Building construction enterprise operation data management method and system

Abstract

The invention provides a building construction enterprise operation data management method and system, and relates to the field of operation data management, and the method comprises the steps: obtaining the operation data of a building construction enterprise, carrying out the recognition and classification of the nodes and types of the data, and carrying out the data division modeling, so as to obtain the distribution rule of the data at different nodes; according to a distribution rule of data at different nodes, generating a data abstract of each node, and aggregating the data abstract of each node to form a data global view; according to the data global view and a preset entity definition of building construction, entities are extracted, node importance scores of all the sub-maps are generated, the node importance scores of the same entities in all the sub-maps are integrated, and a global score is formed; and aligning the entities in each sub-atlas, extracting the relationship between the entities, and carrying out atlas combination to obtain a fused atlas. The method is used for overcoming the defect that a unified data view is difficult to form in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of business data management, and particularly to a business data management method and system for construction enterprises. Background Art

[0002] With the continuous development of big data technology, construction enterprises can collect, process, and analyze a large amount of business data, including aspects such as project cost, progress, quality, and safety, providing strong data support for the enterprise's decision-making. Information technologies such as cloud computing, the Internet of Things, and artificial intelligence also play an important role in the business data management of construction enterprises, enabling real-time collection, transmission, and processing of data, and improving the efficiency and accuracy of data management.

[0003] In the context of big data, the business decisions of construction enterprises increasingly rely on data. Enterprises use data analysis to gain insights into market trends, customer needs, and internal operations, so as to formulate more scientific and reasonable business strategies. With the help of information technology means, enterprises can monitor key data such as project progress and cost expenditure in real time, and conduct analysis and early warning, which helps enterprises discover and solve problems in a timely manner and reduce business risks.

[0004] Due to the business characteristics of the construction industry, data is often scattered in various projects, departments, and systems, making it difficult to form a unified data view, resulting in great difficulty in data governance and difficulty in ensuring data quality. Summary of the Invention

[0005] The present invention provides a business data management method and system for construction enterprises to solve the defect that in the prior art, data is often scattered in various projects, departments, and systems, making it difficult to form a unified data view.

[0006] On the one hand, the present invention provides a business data management method for construction enterprises, including: Obtain the business data of construction enterprises, and identify and classify the nodes and types of the data to obtain the classified data; According to the classified data, perform data distribution modeling to obtain the distribution law of the data at different nodes; According to the distribution law of the data at different nodes, generate data summaries for each node, and aggregate the data summaries of each node to form a global data view; According to the global data view and the preset entity definitions of construction, extract entities and generate node importance scores for each sub-graph, and integrate the node importance scores of the same entities in each sub-graph to form a global score; According to the global score, align the entities in each sub-graph, extract the relationships between entities and perform graph merging to obtain a merged graph; Based on the fused atlas, detect and correct the inconsistencies in the data in the atlas to obtain the corrected atlas; Construct a multi-dimensional data display interface according to the corrected atlas.

[0007] Furthermore, obtain the business data of construction enterprises, and identify and classify the nodes and types of the data to obtain the classified data, including: Obtain the business data of construction enterprises from different data sources, and the data sources include the project department management system, the financial department system, the supply chain management system, the human resources system, and the qualification management system; Obtain the organizational structure and business processes of construction enterprises, identify the nodes of business data, and assign a unique identifier to each node. The nodes include project departments, departments, and system modules; Classify the data of each node to obtain the classified data. The types include structured data, unstructured data, and real-time data.

[0008] Furthermore, according to the classified data, conduct data distribution modeling to obtain the distribution law of data at different nodes, including: Perform standardization processing on the classified data to obtain the standardized data; According to the standardized data, count the data volume, data update frequency, and field overlap degree at each node, and use a statistical model to construct a data distribution model; Analyze the data distribution model to obtain the distribution law of data at different nodes.

[0009] Furthermore, according to the distribution law of data at different nodes, generate data summaries for each node, and aggregate the data summaries of each node to form a global data view, including: Determine the data summary indicators for each node according to the data distribution law and analysis requirements; For each node, calculate the summary indicators according to the determined summary indicators, and organize the calculated summary indicators into data summaries; Aggregate each data summary to obtain the aggregated data summary; Organize the aggregated data summary into a global data view.

[0010] Furthermore, according to the global data view and the preset entity definitions of construction, extract entities and generate node importance scores for each sub-atlas, and integrate the node importance scores of the same entities in each sub-atlas to form a global score, including: Preset entity types according to the business characteristics and requirements of the construction field. The entity types include projects, tasks, resources, and departments; Extract entities that match the preset entity types from the data according to the global view of the data to obtain the extracted entities; Construct each sub-graph based on the extracted entities; For each node in each sub-graph, use Calculate the importance score of each node, where is each node, is each node is the importance score of is the set of nodes pointing to node , represents an edge from node u to node v, then it is said that node u points to node v, and node u belongs to the set M(v), outdeg is node is the out-degree of is the damping factor; Integrate the node importance scores of each node in different sub-graphs, and use to calculate the global score, where is the global score of entity , is entity in the -th sub-graph score, is the -th sub-graph weight, represents the number of sub-graphs, represents the sum of all terms from to .

[0011] Furthermore, according to the global score, align the entities in each sub-graph, extract the relationships between entities and perform graph merging to obtain the merged graph, including: According to the global score, use entity alignment technology to match the same or similar entities in different sub-graphs to obtain the matching result; According to the matching result and the preset alignment rules, perform entity alignment operations to align the same entities in different sub-graphs to obtain the aligned entities; According to the aligned entities, use relationship extraction technology to extract the relationships between entities from each sub-graph; According to the relationships between entities, perform graph merging to obtain the merged graph.

[0012] Furthermore, according to the merged graph, detect and correct the inconsistencies in the graph data to obtain the corrected graph, including: Based on the fused graph, calculate the hash value of the global unique identifier for the attributes of each entity, and compare whether the hash values from different sources are consistent to obtain the attribute detection result; Based on the fused graph, construct an entity relationship matrix and check for contradictory relationships to obtain the relationship detection result; Based on the fused graph, verify whether the graph conforms to a predefined pattern and count the proportion of isolated nodes to obtain the structure detection result; Based on the attribute detection result, relationship detection result, and structure detection result, perform correction and verification to obtain the corrected graph.

[0013] On the other hand, a business data management system for construction enterprises, characterized by comprising: An acquisition module, configured to acquire the business data of a construction enterprise, identify and classify the nodes and types of the data to obtain the classified data; A processing module, configured to perform data distribution modeling based on the classified data to obtain the distribution law of the data at different nodes; generate data summaries for each node according to the distribution law of the data at different nodes, and aggregate the data summaries of each node to form a global data view; extract entities and generate node importance scores for each sub-graph according to the global data view and the preset entity definitions of construction; integrate the node importance scores of the same entity in each sub-graph to form a global score; align the entities in each sub-graph according to the global score, extract the relationships between the entities and perform graph merging to obtain the fused graph; perform inconsistency detection on the data in the fused graph and perform correction to obtain the corrected graph; construct a multi-dimensional data display interface according to the corrected graph.

[0014] On the other hand, the present invention also provides a business data management system for construction enterprises, including an acquisition module and a processing module.

[0015] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, it implements the business data management method for construction enterprises as described in any one of the above.

[0016] On the other hand, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the business data management method for construction enterprises as described in any one of the above.

[0017] On the other hand, the present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the business data management method for construction enterprises as described in any one of the above.

[0018] The business data management method and system for construction enterprises provided by the present invention achieve cross-project and cross-department data association by breaking information silos, which brings significant beneficial effects. It improves the availability and value of data, enables data between different departments and projects to complement and verify each other, thus providing more comprehensive and accurate information. Data integration promotes communication and collaboration within the enterprise, enabling each department to make decisions and plans based on a common data foundation, reducing misunderstandings and conflicts caused by inconsistent information. Data integration also helps the enterprise discover new business opportunities and potential risks. By mining cross-project and cross-department data associations, the enterprise can gain insights into key information such as market trends and customer needs, providing strong support for strategic decision-making. Extracting entities and generating node importance scores for each sub-graph according to the global data view and the preset construction entity definition helps enhance the relevance between data, form a more complete and accurate knowledge graph. Integrating the node importance scores of the same entities in each sub-graph to form a global score and aligning and merging the sub-graphs based on this can significantly improve the quality and consistency of the graph. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0020] Figure 1 is a schematic flowchart of the business data management method for construction enterprises provided by an embodiment of the present invention; Figure 2 is a schematic diagram of the business data management system for construction enterprises provided by an embodiment of the present invention; Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0022] Figure 1 is one of the schematic flowcharts of the business data management method for construction enterprises provided by an embodiment of the present invention.

[0023] As Figure 1 shown, the business data management method for construction enterprises provided by the embodiments of the present invention mainly includes the following steps: 11. Obtain the business data of construction enterprises, identify and classify the nodes and types of the data to obtain the classified data; 12. According to the classified data, perform data distribution modeling to obtain the distribution law of the data at different nodes; 13. According to the distribution law of the data at different nodes, generate data summaries for each node, and aggregate the data summaries of each node to form a global data view; 14. According to the global data view and the preset entity definitions of construction, extract entities and generate node importance scores for each sub-graph, and integrate the node importance scores of the same entities in each sub-graph to form a global score; 15. According to the global score, align the entities in each sub-graph, extract the relationships between the entities and perform graph merging to obtain a merged graph; 16. According to the merged graph, detect and correct the inconsistencies in the data in the graph to obtain a corrected graph; 17. According to the corrected graph, construct a multi-dimensional data display interface.

[0024] In the embodiment of the present invention, all kinds of data generated by the construction enterprise during the operation process are comprehensively collected to provide a basis for subsequent analysis. By identifying and classifying data nodes and types, complex data are organized into an orderly structure to facilitate subsequent processing and analysis. For example, project data, personnel data, material data, etc. are classified separately to make data management clearer. The ordering of data improves the manageability and comprehensibility of data, provides a standardized data basis for subsequent data modeling and analysis, and reduces the complexity and error rate of data processing; for different types of data nodes, corresponding data models are established, and the distribution characteristics of data at different nodes are deeply mined to help enterprises understand the performance of data in various business links. For example, the distribution law of project progress data can reflect the execution efficiency of the project, revealing the inherent law of data at different nodes, providing data support for the decision-making of the enterprise, and predicting the change trend of data through the model, and discovering potential problems and opportunities in advance; the data of each node is refined and summarized to generate a concise and clear data summary, highlighting key information, and the data summary of each node is aggregated to form a comprehensive data global view, so that the enterprise can grasp the business status as a whole, provide intuitive data display, and facilitate the enterprise management to quickly understand the overall operation of the enterprise. The data global view helps to discover the relationship and mutual influence between data at different nodes. , providing a basis for cross-departmental collaboration and decision-making; extracting entities related to construction, such as projects, personnel, equipment, etc., from the global view of the data, by generating node importance scores for each sub-graph, evaluating the importance of each entity in different business dimensions, integrating the scores of the same entity, forming a global score, comprehensively reflecting the comprehensive importance of the entity in the entire enterprise operation, clarifying the status of the key entities of the enterprise in different business links, helping the enterprise to reasonably allocate resources, providing a quantitative reference basis for the enterprise's strategic planning and decision-making, and improving the scientificity and accuracy of decision-making; through entity alignment, eliminating the differences of the same entities in different sub-graphs, ensuring data consistency, and extracting the relationship between entities , build a complete entity relationship network to reflect the overall picture of the enterprise's business, merge sub-graphs to form a fused graph, provide a unified data view, improve the accuracy and consistency of data, avoid data conflicts and redundancy, and the fused graph provides enterprises with more comprehensive and in-depth business insights, which helps to discover new business opportunities and problems; detect data inconsistencies in the fused graph, such as attribute conflicts, relationship contradictions, etc., correct inconsistent data, ensure the correctness and reliability of graph data, improve the quality of graph data, and ensure the accuracy of subsequent analysis and decision-making. The corrected graph can more truly reflect the actual business situation of the enterprise and provide support for the refined management of the enterprise;The corrected atlas data is presented to users in an intuitive and understandable manner, providing multi-dimensional data views to support users in querying, analyzing, and visually displaying data according to different needs, improving data accessibility and usability, enabling enterprise management and employees to more conveniently access and utilize data. The multi-dimensional data display interface helps users deeply understand the enterprise's business situation, discover potential problems and trends, and provides strong support for the enterprise's decision-making.

[0025] As Figure 1 shown, 11, obtain the operation data of construction enterprises, and identify and classify the nodes and types of the data to obtain the classified data, including: 111. Obtain the operation data of construction enterprises from different data sources, where the data sources include project department management systems, financial department systems, supply chain management systems, and human resources systems; 112. Obtain the organizational structure and business processes of construction enterprises, identify the nodes of the operation data, and assign a unique identifier to each node. The nodes include project departments, departments, and system modules; 113. Classify the data of each node by type to obtain the classified data, where the types include structured data, unstructured data, and real-time data.

[0026] In the embodiments of the present invention, various business data of construction enterprises are comprehensively integrated to break data islands, enabling enterprises to obtain comprehensive information from multiple dimensions. Different data sources cover different aspects of enterprise operations, such as progress data of project departments, fund data of financial departments, material data of supply chain systems, and personnel data of human resources, etc., providing a rich data foundation for the comprehensive analysis and decision-making of enterprises; improving the integrity and accuracy of data, avoiding information loss or inconsistency caused by scattered data, providing sufficient data support for subsequent data analysis and mining, and helping to discover potential problems and opportunities in enterprise operations; deeply understanding the organizational structure and business processes of enterprises, clarifying the flow path and generation links of data within the enterprise, refining the business activities of enterprises into specific data units by identifying the nodes of business data, facilitating the management and analysis of data, assigning unique identifiers to each node to ensure the uniqueness and traceability of data in different systems and links, establishing a clear data management system, making the source and destination of data more clear, improving the efficiency of data management, providing a basis for data integration and sharing, facilitating data interaction and collaborative work between different departments and systems, and helping enterprises to optimize and reorganize business processes, and discovering bottlenecks and improvement points in business through the analysis of data nodes; classifying data at different nodes to facilitate the adoption of different processing and analysis methods according to the characteristics of data. Structured data is convenient for statistical analysis and modeling, unstructured data can mine text information and knowledge therein, and real-time data can reflect the latest operation status of enterprises, improving the pertinence and efficiency of data processing. Different types of data can be processed using the most suitable technologies and tools, providing a richer perspective for the data analysis and decision-making of enterprises. For example, business strategies can be adjusted in a timely manner by analyzing real-time data, and potential market demands and customer feedback can be discovered by mining unstructured data.

[0027] As Figure 1 shown in 12, according to the classified data, data distribution modeling is carried out to obtain the distribution law of data at different nodes, including: 121. Standardize the classified data to obtain the standardized data; 122. According to the standardized data, count the data volume, data update frequency, and field overlap degree at each node, and use a statistical model to construct a data distribution model; 123. Analyze the data distribution model to obtain the distribution law of data at different nodes.

[0028] In the embodiments of the present invention, by deeply exploring the characteristics and behavior patterns of the classified data on different nodes, a scientific basis is provided for the enterprise's data management and business decision-making, helping the enterprise understand the distribution of data in each business link, discover the imbalance and potential problems in the data distribution, reveal the internal laws of the data on different nodes, enabling the enterprise to more reasonably allocate resources and optimize the business process, providing a basis for subsequent data analysis, mining and application, and improving the utilization value of data and the accuracy of decision-making; eliminating the dimensional difference and format inconsistency problems of the data between different data sources and nodes, making the data comparable, improving the quality and consistency of the data, providing a reliable basis for subsequent data analysis and modeling, enabling the data on different nodes to be analyzed and compared under the same standard, avoiding analysis errors caused by different data formats and dimensions, simplifying the complexity of data processing and analysis, and improving the efficiency and accuracy of data processing; by statistically analyzing the key indicators of the data on each node, comprehensively understanding the distribution characteristics of the data, and using statistical models to construct a data distribution model, the distribution law of the data on different nodes can be more accurately described, providing quantitative data distribution information, such as the size of the data volume can reflect the importance of the node, the data update frequency can reflect the timeliness of the data, and the field overlap degree can reveal the data association degree between nodes; deeply mining the information contained in the data distribution model, revealing the distribution law and trend of the data on different nodes, providing strong support for the enterprise's data strategic planning and business decision-making, helping the enterprise better understand and utilize the data, clarifying the distribution characteristics of the data on different nodes, such as which nodes have concentrated data and which nodes have frequent data updates, etc., and helping the enterprise formulate targeted data management strategies.

[0029] As Figure 1 shown in 13, according to the distribution law of the data on different nodes, generate the data summary of each node, and aggregate the data summaries of each node to form a global data view, including: 131. Determine the data summary indicators of each node according to the data distribution law and analysis requirements; 132. For each node, calculate the summary indicators according to the determined summary indicators, and organize the calculated summary indicators into a data summary; 133. Aggregate each data summary to obtain the aggregated data summary; 134. Organize the aggregated data summary into a global data view.

[0030] In the embodiments of the present invention, complex data distribution rules are transformed into concise and clear data summaries, which facilitate enterprise management and decision-makers to quickly understand the key information of each node. By aggregating the data summaries of each node, a comprehensive global data view is formed, enabling the enterprise to grasp the overall operating conditions, discover the associations and mutual influences between different nodes, improve the readability and comprehensibility of the data, lower the threshold of data analysis, enable non-professionals to quickly obtain valuable information, provide strong support for the enterprise's strategic planning and decision-making, help the enterprise discover potential problems and opportunities, and optimize resource allocation and business processes; according to the data distribution characteristics and analysis objectives of different nodes, appropriate summary indicators are selected to ensure that the data summaries can accurately reflect the key information of the nodes, making the data summaries targeted and practical, meeting the needs of different users, improving the quality and effectiveness of the data summaries, avoiding information redundancy and unnecessary details, and providing a clear direction and standard for subsequent data summary calculation and collation; according to the determined summary indicators, the data of each node is calculated and analyzed, the key information is extracted, and the calculation results are sorted into standardized data summaries for subsequent aggregation and display; the core data of each node is obtained, providing a basis for the node-level decision-making of the enterprise. The standardized collation of the data summaries improves the consistency and comparability of the data, facilitating data comparison and analysis between different nodes; the data summaries of each node are integrated to form a comprehensive data set, reflecting the overall operating conditions of the enterprise. Through aggregation, data associations and trends between different nodes are discovered, providing support for the cross-departmental collaboration and overall decision-making of the enterprise; a more macroscopic data perspective is provided, enabling the enterprise to grasp the overall operating situation, discover potential problems and opportunities. The aggregated data summaries help the enterprise conduct comprehensive analysis and evaluation, improving the scientificity and accuracy of decision-making; the aggregated data summaries are presented in an intuitive and easy-to-understand manner to form a global data view, providing a comprehensive and clear data display platform for enterprise management and decision-makers, facilitating their data query, analysis and decision-making; the visualization degree of the data is improved, making the data more intuitive and vivid, facilitating user understanding and use. The global data view helps the enterprise discover the rules and trends in the data, providing strong support for the enterprise's strategic planning and business adjustment; Suppose there are three nodes (Node1, Node2, Node3), and each node has a data summary regarding a certain key field, including the data volume and the mean value. The specific data is as follows: Node1: Data volume , Node2: Data volume , Node3: Data volume , and the aggregated data volume is: , and the aggregated mean value is: , and the global data view is Total data volume: 5000, total mean 。

[0031] As Figure 1 shown in 14, according to the global view of data and the predefined entity definitions for building construction, entities are extracted and the node importance scores of each sub-graph are generated. The node importance scores of the same entities in each sub-graph are integrated to form a global score, including: 141. According to the business characteristics and requirements in the field of building construction, entity types are predefined, and the entity types include projects, tasks, resources, and departments; 142. According to the global view of data, entities that conform to the predefined entity types are extracted from the data to obtain the extracted entities; 143. According to the extracted entities, each sub-graph is constructed; 144. For each node in each sub-graph, use to calculate the importance score of each node, where is each node, is each node 's importance score, is the set of nodes pointing to node , represents an edge from node u to node v, then it is said that node u points to node v, and node u belongs to the set M(v), outdeg is node 's out-degree, is the damping factor; 145. Integrate the node importance scores of each node in different sub-graphs, and use to calculate the global score, where is the global score of entity , is the score of entity in the -th sub-graph, is the weight of the -th sub-graph, represents the number of sub-graphs, represents the sum of all terms from to .

[0032] In the embodiments of the present invention, the core entities that need to be concerned in the field of building construction are clarified, providing a basic framework for subsequent data extraction and graph construction, making data processing and analysis more targeted and professional, meeting the actual business needs of the building construction industry, improving the efficiency and accuracy of data processing, avoiding the interference of irrelevant data, providing a clear standard for entity recognition and classification in subsequent steps, and helping to construct more meaningful sub-graphs; according to the global data view, entities that meet the preset entity types are extracted from the data to obtain the extracted entities, and key entities related to building construction operations are screened out from the massive data, providing data support for subsequent graph construction, ensuring that the extracted entities accurately reflect the actual business situation, improving the quality and usability of the data, obtaining an entity set closely related to the business, providing a reliable data basis for subsequent analysis and decision-making, reducing the workload of data processing, and improving the efficiency of data processing; the extracted entities are classified and organized according to different business dimensions to form multiple sub-graphs, such as project-task sub-graphs, resource-department sub-graphs, etc., facilitating in-depth analysis and mining of different types of entity relationships, discovering potential business rules and problems, providing a clearer and more organized data display method, helping users better understand the relationships between data, and providing a structured data basis for subsequent node importance scoring and global scoring calculation; for each node in each sub-graph, the importance score of each node is calculated using the PageRank algorithm idea, evaluating the importance of each node in the sub-graph, considering the connection relationships between nodes and the importance of other nodes pointing to this node, helping to discover the key nodes in the sub-graph, providing a basis for the enterprise's resource allocation and decision-making, obtaining the quantitative importance score of each node, and making the comparison of node importance more objective and accurate. By analyzing the importance scores of nodes, the core nodes and potential influencing nodes in the sub-graph can be discovered, providing a direction for business optimization; comprehensively considering the performance of nodes in different sub-graphs, the global importance score of the nodes is obtained, reflecting the comprehensive status of the nodes in the entire building construction business, providing comprehensive data support for the enterprise's strategic planning and decision-making, and avoiding the limitations of a single sub-graph; Suppose there are two sub-graphs: project-task sub-graph and resource-department sub-graph. In the project-task sub-graph, the PageRank value of project A is 0.3. In the resource-department sub-graph, the degree centrality of project A (as the object of resource allocation) is 0.4. Suppose the weight of the project-task sub-graph is 0.6 and the weight of the resource-department sub-graph is 0.4. The global score calculation is as follows: Project A .

[0033] Such as Figure 1As shown in 15, according to the global score, align the entities in each sub-graph, extract the relationships between the entities, and merge the graphs to obtain the merged graph, including: 151. According to the global score, use entity alignment technology to match the same or similar entities in different sub-graphs to obtain the matching result; 152. According to the matching result and the preset alignment rules, perform entity alignment operations to align the same entities in different sub-graphs to obtain the aligned entities; 153. According to the aligned entities, use relationship extraction technology to extract the relationships between the entities from each sub-graph; 154. According to the relationships between the entities, perform graph merging to obtain the merged graph.

[0034] In the embodiment of the present invention, according to the global score, entity alignment technology is used to match the same or similar entities in different sub-graphs to obtain the matching result. Using the global score as a reference improves the accuracy and efficiency of entity matching, ensures that the same or similar entities can be correctly identified in different sub-graphs, provides a basis for subsequent entity alignment operations, guarantees the accuracy and reliability of alignment, obtains the matching result of the same or similar entities in different sub-graphs, provides a clear goal for subsequent alignment operations, improves the accuracy of entity alignment, and reduces the cases of mis-matching and missed-matching; according to the matching result and the preset alignment rules, perform entity alignment operations to align the same entities in different sub-graphs to obtain the aligned entities. According to the preset alignment rules, merge and align the same entities in the matching result, eliminate data redundancy, ensure that the aligned entities have consistent representations and attributes in different sub-graphs, improve data consistency, obtain the set of aligned entities, provide a unified data basis for subsequent relationship extraction and graph merging, improve the quality and usability of data, and avoid analysis errors caused by inconsistent entities; according to the aligned entities, use relationship extraction technology to extract the relationships between the entities from each sub-graph, mine the relationships between them from the aligned entities, enrich the information content of the graph, provide relationship data for graph merging, ensure that the merged graph can accurately reflect the business connections between entities, obtain the set of relationships between entities, provide richer semantic information for the construction of the graph, help discover potential business rules and associations, and provide more comprehensive support for the decision-making of enterprises; according to the relationships between the entities, perform graph merging to obtain the merged graph, integrate the entities and relationships in different sub-graphs to form a unified graph structure, improve the integrity and consistency of the graph, provide more powerful support for subsequent data analysis and applications, obtain the merged graph, and can more comprehensively and accurately display the business information in the field of building construction.

[0035] AsFigure 1 As shown in 16, based on the fused graph, perform inconsistency detection on the data in the graph and make corrections to obtain a corrected graph, including: 161. Based on the fused graph, calculate the hash value of the global unique identifier for the attributes of each entity, and compare whether the hash values from different sources are consistent to obtain the attribute detection result; 162. Based on the fused graph, construct an entity relationship matrix and check for contradictory relationships to obtain the relationship detection result; 163. Based on the fused graph, verify whether the graph conforms to the predefined pattern and count the proportion of isolated nodes to obtain the structure detection result; 164. Based on the attribute detection result, relationship detection result, and structure detection result, make corrections and validations to obtain a corrected graph.

[0036] In the embodiments of the present invention, according to the fused graph, for the attributes of each entity, calculate the hash value of its globally unique identifier, and compare whether the hash values from different sources are consistent to obtain the attribute detection result. By calculating the hash value of the globally unique identifier, the uniqueness and consistency of entity attributes are ensured, effectively detecting conflicts or inconsistencies in data from different sources at the attribute level, providing accurate positioning information for subsequent data correction, helping to identify which entity attributes need to be adjusted, quickly discovering data inconsistencies at the attribute level, such as different data sources recording different attributes for the same entity, improving data quality, ensuring the accuracy and reliability of entity attributes in the fused graph, and avoiding analysis deviations caused by attribute errors; according to the fused graph, construct an entity relationship matrix, and check whether there are contradictory relationships to obtain the relationship detection result. Constructing the entity relationship matrix can clearly display the relationship network between entities, facilitating the inspection of whether there are contradictory relationships, such as cyclic relationships, conflict relationships, etc., helping to discover unreasonable associations in the graph, ensuring the logic and correctness of entity relationships, accurately identifying contradictory relationships in the graph, avoiding incorrect conclusions caused by incorrect relationships in subsequent analysis and applications, enhancing the semantic consistency and credibility of the graph, and making the graph more accurately reflect the business logic in the construction field; according to the fused graph, verify whether the graph conforms to the predefined pattern, and count the proportion of isolated nodes to obtain the structure detection result. Verifying whether the graph conforms to the predefined pattern can ensure that the structure of the graph conforms to the expected business rules and data specifications. Counting the proportion of isolated nodes helps to discover possible data missing or connection problems in the graph. These isolated nodes may be manifestations of incomplete data collection or insufficient relationship mining, ensuring the rationality and standardization of the graph structure, making it conform to the data organization requirements in the construction field, and providing a direction for subsequent data supplementation and relationship improvement by identifying isolated nodes, improving the integrity and usability of the graph; according to the attribute detection result, relationship detection result, and structure detection result, perform correction and verification to obtain the corrected graph. Combining the previous detection results, comprehensively correct the attributes, relationships, and structures in the graph, solve the discovered data inconsistencies, contradictory relationships, and structure problems, and verify the corrected graph again to ensure the effectiveness and correctness of the correction operation, ensuring that the graph quality reaches a high standard, and obtaining a high-quality, accurate, and reliable corrected graph, which can more accurately reflect the actual business situation in the construction field.

[0037] As Figure 2 shown, a business data management system 20 for construction enterprises includes: An acquisition module 21, configured to acquire the business data of construction enterprises, and identify and classify the nodes and types of the data to obtain the classified data; A processing module 22, configured to perform data distribution modeling based on the classified data to obtain the distribution law of the data at different nodes; generate data summaries for each node according to the distribution law of the data at different nodes, and aggregate the data summaries of each node to form a global data view; extract entities and generate node importance scores for each sub-graph according to the global data view and the predefined entity definitions of building construction, integrate the node importance scores of the same entity in each sub-graph to form a global score; align the entities in each sub-graph according to the global score, extract the relationships between the entities and perform graph merging to obtain a fused graph; detect and correct the inconsistencies in the data in the graph according to the fused graph to obtain a corrected graph; and construct a multi-dimensional data display interface according to the corrected graph.

[0038] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0039] As Figure 3 shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communication bus 640. Among them, the processor 610, the communications interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the business data management method for building construction enterprises.

[0040] In addition, when the logic instructions in the above-mentioned memory 630 are implemented in the form of software functional units and sold or used as an independent product, they may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, or a part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program codes.

[0041] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the construction enterprise operation data management method provided by each of the above methods.

[0042] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the construction enterprise operation data management method provided by each of the above methods.

[0043] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0044] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solutions, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A management method for the business operation data of a construction enterprise, characterized in that, Including: Obtain the business data of construction enterprises, identify and classify the nodes and types of the data to obtain the classified data; According to the classified data, perform data distribution modeling to obtain the distribution law of the data at different nodes; According to the distribution law of the data at different nodes, generate data summaries for each node, and aggregate the data summaries of each node to form a global data view; According to the global data view and the preset entity definitions of construction, extract entities and generate node importance scores for each sub-graph, and integrate the node importance scores of the same entities in each sub-graph to form a global score; According to the global score, align the entities in each sub-graph, extract the relationships between entities and perform graph merging to obtain the merged graph; According to the merged graph, detect and correct the inconsistencies in the data in the graph to obtain the corrected graph; According to the corrected graph, construct a multi-dimensional data display interface.

2. The method for managing the business data of a construction enterprise according to claim 1, wherein Obtain the business data of construction enterprises, identify and classify the nodes and types of the data to obtain the classified data, including: Obtain the business data of construction enterprises from different data sources, and the data sources include project department management systems, financial department systems, supply chain management systems, human resources systems, and qualification management systems; Obtain the organizational structure and business processes of construction enterprises, identify the nodes of business data, and assign a unique identifier to each node. The nodes include project departments, departments, and system modules; Classify the data of each node by type to obtain the classified data. The types include structured data, unstructured data, and real-time data.

3. The method for managing the business data of a construction enterprise according to claim 2, wherein According to the classified data, perform data distribution modeling to obtain the distribution law of the data at different nodes, including: Perform standardization processing on the classified data to obtain the standardized data; According to the standardized data, count the data volume, data update frequency, and field overlap degree on each node, and use a statistical model to construct a data distribution model; Analyze the data distribution model to obtain the distribution law of the data at different nodes.

4. The method for managing the operation data of a construction enterprise according to claim 3, characterized in that, According to the distribution law of the data at different nodes, generate data summaries for each node, and aggregate the data summaries of each node to form a global data view, including: Determine the data summary indicators for each node according to the data distribution law and analysis requirements; For each node, calculate the summary indicators according to the determined summary indicators, and organize the calculated summary indicators into data summaries; Aggregate each data summary to obtain the aggregated data summary; Organize the aggregated data summary into a global data view.

5. The management method for the operation data of a construction enterprise according to claim 4, characterized in that According to the global data view and the preset entity definitions of construction, extract entities and generate node importance scores for each sub-graph, and integrate the node importance scores of the same entities in each sub-graph to form a global score, including: Preset entity types according to the business characteristics and requirements of the construction field. The entity types include projects, tasks, resources, and departments; Extract entities that meet the preset entity types from the data according to the global data view to obtain the extracted entities; Construct each sub-graph according to the extracted entities; For each node in each sub-graph, use to calculate the importance score of each node, where is each node, is each node 's importance score, is the set of nodes pointing to node ; represents an edge from node u to node v, then it is said that node u points to node v, and node u belongs to the set M(v), outdeg is the out-degree of node ; is the damping factor; Integrate the node importance scores of each node in different sub-graphs, and use to calculate the global score, where is the global score of entity , is the score of entity in the -th sub-graph, is the weight of the -th sub-graph, represents the number of sub-graphs, means summing all terms from to .

6. The method for managing the business operation data of a construction enterprise according to claim 5, characterized in that Align the entities in each sub-graph according to the global score, extract the relationships between entities and merge the graphs to obtain the merged graph, including: According to the global score, use entity alignment technology to match the same or similar entities in different sub-graphs to obtain the matching result; According to the matching result and the preset alignment rules, perform entity alignment operations to align the same entities in different sub-graphs to obtain the aligned entities; According to the aligned entities, use relationship extraction technology to extract the relationships between entities from each sub-graph; According to the relationships between entities, perform graph merging to obtain the merged graph.

7. The method for managing the operation data of a construction enterprise according to claim 6, wherein According to the merged graph, detect and correct the inconsistencies in the data in the graph to obtain the corrected graph, including: According to the merged graph, calculate the hash value of the global unique identifier for the attributes of each entity, and compare whether the hash values from different sources are consistent to obtain the attribute detection result; According to the merged graph, construct an entity relationship matrix and check for contradictory relationships to obtain the relationship detection result; According to the merged graph, verify whether the graph conforms to the predefined pattern and count the proportion of isolated nodes to obtain the structure detection result; According to the attribute detection result, relationship detection result, and structure detection result, perform corrections and validations to obtain the corrected graph.

8. An operating data management system for construction enterprises, characterized in that, Including: An acquisition module for acquiring the business data of construction enterprises, identifying and classifying the nodes and types of the data to obtain the classified data; A processing module for performing data distribution modeling based on the classified data to obtain the distribution law of the data at different nodes; generating data summaries for each node according to the distribution law of the data at different nodes, aggregating the data summaries of each node to form a global data view; extracting entities and generating the node importance scores of each sub-graph according to the global data view and the preset entity definitions of construction; integrating the node importance scores of the same entities in each sub-graph to form the global score; According to the global score, align the entities in each sub-graph, extract the relationships between entities and merge the graphs to obtain the merged graph; According to the merged graph, detect and correct the inconsistencies in the data in the graph to obtain the corrected graph; construct a multi-dimensional data display interface according to the corrected graph.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the construction enterprise business data management method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the construction enterprise business data management method according to any one of claims 1 to 7.

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