Hydraulic ring geological information system based on business full life cycle digital intelligence

Abstract

The invention provides a hydraulic ring geological information system based on business full life cycle digital intelligence, and the system comprises five parts: infrastructure, a data center, a functional center, an application scene, and a user group. A hydraulic ring geological service is divided into four fixed basic process units including service deployment, service execution, service inspection and data management and application, and digital closed-loop management which is based on a PDCA ring and covers the full life cycle of the hydraulic ring geological service is constructed; a hydraulic ring big data center composed of hydraulic ring subject knowledge, technical rules, business knowledge, an algorithm model and business data is established, and intelligent control over the hydraulic ring geological business process is achieved based on the big data technology and the artificial intelligence technology; based on a software engineering principle, a modular design method, multiple software design modes, a micro-service architecture and an interface technology are adopted, and five function modules of service management, business management, technology management, model management and data management are divided.

Description

Technical Field

[0001] The present invention relates to the field of digital and intelligent construction of hydrogeology, engineering geology and environmental geology information, and particularly relates to a hydrogeology, engineering geology and environmental geology information system based on digital and intelligent throughout the entire business life cycle. Background Art

[0002] With the gradual and extensive application of advanced information technologies in traditional industries, traditional hydrogeology, engineering geology and environmental geology information systems usually adopt a monolithic architecture, having problems such as high system coupling degree, poor scalability and difficult maintenance. With the development of big data, AI, microservices technology and the digital and intelligent transformation of traditional geological industries such as hydrogeology, engineering geology and environmental geology, there is an urgent need for a digital and intelligent system architecture that can cover the entire business life cycle of hydrogeology, engineering geology and environmental geology operations, so as to realize the intelligence of business processes and data-driven management, comprehensively apply advanced technologies such as big data, AI and microservices, achieve the close combination of traditional geological industries such as hydrogeology, engineering geology and environmental geology and information technology, and promote the digital and intelligent transformation of the entire business life cycle and the digital and intelligent transformation and upgrading development of the industry. This is an inevitable requirement for the development of traditional geological industries such as hydrogeology, engineering geology and environmental geology.

[0003] Existing software systems have problems such as insufficient digitalization of the entire business life cycle of hydrogeology, engineering geology and environmental geology operations and insufficient intelligence in the control of hydrogeology, engineering geology and environmental geology business processes, and difficulties in expanding new business forms and new technologies. Specifically, it mainly includes: (1) There is a lack of digital closed-loop linkage in each business link, and the digital closed-loop management of the entire business life cycle of hydrogeology, engineering geology and environmental geology operations has not been realized. Existing systems mainly focus on the digitalization of the geological data collection (survey) process, and rarely involve aspects such as the front-end work deployment, the back-end data quality evaluation (review) and sharing and utilization management. The business processes are fragmented, there is a lack of digital closed-loop linkage in each business link, and the digital closed-loop management of the entire business life cycle of hydrogeology, engineering geology and environmental geology operations has not been formed. (2) There is a lack of dynamic control ability of business processes based on big data and AI, and the intelligence level of the control of hydrogeology, engineering geology and environmental geology business processes is low. Existing systems mainly focus on the digital implementation of the technical method process of geological data collection (survey). The combination of basic disciplinary knowledge, technical method rules involved in various technical methods of hydrogeology, engineering geology and environmental geology data collection (survey) and digital technology is insufficient. There is a lack of dynamic control ability of business processes based on big data and AI, and the digital and intelligent level of hydrogeology, engineering geology and environmental geology operations is low. (3) The flexibility and scalability of the system are poor, and it is difficult to meet the requirements of expanding new business forms and new technologies. Existing systems mainly focus on the digital implementation of mature geological survey technical methods, with obvious characteristics of high cohesion and high coupling. The flexibility and scalability of the system are poor, and it is difficult to adjust and improve existing functions and expand new functions brought by new business forms and new technologies. Summary of the Invention

[0004] The purpose of the present invention is to provide a hydrogeology, engineering geology and environmental geology information system based on digital and intelligent throughout the entire business life cycle.

[0005] The problems to be solved by the present invention are as follows: It relates to the design and development of a hydrogeology, engineering geology and environmental geology information system, aiming to improve the efficiency and management level of hydrogeology, engineering geology and environmental geology operations through digital, intelligent and modular means. It intends to solve three technical problems: (1) There is a lack of digital closed-loop linkage in each business link of the existing software system, and the digital closed-loop management of the entire life cycle of hydrogeology, engineering geology and environmental geology operations has not been achieved; (2) The existing software system lacks the dynamic control ability of the business process based on big data and AI, and the intelligent control of the hydrogeology, engineering geology and environmental geology business process has not been realized, with a low degree of digital intelligence; (3) The existing software system has poor flexibility and scalability and is difficult to meet the requirements of the expansion of new business forms and new technologies.

[0006] A hydrogeology, engineering geology and environmental geology information system based on the digital intelligence of the entire business life cycle, comprising: Infrastructure: It includes data collection, network communication, data storage and processing servers, and user terminal devices. The basic information and status information of the infrastructure are used as system data, stored and updated regularly using a MySQL database. Based on the basic information and status information of the infrastructure, the system monitors and discovers abnormal device states and issues monitoring and warning instructions; Data center: According to the data source, characteristics and uses, it is divided into five types for management: floor data, knowledge data, model data, business data and system data. Based on the characteristics of multi-source, heterogeneous and large volume of hydrogeology, engineering geology and environmental geology data, a hybrid storage mode combining a PostgreSQL+MySQL+Hadoop database, descriptive electronic files, multimedia files, and graphic files is adopted. By integrating floor data, knowledge data, model data and business data, multi-source data fusion, big data technology and artificial intelligence technology are used to simulate domain experts to construct a knowledge graph of hydrogeology, engineering geology and environmental geology and the association model between business and data, assisting in intelligent design of business deployment, intelligent control of business execution, intelligent analysis of business inspection, and intelligent prediction of data application. Access permission control is set for various types of data, and according to the access permission control, data query, retrieval, statistical analysis functions are provided; Functional Center: The hydrogeology, engineering geology and environmental geology (HEG) business is divided into four fixed basic process units: business deployment, business execution, business inspection, and data management and application. A digital closed-loop management covering the entire life cycle of the HEG business based on the PDCA cycle is constructed. An HEG big database consisting of HEG discipline knowledge, technical rules, business knowledge, algorithm models, and historical data is established. Based on big data and artificial intelligence technologies, intelligent control of the HEG business process driven by big data is carried out. Using the modular design method, the management contents in five aspects of service application, business process, technical method, algorithm model, and data resource are separated and divided into five independent functional modules: service management, business management, technical management, model management, and data management. The software design pattern of the abstract factory is adopted between the modules, and loose coupling connection is carried out through interfaces, and the microservice development framework is used for integration; Application Scenario: Regarding the industrial field - business type and business type - technical method as the factory - product family pair, the software design pattern of the abstract factory and interface technology are used to construct a process model of the HEG information system based on the digitalization of the entire life cycle of the business. Based on the future expansion of the industrial field, the extension of the industrial chain, and the change of technical methods, the applicable industrial fields, business types, and technical methods are extended; User Groups: Include HEG engineers who carry out HEG data collection and engineering project construction work, HEG researchers who carry out HEG three-dimensional modeling and digital twin construction work, project managers who carry out the full life cycle management of project implementation, those who carry out industry management work in resource development, engineering construction, disaster prevention and control, and ecological restoration, those who carry out remote command work for emergencies, query and retrieve HEG information and services, covering all roles in the HEG industrial chain.

[0007] Furthermore, the infrastructure includes data collection, network communication, data storage and processing servers, and user terminal devices, including: Data Collection Equipment: Includes general on-site manual collection equipment and remote real-time automated collection equipment for HEG. The data collected by the on-site manual collection equipment is quality-checked and controlled by domain experts. The data is manually input into the data entry system and intelligently collected using OCR technology and ASR technology, and then transmitted to the functional center for processing. The data collected by the remote real-time automated collection equipment is checked and controlled for data quality using a data verification algorithm that conforms to discipline knowledge and technical rules, and is transmitted to the functional center for processing through the protocol interface and Internet of Things communication technology. Sensitive data is desensitized using encryption technology, and secure transmission protocols and access control are used. The collected data is stored in the business database, and the remote real-time automated collection equipment has the function of local short-term data storage; Network communication devices: including wide area Internet, local area Internet, Internet of Things, routers, switches, and firewall facilities. A distributed collaborative network architecture and load balancing are adopted, and a network security protection system is constructed by using secure transmission protocols, encryption and decryption technologies, setting up firewalls, deploying intrusion detection and prevention systems, and network monitoring and operation and maintenance tools; Data storage and processing servers: including self-built computer rooms and file servers, database servers, and application servers provided by service providers. The storage server supports massive data storage by configuring large-capacity hard disks and SSDs, and adopts RAID, load balancing, cluster architecture, data redundancy, and disaster recovery management. The application server uses computing servers and configures two sets of devices on a 1:1 scale; User terminal devices: including service consoles, user computers, display screens, tablets, and mobile phones. User terminals control the differential use of system functions through protocol interfaces according to user permissions.

[0008] Furthermore, the data center: is divided into five types, namely floor data, knowledge data, model data, business data, and system data, for management according to data sources, characteristics, and uses, including: Knowledge data: including subject knowledge data, technical rule data, business knowledge data, and material catalog data. The material catalog data is converted into PDF file format and saved as original materials in the material catalog database, and the material information is registered in the material catalog. By combining machine learning and artificial learning, and according to unified standardization and structuring requirements, after data cleaning, data integration, and data transformation, it is saved in three sub-databases of subject knowledge data, technical rule data, and business knowledge data in the form of PostgreSQL + MySQL + Hadoop databases, descriptive electronic files, multimedia files, and graphic files. Based on the subject knowledge data, instant text and graphic prompt functions are provided, and a data dictionary is provided as a standard option. Using the responsibility chain design pattern, business processes are assembled according to technical rule data and business knowledge data. Based on AOP technology and interface technology, instant quality verification, error warning, and auxiliary acceptance of data are carried out; Model data: created when designing models, and stores functional concept model design schemes, structured model elements, text descriptions, audio and video, and spatial graphic data in the form of MySQL databases, descriptive electronic files, multimedia files, and graphic files respectively. Model parameters are updated when modifying and optimizing the model in service configuration and optimization, business configuration, and technical configuration, providing a prototype template for business management; Business data: It is stored in the form of PostgreSQL + MySQL + Hadoop databases, descriptive electronic files, multimedia files, and graphic files. During business management, a business data structure for the corresponding business is created. Based on factors such as the reliability of technical methods, the accuracy of instrumentation, the level of technical personnel, the compliance of business processes, data integrity, and data timeliness, the Hydrogeology, Engineering Geology, and Environmental Geology Data Quality Trust Index (HEEDQI) is used for quality assessment and quality annotation. The calculation formula of HEEDQI is as follows: , where represents the number of quality factors participating in the calculation of the Hydrogeology, Engineering Geology, and Environmental Geology data quality trust index of the dataset, represents the th quality factor, including the reliability of technical methods, the accuracy of instrumentation, the level of technical personnel, the compliance of business processes, data integrity, and data timeliness, represents the th total number of samples of the quality factor, that is, the total number of technical methods adopted, represents the th th sample of the quality factor, represents the th weight of the quality factor, The value ranges from 0 to 1, , represents the th th single-factor data quality trust index of the sample of the quality factor, which is determined by machine learning values and domain experts, The value ranges from 0 to 1, represents the manual intervention correction coefficient, which is obtained by converting the scores from the inspection and acceptance by domain experts, The value ranges from 0 to 1. For business data with a HEEDQI index greater than the predetermined threshold, after data cleaning and data conversion, it is integrated into the corresponding thematic database of the floor data, and at the same time, the HEEDQI index is recorded for quality annotation; Floor data: New data collected and observed in the overlay update area is superimposed. According to the method in the business data, the data is aggregated to form a Hydrogeology, Engineering Geology, and Environmental Geology data lake, and the system floor data is constructed. The floor data establishes a unified database structure and data dictionary according to the thematic domain conceptual model, specifications, and standardization and structuring requirements, and undergoes data cleaning, data integration, and data conversion processing; System data: At the same time as the work is deployed, according to user permissions, applying basic domain knowledge and technical rules, following the principle that the agreement is greater than the configuration, the system database structure and system data are established. The system data is intelligently set and modified according to business technical rules and system operating status, as well as user configuration modifications, and is stored in the form of MySQL databases and XML files.

[0009] Further, it is divided into five independent functional modules of service management, business management, technical management, model management, and data management, including: Service management module: Responsible for service registration, discovery, invocation, and monitoring of the system. Based on the modular design method, it is vertically divided into five modules: industrial field, business type, technical method, algorithm model, and data resource. It adopts a microservices development framework, microservices discovery and management tools, integrated containerized deployment, and service orchestration tools for automated management and expansion of microservices. It provides a unified API gateway, integrates user authentication and authorization protocols and network security protocol management for microservice invocation and permission control, and uses RESTful API and message queues to achieve communication between services. The service management module adopts the adapter pattern to provide microservice management functions; Business management module: Adopts the modular design method, various software design patterns, and interface technologies to provide intelligent control over the business process management processes and management elements of various application scenarios, industrial fields, and business types; Based on the builder software design pattern, a PDCA process model for hydrogeology, engineering geology, and environmental geology (hydrogeology, engineering geology, and environmental geology, abbreviated as "hydrogeology, engineering geology, and environmental geology") business is constructed. The life cycle of hydrogeology, engineering geology, and environmental geology technical methods is divided into four fixed PDCA basic process units: business deployment Plan, business execution Do, business inspection Check, and data management and application Application. The business process abstract builder interface is used as the top-level interface for various business processes, and public interfaces for business deployment, business execution, business inspection, and data management and application are provided externally; Based on the abstract factory design pattern, a business process structure model for hydrogeology, engineering geology, and environmental geology is constructed. The industrial field and the specific industrial field are used as the abstract factory interface and the specific factory class respectively, and various types of hydrogeology, engineering geology, and environmental geology businesses are used as hydrogeology, engineering geology, and environmental geology business products. The above-mentioned business process abstract builder interface is inherited to construct different hydrogeology, engineering geology, and environmental geology business abstract product interfaces, providing specific hydrogeology, engineering geology, and environmental geology business implementations for different industrial fields; Adopts the bridge design pattern to integrate the hydrogeology, engineering geology, and environmental geology business management process and management elements into the business process model, replaces the inheritance relationship with a composition relationship, and establishes a basic template for the hydrogeology, engineering geology, and environmental geology business process system in different application scenarios according to hydrogeology, engineering geology, and environmental geology subject knowledge and technical rules. According to the principle that convention is better than configuration, it adopts the prototype design pattern, database storage technology, and Java reflection mechanism. Through object replication, it provides default configurations for business processes in different application scenarios, and drives the automated flow of corresponding hydrogeology, engineering geology, and environmental geology business processes according to the default configurations. Before and during the execution of the business process, configuration parameters are modified, and the business process is adjusted according to the new configuration parameters; During the execution of the business process, according to the specific configuration of each management element by the business deployment unit, the binding mechanism, and the preset business inspection interface, the AOP technology and interface technology are used to access the artificial intelligence-assisted business inspection module based on disciplinary knowledge and technical rules, so as to realize the intelligent inspection of business process configuration, process execution process, and achievement data quality; Technical management module: Make corresponding settings based on the method described in the business management module; Algorithm model module: Based on the hydrogeological, environmental geological, and engineering geological business knowledge, disciplinary knowledge, and technical specifications, screen the core algorithms of each functional module, construct an algorithm model template including the basic processes of various algorithms, the model index system describing the algorithms, and the index parameter values, and use a structured database to store the business process steps, model indexes, and model parameter values of various algorithm models, and establish a classified algorithm model template database to support the operation of service management, business management, technical management, model management, and data management modules; Based on the algorithm model template database, adopt the modular design method, responsibility chain design pattern, Java reflection mechanism, and database code generation technology to construct the model function body, and automatically optimize and manually modify the model parameters through machine learning; Data management module: Based on the hydrogeological, environmental geological, and engineering geological disciplinary knowledge, technical rules, business knowledge, and artificial intelligence technology, construct a hydrogeological, environmental geological, and engineering geological knowledge, technical rules, and business knowledge system and knowledge graph, establish a hydrogeological, environmental geological, and engineering geological data structure model and a metadata database, and use a structured database for storage, and classify and construct a hydrogeological, environmental geological, and engineering geological data center composed of floor data, knowledge data, model data, business data, and system data; Adopt the hybrid storage mode of PostgreSQL+MySQL+Hadoop to manage hydrogeological, environmental geological, and engineering geological data, use Hadoop and Spark to store and process hydrogeological, environmental geological, and engineering geological big data, provide data permission management and security audit functions, use cache technology to store and quickly retrieve data, and access large language models, artificial intelligence agents, and knowledge graphs to assist in updating and mining the data in the data center.

[0010] The beneficial effects of the present invention: Based on the basic unit of the hydrogeological, environmental geological, and engineering geological business process and the PDCA cycle, realize the digital and intelligent closed-loop management of the entire life cycle of the hydrogeological, environmental geological, and engineering geological business; Based on big data technology and artificial intelligence technology, realize the intelligent control of the hydrogeological, environmental geological, and engineering geological business process; Adopt modular, high cohesion, low coupling design, and microservice architecture to improve the security, flexibility, and scalability of the system; Support business applications in multiple industrial fields such as hydrogeology, geothermal mineral springs, geological engineering, geological disasters, and ecological restoration. Brief Description of the Drawings

[0011] Figure 1 It is a schematic diagram of the functional modules of a hydrogeological, environmental geological, and engineering geological information system based on the digital and intelligent full life cycle of the business; Figure 2 It is a schematic diagram of the hydrogeology, engineering geology and environmental geology information system architecture based on the digitalization of the entire business life cycle. Specific implementation manners

[0012] The following further clearly and completely describes the present invention, but the protection scope of the present invention is not limited thereto.

[0013] A hydrogeology, engineering geology and environmental geology information system based on the digitalization of the entire business life cycle includes: Infrastructure: The infrastructure provides basic support for the hydrogeology, engineering geology and environmental geology digital information system, including data acquisition, network communication, data storage and processing servers, and user terminal devices. The infrastructure is the basis for realizing the acquisition, transmission, storage, processing and shared application of hydrogeology, engineering geology and environmental geology and business (project) management data. The basic information and status information of the infrastructure are used as system data and stored and updated regularly using a MySQL database; the system calls the system data regularly to understand the basic information and status information of the infrastructure, so as to timely detect abnormal device status and issue monitoring and warning instructions.

[0014] Data center: The data center is divided into five types, namely floor data, knowledge data, model data, business data and system data, for management according to data sources, characteristics and uses. It adopts a hybrid storage mode combining a PostgreSQL+MySQL+Hadoop database, descriptive electronic files, multimedia files, and graphic files to adapt to the characteristics of multi-source, heterogeneous and large-volume hydrogeology, engineering geology and environmental geology data, and meet the requirements of unified management and shared use of hydrogeology, engineering geology and environmental geology big data. The present invention combines floor data, knowledge data, model data and business data, and uses multi-source data fusion, big data technology and artificial intelligence technology to simulate domain experts to construct a knowledge graph of hydrogeology, engineering geology and environmental geology and an association model between business and data, to assist in realizing intelligent design of business deployment, intelligent control of business execution, intelligent analysis of business inspection, and intelligent prediction of data application. Access permission control is set for various types of data, and according to the access permission control, data query and retrieval, and statistical analysis functions are provided.

[0015] Functional Center: The functional center is the core of the hydrogeology, engineering geology, and environmental geology digital geological information system. In this invention, the hydrogeology, engineering geology, and environmental geology business is divided into four fixed basic process units: business deployment (Plan), business execution (Do), business inspection (Check), and data management and application (Application). A digital closed-loop management covering the entire life cycle of the hydrogeology, engineering geology, and environmental geology business is constructed based on the PDCA cycle. A large hydrogeology, engineering geology, and environmental geology database composed of hydrogeology, engineering geology, and environmental geology discipline knowledge, technical rules, business knowledge, algorithm models, and historical data is established. Based on big data and artificial intelligence technologies, intelligent control of the hydrogeology, engineering geology, and environmental geology business process driven by big data is realized. Using the modular design method, the management content in five aspects of service application, business process, technical method, algorithm model, and data resource is separated and divided into five independent functional modules: service management (SM), business management (BM), technical management (TM), model management (MM), and data management (DM). The abstract factory software design pattern is adopted between the modules, and loose coupling connections are made through interfaces. The microservice development framework is used for integration, supporting the rapid integration of new functions and enhancing the flexibility and scalability of the system.

[0016] Application Scenarios: In this invention, by taking the industrial field - business type and business type - technical method as the factory - product family pairs at the same time, an abstract factory software design pattern and interface technology are used to construct a process model of the hydrogeology, engineering geology, and environmental geology digital geological information system based on the digitalization of the entire life cycle of the business, enhancing the flexibility and scalability of the system, supporting the application of multiple industrial fields, multiple business types, and multiple technical methods, and being able to flexibly expand the applicable industrial fields, business types, and technical methods according to the future expansion of industrial fields, the extension of industrial chains, and the changes in technical methods. The industrial fields applicable to this invention include but are not limited to hydrogeology, geothermal mineral springs, geological engineering, geological disasters, and ecological restoration. The applicable business types include but are not limited to investigation and evaluation, engineering exploration, engineering consulting, engineering design, engineering construction, project supervision, dynamic monitoring, and scientific research. The applicable technical methods include but are not limited to data collection, remote sensing interpretation, topographic surveying, geological mapping (field mapping), geophysical exploration, geochemical exploration, mountain engineering, drilling, in-situ testing, sampling and testing, dynamic observation, digital simulation, and special research.

[0017] User Groups: The user groups of this invention include hydrogeology, engineering geology, and environmental geology engineers who carry out hydrogeology, engineering geology, and environmental geology data collection and engineering project construction work; hydrogeology, engineering geology, and environmental geology researchers who carry out three-dimensional modeling and digital twin construction work of hydrogeology, engineering geology, and environmental geology; project managers who carry out the whole life cycle management of project implementation; those who carry out industry management work in resource development, engineering construction, disaster prevention and control, and ecological restoration; those who carry out remote command work for emergencies; and those who query and retrieve hydrogeology, engineering geology, and environmental geology information and services. The user groups can cover all roles in the hydrogeology, engineering geology, and environmental geology industrial chain, and the system has wide applicability.

[0018] ReferenceFigure 1 As shown, it is a schematic diagram of the functional modules of a hydrogeological, engineering geological and environmental geological information system based on the digitalization of the entire business life cycle.

[0019] Furthermore, the infrastructure includes data collection, network communication, data storage and processing servers, and user terminal devices, including: Data collection devices: including general on-site manual collection devices and remote real-time automated collection devices for hydrogeology, engineering geology and environmental geology. The data collected by on-site manual collection devices is quality-checked by domain experts to control data quality. The data is manually entered into the data entry system and intelligently collected using OCR technology and ASR technology, and then transmitted to the functional center (data management module) for processing; for the data collected by remote real-time automated collection devices, data quality is inspected and controlled using data verification algorithms that conform to disciplinary knowledge and technical rules to ensure data integrity and accuracy. Through protocol interfaces and Internet of Things communication technologies, the data is transmitted to the functional center (data management module) for processing; sensitive data is desensitized using encryption technology, and secure transmission protocols and access rights control are used to prevent leakage and illegal tampering, ensuring the security of data transmission; the collected data is stored in the business database, and remote real-time automated collection devices should have the function of local short-term data storage to prevent data loss caused by network anomalies.

[0020] Network communication devices: including wide area Internet, local area Internet, Internet of Things, and routers, switches, and firewall facilities. A distributed collaborative network architecture and load balancing are adopted to improve system reliability and performance; a network security protection system is built by adopting secure transmission protocols, decryption of encryption technology, setting up firewalls, deploying intrusion detection and prevention systems (IDS / IPS), and network monitoring and operation and maintenance tools to ensure network security.

[0021] Data storage and processing servers: including self-built computer rooms and file servers, database servers, application servers, and storage servers provided by service providers. Massive data storage is supported by configuring large-capacity hard disks and SSDs, and data reliability is ensured through RAID; business continuity is guaranteed through load balancing, cluster architecture, data redundancy, and disaster recovery management. The application server uses computing servers to ensure high-computation demand scenarios such as AI training and video rendering; two sets of devices are configured in a 1:1 scale to ensure dual-link load balancing and failover functions.

[0022] User terminal devices: including service consoles, user computers, display large screens, tablets, and mobile phones. User terminals control the differential use of system functions through protocol interfaces according to user permissions, ensuring the open sharing, security, and stability of coefficients and data.

[0023] Furthermore, the data center is divided into five types for management according to data sources, characteristics, and uses, including floor data, knowledge data, model data, business data, and system data, and it includes: Knowledge data: The knowledge data of the present invention includes subject knowledge data, technical rule data, business knowledge data, and data catalog data.

[0024] The subject knowledge data includes professional term data and knowledge structure data of the subject fields required for establishing the hydrogeology, environment, and engineering (HGE) subject system. The professional term data may include basic geography professional term data, basic geology professional term data, geophysics professional term data, geochemistry professional term data, hydrogeology professional term data, geothermal spring professional term data, geological engineering professional term data, geological disaster professional term data, and ecological restoration professional term data. The professional term data can be increased according to the expansion of the industrial field. The knowledge structure data may include basic geography knowledge structure data, basic geology knowledge structure data, geophysics knowledge structure data, geochemistry professional knowledge structure data, hydrogeology knowledge structure data, geothermal spring knowledge structure data, geological engineering knowledge structure data, geological disaster knowledge structure data, and ecological restoration knowledge structure data. The knowledge structure data can be increased according to the expansion of the industrial field.

[0025] Technical rule data includes hydrogeological, environmental and engineering geological business processes, as well as business process data and technical quality index data of technical methods adopted in the process of carrying out hydrogeological, environmental and engineering geological business. Hydrogeological, environmental and engineering geological business process data may include investigation and evaluation business process data, engineering exploration business process data, engineering consulting business process data, engineering design business process data, engineering construction business process data, engineering supervision business process data, dynamic monitoring business process data, and scientific research business process data. The business process data can be increased or adjusted according to the extension or adjustment of the industrial chain; The investigation and evaluation business process data may include hydrogeological investigation and evaluation business process data, geothermal and mineral spring investigation and evaluation business process data, geological engineering investigation and evaluation business process data, geological disaster investigation and evaluation business process data, and ecological restoration investigation and evaluation business process data. The investigation and evaluation business process data can be increased according to the expansion and subdivision of the industrial field; The business process data of other hydrogeological, environmental and engineering geological business types can be inferred by analogy. The technical quality index data of hydrogeological, environmental and engineering geological business may include investigation and evaluation technical quality index data, engineering exploration technical quality index data, engineering consulting technical quality index data, engineering design technical quality index data, engineering construction technical quality index data, engineering supervision technical quality index data, dynamic monitoring technical quality index data, and scientific research technical quality index data. The technical quality index data can be increased or adjusted according to the extension or adjustment of the industrial chain; The investigation and evaluation technical quality index data may include hydrogeological investigation and evaluation technical quality index data, geothermal and mineral spring investigation and evaluation technical quality index data, geological engineering investigation and evaluation technical quality index data, geological disaster investigation and evaluation technical quality index data, and ecological restoration investigation and evaluation technical quality index data. The investigation and evaluation technical quality index data can be increased according to the expansion and subdivision of the industrial field; The technical quality index data of other hydrogeological, environmental and engineering geological business types can be inferred by analogy. The business process data of hydrogeological, environmental and engineering geological technical methods may include data collection business process data, remote sensing interpretation business process data, topographic survey business process data, geological mapping (field mapping) business process data, physical exploration business process data, chemical exploration business process data, mountain engineering business process data, geological drilling business process data, on-site test business process data, sampling and testing business process data, dynamic monitoring business process data, digital simulation business process data, and special research business process data. The business process data of hydrogeological, environmental and engineering geological technical methods can be increased or adjusted according to the introduction and subdivision of new technical methods; The remote sensing interpretation business process data may include hydrogeological remote sensing interpretation business process data, geothermal and mineral spring remote sensing interpretation business process data, geological engineering remote sensing interpretation business process data, geological disaster remote sensing interpretation business process data, and ecological restoration remote sensing interpretation business process data. The remote sensing interpretation business process data can be increased according to the expansion and subdivision of the industrial field;The same applies to other hydrogeology, engineering geology, and environmental geology technical method business process data. The technical quality index data of hydrogeology, engineering geology, and environmental geology technical methods can include data on the technical quality index of data collection, remote sensing interpretation, topographic survey, geological mapping (field mapping), physical exploration, chemical exploration, mountain engineering, geological drilling, on-site testing, sampling and testing, dynamic monitoring, digital simulation, and special research. The technical quality index data of hydrogeology, engineering geology, and environmental geology technical methods can be increased or adjusted according to the introduction and subdivision of new technical methods; the technical quality index data of remote sensing interpretation can include data on the technical quality index of hydrogeological remote sensing interpretation, geothermal and mineral spring remote sensing interpretation, geological engineering remote sensing interpretation, geological disaster remote sensing interpretation, and ecological restoration remote sensing interpretation. The technical quality index data of remote sensing interpretation can be increased according to the expansion and subdivision of industrial fields; the same applies to other hydrogeology, engineering geology, and environmental geology technical method technical quality index data.

[0026] Business knowledge data can include business management process knowledge data and business management element knowledge data. Business management process knowledge data can include project planning knowledge data, project application knowledge data, project bidding knowledge data, project design knowledge data, project implementation knowledge data, result compilation knowledge data, result review knowledge data, project acceptance knowledge data, project assessment knowledge data, data management knowledge data, result application knowledge data, and after-sales service knowledge data. Business management process knowledge data can be adjusted according to the optimization of business management processes. Business management element knowledge data can include work objective knowledge data, work location knowledge data, work task knowledge data, work process knowledge data, work time knowledge data, technical quality knowledge data, human resources knowledge data, work materials knowledge data, work cost knowledge data, and work result knowledge data. Business management element knowledge data can be adjusted according to the optimization of business management elements.

[0027] Data on the data catalog can include data on the technical specification catalog, professional book catalog, technical manual catalog, legal document catalog, policy document catalog, journal literature catalog, technical report catalog, and online article catalog; the data catalog can be adjusted according to the increase in data source types.

[0028] The present invention establishes a disciplinary knowledge database reflecting the knowledge structure system of hydrogeology, engineering geology, and environmental geology, a technical rule database reflecting the business process and technical method system of hydrogeology, engineering geology, and environmental geology, a business knowledge database reflecting the business management knowledge system of hydrogeology, engineering geology, and environmental geology, and a data catalog database indicating the data sources of the above three types of databases, jointly constructing the hydrogeology, engineering geology, and environmental geology knowledge data.

[0029] First, collect hydrogeological, engineering geological and environmental geological technical specifications, professional books, technical manuals, legal documents, policy documents, journal articles, technical reports, and online articles through data collection and online retrieval methods. Scan the paper documents and convert them, together with various electronic documents such as collected drawings, texts, and tables, into PDF file format for storage as original materials in the data catalog database, and register the material information in the data catalog. The data catalog can be optionally saved in a MySQL database.

[0030] Then, adopt a combination of machine learning and artificial learning for the collected materials. After data cleaning, data integration, and data conversion according to unified standardization and structuring requirements, save them in three sub-databases of subject knowledge data, technical rule data, and business knowledge data in the form of PostgreSQL + MySQL + Hadoop databases, descriptive electronic files, multimedia files, and graphic files.

[0031] Finally, the use of subject knowledge data can assist in providing instant text and graphic prompt functions during business deployment, business execution, business inspection, data management, and application operations to reduce misoperations; provide a data dictionary as a standardization option to achieve standardized data collection; use the responsibility chain design pattern to assemble business processes based on technical rule data and business knowledge data to achieve intelligent design of business deployment and intelligent flow of business execution; based on AOP technology and interface technology, use technical rule data to achieve instant quality verification, error warning, and auxiliary acceptance functions of data, improving work efficiency and quality.

[0032] Model data: Model data can include integrated service model data, business process model data, technical method model data, data management model data, model management model data, map rendering model data, geological calculation model data, and general algorithm model data. Model data is created when designing models and stores functional concept model design solutions, structured model elements, text descriptions, audio and video, and spatial graphic data in the form of MySQL databases, descriptive electronic files, multimedia files, and graphic files respectively. Model parameters can be updated when modifying (optimizing) the models during service configuration and optimization, business configuration, and technical configuration. Model data is used to support service management, business management, technical management, data management, model management, map rendering, hydrogeological, engineering geological and environmental geological calculations, and artificial intelligence algorithm implementation, providing a prototype template for business management.

[0033] Business data: Business data includes data continuously added and updated in the entire life cycle business processes of hydrogeology, engineering geology, and environmental geology business deployment, business execution, business inspection, data management, and application. Business data includes business basic information data, business management information data, business deployment data, business execution data, business inspection data, data management and application data, digital modeling data, and prediction and evaluation data.

[0034] According to the characteristics of multi-source, heterogeneous, and large volume of hydrogeology, engineering geology, and environmental geology business data, it is stored in the form of PostgreSQL + MySQL + Hadoop databases, descriptive electronic files, multimedia files, and graphic files. When conducting business management (business deployment), a business data structure for the corresponding business is created. In this invention, for the data formed during various technical methods, various business types, and the overall execution process of the project, a hydrogeology, engineering geology, and environmental geology data quality credibility index (HEEDQI) is used for quality assessment and quality annotation based on factors such as the reliability of technical methods, the accuracy of instrument and equipment, the level of technical personnel, the compliance of business processes, data integrity, and data timeliness. The calculation formula of HEEDQI is as follows: , where represents the number of quality factors participating in the calculation of the hydrogeology, engineering geology, and environmental geology data quality credibility index of the dataset, represents the th quality factor, including the reliability of technical methods, the accuracy of instrument and equipment, the level of technical personnel, the compliance of business processes, data integrity, and data timeliness, represents the total number of samples of the th quality factor, that is, the total number of technical methods adopted, represents the th sample of the th quality factor, represents the weight of the th quality factor, with a value ranging from 0 to 1, , represents the th quality factor's th sample's single-factor data quality credibility index, determined by machine learning values and domain experts, with a value ranging from 0 to 1, represents the manual intervention correction coefficient, obtained by converting the scores from domain expert inspection and acceptance, with a value ranging from 0 to 1; Calculate the average quality of the samples for each quality factor, assign weights to each factor according to business requirements, where the weights reflect their importance to the overall quality. Integrate the contributions of each factor through the weighted sum formula to form a comprehensive quality credibility index that synthesizes the weighted contributions of multi-dimensional quality factors and quantifies the overall quality of business data. The weights Through expert empowerment method and dynamic optimization of machine learning, it adapts to the changes in business requirements, and introduces an artificial correction coefficient on the basis of weighted sum , allowing domain experts to adjust the results according to actual business experience to make up for the limitations of machine learning models.

[0035] For business data with a HEEDQI index greater than a predetermined threshold (determined by machine learning and domain expert evaluation), after data cleaning and data conversion, it is integrated into the corresponding thematic database of the floor data (while recording the HEEDQI index for quality annotation), realizing data convergence.

[0036] Floor data: Floor data may include basic geographical data, geological structure data, geophysical data, geochemical data, hydrogeological data, geothermal mineral spring data, ecological environment data, geological engineering data, and geological disaster data; Floor data can be increased according to the expansion of the industrial field.

[0037] The present invention collects basic geographical, geological structure, geophysical, and geochemical basic data and historical data of themes in the field of hydrogeology, engineering geology, and environmental geology, superimposes and updates new data collected and observed in the field, converges data according to the method in business data, forms a hydrogeology, engineering geology, and environmental geology data lake, constructs system floor data, and according to the thematic domain conceptual model, specifications, and standardization and structuring requirements, establishes a unified database structure and data dictionary, and after data cleaning, data integration, and data conversion processing, comprehensively stores in the form of PostgreSQL+MySQL+Hadoop databases, descriptive electronic files, multimedia files, and graphic files to adapt to the characteristics of multi-source, heterogeneous, and large-volume thematic data.

[0038] The floor data mainly provides basic data for digital modeling, prediction and evaluation, scheme design, and scientific decision-making. System data: System data includes permission management data, interface management data, and system configuration data that support the operation of the system. When the system is deployed, according to user permissions, application domain basic knowledge, and technical rules, following the principle that convention is greater than configuration, it establishes a system database structure and system data (parameters). The system data (parameters) are intelligently set and modified according to business technical rules and system operation status, as well as user configuration modification. The system data is stored in the form of MySQL databases and XML files, mainly used to support and guide the operation of the system.

[0039] Furthermore, it is divided into five independent functional modules: service management, business management, technical management, model management, and data management, including: Service Management Module: The service management module is responsible for service registration, discovery, invocation, and monitoring of the system. Based on the modular design method, it is vertically divided into five modules: industrial field, business type, technical method, algorithm model, and data resource. Horizontally, it is divided into industrial fields such as hydrogeology, geothermal mineral springs, geological engineering, geological disasters, and ecological restoration. The industrial fields can be extended according to actual needs; business types such as investigation and evaluation, engineering exploration (survey), engineering consulting, engineering design, engineering construction, engineering supervision, dynamic monitoring, and scientific research. The business types can be extended and adjusted according to the actual situation of the industrial chain; technical methods such as data collection, remote sensing interpretation, topographic survey, geological mapping (field mapping), physical exploration, chemical exploration, mountain engineering, geological drilling, on-site tests, sampling and testing, dynamic monitoring, digital simulation, and special research. The technical methods can be extended according to the actual needs of new technologies and new methods; algorithm models such as integrated services, business processes, technical methods, data management, model management, map rendering, geological calculation, and general algorithms; data resources such as floor data, knowledge data, model data, model data, business data, and system data. The present invention designs micro-service functional entities with single functions based on various industrial fields, various business types, various technical methods, and various algorithm models, which can be extended according to specific application scenarios and actual situations. Using a micro-service development framework (including Spring Cloud), adopting micro-service discovery and management tools (including Nacos), and integrating containerized deployment and service orchestration tools (including Kubernetes) to achieve automated management and expansion of micro-services; providing a unified API gateway (including SpringCloud Gateway), integrating user authentication and authorization protocols (including OAuth2.0) and network security protocols (including SSL / TLS) to manage the invocation and permission control of micro-services, and using RESTful API and message queues to achieve communication between services to ensure efficient communication between micro-services. The service management module adopts the adapter pattern, that is, by adding pre-processing before invocation and post-processing after invocation, integrating internal and external functional components (including QGIS API), providing basic services and service construction such as permission management, interface management, business management, model management, map rendering, log auditing, system monitoring, system configuration, and help system, and micro-service management functions such as service registration, service configuration, service retrieval, service invocation, service stop, service optimization, service deletion, and service statistics, coordinating the efficient, safe, and stable operation of the entire system, building a hydrogeological, engineering geological, and environmental geological information system based on a micro-service architecture, and enhancing the flexibility and scalability of the system.

[0040] Business Management Module: The business management module is responsible for managing the business processes throughout the entire life cycle of hydrogeology, engineering geology, and environmental geology (hydrogeology, engineering geology, and environmental geology, hereinafter referred to as "hydrogeology, engineering geology, and environmental geology") business, supporting the flow of business processes throughout the entire life cycle of hydrogeology, engineering geology, and environmental geology business. Using modular design methods, various software design patterns, and interface technologies, it provides intelligent control over the business process management processes and management elements in various application scenarios (industrial fields) and various business types.

[0041] First, based on the builder software design pattern, a PDCA process model for hydrogeology, engineering geology, and environmental geology business is constructed. The life cycle of hydrogeology, engineering geology, and environmental geology technical methods is divided into four fixed PDCA basic process units (PdcaBase): business deployment (Plan), business execution (Do), business inspection (Check), and data management and application (Application), which are responsible for the configuration, implementation, inspection, and data management and application of hydrogeology, engineering geology, and environmental geology business processes respectively. In this invention, the builder software design pattern is adopted, and business deployment, business execution, business inspection, and data management and application are used as fixed unit modules of the business process abstract builder interface to solidify the four basic process units. At the same time, the business process abstract builder interface (AbstractBusiness, inheriting the PdcaBase interface of the PDCA basic process unit) is used as the top-level interface for various business processes, providing public interfaces for business deployment, business execution, business inspection, and data management and application externally. The builders of various business processes are independent of each other, providing their respective implementation details independently, enhancing the flexibility and scalability of business processes. The business result data can be applied to the optimization and re-deployment of the business deployment model. Restart business deployment - business execution - business inspection - data management and application to construct a business process PDCA loop; it can also provide basic data for the next business process, assist in business deployment, and start a new business process PDCA loop. The division of the basic process units of the business process covers the entire life cycle of hydrogeology, engineering geology, and environmental geology business. The hydrogeology, engineering geology, and environmental geology business process constructed based on the basic process units can achieve closed-loop management of hydrogeology, engineering geology, and environmental geology business processes.

[0042] Then, based on the abstract factory design pattern, a structural model of the hydrogeology, engineering geology, and environmental geology business process is constructed. The hydrogeology, engineering geology, and environmental geology business can be divided into types such as investigation and evaluation, engineering exploration, engineering consulting, engineering design, engineering construction, engineering supervision, dynamic monitoring, and scientific research. For different application scenarios (industrial fields), the work processes, work contents, and technical methods of the hydrogeology, engineering geology, and environmental geology business are not completely the same. Using the abstract factory design pattern, the industrial field and the specific industrial field (including hydrogeology) are used as the abstract factory interface (AbstractField) and the specific factory classes (ConcreteField1~ConcreteFieldn) respectively, and various types of hydrogeology, engineering geology, and environmental geology business (including engineering exploration) are used as the hydrogeology, engineering geology, and environmental geology business products. By inheriting the above-mentioned business process abstract builder interface (AbstractBusiness), different hydrogeology, engineering geology, and environmental geology business abstract product interfaces (AbstractBusiness1~AbstractBusinessm) are constructed. Each industrial field (factory) can have (i.e., produce) multiple hydrogeology, engineering geology, and environmental geology businesses (products), that is, through different specific implementations (ConcreteBusiness11~ConcreteBusinessmn) of various types of hydrogeology, engineering geology, and environmental geology business interfaces (AbstractBusiness1~AbstractBusinessm), the specific hydrogeology, engineering geology, and environmental geology business (products, including geothermal exploration) corresponding to different industrial fields can be provided (produced). Using the abstract factory design pattern, when adding a new type of business (product family), there is no need to modify the original code, meeting the open-closed principle of software development. Only the modification needs to be made in the industrial field (factory) class, enhancing the extensibility of the hydrogeology, engineering geology, and environmental geology business types.

[0043] Finally, the bridge design pattern is adopted to integrate the hydrogeology, engineering geology and environmental geology business management processes (project planning, project application, project design, project implementation, result compilation, result review, project acceptance, project assessment, data management, result application and after-sales service) and management elements (work objectives, work locations, work tasks, work processes, work time, technical quality indicators, human resources, work materials, work costs, work results) into the business process model. The inheritance relationship is replaced by a composition relationship, separating the abstract part from the specific implementation part of the management elements, reducing the coupling degree of the two variable dimensions of abstraction and implementation, and enhancing flexibility and expandability. At the same time, based on the knowledge and technical rules of the hydrogeology, engineering geology and environmental geology disciplines, a basic template of the hydrogeology, engineering geology and environmental geology business process system for different application scenarios is established. According to the principle that convention is better than configuration, the prototype design pattern, database (including XML) storage technology, and Java reflection mechanism are used to provide default configurations of business processes for different application scenarios through object replication, and the automated flow of corresponding hydrogeology, engineering geology and environmental geology business processes is driven according to the default configurations, supporting the automated flow of hydrogeology, engineering geology and environmental geology business in different application scenarios; AOP technology and interface technology are used to modify configuration parameters before and during the execution of the business process according to specific application scenarios and special situations, and adjust the business process according to the new configuration parameters, improving the flexibility and expandability of the system; During the execution of the business process, according to the specific configuration of each management element by the business deployment unit, through the binding mechanism and preset business inspection interfaces, the association between user operations and the business process model and business process model data is realized. Based on the knowledge of the hydrogeology, engineering geology and environmental geology disciplines, technical rules, artificial intelligence technology and AOP technology, intelligent control functions such as process guidance and operation tips, preselection and pre-filling and standardized selection, data verification and error warning are dynamically provided, improving the intelligence level of the system, reducing cumbersome business configuration and deployment work and user misoperations, and improving the work efficiency, data integrity and quality of hydrogeology, engineering geology and environmental geology business; During and after the execution of the business process, AOP technology and interface technology can be used to integrate an artificial intelligence-assisted business inspection module based on disciplinary knowledge and technical rules to realize intelligent inspection of business process configuration, process execution process and result data quality, greatly improving the intelligence level and the work efficiency of resource utilization, technical personnel and review experts.

[0044] Technical management module: The technical management module is responsible for the management of technical methods throughout the life cycle of hydrogeology, engineering geology and environmental geology business, supporting the process flow of technical methods throughout the life cycle of hydrogeology, engineering geology and environmental geology business. Using modular design methods, various software design patterns and interface technologies, it provides intelligent control of various application scenarios (various business types in each industrial field) and various technical method process management processes and management elements.

[0045] First, a PDCA process model for hydrogeology, engineering geology and environmental geology (HGE) technology is constructed based on the builder design pattern. Similarly, the life cycle of HGE technology methods is divided into four fixed basic process units: business deployment (Plan), business execution (Do), business inspection (Check), and data management and application (Application), which are responsible for the configuration, implementation, inspection, data management and application of HGE technology methods respectively. The present invention adopts the builder software design pattern, taking business deployment, business execution, business inspection, and data management and application as fixed unit modules of the abstract builder interface for technology methods to solidify the four basic process units. At the same time, the abstract builder interface for technology methods (AbstractMethod, inheriting the PDCA basic process unit interface PdcaBase) is used as the top-level interface for various technology methods, providing public interfaces for business deployment, business execution, business inspection, data management and application externally. The builders of various technology methods are independent of each other, independently providing their respective implementation details, enhancing the flexibility and scalability of technology methods. The business result data can be applied to the optimization and re-deployment of the business deployment model. The business deployment - business execution - business inspection - data management and application are restarted to construct a business process PDCA loop; it can also provide basic data for the next business process to assist business deployment and start a new business process PDCA loop. The division of the basic process units of technology methods covers the entire life cycle of HGE geological technology methods. The business process of technology methods constructed based on the basic process units can realize the closed-loop management of the business process of HGE technology methods.

[0046] Then, a process structure model for HGE technology methods is constructed based on the abstract factory design pattern. HGE geological technology methods can be divided into types such as data collection, remote sensing interpretation, topographic survey, geological mapping (field mapping), geophysical exploration, geochemical exploration, mountain engineering, drilling, in-situ testing, sampling and testing, dynamic observation, digital simulation, and special research. For the business processes, work contents and technical requirements of technology methods in different application scenarios (industrial fields and business types) are not exactly the same. The abstract factory design pattern is adopted. Taking the aforementioned different business types (including engineering exploration) and the business types in specific industrial fields (including hydrogeological engineering exploration) as the abstract factory interfaces (AbstractBusiness1~AbstractBusinessm) and the concrete factory classes (ConcreteBusiness11~Concrete Businessmn), taking various types of technical methods (including geophysical exploration) as the technical method products, inheriting the above-mentioned technical method abstract builder interface (AbstractMethod) to construct different types of technical method abstract product interfaces (AbstractMethod1~AbstractMethodx), various business types (factories) in each industrial field can have (i.e., produce) multiple technical methods (products), that is, through different specific implementations (ConcreteMethod111~ConcreteMethodxmn) of various types of technical method interfaces (AbstractMethod1~AbstractMethodx), provide (produce) the specific technical methods (products, including geophysical exploration for hydrogeological survey and evaluation) corresponding to different industrial fields (including hydrogeology) and different business types (survey and evaluation). Using the abstract factory software design pattern, when adding a new type of technical method (product family), there is no need to modify the original code, meeting the open-closed principle of software development. Only the modification needs to be made in the business type (factory) class, enhancing the extensibility of technical methods.

[0047] Finally, the bridge design pattern is adopted to incorporate the technical method management elements (work objectives, work locations, work tasks, work processes, work times, technical quality indicators, human resources, work materials, work costs, work results) into the technical process model. By replacing the inheritance relationship with a composition relationship and separating the abstract part from the specific implementation part of the management elements, the coupling degree of the two variable dimensions of abstraction and implementation can be reduced, enhancing flexibility and expandability. At the same time, based on the knowledge and technical rules of the hydrogeology, engineering geology, and environmental geology discipline, a basic template for the hydrogeology, engineering geology, and environmental geology business technical method system for different application scenarios is established. According to the principle that convention is better than configuration, the prototype design pattern, database (including XML) storage technology, and Java reflection mechanism are used. Through object replication, the default configuration of technical methods for different application scenarios is provided, and the automated flow of corresponding technical methods is driven according to the default configuration to support the automated flow of hydrogeology, engineering geology, and environmental geology services in different application scenarios; AOP technology and interface technology are used to modify the configuration parameters before the start and during the execution of the technical method process according to specific application scenarios and special situations, and adjust the technical method process according to the new configuration parameters, improving the flexibility and expandability of the system; During the execution of the technical method, according to the specific configuration of each management element by the business deployment unit, through the binding mechanism and preset business inspection interfaces, the association between user operations and the technical method model and the data of the technical method model is realized. Based on the knowledge of the hydrogeology, engineering geology, and environmental geology discipline, technical rules, artificial intelligence technology, and AOP technology, intelligent control functions such as process guidance and operation tips, preselection and pre-filling and standardized selection, data verification and error warning are dynamically provided, improving the intelligence level of the system, reducing cumbersome business configuration and deployment work and user misoperations, and improving the work efficiency, data integrity, and quality of hydrogeology, engineering geology, and environmental geology services; During and after the execution of the technical method process, AOP technology and interface technology can be used to incorporate an artificial intelligence-assisted business inspection module based on disciplinary knowledge and technical rules to realize the intelligent inspection of the technical method process configuration, process execution process, and result data quality, greatly improving the intelligence level and the work efficiency of resource utilization, technical personnel, and review experts.

[0048] Algorithm model module: The algorithm model module is responsible for the management and optimization of the algorithm models throughout the life cycle of hydrogeology, engineering geology, and environmental geology services. Using the modular design method, prototype software design pattern, and interface technology, it provides support for the intelligent control of each functional module.

[0049] First, based on the knowledge of hydrogeology, environmental geology, and engineering geology, disciplinary knowledge, and technical specifications, core algorithms for each functional module are screened, including the permission management algorithm for microservice management, the business statistics algorithm for hydrogeology, environmental geology, and engineering geology business management, the cross-section drawing algorithm for technical method management, the model construction algorithm for model management, and the professional term description algorithm for data management. An algorithm model template is constructed, including the basic processes of various algorithms, the model index system for describing algorithms, and the index parameter values. A structured database (including MySQL) is used to store the business process steps, model indexes, and model parameter values of various algorithm models, and a classified algorithm model template database is established to support the operation of service management, business management, technical management, model management, and data management modules.

[0050] Then, based on the algorithm model template database, using the modular design method, the responsibility chain design pattern, the Java reflection mechanism, and the database code generation technology, a model functional body is constructed. With the support of artificial intelligence technology, through machine learning, automatic optimization, and manual modification of model parameters, the evolution of the model is realized using the prototype design pattern, improving the resource reuse rate. The map rendering in the map compilation process, the geological calculations in the implementation process of hydrogeology, environmental geology, and engineering geology business, and general algorithms are solidified and implemented, and a corresponding algorithm model library is established.

[0051] Finally, by providing a calling interface for the model, intelligent processing of functional modules is supported, and model usage is realized.

[0052] Data management module: The data management module is responsible for the unified management of data, supporting data-driven intelligent control of the hydrogeology, environmental geology, and engineering geology business process. Based on the knowledge of hydrogeology, environmental geology, and engineering geology, technical rules, business knowledge, and artificial intelligence technology, a knowledge system and knowledge graph of hydrogeology, environmental geology, and engineering geology are constructed. A hydrogeology, environmental geology, and engineering geology data structure model and a metadata database are established, and a structured database (including MySQL) is used for storage; a hydrogeology, environmental geology, and engineering geology data center composed of floor data, knowledge data, model data, business data, and system data is constructed by classification; a hybrid storage mode (PostgreSQL + MySQL + Hadoop) is used to manage multi-source, multi-class, large-volume, multi-dimensional, multi-temporal, and heterogeneous hydrogeology, environmental geology, and engineering geology data, and Hadoop and Spark are used to store and process hydrogeology, environmental geology, and engineering geology big data; permission management and security audit functions for data are provided, and data is stored and quickly retrieved through caching technology to reduce the number of requests to the database server, thereby improving the response speed and reducing network load. By accessing large language models (including DeepSeek), artificial intelligence agents (including Manus), and knowledge graphs (including Neo4j), the update and mining of data in the data center are assisted, and the update efficiency and utilization rate of data are improved.

[0053] Reference Figure 2As shown in the figure, it is a schematic diagram of the hydrogeology, engineering geology and environmental geology information system architecture based on the digitalization of the entire business life cycle.

[0054] The present invention provides a hydrogeology, engineering geology and environmental geology information system based on the digitalization of the entire business life cycle. The system architecture includes five parts: infrastructure, data center, function center, application scenarios and user groups. The hydrogeology, engineering geology and environmental geology business is divided into four fixed basic process units: business deployment, business execution, business inspection, data management and application, and a digital closed-loop management covering the entire life cycle of the hydrogeology, engineering geology and environmental geology business based on the PDCA cycle is constructed; a hydrogeology, engineering geology and environmental geology big data center composed of hydrogeology, engineering geology and environmental geology discipline knowledge, technical rules, business knowledge, algorithm models and business data is established, and intelligent control of the hydrogeology, engineering geology and environmental geology business process is realized based on big data technology and artificial intelligence technology; based on the principles of software engineering, using modular design methods, various software design patterns, microservice architecture and interface technology, it is divided into five function modules: service management, business management, technical management, model management and data management.

Claims

1. A hydrogeological, engineering geological and environmental geological information system based on the digitalization of the entire business life cycle, characterized in that, Including: Infrastructure: It includes data collection, network communication, data storage and processing servers, and user terminal devices. The basic information and status information of the infrastructure are used as system data, stored in a MySQL database and updated regularly. Based on the basic information and status information of the infrastructure, the system monitors and discovers abnormal device statuses and issues monitoring and warning instructions; Data Center: It is divided into five types for management according to data sources, characteristics, and uses, namely floor data, knowledge data, model data, business data, and system data. Based on the characteristics of multi-source, heterogeneous, and large-volume hydrogeology, engineering geology, and environmental geology (hydrogeology, engineering geology, and environmental geology, hereinafter referred to as "hydrogeology, engineering geology, and environmental geology") data, a hybrid storage mode combining PostgreSQL + MySQL + Hadoop databases, descriptive electronic files, multimedia files, and graphic files is adopted. By integrating floor data, knowledge data, model data, and business data, multi-source data fusion, big data technology, and artificial intelligence technology are used to simulate domain experts to construct a knowledge graph of hydrogeology, engineering geology, and environmental geology and an association model between business and data, assisting in intelligent design of business deployment, intelligent control of business execution, intelligent analysis of business inspection, and intelligent prediction of data application. Access permission control is set for various types of data, and data query, retrieval, statistical analysis functions are provided according to access permission control; Function Center: The hydrogeology, engineering geology, and environmental geology business is divided into four fixed basic process units: business deployment, business execution, business inspection, and data management and application. A digital closed-loop management covering the entire life cycle of hydrogeology, engineering geology, and environmental geology business based on the PDCA cycle is constructed. A large database of hydrogeology, engineering geology, and environmental geology consisting of hydrogeology, engineering geology, and environmental geology discipline knowledge, technical rules, business knowledge, algorithm models, and historical data is established. Based on big data and artificial intelligence technology, intelligent control of the hydrogeology, engineering geology, and environmental geology business process driven by big data is carried out. Using the modular design method, the management contents of five aspects, namely service application, business process, technical method, algorithm model, and data resource, are separated and divided into five independent function modules: service management, business management, technical management, model management, and data management. The modules use the abstract factory software design pattern and are loosely coupled through interfaces and integrated using the microservice development framework; Application Scenario: Taking the industrial field - business type and business type - technical method as the factory - product family pair, an abstract factory software design pattern and interface technology are used to construct a process model of a hydrogeology, engineering geology, and environmental geology information system based on the digitalization of the entire business life cycle. Based on the future expansion of the industrial field, the extension of the industrial chain, and the change of technical methods, the applicable industrial fields, business types, and technical methods are expanded; User Groups: It includes hydrogeology, engineering geology, and environmental geology engineers who carry out hydrogeology, engineering geology, and environmental geology data collection and engineering project construction work, hydrogeology, engineering geology, and environmental geology researchers who carry out three-dimensional modeling and digital twin construction work of hydrogeology, engineering geology, and environmental geology, project managers who carry out the whole life cycle management of project implementation, those who carry out industry management work in resource development, engineering construction, disaster prevention and control, and ecological restoration, those who carry out remote command work for emergencies, and those who query and retrieve hydrogeology, engineering geology, and environmental geology information and services, covering all roles in the hydrogeology, engineering geology, and environmental geology industrial chain.

2. The hydrogeological, engineering geological and environmental geological information system based on the digitalization of the entire business life cycle as claimed in claim 1, wherein The infrastructure includes data collection, network communication, data storage and processing servers, and user terminal devices, including: Data collection devices: including general on-site manual collection devices for hydrogeology, engineering geology, and environmental geology, and remote real-time automated collection devices. The data collected by on-site manual collection devices is quality-checked by domain experts to control data quality. The data is manually input into the data entry system, and intelligent data collection is also carried out using OCR technology and ASR technology, and then transmitted to the function center for processing. For the data collected by remote real-time automated collection devices, data quality is inspected and controlled using a data verification algorithm that conforms to disciplinary knowledge and technical rules. Through the protocol interface and Internet of Things communication technology, it is transmitted to the function center for processing. Sensitive data is desensitized using encryption technology, and secure transmission protocols and access rights control are adopted. The collected data is stored in the business database. Remote real-time automated collection devices have the function of short-term local data storage; Network communication devices: including wide-area Internet, local-area Internet, Internet of Things, and routers, switches, and firewall facilities. A distributed collaborative network architecture and load balancing are adopted. A network security protection system is built by using secure transmission protocols, decryption of encryption technology, setting up firewalls, deploying intrusion detection and prevention systems, and network monitoring and operation and maintenance tools; Data storage and processing servers: including self-built computer rooms and file servers, database servers, and application servers provided by service providers. The storage server supports massive data storage by configuring large-capacity hard disks and SSDs, and adopts RAID, load balancing, cluster architecture, data redundancy, and disaster recovery management. The application server uses computing servers and configures two sets of devices in a 1:1 scale; User terminal devices: including service consoles, user computers, display large screens, tablet computers, and mobile phones. User terminals control the differential use of system functions through protocol interfaces according to user permissions.

3. A hydrogeological, engineering geological and environmental geological information system based on the digitalization of the entire business life cycle as claimed in claim 1, characterized in that, The data center: According to the data source, characteristics, and uses, it is divided into five types: floor data, knowledge data, model data, business data, and system data for management, including: Knowledge data: including disciplinary knowledge data, technical rule data, business knowledge data, and data catalog data. The data catalog data is converted into PDF file format and saved as original materials in the data catalog database, and the material information is registered in the data catalog. Using a combination of machine learning and manual learning, according to unified standardization and structuring requirements, after data cleaning, data integration, and data conversion, it is saved in three sub-databases: disciplinary knowledge data, technical rule data, and business knowledge data, in the form of PostgreSQL + MySQL + Hadoop databases, descriptive electronic files, multimedia files, and graphic files. Based on the disciplinary knowledge data, an instant text and graphic prompt function is provided, and a data dictionary is provided as a standard option. Using the responsibility chain design pattern, business processes are assembled according to technical rule data and business knowledge data. Based on AOP technology and interface technology, instant quality verification, error warning, and auxiliary acceptance of data are carried out; Model data: Created when designing the model, and stored in the form of MySQL database, descriptive electronic files, multimedia files, and graphic files for the structured model elements, text descriptions, audio-visual, and spatial graphic data of the functional concept model design solution. The model parameters are updated when modifying and optimizing the model in service configuration and optimization, business configuration, and technical configuration, providing a prototype template for business management; Business data: It is stored in the form of PostgreSQL + MySQL + Hadoop databases, descriptive electronic files, multimedia files, and graphic files. During business management, a business data structure for the corresponding business is created. Based on factors such as the reliability of technical methods, the accuracy of instrumentation, the level of technical personnel, the compliance of business processes, data integrity, and data timeliness, the Hydrogeology, Engineering Geology, and Environmental Geology Data Quality Trust Index (HEEDQI) is used for quality assessment and quality annotation. The calculation formula of HEEDQI is as follows: , where represents the number of quality factors participating in the calculation of the Hydrogeology, Engineering Geology, and Environmental Geology data quality trust index of the dataset, represents the th quality factor, including the reliability of technical methods, the accuracy of instrumentation, the level of technical personnel, the compliance of business processes, data integrity, and data timeliness, represents the total number of samples of the th quality factor, that is, the total number of technical methods adopted, represents the th sample of the th quality factor, represents the th weight of the quality factor, The value ranges from 0 to 1, , represents the th single-factor data quality trust index of the th sample of the quality factor, which is determined by machine learning values and domain experts, The value ranges from 0 to 1, represents the manual intervention correction coefficient, which is obtained by converting the inspection and acceptance scores of domain experts, The value ranges from 0 to 1. For business data with a HEEDQI index greater than the predetermined threshold, after data cleaning and data transformation, it is integrated into the corresponding thematic database of the floor data, and the HEEDQI index is recorded for quality annotation; Floor data: Overlay and update the newly collected and observed data in the field, converge the data according to the methods in the business data to form a hydrogeology, engineering geology, and environmental geology data lake, and construct the system floor data. The floor data establishes a unified database structure and data dictionary according to the thematic domain concept model, specifications, and standardization and structuring requirements, and undergoes data cleaning, data integration, and data conversion processing; System data: According to user permissions during work deployment, applying basic application domain knowledge and technical rules, and following the principle that agreements are greater than configurations, establish the system database structure and system data. The system data is intelligently set and modified according to business technical rules and system operation status, as well as user configuration modifications, and is stored in the form of MySQL database and XML files.

4. A hydrogeological, engineering geological and environmental geological information system based on the digitalization of the entire business life cycle as claimed in claim 1, characterized in that, Divided into five independent functional modules: service management, business management, technical management, model management, and data management, including: Service management module: Responsible for service registration, discovery, invocation, and monitoring of the system. Based on the modular design method, it is vertically divided into five modules: industrial domain, business type, technical method, algorithm model, and data resource. Adopting a microservice development framework, using microservice discovery and management tools, integrated containerized deployment, and service orchestration tools for automated management and expansion of microservices. Provide a unified API gateway, integrate user authentication and authorization protocols and network security protocols to manage the invocation and permission control of microservices. Use RESTful API and message queues to achieve communication between services. The service management module adopts the adapter pattern to provide microservice management functions; Business management module: Adopting the modular design method, various software design patterns, and interface technologies to provide intelligent control over the business process management processes and management elements in various application scenarios, industrial domains, and business types; Based on the builder software design pattern, construct the PDCA process model for hydrogeology, engineering geology, and environmental geology business. Divide the life cycle of hydrogeology, engineering geology, and environmental geology technical methods into four fixed PDCA basic process units: business deployment Plan, business execution Do, business inspection Check, and data management and application Application. Abstract the business process builder interface as the top-level interface for various business processes, and provide public interfaces for business deployment, business execution, business inspection, and data management and application; Based on the abstract factory design pattern, construct the business process structure model for hydrogeology, engineering geology, and environmental geology. Use the industrial domain and specific industrial domain as the abstract factory interface and specific factory class respectively, and use various types of hydrogeology, engineering geology, and environmental geology businesses as hydrogeology, engineering geology, and environmental geology business products. Inherit the above business process abstract builder interface to construct different hydrogeology, engineering geology, and environmental geology business abstract product interfaces, providing specific hydrogeology, engineering geology, and environmental geology business implementations corresponding to different industrial domains; Adopt the bridge design pattern, integrate the hydrogeology, engineering geology and environmental geology business management process and management elements into the business process model, replace the inheritance relationship with the composition relationship, and establish the basic template of the hydrogeology, engineering geology and environmental geology business process system for different application scenarios according to the knowledge and technical rules of the hydrogeology, engineering geology and environmental geology discipline. According to the principle of convention over configuration, adopt the prototype design pattern, database storage technology, and Java reflection mechanism, and provide the default configuration of the business process for different application scenarios through object replication. Before the business process starts and during the execution process, modify the configuration parameters and adjust the business process according to the new configuration parameters; During the execution of the business process, according to the specific configuration of each management element by the business deployment unit, the binding mechanism and the preset business inspection interface, use AOP technology and interface technology to integrate the artificial intelligence-assisted business inspection module based on discipline knowledge and technical rules to realize the intelligent inspection of the business process configuration, process execution process and result data quality; Technical management module: Make corresponding settings based on the method described in the business management module; Algorithm model module: Based on the hydrogeology, engineering geology and environmental geology business knowledge, discipline knowledge and technical specifications, screen the core algorithms of each functional module, construct an algorithm model template including the basic processes of various algorithms, the model index system describing the algorithms and the index parameter values, and use a structured database to store the business process steps, model indexes and model parameter values of various algorithm models, and establish a classified algorithm model template database to support the operation of the service management, business management, technical management, model management and data management modules; Based on the algorithm model template database, adopt the modular design method, responsibility chain design pattern, Java reflection mechanism and database code generation technology to construct the model function body, and automatically optimize and manually modify the model parameters through machine learning; Data management module: Based on the hydrogeology, engineering geology and environmental geology discipline knowledge, technical rules, business knowledge and artificial intelligence technology, construct the hydrogeology, engineering geology and environmental geology discipline knowledge, technical rules, business knowledge system and knowledge graph, establish the hydrogeology, engineering geology and environmental geology data structure model and metadata database, and use a structured database to store and classify the construction of the hydrogeology, engineering geology and environmental geology data center composed of floor data, knowledge data, model data, business data and system data; Adopt the hybrid storage mode PostgreSQL + MySQL + Hadoop to manage the hydrogeology, engineering geology and environmental geology data, use Hadoop and Spark to store and process the hydrogeology, engineering geology and environmental geology big data, provide the data permission management and security audit functions, use the cache technology to store and quickly retrieve the data, and integrate the large language model, artificial intelligence agent, and knowledge graph to assist the data center data for update and mining.

Images