A big data-based local industry investment attraction intelligent matching and project landing management and control system, method, device and storage medium

Abstract

This invention relates to a system, method, equipment, and storage medium for intelligent matching and project implementation management of local industry investment promotion based on big data, belonging to the field of smart investment promotion big data technology. This invention aims to solve the technical problems of severe data silos in existing industry investment promotion, low accuracy in project matching, fragmented project implementation management, and the inability to dynamically optimize investment promotion strategies. The system constructs a big data foundation for investment promotion through multi-source data aggregation and governance, builds an industry knowledge base including industrial chain maps, resource carrying capacity, and policy tags, and achieves intelligent matching and customized solution output for investment projects based on multi-dimensional weighted algorithms, while establishing a closed-loop management system for the entire project lifecycle. This invention transforms investment promotion work from experience-driven to data-driven intelligence, effectively improving the accuracy of investment matching and the conversion rate of project implementation, and adapting to the needs of precise investment promotion and digital management of local industrial chains.

Description

Technical Field

[0001] This invention relates to the field of smart investment promotion big data technology, specifically to a system, method, equipment, and storage medium for intelligent matching and project implementation management of local industry investment promotion based on big data. Background Technology

[0002] Local industry investment promotion is a core tool for regional economic transformation and upgrading, strengthening and supplementing industrial chains, and cultivating tax revenue. Precise investment promotion and efficient project implementation are the core objectives of local government investment promotion efforts. Currently, domestic local industry investment promotion and project implementation management generally suffer from pain points such as low levels of intelligence, severe data silos, insufficient matching accuracy, fragmented implementation management, and lack of full-process traceability.

[0003] Current investment promotion efforts rely heavily on manual experience. Investment promotion personnel screen projects through offline meetings, enterprise directories, and manual comparison of regional industrial policies, land, energy consumption, and labor resources, which is highly subjective and limited. On one hand, regional industrial resource data, policy data, idle infrastructure data, and energy consumption data are scattered across multiple departments such as the Development and Reform Commission, the Industry and Information Technology Bureau, the Natural Resources Bureau, and the Human Resources and Social Security Bureau, lacking a unified data foundation. This results in outdated data, inconsistent data standards, and an inability to achieve cross-departmental data linkage and analysis. On the other hand, traditional investment promotion models rely solely on single industry categories for project matching, failing to consider multiple dimensions such as regional industrial chain maps, industry compatibility, resource carrying capacity, and policy matching. This leads to low accuracy in investment matching, mismatches of high-quality projects, indiscriminate introduction of inefficient projects, and gaps in industrial chain investment promotion, resulting in a waste of investment promotion resources.

[0004] Meanwhile, existing project implementation management models mostly rely on offline ledgers and periodic manual reporting, lacking end-to-end digital management capabilities. Information is fragmented across project signing, initiation, land approval, construction, and operation, making it impossible to monitor project progress, bottlenecks, and resource utilization in real time. This results in problems such as project delays, problem shirking, abandoned projects, and low implementation conversion rates. Furthermore, existing investment promotion systems cannot achieve big data-driven review of investment promotion results, intelligent prediction of supply chain gaps, or dynamic optimization of investment promotion strategies, making them ill-suited to the demands of precise investment promotion, supply chain-based investment promotion, and closed-loop implementation management in the new era.

[0005] In summary, existing technologies suffer from technical deficiencies such as data fragmentation, low level of intelligent matching, lack of closed-loop implementation management, and absence of dynamic optimization mechanisms. There is an urgent need for a big data-based, end-to-end, intelligent, and closed-loop intelligent matching and project implementation management system for local industry investment promotion. Summary of the Invention

[0006] The purpose of this invention is to address the technical problems of severe data silos in existing local industry investment promotion work, low accuracy in matching projects with regional resources, fragmented project implementation management, lack of full-process traceability, and inability to dynamically optimize investment promotion strategies. This invention provides a big data-based intelligent matching and project implementation management system for local industry investment promotion, achieving integrated aggregation of regional industry data, intelligent and accurate matching of investment projects, digital closed-loop management of the entire project implementation process, real-time analysis of investment promotion trends, and dynamic optimization of strategies. This significantly improves the efficiency of local industry investment promotion and the conversion rate of project implementation, and helps to precisely strengthen and supplement the industrial chain.

[0007] To achieve the above objectives, the present invention provides a Compared with existing technologies, the technical solution proposed in this application has the following beneficial effects: by breaking down data silos among departments through big data aggregation and governance technology, a unified local investment promotion big data foundation and industry knowledge base are constructed, realizing integrated, standardized, and dynamic management of regional industrial resources, policies, projects, and enterprise data, and solving the technical problems of fragmented, outdated, and uninterrupted analysis of traditional investment promotion data; Employing a multi-dimensional weighted intelligent matching algorithm, which integrates multiple factors such as industry compatibility, policy matching, resource carrying capacity, compliance, and industrial chain value, this algorithm replaces the traditional manual single-dimensional matching mode, significantly improving the accuracy of investment project matching, achieving targeted and precise investment attraction along the industrial chain, effectively avoiding the problems of inefficient project introduction and resource mismatch, and improving the utilization rate of investment resources. A closed-loop management and control system for the entire lifecycle of a project, from initial intent to production and contract fulfillment, has been established. This system enables real-time monitoring of the progress of each stage of the project, intelligent early warning of bottlenecks, and tracing and accountability for problems. It solves the problems of fragmented implementation management, loss of progress control, and low implementation conversion rate in traditional projects, and makes the entire process of investment projects traceable, controllable, and assessable. It possesses the ability to dynamically assess the situation and iteratively optimize algorithms. Based on big data, it can review the effectiveness of investment promotion, predict gaps in the industrial chain, and dynamically optimize investment promotion strategies and matching models. This enables investment promotion work to transform and upgrade from "experience-driven" to "data-driven and intelligent iteration," thus meeting the long-term high-quality development needs of regional industries. A comprehensive data security protection system is configured to achieve hierarchical data management, data anonymization and encryption, and operation traceability, ensuring the security and confidentiality of government investment data and enterprise project data, and conforming to the security specifications of government digital platforms. Attached Figure Description

[0009] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a diagram illustrating the method steps of the present invention. Detailed Implementation

[0010] The technical solutions in the embodiments of the present invention will be clearly and completely described and discussed below with reference to the accompanying drawings. Obviously, what is described here is only a part of the examples of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0011] It should be noted that when a component is described as "fixed to" another component, it can be directly on the other component or may have a component in between. When a component is considered "connected to" another component, it can be directly connected to the other component or may have a component in between. When a component is considered "set on" another component, it can be directly set on the other component or may have a component in between. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0012] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Example

[0013] This embodiment provides a big data-based intelligent matching and project implementation management system for local industry investment promotion. Deployed on a dedicated government cloud server, it adopts a layered architecture, consisting of a data acquisition layer, data governance layer, knowledge construction layer, intelligent matching layer, implementation management layer, decision optimization layer, visualization layer, and security protection layer. Each layer is interconnected, with unidirectional data flow and closed-loop functional empowerment. The system comprises seven core modules and subdivided functional units. All modules and units communicate and interact based on a unified government data interface, forming a fully closed-loop data flow, adaptable to digital application scenarios for local industry investment promotion at multiple levels, including districts, counties, industrial parks, and economic development zones.

[0014] The overall system logic is as follows: The big data aggregation and governance module serves as the data source, providing standardized and dynamically updated basic data for all upper-level modules; the industry knowledge base construction module relies on the governed data to build industry models, resource models, and policy models, providing core algorithm support for intelligent matching; the investment promotion intelligent matching module calls the knowledge base model to complete accurate project screening and solution output, providing effective project sources for the project implementation management module; the project implementation closed-loop management module undertakes matching projects, realizes full life-cycle digital supervision, and accumulates effective project implementation data; the situation analysis and strategy optimization module reversely retrieves knowledge base data and project implementation data to complete algorithm weight iteration and investment promotion strategy optimization, achieving intelligent self-upgrading of the system; the visualization display module fully integrates data from all levels to achieve visualized presentation; and the data security protection module runs through the entire system process, ensuring the security and compliance of all data collection, transmission, calculation, storage, and display links.

[0015] This invention constructs a complete closed-loop data flow system, where the overall data flows and empowers layer by layer according to fixed business logic. The system first completes the comprehensive collection of multi-source raw investment promotion data, then cleans, standardizes, and integrates the collected raw data to obtain standardized and usable data. Based on the high-quality data obtained through this process, an industry knowledge base model is built and continuously iterated and updated. Based on the iterated industry knowledge base, intelligent analysis and multi-dimensional intelligent matching calculations of investment project requirements are performed. The matching calculation results are combined to accurately screen high-quality investment projects and output customized investment promotion solutions. High-quality projects are incorporated into the system for full lifecycle implementation management, while simultaneously accumulating comprehensive and effective data on project progress, contract fulfillment, and operation. The accumulated massive project and industry data are used to conduct comprehensive analysis of investment promotion trends and intelligent prediction of regional industrial chain gaps. Based on the trend analysis and industrial chain prediction results, the weights of each dimension of the matching algorithm are dynamically updated adaptively, simultaneously optimizing regional investment promotion strategies. Finally, the system's full business data, analysis results, and optimization strategies are visualized. Data from each business segment can be synchronized in real time and updated iteratively, forming a smart investment promotion data operation system that can self-review, self-optimize, and continuously iterate.

[0016] Based on the above overall architecture and data flow logic, the specific implementation process of this system is as follows: S1. Multi-source data aggregation and standardized governance to build a unified data foundation for the system. This step is completed collaboratively by the big data aggregation and governance module and its subordinate multi-source data acquisition unit, data standardization processing unit, data fusion and association unit, and incremental update unit, forming the data foundation for all system functions. Specifically, the multi-source data acquisition unit obtains comprehensive investment promotion-related data through three compliant acquisition methods: First, it connects to government interfaces to synchronize structured government data from departments such as the Development and Reform Commission, Industry and Information Technology, Natural Resources, Ecology and Environment, Human Resources and Social Security, Taxation, and Administrative Approval Bureau, including industry directories, land and factory vacancy information, energy consumption and environmental protection indicators, employment and talent data, enterprise tax data, and approval process data; second, it synchronizes databases periodically to capture local historical investment project ledgers, implemented project performance data, and industry statistical annual report data; and third, it uses compliant web crawlers to collect semi-structured and unstructured industry public opinion data, such as investment dynamics of leading companies in the industry, industry transfer trends, and investment promotion policies from other regions.

[0017] After data collection, the data standardization processing unit unifies and organizes the multi-source heterogeneous data, eliminating differences in data fields, inconsistent formats, and non-uniform coding across different departments. It also batch-removes null values, duplicates, and invalid data, creating a standardized data field system adapted to investment promotion scenarios. Subsequently, the data fusion and association unit uses unified industry codes, regional sector codes, and unique project pre-codes as core association keys to link and bind scattered industry, resource, policy, enterprise, and project data, achieving a data fusion effect of "one data per industry, one ledger per region, and full information per project." Finally, the incremental update unit employs a mechanism of daily scheduled incremental synchronization and real-time updates for major data points to iteratively update the big data foundation, ensuring data timeliness. The standardized data after governance is synchronized in real-time to the industry knowledge base construction module and the situation analysis and strategy optimization module, providing data support for subsequent modeling, analysis, and optimization.

[0018] S2. Construction of a multi-dimensional industry knowledge base to build a core intelligent matching model system. This step is completed collaboratively by the industry knowledge base construction module and its subordinate industry chain map construction unit, resource carrying capacity assessment unit, and policy intelligent decomposition unit. This module receives standardized data from the big data aggregation and governance module in one direction, and completes the transformation of static data into dynamic intelligent models, which is the core decision basis for intelligent matching.

[0019] The industrial chain mapping unit, based on regional industrial foundation data, sorts out the upstream and downstream industrial chain system, supporting industrial system, and related industrial system of local leading, pillar, and emerging industries. It accurately marks missing links, weak supporting links, and high value-added links in the industrial chain, constructing a visualized and iterative regional industrial chain knowledge graph to clarify the core directions for regional investment attraction, chain supplementation, and chain strengthening. The resource carrying capacity assessment unit quantifies and grades the region's idle land, standard factory buildings, energy consumption indicators, water resources, labor force, transportation and logistics, and public facilities in various areas. Combined with regional industrial development plans and environmental control requirements, it sets carrying capacity thresholds and upper limits for various resources to prevent the introduction of projects exceeding carrying capacity or that do not comply with regulations. The policy intelligent decomposition unit decomposes national, provincial, municipal, and district-level industrial support, investment incentives, and tax reduction policies in a layered manner. Through keyword extraction and semantic analysis technology, it automatically extracts core support clauses such as land subsidies, factory building reductions, talent subsidies, R&D rewards, and tax rebates, constructing a standardized and tagged policy adaptation library to achieve precise matching between policies and projects.

[0020] The industrial chain map, resource carrying capacity model, and policy tag library output by the three units are integrated to form a complete industrial investment promotion knowledge base, which is synchronized in real time to the investment promotion intelligent matching module to provide model support for multi-dimensional matching of projects.

[0021] S3. Intelligent analysis and multi-dimensional precise matching of project needs, outputting targeted investment promotion results. This step is completed collaboratively by the intelligent investment matching module and its subordinate project demand analysis unit, multi-dimensional weighted matching unit, matching scoring and sorting unit, and customized solution generation unit. This module is bidirectionally connected to the big data aggregation and governance module and the industry knowledge base construction module, and achieves intelligent investment screening based on underlying data and upper-level models.

[0022] First, the project requirements analysis unit uses NLP (Natural Language Processing) technology to intelligently analyze the text information and application materials of projects submitted by enterprises themselves, projects pushed by third-party investment promotion databases, and projects targeted by the system. It accurately extracts the core parameters of the projects, including structured requirements such as industry sub-type, investment scale, land area, energy consumption demand, employment scale, environmental emission standards, and policy demands, and completes the standardized transformation of unstructured project information.

[0023] Subsequently, the multi-dimensional weighted matching unit retrieves the industry chain model, resource carrying capacity threshold, and policy tagging system from the industry knowledge base. Using a pre-set weighted algorithm, it calculates scores for five core dimensions: industry fit score (matching the region's leading industries and industry chain gaps), policy fit score (matching investment promotion and support policies at all levels), resource carrying capacity score (matching land, factory, energy consumption, and labor resources), environmental compliance score (matching regional environmental control and energy consumption requirements), and industry chain value score (the project's contribution to the improvement of the regional industry chain and industrial value-added). The weights of each dimension can be dynamically adjusted according to the region's investment promotion priorities, balancing general applicability with regional customization.

[0024] The matching and ranking unit calculates a comprehensive matching score for projects based on a weighted average of five dimensions. All potential projects are prioritized according to their scores, automatically filtering high-scoring, high-quality targeted projects, marking moderately suitable projects, and eliminating inefficient and non-compliant projects. Finally, the customized solution generation unit combines the project's matching dimensions, core needs, and regional resource and policy advantages to automatically generate a customized investment promotion plan. This plan includes a list of applicable policies, resource allocation suggestions, implementation procedures, and risk warnings. The output project matching results and investment promotion plan are simultaneously pushed to the project implementation closed-loop management module to initiate the project implementation process.

[0025] S4. Closed-loop management of the entire project lifecycle, ensuring traceability and manageability throughout the entire implementation process. This step is completed collaboratively by the project implementation closed-loop management module and its subordinate full lifecycle ledger unit, progress intelligent monitoring unit, checkpoint early warning and handling unit, and performance assessment unit. This module receives high-quality project data output by the investment promotion intelligent matching module, realizes full-process digital closed-loop management from intention to performance, and at the same time accumulates project implementation effectiveness data to support strategy optimization.

[0026] Among them, the full life cycle ledger unit assigns a unique global project code to each intended project that passes the matching and screening, establishes an electronic lifelong ledger, and records all the information, processes, personnel and time nodes of the seven major stages of project intention docking, enterprise due diligence, project signing and filing, license and permit approval agency, land use planning and construction, completion acceptance and commissioning, and long-term performance supervision, so as to achieve "one code to manage the project and full traceability".

[0027] The intelligent progress monitoring unit pre-enters the standard time limits for government processing and investment promotion assessments at each stage, collects real-time progress data for each stage of the project, dynamically compares the actual progress of the project with the standard progress, and automatically identifies lagging projects. The bottleneck early warning and handling unit addresses bottleneck issues such as overdue progress, missing application materials, obstructed administrative approvals, insufficient resource indicators, and failure to meet landing conditions. It issues warnings at three levels: general, important, and urgent, and automatically pushes warning information to the corresponding responsible entities such as the investment promotion center, administrative approval, park management committee, and natural resources department according to the division of responsibilities, urging them to rectify within a specified time and remove obstacles to project implementation.

[0028] After the project officially commences operation, the performance evaluation unit continuously tracks and monitors key indicators such as actual investment performance, capacity release scale, tax contribution, job creation, and output growth rate. It compares the initial commitments made by the project with the actual implementation results, conducts performance rating assessments, and provides regulatory reminders and rectification supervision for projects that fail to meet standards. This completely solves the problem of traditional projects being "heavy on attracting investment, light on supervision, and weak on performance." All project implementation progress, bottlenecks, and performance data are all stored in the system database in real time and synchronized with the situation analysis and strategy optimization module.

[0029] S5, investment promotion trend analysis and dynamic algorithm iteration, enabling intelligent system upgrades. This step is completed collaboratively by the situation assessment and strategy optimization module and its subordinate investment promotion situation analysis unit, industrial chain gap prediction unit, algorithm weight iteration unit, and investment promotion strategy push unit. As the core of the system's intelligent optimization, this module relies on full historical data and real-time data to realize the iterative upgrade of investment promotion work from experience-driven to data-driven.

[0030] The investment promotion trend analysis unit automatically compiles comprehensive core indicators for investment promotion on a monthly, quarterly, and annual basis, including total project matching volume, intelligent matching success rate, project signing rate, project implementation and commencement rate, production and operation rate, regional resource utilization rate, and policy implementation efficiency. Through horizontal and vertical data comparisons, it comprehensively reviews the effectiveness and shortcomings of regional investment promotion efforts. The industrial chain gap prediction unit, based on historical investment promotion data, industrial implementation data, and industrial chain map data, uses big data trend analysis and correlation analysis to intelligently predict future weak links and missing links in the regional industrial chain, thus identifying key investment industries for the next stage in advance.

[0031] The algorithm weight iteration unit is the core intelligent upgrade mechanism of the system. Based on historical project matching scores, implementation success rates, contract fulfillment effects, and industry contributions, it reviews the rationality of the weights for each matching dimension and automatically and dynamically adjusts the algorithm weights for industry adaptation, policy matching, resource carrying capacity, compliance, and industrial chain value, continuously improving the accuracy of project matching. Finally, the investment promotion strategy push unit automatically generates a targeted investment promotion industry list, a key investment promotion enterprise directory, policy optimization suggestions, and resource allocation plans based on the situation analysis results, industrial chain prediction results, and the iterated matching model. This provides accurate data support for government investment promotion decisions and industrial planning adjustments, forming a virtuous cycle of "investment promotion practice - data review - algorithm optimization - strategy upgrade".

[0032] S6, comprehensive data visualization and end-to-end security protection The visualization module fully integrates with data from all levels of the system, building a multi-dimensional visualization system. Through government affairs dashboards, computer management terminals, and mobile work terminals, it displays core content such as the overall industrial landscape, resource reserves, policy compatibility, project matching results, progress of projects under negotiation, bottlenecks in projects under construction, contract fulfillment of projects in production, and overall investment promotion effectiveness. It supports precise querying with hierarchical permissions, custom data filtering, export of reports in multiple formats, and review and analysis of investment promotion trends, meeting the needs of managers and investment promotion staff at different levels.

[0033] The data security protection module covers the entire process of data collection, transmission, calculation, storage, display, and export in the system. Through multiple security mechanisms such as encrypted data storage, hierarchical user access control, anonymization of sensitive enterprise information and government data, full-process operation log traceability, real-time interception of unauthorized access, and data operation record archiving, it comprehensively prevents risks such as data leakage, data tampering, unauthorized access, and unauthorized export. It strictly complies with the security specifications of the government digital platform and ensures the security, integrity, and confidentiality of government investment data and confidential information of enterprise projects.

[0034] The present invention will be further described in detail below with reference to specific embodiments.

[0035] This invention discloses a local industry investment promotion intelligent matching and project implementation management system based on big data, deployed on government cloud servers at all levels, and adapted to local district / county and park-level digital application scenarios for industry investment promotion. The specific implementation process is as follows: S1. Big Data Aggregation and Governance: The system connects to regional government systems such as Development and Reform Commission, Industry and Information Technology Commission, Natural Resources Bureau, Environmental Protection Bureau, and Human Resources and Social Security Bureau through interfaces. It synchronizes regional industrial base data, idle factory and land data, energy consumption and environmental protection indicators, labor resources, investment promotion policies at all levels, historical investment projects and enterprise data. At the same time, it collects investment dynamics and industrial transfer information of leading enterprises in the industry through compliant web crawlers. The collected multi-source heterogeneous data is cleaned, deduplicated, formatted, and fields are unified. Data is associated and integrated with industry codes and regional codes as the core. Data is updated incrementally every day to build a real-time updated investment promotion big data foundation.

[0036] S2. Industry Knowledge Base Construction: Based on the big data foundation after governance, the system automatically draws the industrial chain map of the region's leading industries, marks the missing links and weak supporting links in the upstream and downstream; quantitatively assesses the carrying capacity of land, energy consumption, labor, and supporting facilities resources in each area and sets thresholds; intelligently disassembles investment promotion policies at all levels, extracts core tags such as tax reduction and exemption, factory subsidies, talent support, and land use preferences, and constructs a standardized industry knowledge base to support subsequent intelligent matching and strategy analysis.

[0037] S3. Intelligent Matching of Investment Projects: The system receives projects submitted by enterprises themselves, projects pushed by third-party investment project databases, and targeted mining of industrial project data. It analyzes the project's investment scale, industry category, resource requirements, and compliance indicators using NLP (Natural Language Processing) technology. Based on a multi-dimensional weighted algorithm, it calculates a comprehensive score for the project's suitability with regional industries, policy matching, resource carrying capacity, environmental compliance, and industrial chain supplementation value. The system generates investment priorities based on the scores and automatically generates customized investment plans, policy matching lists, and resource allocation plans for high-scoring, high-quality projects.

[0038] S4. Full-process project implementation management: For matched intended projects, the system assigns a unique project code and establishes a full-lifecycle digital ledger, covering seven major stages: intention connection, due diligence verification, contract signing and filing, permit approval, land use and construction, production and operation, and performance supervision. Standard processing time limits are preset for each stage, and project progress is monitored in real time. Tiered warnings are issued for bottlenecks such as failure to update within time limits, approval obstacles, and insufficient resources, and the warnings are automatically pushed to the relevant personnel responsible for investment promotion, government approval, and park management. After the project is put into production, investment performance, production capacity, tax revenue, and employment indicators are continuously monitored to complete the performance assessment closed loop.

[0039] S5. Situation Analysis and Strategy Optimization: The system automatically calculates key indicators such as investment matching rate, project implementation rate, commencement rate, production rate, and resource utilization rate on a monthly, quarterly, and annual basis, and visualizes the overall investment situation in the region. Based on big data analysis, it predicts gaps in the industrial chain and shortcomings in investment attraction, dynamically adjusts the weights of each dimension of the matching algorithm, optimizes the list of targeted investment enterprises and key industry investment directions, and outputs investment strategy optimization reports to provide data support for government investment decision-making.

[0040] S6. Data Visualization and Security Protection: The system enables the visualization of industry data, matching results, project progress, and investment promotion trends through government affairs dashboards, computer terminals, and mobile devices. It supports hierarchical permission queries, data report export, and trend review. At the same time, it comprehensively protects system data security through data encryption storage, hierarchical permission control, key data anonymization, full traceability of operation logs, and illegal access interception. Example

[0041] This embodiment provides a computer device, such as a smartphone, tablet computer, laptop computer, desktop computer, rack server, blade server, tower server, or cabinet server (including standalone servers or server clusters composed of multiple servers) capable of executing programs. The computer device in this embodiment includes, but is not limited to, a memory and a processor that can be interconnected via a system bus.

[0042] In this embodiment, the memory (i.e., the readable storage medium) includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory can be an internal storage unit of a computer device, such as the hard disk or RAM of the computer device. In other embodiments, the memory can also be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device. Of course, the memory can also include both internal storage units and external storage devices of the computer device. In this embodiment, the memory is typically used to store the operating system and various application software installed on the computer device. In addition, the memory can also be used to temporarily store various types of data that have been output or will be output.

[0043] In some embodiments, the processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor is typically used to control the overall operation of a computer device. In this embodiment, the processor is used to run program code stored in memory or process data.

[0044] This embodiment also provides a computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, App application store, etc., which stores computer programs, and the programs perform corresponding functions when executed by a processor.

[0045] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, nor to combinations thereof. Those skilled in the art can make various changes, modifications, or combinations within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A local industry investment promotion intelligent matching and project implementation management system based on big data, characterized in that, include: The big data aggregation and governance module is used to connect with multiple data sources from local government departments, aggregate relevant data on regional industries, policies, carriers, enterprises, and projects, and complete the cleaning, standardization, fusion, correlation, and incremental updates of multi-source heterogeneous data to build a unified big data foundation for investment promotion. An industry knowledge base construction module is used to construct a regional industrial chain map, a resource carrying capacity assessment system, and a policy adaptation tag library to form an iterative industry investment promotion knowledge base. The industry knowledge base construction module is communicatively connected to the big data aggregation and governance module. The intelligent investment matching module is used to analyze the needs of investment projects, and based on a multi-dimensional weighted matching algorithm, it completes the intelligent matching of projects with regional industries, policies, and resources, and outputs matching scores, investment priorities, and customized investment plans. The intelligent investment matching module is communicatively connected to the big data aggregation and governance module and the industry knowledge base construction module. The big data foundation formed by the big data aggregation and governance module processes the data into the data format and content required by the industry knowledge base construction module and the investment promotion matching module. The industry knowledge base construction module and the investment promotion matching module further process and analyze the data provided by the big data aggregation and governance module.

2. The intelligent matching and project implementation management system for local industry investment promotion based on big data as described in claim 1, characterized in that, Also includes: The project implementation closed-loop management module is communicatively connected to the investment promotion intelligent matching module. The project implementation closed-loop management module is used to establish a digital ledger for the entire life cycle of investment projects, monitor the progress of project advancement in real time, intelligently identify and issue warnings for implementation bottlenecks, and realize closed-loop management of the entire process from initial intention to production and contract fulfillment.

3. The intelligent matching and project implementation management system for local industry investment promotion based on big data as described in claim 2, characterized in that, Also includes: The situation assessment and strategy optimization module is communicatively connected to the industry knowledge base construction module and the project implementation closed-loop management module. The situation assessment and strategy optimization module is used for big data analysis of investment promotion trends, prediction of industrial chain gaps, dynamic iterative matching of algorithm weights, and optimization of regional investment promotion strategies.

4. The intelligent matching and project implementation management system for local industry investment promotion based on big data as described in claim 3, characterized in that, Also includes: The visualization module is used to visualize and display industry data, matching results, project progress, and investment promotion trends, and to output data reports.

5. The intelligent matching and project implementation management system for local industry investment promotion based on big data as described in claim 4, characterized in that, Also includes: The data security protection module is used to implement system data encryption, access control, de-identification processing, and operation traceability to ensure data security.

6. The intelligent matching and project implementation management system for local industry investment promotion based on big data as described in claim 5, characterized in that, The big data aggregation and governance module includes a multi-source data acquisition unit, a data standardization processing unit, a data fusion and association unit, and an incremental update unit; The multi-source data acquisition unit is used to collect structured, semi-structured, and unstructured government and industry data related to investment promotion through interface integration, database synchronization, and compliant web crawling. The data standardization processing unit is used to unify the fields, formats, and coding standards of multi-source data and remove invalid, duplicate, and abnormal data. The data fusion and association unit is used to achieve cross-departmental data linkage and fusion based on industry codes, regional codes, and project codes. The incremental update unit is used to periodically synchronize the latest data from various departments to achieve dynamic iterative updates of the big data foundation.

7. The intelligent matching and project implementation management system for local industry investment promotion based on big data as described in claim 5, characterized in that, The industry knowledge base construction module is configured to: sort out the upstream and downstream and supporting industry system of the region's leading industries, mark the missing links, weak links and value-added links of the industrial chain; quantify and rate the region's land, factory buildings, energy consumption, labor and supporting facilities resources, and set resource carrying capacity thresholds; disassemble multi-level industrial investment promotion policies, extract core support clauses, and build a standardized policy adaptation tag library.

8. The intelligent matching and project implementation management system for local industry investment promotion based on big data as described in claim 5, characterized in that, The intelligent investment matching module is configured to use natural language processing technology to analyze the industry type, investment scale, resource requirements, compliance indicators, and policy demands of investment projects; calculate multi-dimensional scores for projects, including industry suitability score, policy matching score, resource carrying capacity score, environmental compliance score, and industrial chain value score; prioritize investment projects based on the comprehensive matching score and select high-quality targeted investment projects; and automatically generate customized investment plans for policy support, resource allocation, and implementation based on the project matching dimensions.

9. The intelligent matching and project implementation management system for local industry investment promotion based on big data as described in claim 5, characterized in that, The project implementation closed-loop management module includes a full life cycle ledger unit, an intelligent progress monitoring unit, a checkpoint early warning and handling unit, and a performance evaluation unit. The full lifecycle ledger unit assigns a unique code to each project to record information on the entire process of project intention connection, due diligence, contract signing and filing, approval agency, construction progress, commissioning and operation, and performance supervision. The intelligent progress monitoring unit presets standard processing time limits for each stage and compares the actual progress of the project with the standard progress in real time. The aforementioned checkpoint early warning and handling unit provides tiered early warnings for issues such as project delays, missing documents, obstructed approvals, and insufficient resources, and pushes these warnings to the corresponding responsible parties. The performance assessment unit monitors project investment performance, capacity release, tax contribution, and employment creation indicators, and completes closed-loop supervision of performance after project implementation.

10. A local industry investment promotion intelligent matching and project implementation management system based on big data as described in claim 5, characterized in that, The situation analysis and strategy optimization module is configured to regularly collect statistics on key indicators such as investment attraction volume, matching success rate, implementation conversion rate, and resource utilization rate to review the effectiveness of investment attraction work; predict regional industrial development shortcomings and future investment attraction needs based on historical big data; dynamically adjust the algorithm weights of each matching dimension based on the implementation effect of historical projects to optimize the accuracy of the matching model; and automatically generate a targeted investment attraction industry list, a key enterprise list, and policy optimization suggestions.

11. The intelligent matching and project implementation management system for local industry investment promotion based on big data as described in claim 5, characterized in that, The data security protection module is used to provide security protection for at least one of the following: encrypted data storage, hierarchical access control, sensitive data anonymization, full-process operation log traceability, and illegal access interception, so as to achieve comprehensive security protection for government investment data and enterprise project data.

12. A method for intelligent matching and project implementation management of local industry investment promotion based on big data, characterized in that, Applied to the system according to any one of claims 1-11, comprising the following steps: S1. Gather and manage multi-source investment promotion data to build a standardized and dynamically updated big data foundation for investment promotion; S2. Based on big data infrastructure, construct a regional industrial chain map, resource carrying capacity system, and policy tag library to form an industrial investment promotion knowledge base; S3: Intelligent analysis of investment project needs, using multi-dimensional weighted algorithms to accurately match projects with regional resources, select high-quality investment projects and generate customized investment plans; S4. Establish a full lifecycle digital ledger for matched projects, monitor the implementation progress in real time, and intelligently warn of bottlenecks to achieve closed-loop management; S5: Big data analysis of investment trends, prediction of supply chain gaps, dynamic iterative matching algorithm, and optimization of regional investment strategies; S6 provides a visual display of various investment promotion data, while ensuring data security throughout the entire process through a security protection mechanism.

13. A computer device, the computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method of claim 12.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the steps of the method of claim 12.

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