of triples (Subject, Predicate, Object).

[0035] The cost estimation map employs a multi-level index system: Referring to the cost knowledge source data table used in this invention, the cost estimation map also utilizes multi-level indicators. Its system is closely integrated with the coding system of the bill of quantities pricing specification. For example, a top-level node could be "cast-in-place concrete engineering," under which, according to project coding rules, are sub-nodes such as "cast-in-place concrete foundation," "cast-in-place concrete column," and "cast-in-place concrete beam." Each sub-node is associated with specific attributes such as "project characteristics," "project name," "unit of measurement," and "quantity calculation rules." For instance, the node "cast-in-place concrete column" is associated with project characteristics such as "column shape (rectangular column, irregular column)," "concrete strength grade," and "concrete type," as well as its quantity calculation rule "calculated by volume according to the dimensions shown in the design drawings..." This structure tightly binds the pricing rules to the engineering entity.

[0036] Through the aforementioned multi-level indicator system, this embodiment transforms unstructured text knowledge into structured triplet data, providing high-quality data input for subsequent automated graph construction.

[0037] 3. Database instantiation of the graph In this embodiment, Neo4j is used as the graph database storage platform, and the processed construction triplet data and cost triplet data are imported into Neo4j to create two independent knowledge graphs.

[0038] Construction GraphSG: Nodes are assigned a unified "GraphSG" label and additional labels representing their specific types. For example, the source data contains the entity "concrete pouring," which contains specific construction points, such as entity A: "Non-self-compacting concrete must be poured and compacted in layers. The thickness of each layer depends on the vibration method." These entities and their hierarchical relationships are converted into triples, such as (concrete pouring, contains, entity A). This invention loads all such triples into a graph database (Neo4j) to form an initial construction knowledge graph.

[0039] GraphZJ (Concrete Cost Knowledge Graph): Nodes are assigned a unified "GraphZJ" label and additional labels representing their list items. For example, the source data contains the entity "Concrete Pouring and Vibration," which includes specific pricing or process requirements, such as entity B: "Requirements for Concrete Pouring and Vibration." These entities are also converted into triples and loaded into the graph database, forming an independent cost knowledge graph that coexists with the construction drawings.

[0040] Thus, this embodiment has yielded two independent domain knowledge graphs, each with its own internal knowledge structure, but which are not yet connected to each other. These graphs contain entities A and B that need to be aligned. This lays the foundation for subsequent entity alignment and knowledge fusion.

[0041] II. Node Representation Learning As shown in Figure 1 , in step S200, to achieve deep understanding and matching of heterogeneous knowledge, the present invention first needs to transform each entity node representing specific knowledge in the knowledge graph into a machine-computable mathematical vector rich in information, and this process is node representation learning. This embodiment proposes a two-stage representation method that integrates domain features and structural context, as shown in Figure 3 : first, through text preprocessing and the TF-IKGF algorithm, combined with a general word embedding model, an "initial semantic embedding" that can accurately capture the core connotation of professional terms in the construction industry is generated; then, a graph convolutional network (GCN) is used to further integrate the neighborhood structure information of the node into the embedding, and finally an "enhanced node embedding" with both deep semantics and topological correlation is output, laying a solid foundation for subsequent high-precision entity alignment.

[0042] 1. Text Preprocessing Since entity names in the knowledge graph are Chinese natural language texts, standard natural language processing (NLP) preprocessing is required first.

[0043] Chinese word segmentation: the "jieba" word segmentation library is used to segment the names (Name attribute) of all entity nodes in the two knowledge graphs. "jieba" can effectively process Chinese text and segment it into a series of tokens.

[0044] Stop word filtering: after word segmentation, to reduce noise, decrease computational load and highlight core semantics, this embodiment uses a predefined stop word list to filter out words that are ubiquitous but carry little semantic information.

[0045] Taking the name of entity A as an example, the token sequence obtained after word segmentation is: ["dui", "fei", "zi mi shi", "hun ning tu", "bi xu", "fen ceng", "jiao guan", "、", "fen ceng", "dao shi", "。", ...]. After removing stop words (such as "dui", "bi xu", "de"), core semantic tokens are retained: ["fei", "zi mi shi", "hun ning tu", "fen ceng", "jiao guan", "fen ceng", "dao shi",...]. Similarly, after processing the name of entity B, the core tokens obtained are: ["hun ning tu", "jiao zhu", "zhen dao", "yao qiu"] .

[0046] 2. Hybrid Weight Semantic Representation: Integrating Domain Knowledge and General Semantics Traditional entity alignment methods often only use the average of word vectors to represent entities, ignoring the difference in importance of different words in a specific domain knowledge system. This embodiment adopts a hybrid weight representation method to generate an initial embedding vector for each entity that contains both general semantics and reflects domain importance.

[0047] First is the basic semantic embedding: the FastText model.

[0048] The embodiment uses the FastText pre-trained word vector model proposed by Facebook AI Research in 2016 as the source of basic semantics. Compared with models such as Word2Vec and GloVe that rely on complete vocabulary for learning, the core innovation of FastText lies in its subword embedding mechanism. It treats each word as a collection of character n-grams and learns vector representations for these n-grams. The final vector of a word is the sum of all its character n-gram vectors. For example, for the word "concrete", in addition to the vector of the whole word, it also learns the vectors of subwords such as "混", "凝", "土", "混凝", "凝土".

[0049] The advantage of this mechanism is reflected in that in professional fields such as construction engineering, new terms and compound words (such as "post-pouring belt", "tongue-and-groove plate", "self-compacting concrete") emerge in an endless stream. For these Out-of-Vocabulary (OOV) words that may not appear in the general corpus, traditional models cannot generate effective vector representations. However, FastText can synthesize a high-quality vector representation for any out-of-vocabulary word through the known subword vectors that constitute it, which greatly enhances the model's representation ability and generalization for domain-specific terms. In this embodiment, each preprocessed token will be mapped to a 300-dimensional FastText vector.

[0050] Next is the domain knowledge weight: the TF-IKGF algorithm.

[0051] In order to measure the importance of a token in the current knowledge domain, the invention does not use the general TF-IDF, but adopts a TF-IKGF (Term Frequency - Inverse Knowledge Graph Frequency) algorithm that is more suitable for knowledge graph scenarios. The TF-IDF algorithm was originally designed for linear document collections. When directly applied to knowledge graphs with complex topological structures, the concept of "document" becomes ambiguous, and it is difficult to fully utilize the structural information of the graph.

[0052] Therefore, the TF-IKGF algorithm adopted in this embodiment makes adaptive improvements to this: Term Frequency (TF): This part is calculated in accordance with the standard TF and is used to measure the local importance of a term in a single entity name. The higher the frequency of a term appearing in an entity name, the higher its TF value. In entity A, "layering" appears twice, indicating that "layering" is a core concept describing this construction point. The larger its TF value, the better. The calculation formula is: ; in, It is a word element t In entity d The number of times the name appears, the denominator is the entity. d The total number of all terms in the name.

[0053] Inverse Knowledge Graph Frequency (IKGF): This is the core algorithm applied in this invention to adapt to the graph structure. In this embodiment, each triplet in the knowledge graph is treated as a mini-document because it encapsulates an atomic knowledge fact: if a term appears frequently in numerous triplets throughout the entire knowledge base (containing both construction and cost graphs), it indicates high prevalence and low distinguishability, and its importance should be correspondingly suppressed. The formula for calculating IKGF is as follows: ; in, df(t) represents the total number of triples in both the construction drawing and cost drawing datasets, and df(t) is the number of triples containing the term t. Logarithmic smoothing and the increment operation are used to prevent the denominator from being zero and to moderate the numerical scale. This design effectively suppresses the weight of terms that are very common in both datasets (such as "concrete" and "engineering"), while increasing the weight of keywords that only appear under specific topics (such as "slipform" and "list").

[0054] Finally, weighted vectorization is used to generate the initial entity embedding.

[0055] Combining general semantics and domain weights, the initial semantic embedding vector V for each entity node E is... E The weighted average is calculated using the following formula:

[0056] in, t It is a physical entity E morphemes in the name It is its FastText word vector. α and β These are hyperparameters used to adjust the relative importance of TF and IKGF (in this embodiment, they are set experimentally). α=1000, β =0.4), ϵ is a small smoothing factor (such as 0.1) to avoid weights being zero.

[0057] like Figure 4 As shown, this method yields the initial semantic embedding vectors of entities A and B. and These two vectors retain the rich semantics of the general language model while highlighting the professional characteristics of the construction engineering field through TF-IKGF weights.

[0058] 3. Structural and semantic fusion: Graph Convolutional Networks (GCN) An entity's semantic information alone is insufficient; its neighboring nodes in the graph (i.e., its context structure) also contain rich alignment cues. This embodiment employs a Graph Convolutional Network (GCN) to fuse the initial semantic embeddings of nodes with the graph's topological information, generating more discriminative "enhanced node embeddings." GCN, as a cutting-edge graph representation learning technique, can learn feature representations of nodes in the graph end-to-end.

[0059] like Figure 5 As shown, the core idea of ​​GCN originates from graph signal processing and message passing mechanisms. It treats each node in the graph as a signal, aggregating neighborhood information through convolution operations on the graph to update the node's own representation. The propagation rule of a single-layer GCN can be formalized as: ; in, It is an adjacency matrix with self-connections, and I is the identity matrix. yes The degree matrix. H (l) It is the first l The node feature matrix of the layer W (l) It is the trainable weight matrix of this layer. σ It is a non-linear activation function (such as ReLU). The essence of this formula is that the feature of a node in the next layer is a weighted average of its own features and the features of all its neighboring nodes in the current layer, with the weights determined by the graph structure, and then subjected to a linear transformation. W (l) ( ) and nonlinear activation.

[0060] Secondly, adjacency matrix construction and normalization are performed. Based on the triplet relationships in the construction and cost source data, this embodiment constructs adjacency matrix A for the construction map and cost map respectively.SG and A ZJ If a relationship exists between node i and node j, then the corresponding position A(i,j) in the matrix is ​​1; otherwise, it is 0. Since the graph is directed, this embodiment uses a simplified normalization method suitable for directed graphs: ,in D It is the out-degree matrix of the nodes. This row normalization method can be understood as each node passing its "information" evenly to all its outgoing neighbors, effectively preventing the scale explosion or vanishing problem of node features during the aggregation process.

[0061] Finally, GCN-enhanced node embedding is performed, and this invention constructs a deep model containing multiple GCN layers. The initial entity embedding matrix E, calculated using mixed weights, is... initial As input to GCN (i.e., H) (0) And it is fed into a multi-layered GCN model along with the adjacency matrix.

[0062] In the first convolutional layer, the vector V of entity A EA Generate the first hidden representation H EA (1) Next, entity A in the second convolutional layer, H EA (1) This further aggregates the features of its two-hop neighbors. Finally, the matrix containing entities is propagated through multiple layers of GCN and subjected to nonlinear activation (ReLU) to output the final enhanced node embedding matrix E. final .

[0063] It is worth noting that as the number of GCN layers increases, an over-smoothing problem may occur, where the representations of all nodes tend to converge to the same value, thus losing discriminative power. To mitigate this problem, this invention employs a residual-like connection in the GCN model, accumulating the output of each convolutional layer into the final result. The resulting enhanced node embedding is V. EA ′ This design not only allows the model to utilize both shallow (focusing more on local neighborhoods) and deep (focusing more on global structure) information simultaneously, but also effectively preserves the individual characteristics of nodes (including entity A). Similarly, the enhanced embedding V of entity B... EB ′ It also incorporates the structural information from the cost map. After GCN processing, the vector of each node contains its original semantics as well as the structural and semantic information of its multi-hop neighbors, greatly enhancing its expressive power.

[0064] III. Cross-graph entity alignment like Figure 1 As shown, in step S300, the present invention enters the cross-graph entity alignment stage, the core task of which is to accurately identify entity pairs that refer to the same concept or have strong correlations between two independent knowledge graphs. To balance the efficiency of large-scale graph processing with the accuracy of alignment results, this embodiment adopts a two-stage alignment strategy from coarse to fine. This strategy first uses an unsupervised clustering algorithm to embed all nodes in a vector space for rapid grouping, efficiently generating candidate matching sets; then, within each candidate set (cluster), fine-grained matching is performed through high-threshold similarity calculation, thereby filtering out highly reliable aligned entity pairs without relying on any manually labeled data.

[0065] 1. Semi-supervised clustering and candidate entity pair selection After obtaining high-quality node embeddings, this embodiment adopts a coarse-to-fine strategy to find aligned entities.

[0066] First, perform entity vector fusion with the same name (weakly supervised guidance).

[0067] Before performing fully unsupervised clustering, this embodiment introduces a simple heuristic rule as weak supervision. This rule is based on strong prior knowledge: if two entities have the exact same name in construction drawings and cost estimates, they are highly likely to refer to the same concept. Therefore, this embodiment first identifies all pairs of entities with the same name, and then arithmetically averages their respective enhanced embedding vectors generated by GCN, replacing their original vectors with this average vector. This step is an effective bootstrapping strategy, which forcibly narrows the distance between these highly aligned entities in the vector space, providing key "anchors" for subsequent clustering algorithms, enabling the clustering results to better group semantically related but not exactly identical entities together.

[0068] Secondly, candidate entity generation is based on clustering.

[0069] To avoid brute-force comparisons of all possible entity pairs (whose complexity is the product of the number of entities in the two graphs), this invention employs clustering as an efficient candidate generation (or "blocking") strategy, embedding all enhanced nodes of the two graphs after name-matching fusion (including V... EA ′ and V EB ′The data is then merged and partitioned within the same high-dimensional vector space using the unsupervised K-Means clustering algorithm. The goal of K-Means is to find K clusters and minimize the Within-Cluster Sum of Squares (WCSS) of all points within each cluster to the cluster centroid. ; in, C i It is the first i Clusters, x It is the embedding vector of a node in the cluster. μ i It is a cluster C i The algorithm iteratively performs two steps, "assignment" and "update," until convergence. Since the core semantics of entities A and B both point to "concrete pouring and vibration," their enhanced embedding vectors are spatially highly similar. Therefore, entities A and B have a very high probability of being assigned to the same cluster. This embodiment further assigns all nodes to... K Within each cluster, coarse-grained grouping of entities is achieved. Entities within the same cluster have high semantic and structural similarity, which greatly reduces the search space for subsequent precise matching. The choice of K value can be assisted by methods such as the Elbow Method; in this embodiment, a suitable K value was experimentally determined.

[0070] 2. Fine-grained alignment within clusters Within each cluster, the present invention performs a final step of fine alignment.

[0071] Cosine similarity calculation: For each cluster, including the clusters containing entities A and B, this embodiment calculates the cosine similarity between all nodes from the construction drawings and all nodes from the cost drawings within that cluster. The calculated similarity is... V EA ′ and V EB ′ Cosine similarity value: ; Cosine similarity measures the degree of proximity of two vectors in a direction. It is not sensitive to the absolute magnitude of the vectors and is very suitable for similarity judgment in high-dimensional semantic spaces.

[0072] Threshold filtering and matching pair generation: This invention sets a relatively high similarity threshold (e.g., 0.86 or higher). For each node in the construction drawing, within its cluster, it searches for the node in the cost drawing with the highest cosine similarity, and this similarity value is greater than the preset threshold. If found, this pair of entities ((construction node, cost node)) is output as a candidate alignment pair. That is, if Sim(E A E B If the value is higher than the preset threshold, the system determines that entity A and entity B constitute a valid aligned entity pair.

[0073] like Figure 6 The alignment strategy process shown above, through the above steps, automatically generates a high-confidence candidate alignment list including entity A and entity B.

[0074] IV. Validation of Knowledge Alignment and Integration with Knowledge Graphs like Figure 1 As shown, in step S400, the final stage of the method of the present invention is to finally confirm the knowledge alignment relationships generated in the preceding steps to construct a physically unified construction-cost integrated knowledge graph. To ensure the quality and practicality of the final result, this stage includes two core steps: a crucial human-machine collaborative verification process and automated database fusion implementation. First, a human reviews the candidate alignment list generated by the machine to eliminate potential incorrect matches and confirm the correct alignment relationships. Subsequently, as... Figure 7 The knowledge fusion process shown involves the automated fusion module performing atomic "create-link-delete" operations in the graph database based on this verified final list. This structured integration of independent construction knowledge nodes into the corresponding cost knowledge system completes the deep fusion of the two knowledge graphs.

[0075] 1. Manual verification While the aforementioned automated methods can efficiently discover a large number of potential entity alignments, 100% accuracy is difficult to guarantee due to the complexity of natural language and the specificity of domain knowledge. To ensure that the quality of the final fused knowledge graph meets production application standards, this invention incorporates a crucial human-in-the-loop verification step between automated alignment and database fusion: a human reviews the machine-generated candidate alignment list, including entities A and B, eliminating incorrect matches (e.g., entities that, although textually similar, actually refer to different construction stages or pricing rules), and confirming correct matches. This step ensures that the data input into the next fusion process is highly accurate. The final alignment list, after verification, is used to drive subsequent fusion operations.

[0076] This is the final step of the method of the present invention. The embodiment will be executed by an automated fusion module, which is responsible for physically fusing the verified alignment relationships in the graph database to ultimately form a unified knowledge graph that connects construction and cost.

[0077] 2. Establish database connection The fusion module first establishes a secure connection with the running Neo4j database instance.

[0078] 3. Traverse the alignment pairs and perform blending. The fusion module iterates through the manually validated set of entity pairs. For each aligned entity pair (containing entity A and entity B), the following atomic transaction is executed: Locating the parent node: In the cost estimate diagram, find the corresponding "parent" node based on its name. This node will serve as the input point for the merged knowledge.

[0079] Locating child nodes: In the construction drawings, find the corresponding "child" node based on its name.

[0080] Create a new node and migrate information: If both parent and child nodes are found, the system will create a new node in the database. The attributes of this new node (name, description, etc.) are completely copied from the construction node to be merged, such as entity A, but its label is specially set to GraphSGinZJ to identify its source and merging status.

[0081] Establish a new relationship: Between the parent node (cost node), such as entity B, and this newly created GraphSGinZJ node, establish a "contains" (or other custom) relationship. In this way, the previously independent construction knowledge is linked into the cost graph system as a detailed supplement to the cost knowledge.

[0082] Deleting old nodes: To avoid information redundancy and keep the graph clean, after successfully creating new nodes and relationships, the system will delete the original merged node, such as entity A, and all its associated relationships from the construction graph.

[0083] After execution, the previously independent construction point entity A was successfully and structurally integrated into the cost knowledge system, becoming a supplement to the specific technical details of the cost requirement entity B. This atomic operation of "create-link-delete" ensures the smooth transfer and integration of knowledge without disrupting the overall structure of the knowledge map.

[0084] 4. Establish top-level connections After all entity pairs have been merged, the merging module performs a final operation: creating a high-level association, such as SGcomplement (Construction Knowledge Supplement), between the root node of the construction drawing and the root node of the cost drawing. This establishes a connection between the two original knowledge systems at a macro level, allowing users to easily navigate to the knowledge of the other from the entry point of either drawing.

[0085] Through the above specific implementation methods, this invention fully demonstrates how to construct a unified knowledge graph that deeply integrates construction technology and engineering cost knowledge from scratch. This method effectively solves the problem of domain knowledge silos through innovative TF-IKGF algorithms, GCN-enhanced semantic structure representation, semi-supervised clustering alignment, and rigorous database fusion strategies. It provides a solid data foundation and technical path for realizing advanced applications in the construction engineering field, such as intelligent knowledge querying, intelligent question answering, cost prediction, and construction scheme optimization.

[0086] Example 2: like Figure 8 As shown, this embodiment provides a device for fusing construction drawings and cost estimates, including: The initial semantic embedding vector generation module 501 is used to generate an initial semantic embedding vector for each entity node in the construction drawing and cost drawing based on a pre-trained word vector model and combined with statistical features representing the weights of domain terms. The terminal node embedding vector generation module 502 is used to learn and generate a terminal node embedding vector that integrates structural information by using a graph neural network, combining the initial semantic embedding vector and the respective topological structures of the construction drawing and the cost drawing. The entity alignment module 503 is used to first cluster all the final node embedding vectors using an unsupervised clustering algorithm, and then identify aligned entity pairs from different graphs within each cluster based on similarity calculation. The fusion module 504 is used to merge construction drawings and cost drawings in the graph database based on aligned entity pairs, and establish new hierarchical relationships to generate an integrated knowledge graph.

[0087] The specific implementation of each module in this embodiment can be found in Embodiment 1 above, and will not be repeated here. It should be noted that the device provided in this embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0088] Example 3: like Figure 9As shown, this embodiment provides a system for integrating construction drawings and cost estimates, including a processor 602, a memory, an input device 603, a display 604, and a network interface 605 connected via a system bus 601. The processor 602 provides computing and control capabilities. The memory includes a non-volatile storage medium 606 and internal memory 607. The non-volatile storage medium 606 stores an operating system, computer programs, and a database. The internal memory 607 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium 606. When the computer programs are executed by the processor 602, the aforementioned racing car assisted driving method is implemented.

[0089] Example 4: This embodiment provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described racing car assisted driving method.

[0090] The storage medium described in this embodiment can be a disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), USB flash drive, portable hard drive, etc.

[0091] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for integrating construction drawings and cost estimates, characterized in that: Includes the following steps, Based on a pre-trained word vector model and combined with statistical features representing the weights of domain terms, an initial semantic embedding vector is generated for each entity node in the construction drawing and cost drawing. Using a graph neural network, combined with the initial semantic embedding vector and the respective topological structures of the construction drawing and the cost drawing, a final node embedding vector that integrates structural information is learned and generated. To perform entity alignment, firstly, all final node embedding vectors are clustered using an unsupervised clustering algorithm. Then, within each cluster, alignment entity pairs from different graphs are identified based on similarity calculation. Based on aligned entity pairs, construction drawings and cost drawings are integrated in the graph database to establish new hierarchical relationships and generate an integrated knowledge graph. The entity pair alignment step also includes a preprocessing sub-step: identifying entities with the same name in the construction drawings and cost drawings, and replacing their respective final node embedding vectors with a fusion vector, which is the arithmetic mean of the final node embedding vectors of the entities with the same name.

2. The fusion method according to claim 1, characterized in that: The statistical characteristics representing the weight of domain terms are a combination of word frequency and inverse knowledge graph frequency.

3. The fusion method according to claim 1, characterized in that: Graph neural networks are multi-layer graph convolutional networks.

4. The fusion method according to claim 1, characterized in that: The steps to generate the final node embedding vector include: taking the normalized adjacency matrix as input and applying a non-linear activation function after each convolutional layer.

5. The fusion method according to claim 1, characterized in that: The unsupervised clustering algorithm is the K-Means algorithm, and the similarity within clusters is calculated using cosine similarity.

6. The fusion method according to claim 1, characterized in that: The process of integrating construction drawings and cost estimates in a graph database includes: creating a new node that inherits the attributes of entity nodes in the construction drawings; establishing a hierarchical relationship between the new node and the corresponding entity nodes in the cost estimates; and deleting the original entity nodes and their associated relationships in the construction drawings.

7. A fusion apparatus for implementing the fusion method according to any one of claims 1-6, characterized in that, include: The initial semantic embedding vector generation module is used to generate an initial semantic embedding vector for each entity node in the construction drawing and cost drawing based on a pre-trained word vector model and combined with statistical features representing the weights of domain terms. The final node embedding vector generation module is used to learn and generate final node embedding vectors that integrate structural information by using graph neural networks, combining the initial semantic embedding vectors and the respective topological structures of the construction drawings and cost drawings. The entity alignment module is used to first cluster all the final node embedding vectors using an unsupervised clustering algorithm, and then identify aligned entity pairs from different graphs within each cluster based on similarity calculation. The fusion module is used to merge construction drawings and cost drawings in a graph database based on aligned entity pairs, and establish new hierarchical relationships to generate an integrated knowledge graph.

8. A system for integrating construction drawings and cost estimates, characterized in that, It includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the fusion method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the fusion method as described in any one of claims 1-6.

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