A construction enterprise operation data management method and system

Abstract

The application provides a kind of construction enterprise management data management method and system, it is related to the field of management data, including: obtaining construction enterprise management data, and the node and type of data are identified classification, data distribution modeling is carried out, to obtain the distribution law of data in different nodes;According to the distribution law of data in different nodes, the data summary of each node is generated, and the data summary of each node is aggregated to form a global view of data;According to the global view of data and the preset entity definition of construction, extract the entity and generate the node importance score of each sub-graph, integrate the node importance score of the same entity in each sub-graph to form a global score;Align the entity in each sub-graph, extract the relationship between entities and merge the graph to obtain the fused graph.The application is used to solve the defect that it is difficult to form a unified data view in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of business data management, and particularly relates to a construction enterprise business data management method and system. BACKGROUND

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

[0003] Under the background of big data, the business decisions of construction enterprises are increasingly dependent on data. Enterprises use data analysis to understand market trends, customer needs and internal operating conditions, and to develop more scientific and reasonable business strategies. With the help of information technology, enterprises can monitor key data such as project progress and cost expenditure in real time, analyze and warn, which helps enterprises to discover and solve problems in a timely manner and reduce operational 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 difficulties in data governance and difficulty in ensuring data quality. SUMMARY

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

[0006] In one aspect, the present application provides a construction enterprise business data management method, comprising:

[0007] Obtaining construction enterprise business data and identifying and classifying the nodes and types of the data to obtain classified data;

[0008] According to the classified data, data distribution modeling is performed to obtain the distribution rules of the data in different nodes;

[0009] According to the distribution rules of the data in different nodes, generate data summaries for each node, and aggregate the data summaries of each node to form a global data view;

[0010] According to the data global view and the preset entity definition of the construction, extract the entity and generate the node importance score of each sub-graph, integrate the node importance scores of the same entity in each sub-graph, and form a global score;

[0011] According to the global score, the entities in each sub-graph are aligned, the relationships between the entities are extracted, and the graphs are merged to obtain a fused graph;

[0012] According to the fused graph, inconsistency detection is performed on the data in the graph, and correction is performed to obtain a corrected graph;

[0013] According to the corrected graph, a multi-dimensional data display interface is constructed.

[0014] Further, the construction enterprise operation data is obtained, and the nodes and types of the data are identified and classified to obtain classified data, including:

[0015] The construction enterprise operation data is obtained from different data sources, including project department management system, financial department system, supply chain management system, human resource system and qualification management system;

[0016] The organization structure and business process of the construction enterprise are obtained, the nodes of the operation data are identified, and a unique identifier is assigned to each node, including project department, department, system module;

[0017] The data of each node is classified by type to obtain classified data, including structured data, unstructured data and real-time data.

[0018] Further, according to the classified data, data distribution modeling is performed to obtain the distribution rule of data in different nodes, including:

[0019] The classified data is standardized to obtain standardized data;

[0020] According to the standardized data, the data amount, data update frequency and field overlap degree of each node are calculated, and a statistical model is used to construct a data distribution model;

[0021] The data distribution model is analyzed to obtain the distribution rule of data in different nodes.

[0022] Further, according to the distribution rule of data in different nodes, the data summary of each node is generated, and the data summaries of each node are aggregated to form a global data view, including:

[0023] According to the data distribution rule and analysis requirements, the data summary indicators of each node are determined;

[0024] For each node, the summary indicators are calculated according to the determined summary indicators, and the calculated summary indicators are arranged into a data summary;

[0025] Each data summary is aggregated to obtain the aggregated data summary;

[0026] Organize the aggregated data summary into a global data view.

[0027] Furthermore, based on the global data view and the preset entity definitions for building construction, entities are extracted and node importance scores for each sub-map are generated. The node importance scores of the same entities in each sub-map are then integrated to form a global score, including:

[0028] Based on the business characteristics and needs of the construction industry, entity types are preset, including projects, tasks, resources, and departments;

[0029] Based on the global data view, extract entities that match the preset entity type from the data to obtain the extracted entities;

[0030] Based on the extracted entities, construct each sub-map;

[0031] For each node in each subgraph, use

[0032] ;

[0033] Calculate the importance score for each node, where, It is each node, It is each node Importance rating It points to a node The set of all nodes Indicates traversal A single source node at time, outdeg It is a node The degree of exit, It is the damping factor;

[0034] Integrate the node importance scores of each node in different subgraphs, and use

[0035] ;

[0036] The global score is calculated, where It is a physical entity The overall score It is a physical entity In the Scoring in individual graphs It is the first Weights of individual subgraphs Indicates the number of subgraphs. Indicates from arrive Summing all terms.

[0037] Further, according to the global score, the entities in each sub-graph are aligned, the relationships between entities are extracted and the graphs are merged to obtain the fused graph, including:

[0038] According to the global score, the same or similar entities in different sub-graphs are matched using entity alignment technology to obtain a matching result;

[0039] According to the matching result and the preset alignment rule, an entity alignment operation is performed to align the same entities in different sub-graphs to obtain aligned entities;

[0040] According to the aligned entities, a relationship extraction technology is used to extract the relationships between entities from each sub-graph;

[0041] According to the relationships between entities, the graphs are merged to obtain the fused graph.

[0042] Further, according to the fused graph, the data in the graph is detected for inconsistency and corrected to obtain a corrected graph, including:

[0043] According to the fused graph, the hash value of the global unique identifier of each entity's attribute is calculated, and the hash values of different sources are compared for consistency to obtain an attribute detection result;

[0044] According to the fused graph, an entity relationship matrix is constructed to check for contradictory relationships to obtain a relationship detection result;

[0045] According to the fused graph, it is verified whether the graph conforms to the predefined mode, and the proportion of isolated nodes is counted to obtain a structure detection result;

[0046] According to the attribute detection result, the relationship detection result and the structure detection result, the correction and verification are performed to obtain the corrected graph.

[0047] On the other hand, a construction enterprise operation data management system, characterized in that it comprises:

[0048] An acquisition module for acquiring construction enterprise operation data and identifying and classifying the nodes and types of the data to obtain classified data;

[0049] The processing module is configured to perform data distribution modeling according to the classified data to obtain distribution rules of the data in different nodes, generate data summaries of the nodes according to the distribution rules of the data in the different nodes, aggregate the data summaries of the nodes to form a global data view, extract entities and generate node importance scores of each sub-graph according to the global data view and preset entity definitions of the construction project, integrate the node importance scores of the same entities in the sub-graphs to form a global score, align the entities in the sub-graphs according to the global score, extract relationships between the entities and perform graph merging to obtain a fused graph, perform inconsistency detection on the data in the graph according to the fused graph and perform correction to obtain a corrected graph, and construct a multi-dimensional data display interface according to the corrected graph.

[0050] In another aspect, the present application also provides a construction enterprise operation data management system, comprising an acquisition module and a processing module.

[0051] In another aspect, the present application also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the construction enterprise operation data management method as described above when executing the program.

[0052] In another aspect, the present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the construction enterprise operation data management method as described above.

[0053] In another aspect, the present application also provides a computer program product comprising a computer program, wherein the computer program is executable by a processor to implement the construction enterprise operation data management method as described above.

[0054] The building construction enterprise operation data management method and system provided by the application, data integration breaks the information island, realizes cross-project and cross-department data correlation, which brings significant beneficial effects, improves the availability and value of data, enables data between different departments and projects to complement and verify each other, thereby providing more comprehensive and accurate information; data integration promotes communication and cooperation 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, and data integration also helps the enterprise discover new business opportunities and potential risks, by mining cross-project and cross-department data correlation, the enterprise can understand market trends, customer demand and other key information, providing strong support for strategic decision-making; according to the data global view and the preset building construction entity definition, the entity is extracted and the node importance score of each sub-graph is generated, which helps to enhance the correlation between data, form a more complete and accurate knowledge graph, integrate the node importance scores of the same entities in each sub-graph, form a global score, and align and merge the entities in each sub-graph according to the global score, which can significantly improve the quality and consistency of the graph. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0056] Figure 1 is a flowchart of the building construction enterprise operation data management method provided by the embodiment of the present application;

[0057] Figure 2 is a schematic diagram of the building construction enterprise operation data management system provided by the embodiment of the present application;

[0058] Figure 3 is a structural schematic diagram of an electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely in the following combined with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0060] Figure 1Figure 1 is one of process schematic diagrams of the building construction enterprise operation data management method provided by the embodiment of the present application.

[0061] As shown in Figure 1 The building construction enterprise operation data management method provided by the embodiment of the present application mainly includes the following steps:

[0062] 11. Obtain the building construction enterprise operation data, and identify and classify the nodes and types of the data to obtain the classified data;

[0063] 12. According to the classified data, perform data distribution modeling to obtain the distribution rules of the data in different nodes;

[0064] 13. According to the distribution rules of the data in different nodes, generate the data summary of each node, aggregate the data summaries of each node, and form a data global view;

[0065] 14. According to the data global view and the preset entity definition of the building construction, extract the entity and generate the node importance score of each sub-graph, integrate the node importance scores of the same entity in each sub-graph, and form a global score;

[0066] 15. According to the global score, align the entity in each sub-graph, extract the relationship between the entities, and perform graph merging to obtain a fused graph;

[0067] 16. According to the fused graph, perform inconsistency detection on the data in the graph and perform correction to obtain a corrected graph;

[0068] 17. According to the corrected graph, construct a multi-dimensional data display interface.

[0069] In the embodiments of the present application, all kinds of data generated by the construction enterprise in the operation process are comprehensively collected to provide a basis for subsequent analysis. Through identification and classification of data nodes and types, complex data is arranged into an orderly structure, facilitating subsequent processing and analysis. For example, project data, personnel data, and material data are classified respectively, making data management clearer, and improving data order, data manageability, and data comprehensibility, providing a standardized data basis for subsequent data modeling and analysis, and reducing data processing complexity and error rate. For different types of data nodes, corresponding data models are established to deeply mine the distribution characteristics of data at different nodes, helping the enterprise understand the performance of data in each business link. For example, the distribution rule of project progress data can reflect the execution efficiency of the project, revealing the internal rule of data at different nodes, providing data support for enterprise decision-making. Through the model, the change trend of data can be predicted, and potential problems and opportunities can be discovered in advance. The data of each node is refined and summarized to generate a concise and clear data summary, highlighting key information. The data summaries of each node are aggregated to form a comprehensive data global view, enabling the enterprise to grasp the overall business situation, providing an intuitive data display, and facilitating the enterprise management to quickly understand the overall operation of the enterprise. The data global view helps to discover the correlation and mutual influence between different node data, providing a basis for cross-department cooperation and decision-making. From the data global view, entities related to construction, such as projects, personnel, and equipment, are extracted. The importance of each entity in different business dimensions is evaluated by generating node importance scores of each sub-graph. The scores of the same entity are integrated to form a global score, comprehensively reflecting the comprehensive importance of the entity in the entire enterprise operation, and clarifying the position of key entities in different business links, which helps the enterprise to reasonably allocate resources and provides a quantitative reference for the enterprise's strategic planning and decision-making, improving the scientificity and accuracy of decision-making. Through entity alignment, the differences of the same entity in different sub-graphs are eliminated to ensure data consistency. The relationship between entities is extracted to build a complete entity relationship network, reflecting the overall business of the enterprise. The sub-graphs are merged to form a fused graph, providing a unified data view, improving data accuracy and consistency, avoiding data conflicts and redundancy, and providing the enterprise with more comprehensive and in-depth business insights, which helps to discover new business opportunities and problems. The inconsistent data in the fused graph, such as attribute conflicts and relationship contradictions, is detected and corrected to ensure the correctness and reliability of the graph data, improve the quality of the 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, providing support for the fine management of the enterprise.The revised atlas data is displayed to the user in an intuitive and easy-to-understand manner, providing multi-dimensional data views to support users in data querying, analysis and visualization according to different needs, improving data accessibility and usability, making it more convenient for enterprise management and employees to access and utilize data, and the multi-dimensional data display interface helps users to deeply understand the business status of the enterprise, discover potential problems and trends, and provide strong support for enterprise decision-making.

[0070] As shown in Figure 1 11, the operating data of the construction enterprise is obtained, and the nodes and types of the data are identified and classified to obtain classified data, including:

[0071] 111, obtaining the operating data of the construction enterprise from different data sources, the data sources including a project department management system, a financial department system, a supply chain management system, and a human resource system;

[0072] 112, obtaining the organizational structure and business process of the construction enterprise, identifying the nodes of the operating data, and assigning a unique identifier to each node, the nodes including a project department, a department, and a system module;

[0073] 113, classifying the data of each node by type to obtain classified data, the types including structured data, unstructured data, and real-time data.

[0074] In the embodiments of the present application, various types of operating data of the construction enterprise are comprehensively integrated, data islands are broken, the enterprise can obtain comprehensive information from multiple dimensions, different data sources cover different aspects of enterprise operation, such as progress data of the project department, financial data of the financial department, material data of the supply chain system, and personnel data of the human resources, etc., providing rich data basis for comprehensive analysis and decision-making of the enterprise; the data integrity and accuracy are improved, information loss or inconsistency caused by data dispersion is avoided, sufficient data support is provided for subsequent data analysis and mining, which helps to find potential problems and opportunities in enterprise operation; the organizational structure and business process of the enterprise are deeply understood, the flow path and production link of data in the enterprise are clear, the business activities of the enterprise are refined into specific data units by identifying the nodes of operating data, which is convenient for data management and analysis, a unique identifier is assigned to each node to ensure the uniqueness and traceability of data in different systems and links, a clear data management system is established, the source and destination of data are more clear, the data management efficiency is improved, which provides a basis for data integration and sharing, facilitates data interaction and collaborative work between different departments and systems, and helps the enterprise to optimize and reorganize business processes by analyzing data nodes to find bottlenecks and improvement points in business; the data of different nodes are classified, which is convenient for using 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, real-time data can reflect the latest operation status of the enterprise, which improves the pertinence and efficiency of data processing, different types of data can be processed by the most suitable technology and tool, which provides a richer perspective for data analysis and decision-making of the enterprise, for example, real-time data can be analyzed to adjust business strategy in time, unstructured data can be mined to find potential market demand and customer feedback.

[0075] As shown in Figure 1 12, according to the classified data, data distribution modeling is performed to obtain the distribution rule of data in different nodes, including:

[0076] 121, the classified data is standardized to obtain standardized data;

[0077] 122, according to the standardized data, the data amount, data update frequency, and field overlap degree of each node are counted, and a data distribution model is constructed using a statistical model;

[0078] 123, the data distribution model is analyzed to obtain the distribution rule of data in different nodes.

[0079] In the embodiments of the present application, the characteristics and behavior patterns of the classified data on different nodes are deeply explored, scientific basis is provided for data management and business decision of enterprises, and the enterprises are helped to understand the distribution of data in various business links, find the imbalance of data distribution and potential problems, reveal the internal law of data on different nodes, so that the enterprises can more reasonably allocate resources and optimize business processes, provide a basis for subsequent data analysis, mining and application, improve the utilization value of data and the accuracy of decision-making; the dimensional differences and inconsistent formats of data between different data sources and nodes are eliminated, the data is made comparable, the quality and consistency of data are improved, a reliable basis is provided for subsequent data analysis and modeling, so that the data on different nodes can be analyzed and compared under the same standard, the analysis errors caused by different data formats and dimensions are avoided, the complexity of data processing and analysis is simplified, and the efficiency and accuracy of data processing are improved; by statistically analyzing the key indicators of data on each node, the distribution characteristics of data are comprehensively understood, a data distribution model is constructed by using a statistical model, the distribution law of data on different nodes can be more accurately described, and quantitative data distribution information is provided, such as the size of data quantity 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 correlation degree between nodes; the information contained in the data distribution model is deeply mined, the distribution law and trend of data on different nodes are revealed, strong support is provided for data strategic planning and business decision of enterprises, and the enterprises are helped to better understand and utilize data, and the distribution characteristics of data on different nodes are clarified, such as which nodes have concentrated data and which nodes have frequent data updates, which is helpful for enterprises to develop data management strategies.

[0080] As shown in Figure 1 According to the distribution law of data on different nodes, data summaries of each node are generated, and the data summaries of each node are aggregated to form a data global view, including:

[0081] 131、According to the data distribution law and analysis requirements, determine the data summary indicators of each node;

[0082] 132、For each node, calculate the summary indicators according to the determined summary indicators, and arrange the calculated summary indicators into data summaries;

[0083] 133、Aggregate each data summary to obtain an aggregated data summary;

[0084] 134、Arrange the aggregated data summary into a data global view.

[0085] In the embodiments of the present application, the complex data distribution rule is converted into a concise and clear data summary, which facilitates enterprise management and decision-makers to quickly understand the key information of each node. By aggregating the data summaries of each node, a comprehensive data global view is formed, enabling the enterprise to grasp the overall business situation, discover the correlation and mutual influence between different nodes, improve the readability and understandability of data, and reduce the threshold of data analysis, so that non-professionals can also quickly obtain valuable information, providing strong support for the strategic planning and decision-making of the enterprise, helping the enterprise to discover potential problems and opportunities, and optimizing resource allocation and business processes. According to the data distribution characteristics and analysis targets of different nodes, appropriate summary indicators are selected to ensure that the data summary accurately reflects the key information of the node, making the data summary targeted and practical, meeting the needs of different users, improving the quality and effectiveness of the data summary, avoiding information redundancy and unnecessary details, and providing a clear direction and standard for subsequent data summary calculation and arrangement. According to the determined summary indicators, the data of each node is calculated and analyzed, and the key information is extracted, and the calculation results are arranged into a standard data summary, which is convenient 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 arrangement of data summary improves the consistency and comparability of data, facilitating the comparison and analysis of data between different nodes. The data summaries of each node are integrated to form a comprehensive data set, reflecting the overall operation of the enterprise. Through aggregation, the data correlation and trends between different nodes are discovered, providing support for cross-department collaboration and overall decision-making of the enterprise. A more macro data perspective is provided, enabling the enterprise to grasp the overall business situation and discover potential problems and opportunities. The aggregated data summary helps the enterprise to conduct comprehensive analysis and evaluation, improving the scientificity and accuracy of decision-making. The aggregated data summary is displayed in an intuitive and easy-to-understand manner, forming a data global 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 of data is improved, making data more intuitive and visual, facilitating user understanding and use. The data global view helps the enterprise to discover the rules and trends in data, providing strong support for the strategic planning and business adjustment of the enterprise.

[0086] Suppose there are three nodes (Node1, Node2, Node3), each node has a data summary about a certain key field, including data volume and mean value, the specific data is as follows: Node1: data volume , Node2: data volume , Node3: data volume , data volume aggregation is: , mean value aggregation is: , data global view is Total data volume: 5000, overall mean .

[0087] like Figure 1 As shown in Figure 14, based on the global data view and the preset entity definitions for building construction, entities are extracted and node importance scores for each sub-map are generated. The node importance scores of the same entities in each sub-map are then integrated to form a global score, including:

[0088] 141. Based on the business characteristics and needs of the construction industry, preset entity types, including projects, tasks, resources, and departments;

[0089] 142. Based on the global data view, extract entities that match the preset entity type from the data to obtain the extracted entities;

[0090] 143. Construct sub-maps based on the extracted entities;

[0091] 144. For each node in each subgraph, use

[0092] ;

[0093] Calculate the importance score for each node, where, It is each node, It is each node Importance rating It points to a node The set of all nodes Indicates traversal A single source node at time, outdeg It is a node The degree of exit, It is the damping factor;

[0094] 145. Integrate the node importance scores of each node in different sub-graphs, and use...

[0095] ;

[0096] The global score is calculated, where It is a physical entity The overall score It is a physical entity In the Scoring in individual graphs It is the first Weights of individual subgraphs Indicates the number of subgraphs. Indicates from arrive Summing all the terms.

[0097] In the embodiments of the present application, the core entities in the field of building construction are clearly identified, 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 irrelevant data interference, providing a clear standard for entity recognition and classification in subsequent steps, and helping to build more meaningful sub-graphs; according to the global view of data, entities conforming to the preset entity type are extracted from the data to obtain the extracted entities, and the key entities related to the building construction business are selected from the massive data to provide 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, and 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 and resource-department sub-graphs, which facilitate 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, and helping users better understand the relationship between data, providing a structured data basis for subsequent node importance scoring and global scoring calculation; for each node in each sub-graph, the PageRank algorithm is used to calculate the importance score of each node to evaluate the importance of each node in the sub-graph, considering the connection relationship between nodes and the importance of other nodes pointing to the node, which helps to find the key nodes in the sub-graph and provides a basis for resource allocation and decision-making of enterprises, obtaining the quantitative importance score of each node, making the importance comparison of nodes more objective and accurate. By analyzing the importance score of the node, the core node and the potential impact node in the sub-graph can be found, providing a direction for business optimization; the global importance score of the node is obtained by considering the performance of the node in different sub-graphs, reflecting the comprehensive position of the node in the entire building construction business, providing comprehensive data support for the strategic planning and decision-making of enterprises, avoiding the limitations of a single sub-graph;

[0098] 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, assuming 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: Project A .

[0099] For example Figure 1As shown, 15, according to the global score, align the entities in each sub-graph, extract the relationship between entities and perform graph merging to obtain the fused graph, including:

[0100] 151、According to the global score, use entity alignment technology to match the same or similar entities in different sub-graphs to obtain a matching result;

[0101] 152、According to the matching result and the preset alignment rule, perform entity alignment operation to align the same entities in different sub-graphs to obtain aligned entities;

[0102] 153、According to the aligned entities, use relationship extraction technology to extract the relationship between entities from each sub-graph;

[0103] 154、According to the relationship between entities, perform graph merging to obtain the fused graph.

[0104] In the embodiment of the present application, according to the global score, the same or similar entities in different sub-graphs are matched using entity alignment technology to obtain a matching result, the global score is used as a reference to improve the accuracy and efficiency of entity matching, ensuring that the same or similar entities can be correctly identified in different sub-graphs, providing a basis for subsequent entity alignment operations, ensuring the accuracy and reliability of alignment, obtaining the matching result of the same or similar entities in different sub-graphs, providing a clear target for subsequent alignment operations, improving the precision of entity alignment, and reducing the cases of false matching and missed matching; according to the matching result and the preset alignment rule, the entity alignment operation is performed to align the same entities in different sub-graphs to obtain the aligned entities, the same entities in the matching result are merged and aligned according to the preset alignment rule, eliminating data redundancy, ensuring that the aligned entities have consistent representation and attributes in different sub-graphs, improving the consistency of data, obtaining the aligned entity set, providing a unified data basis for subsequent relationship extraction and graph merging, improving the quality and usability of data, and avoiding analysis errors caused by inconsistent entities; according to the aligned entities, the relationship extraction technology is used to extract the relationships between entities from each sub-graph, and the relationships between the aligned entities are mined, enriching the information content of the graph, providing relationship data for graph merging, ensuring that the merged graph can accurately reflect the business relationship between entities, obtaining the relationship set between entities, providing more rich semantic information for the construction of the graph, helping to discover potential business rules and associations, and providing more comprehensive support for enterprise decision-making; according to the relationships between entities, the graph is merged to obtain the fused graph, the entities and relationships in different sub-graphs are integrated to form a unified graph structure, improving the integrity and consistency of the graph, providing stronger support for subsequent data analysis and application, and obtaining the fused graph, which can more comprehensively and accurately display the business information in the field of building construction.

[0105] As shown in Figure 1 , 16, according to the fused graph, the inconsistency of the data in the graph is detected and corrected to obtain the corrected graph, including:

[0106] 161, according to the fused graph, the hash value of the global unique identifier of the attribute of each entity is calculated, and whether the hash values of different sources are consistent is compared to obtain an attribute detection result;

[0107] 162, according to the fused graph, an entity relationship matrix is constructed, and whether there is a contradictory relationship is checked to obtain a relationship detection result;

[0108] 163, according to the fused graph, whether the graph conforms to the predefined mode is verified, and the proportion of isolated nodes is counted to obtain a structure detection result;

[0109] 164. Based on the attribute detection results, relationship detection results, and structure detection results, make corrections and verifications to obtain the corrected map.

[0110] In this embodiment of the invention, based on the fused graph, the hash value of the globally unique identifier of each entity's attributes is calculated. The hash values ​​from different sources are compared to determine if they are consistent, thus obtaining attribute detection results. By calculating the hash value of the globally unique identifier, the uniqueness and consistency of entity attributes are ensured. This effectively detects conflicts or inconsistencies at the attribute level in data from different sources, providing accurate location information for subsequent data correction. It helps identify which entity attributes need adjustment and can quickly discover data inconsistencies at the attribute level, such as different data sources recording the same entity attribute differently. This improves data quality, ensures the accuracy and reliability of entity attributes in the fused graph, and avoids analytical biases caused by attribute errors. Based on the fused graph, an entity relationship matrix is ​​constructed to check for contradictory relationships, thus obtaining relationship detection results. Constructing an entity relationship matrix clearly displays the relationship network between entities, facilitating the checking of contradictory relationships, such as cyclical relationships and conflicting relationships. This helps discover unreasonable associations in the graph, ensuring the logic and correctness of entity relationships. It can accurately identify contradictory relationships in the graph, avoiding erroneous conclusions due to incorrect relationships in subsequent analysis and applications, and improving the semantic consistency and credibility of the graph. The process involves three main steps: First, the graph is optimized to more accurately reflect the business logic of the construction industry. Second, the graph is integrated to verify its conformity to a predefined pattern and to statistically analyze the proportion of isolated nodes. This ensures the graph's structure meets expected business rules and data specifications. Analyzing the proportion of isolated nodes helps identify potential data gaps or connectivity issues, which may indicate incomplete data collection or insufficient relationship mining. This ensures the graph's structure is reasonable and standardized, meeting the data organization requirements of the construction industry. Identifying isolated nodes provides direction for subsequent data supplementation and relationship improvement, enhancing the graph's completeness and usability. Third, based on the attribute, relationship, and structural detection results, corrections and verifications are performed to obtain a revised graph. Combining the previous detection results, the graph's attributes, relationships, and structure are comprehensively corrected to resolve inconsistencies, contradictory relationships, and structural problems. The revised graph is then verified again to ensure the effectiveness and correctness of the correction process, guaranteeing a high standard of graph quality. The result is a high-quality, accurate, and reliable revised graph that more accurately reflects the actual business situation in the construction industry.

[0111] like Figure 2 As shown, a construction enterprise operation data management system 20 includes:

[0112] The acquisition module 21 is configured to acquire the construction enterprise operation data, identify and classify nodes and types of the data, and obtain classified data.

[0113] The processing module 22 is configured to perform data distribution modeling according to the classified data, to obtain distribution rules of the data in different nodes; generate data summaries of the nodes according to the distribution rules of the data in different nodes, aggregate the data summaries of the nodes, and form a data global view; extract entities and generate node importance scores of each sub-graph according to the data global view and preset entity definitions of the construction, integrate the node importance scores of the same entities in the sub-graphs, and form a global score; align the entities in the sub-graphs according to the global score, extract relationships between the entities, and perform graph merging, to obtain a fused graph; perform inconsistency detection on data in the graph according to the fused graph, and perform correction, to obtain a corrected graph; and construct a multi-dimensional data display interface according to the corrected graph.

[0114] Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the application.

[0115] As shown in Figure 3 the electronic device can include a processor 610, a communications interface 620, a memory 630, and a communications bus 640, wherein the processor 610, the communications interface 620, and the memory 630 can communicate with each other through the communications bus 640. The processor 610 can invoke logical instructions in the memory 630 to execute the construction enterprise operation data management method.

[0116] In addition, the logical instructions in the memory 630 described above can be implemented in the form of a software function unit and sold or used as an independent product, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in the embodiments of the application. The foregoing storage medium includes a U disk, 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 disk, and various media that can store program codes.

[0117] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program being stored in a non-transitory computer readable storage medium, and the computer program being capable of executing the construction enterprise operation data management method provided by the above method when executed by a processor.

[0118] In another aspect, the present application also provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program is capable of implementing the construction enterprise operation data management method provided by the above method when executed by a processor.

[0119] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0120] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary general hardware platforms, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in terms of the contribution to the prior art, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in the various embodiments or some parts of the embodiments.

[0121] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for managing operational data of a construction enterprise, characterized in that, include: Obtain operational data from construction companies and identify and classify the nodes and types of the data to obtain categorized data; Based on the classified data, data distribution modeling is performed to obtain the distribution pattern of data at different nodes; Based on the distribution pattern of data across different nodes, a data summary for each node is generated, and the data summaries from each node are aggregated to form a global view of the data. Based on the global data view and the preset entity definitions for building construction, entities are extracted and node importance scores for each sub-map are generated. The node importance scores for the same entities in each sub-map are then integrated to form a global score, including: Based on the business characteristics and needs of the construction industry, entity types are preset, including projects, tasks, resources, and departments; Based on the global data view, extract entities that match the preset entity type from the data to obtain the extracted entities; Based on the extracted entities, construct each sub-map; For each node in each subgraph, use ； Calculate the importance score for each node, where, It is each node, It is each node Importance rating It points to a node The set of all nodes Indicates traversal A single source node at time, outdeg It is a node The degree of exit, It is the damping factor; Integrate the node importance scores of each node in different subgraphs, and use ； The global score is calculated, where It is a physical entity The overall score It is a physical entity In the Scoring in individual graphs It is the first Weights of individual subgraphs Indicates the number of subgraphs. Indicates from arrive Summing all terms; Based on the global score, entities in each sub-graph are aligned, relationships between entities are extracted, and the graphs are merged to obtain a fused graph. This includes: matching identical or similar entities in different sub-graphs using entity alignment technology based on the global score to obtain matching results; performing entity alignment operations based on the matching results and preset alignment rules to align identical entities in different sub-graphs to obtain aligned entities; extracting relationships between entities from each sub-graph using relationship extraction technology based on the aligned entities; and merging the graphs based on the relationships between entities to obtain a fused graph. Based on the fused graph, inconsistency detection and correction are performed on the data in the graph to obtain a corrected graph. This includes: calculating the hash value of the globally unique identifier for each entity's attributes based on the fused graph, and comparing the hash values ​​from different sources to obtain attribute detection results; constructing an entity relationship matrix based on the fused graph and checking for contradictory relationships to obtain relationship detection results; verifying whether the graph conforms to a predefined pattern and calculating the proportion of isolated nodes based on the fused graph to obtain structure detection results; and correcting and verifying based on the attribute detection results, relationship detection results, and structure detection results to obtain the corrected graph. Based on the revised map, a multi-dimensional data display interface is constructed.

2. The method for managing operational data of construction enterprises according to claim 1, characterized in that, Obtain operational data from construction companies and identify and classify the data nodes and types to obtain categorized data, including: The data source includes the project management system, finance department system, supply chain management system, human resources system, and qualification management system. The organizational structure and business processes of construction companies are obtained, the nodes of operational data are identified, and a unique identifier is assigned to each node, including project departments, departments, and system modules. The data at each node is categorized by type to obtain categorized data, which includes structured data, unstructured data, and real-time data.

3. The method for managing operational data of construction enterprises according to claim 2, characterized in that, Based on the categorized data, data distribution modeling is performed to obtain the distribution patterns of the data at different nodes, including: The categorized data is then standardized to obtain standardized data. Based on the standardized data, the amount of data, data update frequency, and field overlap of each node are statistically analyzed, and a data distribution model is constructed using a statistical model. Model and analyze the data distribution to obtain the distribution pattern of the data at different nodes.

4. The method for managing operational data of construction enterprises according to claim 3, characterized in that, Based on the distribution patterns of data across different nodes, a data summary for each node is generated, and these summaries are aggregated to form a global data view, including: Based on the data distribution patterns and analysis requirements, determine the data summary indicators for each node; For each node, a summary index is calculated based on the determined summary index, and the calculated summary index is organized into a data summary. Each data summary is aggregated to obtain the aggregated data summary; Organize the aggregated data summary into a global data view.

5. A data management system for the operation of a construction enterprise, characterized in that, include: The acquisition module is used to acquire operational data of construction companies and identify and classify the nodes and types of the data to obtain classified data. Based on the classified data, data distribution modeling is performed to obtain the distribution pattern of data at different nodes; Based on the distribution pattern of data across different nodes, a data summary for each node is generated, and the data summaries from each node are aggregated to form a global view of the data. Based on the global data view and the preset entity definitions for building construction, entities are extracted and node importance scores for each sub-map are generated. The node importance scores for the same entities in each sub-map are then integrated to form a global score, including: Based on the business characteristics and needs of the construction industry, entity types are preset, including projects, tasks, resources, and departments; Based on the global data view, extract entities that match the preset entity type from the data to obtain the extracted entities; Based on the extracted entities, construct each sub-map; For each node in each subgraph, use ； Calculate the importance score for each node, where, It is each node, It is each node Importance rating It points to a node The set of all nodes Indicates traversal A single source node at time, outdeg It is a node The degree of exit, It is the damping factor; Integrate the node importance scores of each node in different subgraphs, and use ； The global score is calculated, where It is a physical entity The overall score It is a physical entity In the Scoring in individual graphs It is the first Weights of individual subgraphs Indicates the number of subgraphs. Indicates from arrive Summing all terms; The processing module is used to align entities in each sub-graph based on the global score, extract relationships between entities, and merge the graphs to obtain a fused graph. This includes: matching identical or similar entities in different sub-graphs using entity alignment technology based on the global score to obtain matching results; performing entity alignment operations based on the matching results and preset alignment rules to align identical entities in different sub-graphs to obtain aligned entities; extracting relationships between entities from each sub-graph using relationship extraction technology based on the aligned entities; and merging the graphs based on the relationships between entities to obtain the fused graph. Based on the fused graph, inconsistency detection and correction are performed on the data in the graph to obtain a corrected graph. This includes: calculating the hash value of the globally unique identifier for each entity's attributes based on the fused graph, and comparing the hash values ​​from different sources to obtain attribute detection results; constructing an entity relationship matrix based on the fused graph and checking for contradictory relationships to obtain relationship detection results; verifying whether the graph conforms to a predefined pattern and calculating the proportion of isolated nodes based on the fused graph to obtain structure detection results; and correcting and verifying based on the attribute detection results, relationship detection results, and structure detection results to obtain the corrected graph. Based on the revised map, a multi-dimensional data display interface is constructed.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the construction enterprise operation data management method as described in any one of claims 1 to 4.

7. 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 operation data management method as described in any one of claims 1 to 4.

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