Project information management system based on APaas platform and construction method thereof

Abstract

The invention discloses a project information management system based on an APaas platform and a construction method thereof, and relates to the technical field of computer application, in the system, a project name subsystem is used for displaying the project name of each engineering project after a worker executes a trigger instruction for a project name module; the project name module is a trigger module for entering the project name subsystem; the primary information subsystem is used for displaying project main information and project detailed information of the engineering project after a worker executes a trigger instruction for any project name in the engineering project; one piece of project main information comprises a plurality of pieces of project detailed information; and the secondary information subsystem is used for displaying the attribute information of the engineering project after the worker executes the trigger instruction for the project main information and the project detailed information of any engineering project in the primary information subsystem. The project management efficiency and the data integration level are improved, and the problem of information islands is effectively solved.

Description

Technical Field

[0001] The present application relates to the field of computer applications, and in particular to a project information management system based on an APaas platform and a construction method thereof. Background Art

[0002] In the current field of engineering project management, traditional ways of information transmission, such as paper documents, emails and telephone communication, have gradually revealed their limitations. These methods are not only inefficient but also prone to errors, especially when dealing with large-scale, complex modern engineering projects that require extremely high information timeliness, accuracy and security.

[0003] In order to solve these problems, engineering project information management systems came into being, but these systems often have the phenomenon of "information islands". That is, the construction unit, construction unit, design unit, government unit and other parties may each use independent software or systems to manage project information, making it difficult for information to flow efficiently and accurately between different entities. This not only increases communication costs, but may also cause project risks due to inconsistent or delayed information. Summary of the invention

[0004] The purpose of this application is to provide a project information management system based on the APaas platform and a construction method thereof, which can effectively solve the problem of information islands in engineering project management.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a project information management system based on the APaas platform, including: a project name subsystem, a primary information subsystem and a secondary information subsystem.

[0007] The project name subsystem is used to display the project names of various engineering projects after the worker executes the trigger instruction for the project name module; the project name module is a trigger module for entering the project name subsystem.

[0008] The primary information subsystem is used to display the main project information and detailed project information of the engineering project after the worker executes a trigger instruction for any project name in the engineering project; one main project information includes several detailed project information.

[0009] The secondary information subsystem is used to display the attribute information of the engineering project after the worker executes the trigger instruction for the main information and detailed information of the project of any engineering project in the primary information subsystem.

[0010] Optionally, the main project information of the engineering project in the first-level information subsystem includes: basic information, unit information, contract information, personnel information, material and machinery information, knowledge information, drawing information and external information.

[0011] Optionally, the basic information includes project name, project number, project location, project type, project start and end time, project duration, project manager of the construction unit and project manager of the general contractor.

[0012] The unit information includes the competent department, construction unit, construction agency unit, survey unit, cost consulting unit, design unit, drawing review unit, supervision unit, general contractor, subcontractor, service unit, goods unit, labor unit and other units.

[0013] The contract information includes general contract, subcontract, goods contract and service contract.

[0014] The personnel information includes the competent department, construction unit, construction agency unit, survey unit, design unit, review unit, supervision unit, construction unit, service unit, cargo unit and other units.

[0015] The material mechanical information includes a material library and a mechanical library.

[0016] The knowledge information includes a national building regulations library, a company regulations library, a construction practice library and other regulations libraries.

[0017] The drawing information includes architectural drawing information and structural drawing information.

[0018] Optionally, when a worker executes a trigger instruction for contract information of any engineering project in the first-level information subsystem, the attribute information of the engineering project displayed includes contract type, contract name, contract number, contract amount, contract unit, employee name, material machinery and drawing name.

[0019] Optionally, when a worker executes a trigger instruction for personnel information in any engineering project in the first-level information subsystem, the attribute information of the engineering project displayed includes unit name, unit type, employee name, employee ID, employee status, employee position, employee responsibilities, WeChat account, mobile phone number, contract and materials and machinery included.

[0020] In the second aspect, the present application provides a method for constructing a project information management system based on the APaas platform, including:

[0021] Based on the trained deep learning model, the project documents are deeply scanned to obtain engineering project information; the engineering project information includes primary information, secondary information and project name; the deep learning model is a model trained based on BERT or Spacy.

[0022] Based on the engineering project information, a relationship extraction technique is used to determine the association relationship between the primary information, the secondary information and the project name.

[0023] According to the engineering project information and the association relationship, the APaas platform is used to construct a project information framework; the project information framework includes a primary information subsystem, a secondary information subsystem and a project name subsystem.

[0024] Optionally, the association relationship between the primary information and the secondary information is: after selecting a node containing the primary information, an algorithm function is used to determine the secondary information associated with the selected node, and data aggregation technology is used to collect and summarize the secondary information; the algorithm function uses a Cypher query statement to query the secondary information associated with the selected node.

[0025] Optionally, use data aggregation techniques to collect and summarize secondary information, including:

[0026] Gets the identifier of the first-level information and the specified attributes of the second-level information.

[0027] Construct an aggregate query statement according to the identifier and the specified attributes.

[0028] The aggregate query statement is input into a graph database, and all secondary information related to the primary information is retrieved from the graph database.

[0029] The retrieved secondary information is aggregated according to the specified attributes of the secondary information to obtain an aggregated result.

[0030] Optionally, before aggregating the retrieved secondary information according to the specified attributes of the secondary information to obtain the aggregation result, the method further includes:

[0031] Data processing is performed on the retrieved secondary information, specifically including:

[0032] Extract characteristic information of each secondary information; the characteristic information includes the type of node, attribute value and relationship with the primary information.

[0033] Based on the extracted feature information, the retrieved secondary information is filtered and cleaned to obtain secondary information that is directly related to the primary information.

[0034] Optionally, based on the engineering project information, using a relationship extraction technology to determine the association relationship between the primary information, the secondary information and the project name specifically includes:

[0035] The text data in the engineering project information is preprocessed; the data preprocessing includes: cleaning, word segmentation, part-of-speech tagging and named entity recognition.

[0036] Based on rule matching, the relationship between entities in the preprocessed text data is identified to determine the entities and the relationship between them; the rule matching identifies the relationship between entities based on several predefined patterns; the patterns are regular expressions, logical expressions or linguistic templates.

[0037] A trained machine learning model is used to identify the relationships between entities that cannot be identified by the rule matching; the trained machine learning model is a support vector machine, a random forest or a deep learning model.

[0038] The relationship between the entities obtained by rule matching and the relationship between the entities output by the machine learning model are integrated to determine the association relationship between the primary information, the secondary information and the project name.

[0039] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0040] The present application provides a project information management system based on the APaas platform and a construction method thereof. The function of the project name subsystem in the system is to respond to the trigger instructions issued by the worker for the project name module, and then display the specific project names of each engineering project; the function of the primary information subsystem is that when the worker executes a trigger instruction for any project name in the engineering project, it can quickly display the main information of the engineering project and its project details. Among them, the main information of a project is composed of one or more project details; further, in order to meet the needs of deeper information queries, this system also establishes a secondary information subsystem. When the worker executes a trigger instruction for the core information or detailed information of any engineering project displayed in the "primary information subsystem", the subsystem will be responsible for presenting the attribute information of the engineering project. This application aims to effectively solve the problem of information islands by optimizing project management processes and improving data integration, thereby significantly improving the overall efficiency of project management. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 This is a structural diagram of a project information management system based on the APaas platform provided in Example 1 of the present application.

[0043] Figure 2 A flowchart of a project information management method based on an APaas platform is provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0046] Embodiment 1

[0047] like Figure 1 As shown, this embodiment provides a project information management system based on the APaas platform, including: a project name subsystem, a primary information subsystem and a secondary information subsystem.

[0048] The project name subsystem is used to display the project names of various engineering projects after the worker executes the trigger instruction for the project name module; the project name module is a trigger module for entering the project name subsystem.

[0049] The primary information subsystem is used to display the main project information and detailed project information of the engineering project after the worker executes a trigger instruction for any project name in the engineering project; the main project information includes a plurality of detailed project information.

[0050] The secondary information subsystem is used to display the attribute information of the engineering project after the worker executes the trigger instruction for the main information and detailed information of the project of any engineering project in the primary information subsystem.

[0051] Among them, the main project information of the engineering project in the first-level information subsystem includes: basic information, unit information, contract information, personnel information, material and machinery information, knowledge information, drawing information and external information.

[0052] Specifically, basic information is refined into project name, project number, project location, project type, project start and end time, project duration, construction unit project manager, construction general contractor project manager, etc. Unit information is refined into government departments, construction units, construction units, survey units, cost consulting units, design units, review units, supervision units, construction general contractors, construction subcontractors, service units, goods units, labor units, other units, etc. Contract information is refined into general contracting contracts, subcontracting contracts, goods contracts, service contracts, etc. Personnel information is refined into government departments, construction units, construction units, survey units, design units, review units, supervision units, construction units, service units, goods units, other units, etc. Material and machinery information is refined into material library and machinery library. Knowledge information is refined into national building regulations library, company regulations library, construction practice library, other regulations library, etc. Drawing information is refined into architectural drawing information, structural drawing information, etc. External information is broken down into: WeChat information, picture information, BIM information, CAD information, other chat information, etc.

[0053] Specifically, when the worker executes a trigger instruction for the unit information of any engineering project in the first-level information subsystem, the attribute information of the engineering project displayed includes the unit type, unit name, employee in charge (linked with the employee name in the personnel information), project in charge (linked with the project name in the basic information), and contract in charge (linked with the contract name in the contract information), which is refined by the second-level information subsystem.

[0054] When the worker executes the trigger instruction for the contract information of any engineering project in the first-level information subsystem, the attribute information of the engineering project displayed includes the contract type, contract name, contract number, contract amount, contract unit (linked with the unit name in the unit information), employee name (linked with the employee name in the personnel information), material machinery (linked with the material machinery name in the material machinery information), and drawing name (linked with the drawing name in the drawing information).

[0055] When the worker executes the trigger instruction for the personnel information in any engineering project in the first-level information subsystem, the attribute information of the engineering project displayed includes the unit name (linked with the unit name in the unit information), unit type (linked with the unit type in the unit information), employee name, employee ID, employee status (whether resigned), employee position, employee responsibilities, WeChat account, mobile phone number, contract (linked with the contract name in the contract information), and material machinery (linked with the material machinery name in the material machinery information).

[0056] When the worker executes the trigger instruction for the material and machinery information of any engineering project in the first-level information subsystem, the attribute information of the engineering project displayed includes the material and machinery type, material and machinery name, brand name, material and machinery amount, the contract to which it belongs (linked with the contract name in the contract information), and the person in charge of the material and machinery (linked with the employee name in the personnel information).

[0057] When the worker executes the trigger instruction for the knowledge information in any engineering project in the first-level information subsystem, the attribute information of the engineering project displayed includes the regulation type, regulation name, unit to which the regulation belongs (linked with the unit name in the unit information), regulation supervisor (linked with the employee name in the personnel information), regulation enforcer (linked with the employee name in the personnel information), and finer (linked with the employee name in the personnel information).

[0058] When the worker executes the trigger instruction for the drawing information of any engineering project in the first-level information subsystem, the attribute information of the engineering project displayed includes the drawing type, drawing name, the contract to which it belongs (linked with the contract name in the contract information), the person who produced the drawing (linked with the employee name in the personnel information), and the person who reviewed the drawing (linked with the employee name in the personnel information).

[0059] When the worker executes the trigger instruction for the external information in any engineering project in the first-level information subsystem, the attribute information of the engineering project displayed includes the information type, the information sender (linked with the employee name in the personnel information), the information sending unit (linked with the unit name in the personnel information), the information executor (receiver) (linked with the employee name in the personnel information), and the information execution (receiving) unit (linked with the unit name in the personnel information).

[0060] Embodiment 2

[0061] like Figure 2 As shown, this embodiment provides a method for constructing a project information management system based on an APaas platform, including:

[0062] Step 201: Based on the trained deep learning model, perform a deep scan on the project document to obtain engineering project information; the engineering project information includes primary information, secondary information and project name; the deep learning model is a model trained based on BERT or Spacy.

[0063] Step 202: Based on the engineering project information, a relationship extraction technique is used to determine the association relationship between the primary information, the secondary information and the project name.

[0064] Step 203: Based on the engineering project information and the association relationship, an APaas platform is used to construct a project information framework; the project information framework includes a primary information subsystem, a secondary information subsystem and a project name subsystem.

[0065] In some implementations of this embodiment, when executing step 201, the specific steps may be as follows:

[0066] The association relationship between the primary information and the secondary information is: after selecting a node containing the primary information, an algorithm function is used to determine the secondary information associated with the selected node, and data aggregation technology is used to collect and summarize the secondary information; the algorithm function uses a Cypher query statement to query the secondary information associated with the selected node.

[0067] Specifically, data aggregation technology is used to collect and summarize secondary information, including:

[0068] Gets the identifier of the first-level information and the specified attributes of the second-level information.

[0069] Construct an aggregate query statement according to the identifier and the specified attributes.

[0070] The aggregate query statement is input into a graph database, and all secondary information related to the primary information is retrieved from the graph database.

[0071] The retrieved secondary information is aggregated according to the specified attributes of the secondary information to obtain an aggregated result.

[0072] Before aggregating the retrieved secondary information according to the specified attributes of the secondary information to obtain the aggregation result, the following may also be included:

[0073] Data processing is performed on the retrieved secondary information, specifically including:

[0074] Extract characteristic information of each secondary information; the characteristic information includes the type of node, attribute value and relationship with the primary information.

[0075] Based on the extracted feature information, the retrieved secondary information is filtered and cleaned to obtain secondary information that is directly related to the primary information.

[0076] In some implementations of this embodiment, based on the engineering project information, using a relationship extraction technique, determining the association relationship between the primary information, the secondary information, and the project name specifically includes:

[0077] The text data in the engineering project information is preprocessed; the data preprocessing includes: cleaning, word segmentation, part-of-speech tagging and named entity recognition.

[0078] Based on rule matching, the relationship between entities in the preprocessed text data is identified to determine the entities and the relationship between them; the rule matching identifies the relationship between entities based on several predefined patterns; the patterns are regular expressions, logical expressions or linguistic templates.

[0079] A trained machine learning model is used to identify the relationships between entities that cannot be identified by the rule matching; the trained machine learning model is a support vector machine, a random forest or a deep learning model.

[0080] The relationship between the entities obtained by rule matching and the relationship between the entities output by the machine learning model are integrated to determine the association relationship between the primary information, the secondary information and the project name.

[0081] Among them, the connection between the secondary information subsystem and the primary information subsystem is: the secondary information subsystem is a submodule of the primary information subsystem, which is equivalent to the primary information subsystem being a primary directory, and the secondary information being a secondary directory. The secondary system is granularized project information, and the implementation steps of the granularized project information algorithm process are as follows:

[0082] Step 1: Data Association and Mapping

[0083] ①Entity recognition:

[0084] Using pre-trained deep learning models such as BERT or Spacy, we can perform deep scanning of project document information and extract key information from text data. We can not only identify basic entity categories such as project name, unit, personnel, and contract, but also more complex entity types such as project phase, deliverables, budget range, etc. Through continuous training and optimization, we can improve the accuracy and recall rate of the NER system and ensure high accuracy of entity recognition.

[0085] Input: All paper document information in the project, including stamped contract documents, company information, etc.

[0086] The output is: extract nouns from paper document information, such as project name, unit, personnel, and contract, and extract them.

[0087] The trained model can automatically find the approximate location of the primary and secondary information subsystems in paper documents.

[0088] ②Relationship extraction:

[0089] Relation extraction is performed through the trained model. Advanced relation extraction technology is used to extract the extracted entities as nodes and establish boundaries based on their semantic relationships.

[0090] Specifically, the training process is:

[0091] 1) Data preparation and preprocessing:

[0092] Before starting relationship extraction, the text data of the project needs to be preprocessed, including text cleaning, word segmentation, part-of-speech tagging, named entity recognition (NER), etc., in order to provide structured data input for subsequent rule matching and machine learning models.

[0093] 2) Design and application of rule matching:

[0094] Rule matching is based on a series of predefined patterns to identify the relationship between entities. These patterns can be regular expressions, logical expressions or linguistic templates. For example, the following rules can be designed:

[0095] Rule 1: When a verb such as “responsible for” or “manage” appears in a text followed by a project name, the entity in the preceding text is considered to have management responsibility for the project.

[0096] Rule 2: If an entity is followed by a preposition such as "located in" or "at" and then a place name, it can be considered that the entity has a location relationship with the place.

[0097] When applying these rules, you can write a specific parser that checks the entities in the text and the relationships between them one by one according to the rules, thereby identifying some of the relationships.

[0098] 3) Feature extraction and machine learning model training:

[0099] For complex relationships that cannot be covered by rule matching, machine learning methods are needed to identify them. First, features are extracted from the preprocessed text data. These features may include:

[0100] Word sequence: the word sequence around an entity, such as the first N words and the last N words. Part-of-speech tagging: part-of-speech information of an entity and its neighboring words. Dependency parsing results: dependency relationships between entities. Co-occurrence information: the frequency and context of co-occurrence of different entities in a text.

[0101] Next, use these features and the labeled relationship data to train a machine learning model. You can choose a support vector machine (SVM), random forest, or deep learning model. During the training process, the model will learn how to predict the relationship between entities based on the features.

[0102] 4) Relationship identification and result fusion:

[0103] The trained machine learning model will be applied to unlabeled text data to identify the relationship between entities. At the same time, the results of rule matching are fused with the results of the machine learning model to improve the accuracy and coverage of relationship extraction. This is usually achieved through a voting mechanism or a weighted fusion method.

[0104] 5) Result output and application:

[0105] Finally, the results of relation extraction will be output in a structured form, such as a relation triple (entity 1, relation, entity 2). These structured relational data can be used to build project knowledge graphs, support project decision analysis, and provide data support to other intelligent systems.

[0106] Here is an example, assuming there is the following text snippet: "The AI ​​project that XX Company is responsible for building is located in City A, the project manager is Zhang San, and the project budget is 5 million."

[0107] Rule matching: According to Rule 1, it can be identified that there is a management relationship between "XX Company" and "AI Project". Feature extraction: For the part "located in City A", features can be extracted, such as "AI Project" is an entity, "located" is a preposition, and "City A" is a place. Machine learning model training: Use relational data similar to the above features and annotations to train the model. The model will learn that "located" usually indicates the relationship between an entity and a place. Relationship identification: Apply the trained model to the entire text. The model recognizes that there is a location relationship between "AI Project" and "City A". Result fusion: Fusion the results of rule matching and machine learning to obtain a complete relationship extraction result: XX Company manages the AI ​​project, the AI ​​project is located in City A, the project manager is Zhang San, and the project budget is 5 million.

[0108] ③Build a data association graph:

[0109] When constructing a directed graph, we not only consider the direct connection between entities and relationships, but also the multiple relationships and paths between entities to build a more complex graph structure. We assign a unique identifier to each entity and relationship and create an index to optimize the query efficiency and data processing speed of the graph.

[0110] Specifically, the specific process of constructing a data association graph is:

[0111] 1) Graph database initialization: Start the graph database service and perform necessary configurations, such as setting the maximum number of connections, memory usage limits, etc.

[0112] 2) Schema definition: Use the query language of the graph database to define the schema of nodes and edges. The schema should include the types of nodes and edges, the set of allowed attributes, and whether the attributes need to be indexed.

[0113] 3) Node creation: Traverse the relationship extraction results and create a node for each unique entity. Assign a unique identifier to the node and fill in other attributes of the node according to the attributes of the entity.

[0114] 4) Edge creation: Create edges based on the entity-relationship pairs in the relationship extraction results. Edges should connect the correct nodes and carry the type and any relevant attributes that describe the relationship.

[0115] 5) Index creation: For frequently queried attributes, create indexes to improve query performance. The index can be a specific attribute of a node or a relationship type of an edge.

[0116] 6) Graph Verification: Execute queries to ensure that the graph correctly reflects the results of the relationship extraction. Verify the integrity of nodes and edges and the accuracy of attributes.

[0117] 7) Graph optimization: Optimize the graph structure based on query patterns and performance feedback. Optimization measures may include adjusting index strategies, redesigning the storage structure of nodes and edges, etc.

[0118] ④Graph database storage:

[0119] Choose a suitable graph database, such as Neo4j, and optimize the configuration according to the characteristics of the graph and query requirements to ensure efficient data storage and retrieval. Regularly maintain and optimize the graph database, including index reconstruction and data cleaning, to maintain the performance and stability of the database.

[0120] Specifically, the specific process of graph database storage is as follows:

[0121] 1) Graph database selection: Select a suitable graph database system based on project requirements, performance goals, cost budget, and technical support.

[0122] 2) Installation and configuration: Install the selected graph database according to the installation guide and configure it according to hardware resources and expected load, such as memory allocation, storage configuration, etc.

[0123] 3) Performance tuning: Perform performance tuning based on the characteristics of the graph database and workload. This may include adjusting garbage collection strategies, optimizing query execution plans, adjusting connection pool sizes, etc.

[0124] 4) Data import: Import the constructed data association graph into the graph database. Use the data import tool provided by the graph database or write scripts to add nodes and edges in batches.

[0125] 5) Monitoring and maintenance: Monitor the performance indicators of the graph database, such as query response time, memory usage, etc. Perform regular database maintenance, including log cleaning, index reconstruction, data backup, etc.

[0126] In summary, based on the entire "Step 1: Data Association and Mapping", a graph structure ProjectGraph containing all project entities and their relationships is created. The relationships between entities are mapped and stored to facilitate subsequent linkage and query operations.

[0127] (2) Algorithm Step 2: Linkage Mechanism Implementation:

[0128] ①User interface design:

[0129] Design a user-friendly interface and provide multiple view and layout options, such as list view, tree view, network diagram, etc., to suit the preferences and needs of different users. Implement multi-level node selection and operation, allowing users to explore the graph through interactive methods such as dragging, zooming, etc., to improve user experience.

[0130] ②Front-end interaction logic:

[0131] Combined with front-end frameworks such as React or Vue.js, a responsive user interface is implemented to ensure the smoothness of the interface and the real-time nature of the interaction. WebSocket or other real-time communication technologies are used to achieve efficient data exchange between the front and back ends and reduce user waiting time.

[0132] User operation and graph node mapping mechanism: When a user selects a specific entity node (such as a project, unit or person) on the front-end interface, this operation will trigger a series of internal processing flows, the core of which is to convert this operation into a query request for the corresponding node in ProjectGraph. The front-end interface uses a standardized API call mechanism to accurately pass the user's selection in the form of a data request to the back-end service system, requesting the system to retrieve information closely related to the selected node.

[0133] ③Backend service processing:

[0134] Develop powerful backend services and adopt microservice architecture to ensure the independence and scalability of each service. Use advanced query and analysis functions of graph databases, such as Cypher query language, to achieve complex data operations and information retrieval.

[0135] Specifically, the backend service query processing flow: Once the backend service system receives a query request from the frontend, it will use a professional graph database query language (such as Cypher) to write precise query statements based on the established structure of ProjectGraph, aiming to fully retrieve other node information directly or indirectly associated with the selected node. The query process may involve a comprehensive retrieval of all edges connected to the selected node and other nodes pointed to by these edges to ensure the integrity and accuracy of the information.

[0136] ④Linkage effect display:

[0137] Through visualization technology, such as D3.js or Echarts, dynamic data display and interaction are achieved, providing rich visual feedback. Multi-dimensional data filtering and sorting functions are implemented, allowing users to quickly locate and analyze data according to different attributes and conditions. That is, the front-end interface calls the back-end service through the API to query all other entities related to the selected entity in the graph database. The back-end service returns the information of the related entities according to the graph structure ProjectGraph, and highlights or otherwise presents this information on the front-end interface to achieve a linkage effect.

[0138] Specifically, the linkage effect realization mechanism: after completing the query operation, the backend service returns the obtained result data to the front-end interface in a structured form. This data usually contains key content such as the unique identifier, type information and detailed attributes of other nodes associated with the selected node. After receiving the data returned by the backend, the front-end interface will update the interface in real time based on these data contents, including but not limited to highlighting or special presentation of related nodes and edges, so as to achieve intuitive linkage effects and enhance user experience.

[0139] In summary, based on the entire "Step 2: Implementation of the linkage mechanism", users can intuitively view and clarify project information through the interactive interface. For example, clicking on a unit name can view all related projects, employees, and contracts of the unit.

[0140] (3) Algorithm Step 3: Refinement of Secondary Information Modules

[0141] ①Define the algorithm function:

[0142] For each first-level information subsystem, an algorithm function is defined that accepts the identifier of the first-level module as input and retrieves the relevant second-level information from the graph database.

[0143] ②Query data aggregation:

[0144] Use data aggregation techniques, such as SQL queries or graph database aggregation operations, to collect and summarize secondary information subsystems. Utilize the graph database aggregation framework to perform multi-level and multi-level data aggregation to extract valuable information and patterns.

[0145] The data aggregation process is as follows:

[0146] 1) Preparation phase: Get the identifier (primaryKey) of the primary module from the algorithm function defined in step 3① of the algorithm. Determine the attributes (aggregationAttribute) of the secondary information subsystem that needs to be aggregated, such as the cost of the project, the number of employees, etc.

[0147] 2) Build an aggregation query: Based on the primaryKey and aggregationAttribute, build an aggregation query that will retrieve all secondary modules related to the primary module from the graph database and aggregate the specified attributes.

[0148] For example, if you want to calculate the average completion time for all tasks associated with a specific project, you can construct the following query: "query←"MATCH(pm:PrimaryModule{identifier:'"+primaryKey+"'})"+"MATCH(pm)-[:ASSOCIATED_WITH]->(sm:SecondaryModule)"+"WITH sm.completionTime AS time"+"RETURN AVG(time)AS averageCompletionTime"".

[0149] 3) Execute and process the aggregate query: Execute the constructed aggregate query and obtain the results. For example, use the client or API of the graph database to execute the query: results←ExecuteCypherQuery(query). Extract the aggregate value from the result and perform necessary conversion or formatting for use in the application.

[0150] 4) Integrate and output results: Integrate the aggregated results with other information from the first-level module to form a complete view. For example, add the average completion time to the statistics related to the first-level module: comprehensiveStats←CombineStats(basicStats,aggregatedResult); return comprehensive statistics for front-end display or other data processing modules.

[0151] Combined with the above refinement process, the pseudo code of the algorithm function can be expressed as:

[0152] "Function RetrieveAndAggregateSecondaryData(primaryKey:Identifier):ComprehensiveStats

[0153] / / Initialize comprehensive statistics

[0154] comprehensiveStats←{}

[0155] / / Build and execute an aggregate query to calculate the average completion time

[0156] averageCompletionTime←AggregateData("completionTime","AVG","sm.projectId='"+primaryKey+"'")

[0157] / / Integrate the aggregation results into comprehensive statistics

[0158] comprehensiveStats.averageCompletionTime←averageCompletionTime

[0159] / / Perform the same aggregation process on other attributes as needed

[0160] / / For example, calculate total cost, number of employees involved, etc.

[0161] / / Return comprehensive statistics

[0162] return comprehensiveStats

[0163] EndFunction

[0164] Function AggregateData(aggregationAttribute:String,aggregationFunction:String,filterCriteria:String):AggregatedValue

[0165] / / Build an aggregate query

[0166] query←"MATCH(n:SecondaryModule)"+

[0167] "WHERE"+filterCriteria+""+

[0168] "RETURN"+aggregationFunction+"(n."+aggregationAttribute+")ASaggregatedValue"

[0169] / / Execute the query and return the result

[0170] results←ExecuteQuery(query)

[0171] returnresults[0].aggregatedValue

[0172] EndFunction".

[0173] In this algorithm expression process, the RetrieveAndAggregateSecondaryData function first calls the AggregateData function to perform specific data aggregation tasks. The AggregateData function builds and executes the aggregation query and returns the aggregation results. Then, the RetrieveAndAggregateSecondaryData function integrates these results into a comprehensive statistical information object containing multiple statistical indicators and returns this object.

[0174] Through this refined data aggregation process, the algorithm can effectively extract and summarize key secondary information subsystem data from the graph database, providing richer and deeper data support for decision-making.

[0175] ③Information module construction:

[0176] Based on the aggregation results, a detailed view of the secondary information subsystem is dynamically generated, including all details associated with the primary module. When dynamically building the secondary information subsystem, the user's role and permissions are considered to achieve personalized and secure data access control. Through modular and componentized design, the scalability and maintainability of the information module are ensured.

[0177] In summary, based on the entire "Step 3: Refinement of Secondary Information Subsystem", a detailed secondary information subsystem view is created for each primary information subsystem. For example, the secondary module of unit information includes unit type, unit name, and employees in charge.

[0178] In some implementations of this embodiment, the association relationship between the primary information and the secondary information is that after selecting a node containing the primary information, an algorithm function is used to determine the secondary information associated with the selected node; the algorithm function uses a Cypher query statement to query the secondary information associated with the selected node.

[0179] The algorithm function is specifically:

[0180] 1) Input processing:

[0181] The algorithm function receives the identifier of the first-level information subsystem as input. This identifier is unique and is used to accurately locate the corresponding first-level node in the graph database. Input validation: Ensure that the received identifier is in the correct format and does correspond to a valid node in the graph database.

[0182] 2) Query construction:

[0183] Based on the input primary module identifier (primaryKey), a Cypher query statement is constructed, which is used to find secondary modules directly or indirectly related to the primary module in the graph database. The RELATIONSHIP_TYPE in the query statement is a specific edge type, such as MANAGES, PARTICIPATES_IN, etc., which is determined according to the type and business logic of the primary module. identifier:primaryKey is used to accurately match the node attributes of the primary module. *1..2 means matching 1 to 2 hop relationships, and indirect relationships are allowed. s:SecondaryModule and p:PrimaryModule represent the nodes of the secondary and primary modules respectively. collect(distinct r)as relationships is used to collect all relationships of different types and attributes.

[0184] 3) Execute the query:

[0185] Send the constructed query to the graph database and perform the retrieval operation. This usually involves using the graph database's query language, such as Cypher. Wait for the graph database to return the query results, which may include a list of related secondary nodes and their properties.

[0186] 4) Data processing:

[0187] Process the returned results and extract relevant information of each secondary node, such as the node type, attribute value, relationship with the primary node, etc. Filter and clean the data to ensure that only secondary information directly related to the primary module is retained. filteredData = FILTER (data, criteria), where criteria is the filtering condition. sortedData = SORT (filteredData, key), where key is the attribute to sort by. aggregatedData = AGGREGATE (data, operation), where operation is an aggregation operation, such as SUM, COUNT, AVERAGE, etc.

[0188] 5) Data conversion:

[0189] Convert the processed data into a format suitable for front-end display. This includes converting node and edge data into JSON objects or other data structures. If necessary, aggregate or calculate the data, such as calculating the total number of projects related to the first-level module, the average budget, etc.

[0190] 6) Output results:

[0191] The converted data is used as the output of the function and provided to the front-end interface for display. Ensure that the output data structure is clear and easy to understand, and can be correctly parsed and used by the front-end logic.

[0192] Among them, the algorithm function pseudo code expression is as follows:

[0193] "Function RetrieveSecondaryInformation(primaryKey:Identifier):SecondaryData

[0194] / / Initialize the query result list

[0195] secondaryDataList←empty list

[0196] / / Build a Cypher query statement to retrieve secondary information related to the primary module

[0197] / / Assume that primaryKey is the unique identifier of the primary module, such as the project ID or unit name query←"MATCH(p:PrimaryModule{identifier:'"+primaryKey+"'})"+

[0198] "MATCHp-[r:RELATIONSHIP_TYPE*1..2]-(s:SecondaryModule)"+

[0199] "WHERE OT s.isAggregated"+

[0200] "RETURN s,collect(distinctr)as relationships"

[0201] / / Execute Cypher query

[0202] results←ExecuteCypherQuery(query)

[0203] / / Traverse the query results

[0204] foreachresult inresults

[0205] / / Extract secondary module information

[0206] secondaryModule←result.s

[0207] / / Extract relationship type

[0208] relationships←result.relationships

[0209] / / Filter and clean data, remove unnecessary attributes or relationships

[0210] cleanedSecondaryModule←FilterAndCleanData(secondaryModule)

[0211] / / Convert data format and prepare for output

[0212] formattedData←ConvertToOutputFormat(cleanedSecondaryModule,relationships)

[0213] / / Add to the result list

[0214] secondaryDataList.add(formattedData)

[0215] / / Return the processed secondary information data list

[0216] return secondaryDataList

[0217] End Function".

[0218] In addition, this application also covers the linkage mechanism of setting up information modules, and realizes the construction of personalized customized workflows by connecting to traditional information channels, as follows:

[0219] The problem of frequent staff resignation in engineering projects can be effectively solved by linking information components in the primary and secondary information subsystems. Staff information and basic information, unit information, contract information, staff information, material and machinery information, knowledge information, drawing information, and external information are linked one by one. As long as the staff information is changed, all related project information will be updated in a linked manner, so that new employees can have a clearer understanding of the work content they are responsible for in the entire engineering project and can better hand over the work of the previous staff who left.

[0220] At the same time, clicking on relevant components on the Apaas platform can trigger the corresponding project information, thereby visualizing the project information in real time.

[0221] Moreover, through the external information module, the Apaas platform can be connected to the traditional project information transmission channels, and WeChat, BIM file information, CAD files and other information can be directly imported into the Apaas platform. The Apaas platform then stores all files in the cloud, and then saves the file information, and links the external information with the personnel information, so as to achieve the openness and transparency of the entire process from the source of information to the end of information, which can facilitate the subsequent file information search, retrieval, and traceability, and gradually get rid of the information islands caused by different units using different information transmission tools.

[0222] Both the primary and secondary information subsystems can be dragged and dropped to form information workflows according to the actual project situation. The initiator can connect the corresponding information approvers in sequence according to the actual project process, and can require approval, filling, copying, and other operations, and finally return the information to the initiator to form an information closed loop. At the same time, each node is linked to the corresponding secondary information subsystem personnel information. As long as the process is stagnant at the approval node, click on the information of the person at the node, and the AI-edited promotion statement will automatically pop up: "@Employee WeChat account, XX workflow has been delivered to you, please advance the progress of the process as soon as possible, thank you!" This sentence can be copied and pasted into the corresponding work group, or you can directly click on the AI ​​voice urging, and the robot will call the node personnel and urge them to speed up the process by voice.

[0223] Specifically, the algorithm steps to achieve the above functions are as follows:

[0224] Step 1: Realize intelligent information linkage and integration of traditional information channels:

[0225] ① Change monitoring and notification details:

[0226] Implement a Change Manager to monitor key nodes such as change requests and design changes in engineering construction projects in real time.

[0227] Utilize workflow management systems to automatically trigger change notifications related to project schedules and cost budgets, ensuring that unit project leaders receive updates in a timely manner.

[0228] ②Information synchronization and integration refinement:

[0229] Develop integrated interfaces with project management software (such as Primavera P6, MS Project) and BIM collaboration platforms (such as Autodesk BIM 360) to synchronize project information such as construction logs and quality checklists. Use data converters to convert engineering data in different formats such as 2D drawings and 3D models into unified electronic spreadsheets for centralized storage and management in the cloud.

[0230] Step 2: Optimize workflow and achieve intelligent handover:

[0231] ① Optimization and refinement of work flow:

[0232] Apply engineering project management software to collect real-time data during project execution, such as schedule tracking and cost control data, to analyze the efficiency of the workflow.

[0233] Leverage key performance indicators and quality metrics to assess the performance of your workflow and identify opportunities for improvement.

[0234] ② Refinement of the intelligent handover system:

[0235] Utilize information from the project management knowledge base and combine it with expert system technology to provide decision support for the handover process. Develop a handover coordinator that can automatically organize project documents, such as construction contracts, safety regulations, etc., and generate a handover checklist to ensure the integrity and traceability of information.

[0236] The above operations greatly improve the efficiency of information transmission of engineering projects, reduce the loss of information transmission, and reduce the information handover problems caused by employee resignation. At the same time, it can also customize the work flow according to the actual engineering projects.

[0237] In addition, this application also covers the division of information confidentiality rights, the hierarchical classification of rights, and the responsibility system for information transmission, as follows:

[0238] Different confidentiality permissions are set for different units. For example, if the government department and construction unit are set to the first-level confidentiality permission, they can view and modify all project information. The construction unit and the supervision unit are set to the second-level confidentiality permission, and can only view and modify the project information of the units below the second level. The general contractor is set to the third-level confidentiality permission. The survey unit, cost consulting unit, design unit, and drawing review unit are also set to the third-level permission unit. The construction subcontractor, service unit, cargo unit, labor unit, and other units are set to the fourth-level confidentiality permission. All permission units can only see the project information below the scope of permission. When you want to view the above permission information, the low-level permission unit can immediately build a work information process and ask the high-level permission unit for approval and consent to view the modification. At the same time, each layer of information transmission process has corresponding nodes and corresponding personnel information, and its modification records have unique ID codes. At the same time, when the information is transmitted out of the APaas platform, a warning will pop up and an employee information watermark will be attached, so that the information security of the project is guaranteed.

[0239] The implementation steps are as follows:

[0240] Step 1: Define the permission model and access control strategy.

[0241] 1) Permission model construction: Design a role-based access control (RBAC) model to define the permissions of different roles (such as government departments, construction units, construction units, etc.). Assign corresponding confidentiality permission levels to each role, such as level 1, level 2, level 3, and level 4.

[0242] 2) Access control strategy formulation: Develop an access control list (ACL) to clarify the scope of project information that each role can access and operate. Set up permission inheritance and cross-authority rules to achieve hierarchical transmission of permissions in the organizational structure.

[0243] Step 2: Implement dynamic permission allocation and information flow construction:

[0244] 1) Dynamic permission allocation: Develop a permission allocation system to automatically assign corresponding confidentiality permissions based on the user's unit and role. The system should be able to dynamically adjust the user's permission settings based on the project organizational structure and personnel changes.

[0245] 2) Information flow construction: Design a workflow engine to allow users of low-authority units to build work information flows as needed and submit them to high-authority units for approval. The workflow template should include key information such as the requested permission level and the scope of information to be viewed or modified.

[0246] Step 3: Information transfer responsibility and security audit:

[0247] 1) Implementation of the responsibility system for information transmission: define a unique ID code for each information transmission and associate it with the specific requester and approver. Track each node of information transmission through the workflow engine to ensure that each step of the operation has a clear person in charge.

[0248] 2) Security audit and watermark mechanism: Deploy a security audit system to record detailed logs of all information access and operations, including time, user, operation type, etc. Implement watermark technology to attach employee information as an irremovable watermark to the transmitted information to prevent unauthorized information leakage.

[0249] Step 4: Permission approval and information update:

[0250] 1) Permission approval process: When a user from a low-authority unit needs to access high-authority information, he / she submits an approval request through the workflow engine. The high-authority unit approver views the request details in the approval interface and decides whether to authorize it.

[0251] 2) Information update and feedback: Once the approval is passed, the system automatically updates the permission settings of the requesting user and notifies the user. If the approval is rejected, the system records the rejection reason and provides feedback to the requesting user.

[0252] In summary, this application has the following technical effects:

[0253] 1) This application aims to effectively solve the problem of information islands by optimizing project management processes and improving data integration, thereby significantly improving the overall efficiency of project management.

[0254] 2) This application is based on the Apaas platform to construct project information management methods, systems and computer storage media, which overcomes the low learning ability of engineering personnel and their inability to adapt to existing new information transmission software. Based on APaas's zero code, it is easy to build work information processes based on the actual processes of engineering projects, and subsequent updates and maintenance are simple. At the same time, through a high-granularity secondary information subsystem, it achieves high efficiency in information transmission and personnel resignation information handover, and external information channels to achieve the storability of external information. Finally, different confidentiality authority levels are set, and personnel information is set responsible for information transmission and outgoing transmission, so that engineering project information can be traced, thereby improving the security of engineering information.

[0255] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0256] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A project information management system based on APaas platform, characterized in that: include: Project name subsystem, primary information subsystem and secondary information subsystem; The project name subsystem is used to display the project names of various engineering projects after the worker executes the trigger instruction for the project name module; the project name module is a trigger module for entering the project name subsystem; The primary information subsystem is used to display the main project information and detailed project information of the engineering project after the worker executes the trigger instruction for any project name in the engineering project; the main project information includes a plurality of detailed project information; The secondary information subsystem is used to display the attribute information of the engineering project after the worker executes the trigger instruction for the main information and detailed information of the project of any engineering project in the primary information subsystem.

2. According to claim 1, a project information management system based on the APaas platform is characterized in that: The main project information of the engineering project in the first-level information subsystem includes: basic information, unit information, contract information, personnel information, material and machinery information, knowledge information, drawing information and external information.

3. A project information management system based on APaas platform according to claim 2, characterized in that: The basic information includes the project name, project number, project location, project type, project start and end time, project duration, project manager of the construction unit and project manager of the general contractor; The unit information includes the competent department, construction unit, construction agency, survey unit, cost consulting unit, design unit, drawing review unit, supervision unit, general contractor, subcontractor, service unit, goods unit, labor unit and other units; The contract information includes general contract, subcontract, goods contract and service contract; The personnel information includes the competent department, construction unit, construction agency, survey unit, design unit, drawing review unit, supervision unit, construction unit, service unit, cargo unit and other units; The material and mechanical information includes a material library and a mechanical library; The knowledge information includes national building regulations database, company regulations database, construction practice database and other regulations database; The drawing information includes architectural drawing information and structural drawing information.

4. According to claim 3, a project information management system based on the APaas platform is characterized in that: When the worker executes the trigger instruction for the contract information of any engineering project in the first-level information subsystem, the attribute information of the engineering project displayed includes the contract type, contract name, contract number, contract amount, contract unit, employee name, material machinery and drawing name.

5. According to claim 3, a project information management system based on the APaas platform is characterized in that: When the worker executes the trigger instruction for the personnel information of any engineering project in the first-level information subsystem, the attribute information of the engineering project displayed includes the unit name, unit type, employee name, employee ID, employee status, employee position, employee responsibilities, WeChat account, mobile phone number, contract and materials and machinery included.

6. A method for constructing a project information management system based on the APaas platform, characterized in that: include: Based on the trained deep learning model, the project documents are deeply scanned to obtain engineering project information; the engineering project information includes primary information, secondary information and project name; the deep learning model is a model trained based on BERT or Spacy; Based on the engineering project information, using relationship extraction technology to determine the association relationship between the primary information, the secondary information and the project name; According to the engineering project information and the association relationship, the APaas platform is used to construct a project information framework; the project information framework includes a primary information subsystem, a secondary information subsystem and a project name subsystem.

7. The method for constructing a project information management system based on an APaas platform according to claim 6, characterized in that: The association relationship between the primary information and the secondary information is: after selecting a node containing the primary information, an algorithm function is used to determine the secondary information associated with the selected node, and data aggregation technology is used to collect and summarize the secondary information; the algorithm function uses a Cypher query statement to query the secondary information associated with the selected node.

8. The method for constructing a project information management system based on the APaas platform according to claim 7 is characterized in that: Use data aggregation techniques to collect and summarize secondary information, including: Get the identifier of the first-level information and the specified attributes of the second-level information; Constructing an aggregate query statement according to the identifier and the specified attribute; Inputting the aggregate query statement into a graph database, and retrieving all secondary information related to the primary information from the graph database; The retrieved secondary information is aggregated according to the specified attributes of the secondary information to obtain an aggregated result.

9. The method for constructing a project information management system based on the APaas platform according to claim 8 is characterized in that: Before aggregating the retrieved secondary information according to the specified attributes of the secondary information to obtain the aggregation result, the following is also included: Data processing is performed on the retrieved secondary information, specifically including: Extracting feature information of each secondary information; the feature information includes the type of node, attribute value and relationship with the primary information; Based on the extracted feature information, the retrieved secondary information is filtered and cleaned to obtain secondary information that is directly related to the primary information.

10. The method for constructing a project information management system based on an APaas platform according to claim 9, characterized in that: Based on the engineering project information, using the relationship extraction technology, determining the association relationship between the primary information, the secondary information and the project name specifically includes: Performing data preprocessing on the text data in the engineering project information; the data preprocessing includes: cleaning, word segmentation, part-of-speech tagging and named entity recognition; Based on rule matching, the relationship between entities in the preprocessed text data is identified to determine the entities and the relationship between the entities; the rule matching is based on a number of predefined patterns to identify the relationship between entities; the pattern is a regular expression, a logical expression or a linguistic template; A trained machine learning model is used to identify the relationship between entities that cannot be identified by the rule matching; the trained machine learning model is a support vector machine, a random forest or a deep learning model; the relationship between the entities obtained by rule matching and the relationship between the entities output by the machine learning model are integrated to determine the association relationship between the primary information, the secondary information and the project name.

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