Regional industry investment resource dynamic updating and optimization system based on artificial intelligence platform

Abstract

The application discloses an area industry investment resource dynamic updating and optimizing system based on an artificial intelligence platform and belongs to the technical field of government affairs services, and comprises an information acquisition module, which is used for acquiring multi-source data of a target area, the multi-source data including enterprise business information, local government open policy files, land / warehouse resource data, upstream and downstream relationships in an industrial chain, investment history signing data, public opinion and enterprise dynamics, competitive regional investment data and talent and college resource, and updated data is acquired from the acquired multi-source data; a data processing module, the data processing module comprising an acquisition unit and an analysis unit; the acquisition unit is used for acquiring relevant changes, and the relevant changes are acquired based on the local government open policy files. The application realizes dynamic optimization and accurate placement of investment resources by integrating multi-source data and tracking updates, and combining deep policy and market insight and quantitative evaluation analysis, and helps the healthy development of regional economy.

Description

Technical Field

[0001] This invention relates to the field of government service technology, and in particular to a system for dynamically updating and optimizing regional industrial investment resources based on an artificial intelligence platform. Background Technology

[0002] In modern economic society, in order to optimize the structure and proportion of industries within a region and make them more in line with the needs and trends of economic development, it is necessary to attract investment to promote the adjustment and optimization of the industrial structure.

[0003] In this area of ​​research, application CN202411109405.9 provides a method, apparatus, equipment, and storage medium for optimizing regional industrial structure. This technical solution combines a table showing the relationship between the International Patent Classification (IPC) and a custom industrial classification, as well as a table showing the relationship between the IPC and strategic emerging industries classification, to analyze the industrial endowment of a target region, obtaining the analysis results. Then, combining a strategic emerging industries map and a custom industries map, based on the industrial endowment analysis results, it performs an industrial structure optimization analysis of the target region, identifying the key development industries for that region. This technical solution can intuitively reveal the current industrial status of a target region.

[0004] Another application, CN202310607404.6, provides a dynamic display system for investment promotion services based on big data and cloud computing. This technical solution includes a main control module, and data acquisition, data processing, display planning, and dynamic display modules connected to the main control module. It collects and retrieves information on investment promotion, urban infrastructure, and development plans, and integrates and analyzes this information using big data to match relevant land parcels. Finally, it presents comprehensive urban information to businesses in a dynamic, combined text and graphics format, facilitating a deeper understanding of various aspects of the city and providing a reliable basis for assessing urban development potential and formulating investment plans. This technical solution improves the efficiency and reduces the cost of urban investment promotion.

[0005] In practice, investment promotion requires the ability to perceive industrial transfer signals in real time, automatically update enterprise intelligence, and dynamically optimize matching strategies. However, the investment promotion approach of "data collection + manual screening + static map" mentioned above lacks quantitative evaluation capabilities, resulting in a lack of a holistic perspective for optimizing and updating investment resources, and consequently missing investment opportunities. Summary of the Invention

[0006] In view of the problems existing in the field of government service technology, the present invention is proposed.

[0007] Therefore, one of the objectives of this invention is to provide a system for dynamic updating and optimization of regional industrial investment resources based on an artificial intelligence platform. By integrating and tracking multi-source data, and combining in-depth policy and market insights with quantitative evaluation and analysis, the system achieves dynamic optimization and precise allocation of investment resources, thereby contributing to the healthy development of the regional economy.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] This invention provides a system for dynamically updating and optimizing regional industrial investment resources based on an artificial intelligence platform, including:

[0010] The information acquisition module is used to acquire multi-source data of the target region. The multi-source data includes enterprise business information, local government public policy documents, land / factory resource data, upstream and downstream relationships of the industrial chain, historical investment signing data, public opinion and enterprise dynamics, investment data of competing regions, and talent and university resources. The module also acquires updated data from the acquired multi-source data.

[0011] The data processing module includes an acquisition unit and an analysis unit;

[0012] The acquisition unit is used to acquire relevant changes, which are acquired based on the publicly available policy documents of the local government, as shown below:

[0013] Based on publicly available policy documents from local governments, relevant changes in land / factory resource data, public opinion and enterprise dynamics, and investment promotion data from competing regions are obtained; among which, the relevant changes in land / factory resource data include changes in the location, area, use, and starting price of land parcels;

[0014] The relevant changes in public opinion and corporate dynamics include changes in public opinion and corporate dynamics on news websites and social forums, and relevant monitoring is carried out based on the changes in public opinion and corporate dynamics;

[0015] The relevant changes in the investment promotion data of the competing regions include collecting investment promotion policy documents, industrial plans and preferential measures issued by the governments of the competing regions.

[0016] The analysis unit responds to the acquisition unit to perform relevant analysis, including relevant analysis based on the industry plan, as shown below:

[0017] Obtain changes in industrial planning, including changes in positioning and objectives, industrial direction, spatial layout, and policies and support mechanisms.

[0018] Based on the changes in the acquired industrial planning, analyze the differences between the current industrial planning of the target region and the industrial planning of the competing regions;

[0019] A data fusion and update module, comprising an extraction unit, a calculation unit, and an update unit;

[0020] The extraction unit is used to perform evaluation extraction based on the differences. The evaluation extraction is based on the ratings given by enterprises in the target region to the differences. Based on these ratings, the bias of enterprises in the target region towards changes in industrial planning is obtained.

[0021] The calculation unit is used to perform relevant calculations, including calculating the correlation between the business operations of enterprises in the target region and the bias selection;

[0022] The updating unit is used to dynamically update and optimize investment resources based on the correlation, including prioritizing the changes in industrial planning corresponding to the direction with high correlation as the primary investment resources for updating and optimization.

[0023] In a preferred embodiment of the present invention, the calculation unit calculates the correlation between the business operations of enterprises in the target region and the bias selection, based on the following formula:

[0024] ;

[0025] In the formula, Indicates the first Business operation characteristic values ​​of an individual enterprise;

[0026] Represents all enterprises The mean;

[0027] Indicates the first The score of each enterprise's preference for changes in industrial planning;

[0028] Represents all enterprises The mean;

[0029] This indicates the total number of businesses in the target region;

[0030] This represents the correlation coefficient.

[0031] In a preferred embodiment of the present invention, the following formula is also included:

[0032] ;

[0033] In the formula, Indicates the first Individual companies tend to "support" changes in industry planning;

[0034] Indicates the first The first business characteristic of an enterprise;

[0035] Indicates the first The second business characteristic of an enterprise;

[0036] Indicates the first The first company One business characteristic;

[0037] Represents the intercept term;

[0038] Indicates the first Regression coefficients for each business characteristic;

[0039] This indicates the probability that the corresponding company is inclined to support it.

[0040] In a preferred embodiment of the present invention, the matching degree between the business operations and bias selection of enterprises in the target region is obtained based on the calculation results, as shown below:

[0041] ;

[0042] In the formula, Indicates the first The degree of alignment between an enterprise's business operations and its preferred choices;

[0043] Indicates the first The weight of each business feature;

[0044] Indicates the first Normalized values ​​of each business feature;

[0045] Indicates the first The score of each enterprise's preference for changes in industrial planning;

[0046] This represents the total number of business features.

[0047] In a preferred embodiment of the present invention, the relevant monitoring in the acquisition unit includes monitoring the target enterprise's production expansion, relocation, financing news, senior management changes, litigation disputes, and negative events.

[0048] In a preferred embodiment of the present invention, the changes in positioning and objectives within the analysis unit include functional positioning adjustments, replacement of leading industries, revision of quantitative objectives, and addition / removal of industry categories.

[0049] The changes in the industrial direction include adjustments to the direction of industrial integration;

[0050] The changes in the spatial layout include adjustments to functional zoning, land use standards, and the direction of industrial transfer;

[0051] The changes in the aforementioned policies and safeguard mechanisms include changes in fiscal support and adjustments to land policies.

[0052] In a preferred embodiment of the present invention, relevant statistics are performed based on the calculation results. The relevant statistics are based on the changes in the industrial plan, including the proportion of matching degree between different business operations and corresponding preference selections. The proportions are sorted in descending order, and the investment promotion resources are updated and optimized based on the sorting results.

[0053] In a preferred embodiment of the present invention, the relevant statistics further include obtaining relevant information based on enterprises in the target region, as shown below:

[0054] The business operations of enterprises in the target region are categorized, and the number of enterprises corresponding to each business operation is obtained based on the categorization results.

[0055] The relevant processing is based on the number of enterprises, including classifying the business operations of more than 50% of the enterprises as Class I business operations, more than 30% of the enterprises as Class II business operations, and more than 20% of the enterprises as Class III business operations;

[0056] Based on the changes in the aforementioned industry plan, the preference for enterprises corresponding to Category I business operations will be updated and optimized as the primary investment attraction resource, the preference for enterprises corresponding to Category II business operations will be updated and optimized as the secondary investment attraction resource, and the preference for enterprises corresponding to Category III business operations will be updated and optimized as the secondary investment attraction resource.

[0057] In a preferred embodiment of the present invention, the following is provided: based on the bias selection of changes in industrial planning, a response rate to changes in industrial planning is preset among enterprises corresponding to the Class I, Class II, and Class III business operations; if the response rate of enterprises corresponding to Class I, Class II, and Class III business operations is lower than the preset response rate, then when updating and optimizing investment resources in the future, the bias selection of enterprises with response rates lower than the preset response rate will be eliminated.

[0058] Beneficial effects:

[0059] 1. This invention achieves the integration of multi-source heterogeneous data through information acquisition, including but not limited to enterprise business registration information, policy documents, land resources, industrial chain relationships, etc., to build a comprehensive investment promotion information database. It can also update the data, continuously track and capture the latest data changes, and ensure that investment promotion decisions are based on the latest and most accurate information, thereby improving the timeliness of decision-making.

[0060] 2. This invention takes policy documents as its core, enabling the system to accurately identify changes in key elements such as land / factory resources, public opinion dynamics, and competitor regional investment promotion policies, providing a reference for adjusting investment promotion strategies; at the same time, it conducts multi-dimensional analysis of changes in industrial planning, covering aspects such as positioning objectives, industrial direction, spatial layout, and policy guarantees, providing a scientific basis for the optimal allocation of investment promotion resources;

[0061] 3. This invention, through quantitative assessment of enterprise preferences, can evaluate the degree of preference of enterprises in the target region for changes in industrial planning, thereby achieving a precise grasp of enterprise needs. Furthermore, by calculating the correlation between enterprise business operations and preference choices, it provides quantitative support for the precise allocation of investment promotion resources, improving resource utilization efficiency. Based on the results of quantitative analysis, the system can dynamically adjust investment promotion resources to ensure that resources are tilted towards areas with high relevance and high preference, thereby maximizing investment promotion benefits.

[0062] 4. By statistically analyzing the matching degree between different business operations and preference choices, the priority of updating investment promotion resources can be intelligently sorted to improve the targeting and efficiency of investment promotion work; at the same time, a preset threshold for enterprise response rate can be set, and investment promotion strategies can be flexibly adjusted according to the actual response situation to ensure the effectiveness and adaptability of the strategy.

[0063] 5. Furthermore, for companies with response rates lower than the preset value, the system can automatically remove them and adjust them in future investment promotion resource updates to ensure the continuous optimization of investment promotion strategies. Attached Figure Description

[0064] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a schematic diagram of the modular structure of the regional industry investment promotion resource dynamic updating and optimization system based on an artificial intelligence platform, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the process structure of an embodiment of the present invention;

[0065] The diagram is labeled as follows: 110 - Information acquisition module; 120 - Data processing module; 1201 - Acquisition unit; 1202 - Analysis unit; 130 - Data fusion and update module; 1301 - Extraction unit; 1302 - Calculation unit; 1303 - Update unit. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0067] Because the existing investment promotion method of "data collection + manual screening + static map" lacks quantitative evaluation capabilities, it lacks a holistic perspective for optimizing and updating investment resources, thus missing investment opportunities.

[0068] Based on this, this invention proposes a dynamic updating and optimization system for regional industrial investment promotion resources based on an artificial intelligence platform. By integrating multi-source data and tracking updates in real time, and combining in-depth policy and market insights with quantitative evaluation and analysis, it achieves dynamic optimization and precise allocation of investment promotion resources. The system can sensitively capture policy and market changes, deeply analyze changes in industrial planning, and scientifically assess the correlation between enterprise business operations and changes in industrial planning, guided by enterprise needs and preferences. This allows for dynamic adjustment of investment promotion resource priorities, improving investment promotion efficiency and decision-making quality. Furthermore, by using a preset response rate mechanism to maintain strategic flexibility, it achieves precise, scientific, and continuous optimization of regional industrial investment promotion, contributing to the healthy development of the regional economy.

[0069] The present solution will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0070] Reference Figures 1 to 2 As one embodiment of the present invention, this embodiment provides a regional industrial investment promotion resource dynamic updating and optimization system based on an artificial intelligence platform, including:

[0071] The information acquisition module 110 is used to acquire multi-source data of the target region. The multi-source data includes enterprise business registration information (which can be obtained through scheduled crawling and change push), local government public policy documents, land / factory resource data (which can be obtained through the interface of the Natural Resources Bureau), upstream and downstream relationships of the industrial chain (which can be automatically extracted through knowledge graph), historical investment signing data (which can be obtained through internal system integration), public opinion and enterprise dynamics (which can be obtained through real-time monitoring through news API), investment data of competitor regions (which can be obtained through relevant public information), and talent and university resources (which can be obtained through public data of the education department). The module also acquires updated data from the acquired multi-source data.

[0072] This module enables the system to build a rich database of investment information by comprehensively collecting this data.

[0073] At the same time, the acquisition of multi-source data ensures the comprehensiveness of investment promotion information, providing a solid foundation for subsequent data analysis and decision-making;

[0074] It can also obtain updated data in real time, ensuring the timeliness and accuracy of investment information, and helping to keep abreast of market dynamics and policy changes.

[0075] Data processing module 120, which includes acquisition unit 1201 and analysis unit 1202;

[0076] Acquisition unit 1201 is used to acquire relevant changes, which are based on publicly available policy documents from local governments, as shown below:

[0077] Based on publicly available policy documents from local governments, we obtain relevant changes in land / factory resource data, public opinion and enterprise dynamics, and investment promotion data from competing regions. Among these changes, the relevant changes in land / factory resource data include changes in the location, area, use, and starting price of land parcels (obtained from the official websites of local natural resources bureaus by querying land acquisition approvals, land acquisition announcements, and land supply plans).

[0078] The relevant changes in public opinion and corporate dynamics include changes in public opinion and corporate dynamics on news websites and social forums, and relevant monitoring is carried out based on changes in public opinion and corporate dynamics;

[0079] Relevant changes in competitor region investment promotion data include collecting investment promotion policy documents, industrial plans and preferential measures issued by the governments of competitor regions (e.g., monthly comparison of policy strength (tax incentives, subsidy standards), and month-on-month changes in the number of signed projects / investment amount);

[0080] By capturing key information on policy and market changes, we can provide timely information for adjusting our investment promotion strategies.

[0081] Meanwhile, monitoring various types of data helps to gain a deeper understanding of market dynamics and enterprise needs, thereby improving the accuracy of investment attraction.

[0082] Analysis unit 1202 is a response acquisition unit used for relevant analysis, including relevant analysis based on industry planning, as shown below:

[0083] To obtain information on changes in industrial planning (this involves not just checking whether the planning document has been changed, but tracking the dynamic adjustments of every executable element in the plan), changes include changes in positioning and objectives, industrial direction, spatial layout, and policies and support mechanisms.

[0084] Based on the changes in the acquired industrial plans, analyze the differences between the current industrial plans of the target region and those of the competing regions.

[0085] A multi-dimensional analysis of changes in industrial planning provides a scientific basis for the optimal allocation of investment resources.

[0086] Furthermore, comparative analysis can clearly identify the competitive advantages and disadvantages of target regions, providing strong support for formulating targeted investment promotion strategies.

[0087] The data fusion and update module 130 includes an extraction unit 1301, a calculation unit 1302, and an update unit 1303.

[0088] The extraction unit 1301 is used to perform evaluation extraction based on the differences. The evaluation extraction is based on the rating of the differences by enterprises in the target region. Based on this rating, the bias of enterprises in the target region towards changes in industrial planning is obtained.

[0089] Based on the ratings given by businesses in the target region to changes in industrial planning, a biased selection evaluation is extracted. This rating reflects the businesses' acceptance and preference for changes in industrial planning.

[0090] This allows the region to be guided by the actual needs and preferences of enterprises, ensuring that the updating and optimization of investment resources meet market demands.

[0091] The calculation unit 1302 is used to perform relevant calculations, including calculating the correlation between the business operations and preference choices of enterprises in the target region;

[0092] In reality, a company's preference for changes in industrial planning is essentially a reflection of its own business operations. Companies don't make "abstract judgments" about industrial planning; rather, they interpret the changes through the lens of their own business operations. Who benefits from the plan and who doesn't depends on the company's current business structure, resource endowment, and competitive position.

[0093] Therefore, calculating the correlation between business operations and preference choices of enterprises in a target region is of practical significance.

[0094] This calculation process takes into account factors such as the company's business operation characteristics and its bias score in response to changes in industry planning.

[0095] By calculating the correlation, the relationship between a company's business operations and changes in industrial planning can be clarified, providing data support for the precise allocation of investment resources.

[0096] Furthermore, making decisions based on quantitative analysis results improves the scientific rigor and accuracy of the decisions.

[0097] The update unit 1303 is used to dynamically update and optimize investment resources based on relevance, including prioritizing the changes in industrial planning corresponding to the most relevant areas as the primary investment resources for updating and optimization.

[0098] It enables dynamic updating and optimization of investment resources, ensuring efficient utilization of resources and maximizing investment attraction results. It can also prioritize resources based on their relevance, improving the targeting and efficiency of investment attraction efforts.

[0099] Within the calculation unit, the correlation between the business operations and preference choices of enterprises in the target region is calculated using the following formula:

[0100] ;

[0101] In the formula, Indicates the first The business operation characteristics of an enterprise (such as industry chain position score, core technology density, profit margin level);

[0102] Represents all enterprises The mean;

[0103] Indicates the first The score of an enterprise's preference for changes in industrial planning (e.g., support = 1, neutral = 0, resistance = -1).

[0104] Represents all enterprises The mean;

[0105] This indicates the total number of businesses in the target region;

[0106] This represents the correlation coefficient.

[0107] It also includes calculations based on the following formula:

[0108] ;

[0109] In the formula, Indicates the first Individual companies tend to "support" changes in industry planning;

[0110] Indicates the first The first business characteristic of an enterprise (e.g., whether it is a high-tech enterprise);

[0111] Indicates the first The second business characteristic of an enterprise (e.g., debt-to-equity ratio);

[0112] Indicates the first The first company One business characteristic;

[0113] Represents the intercept term;

[0114] Indicates the first Regression coefficients for each business characteristic;

[0115] A value greater than 0 indicates that the stronger the feature, the more it is biased towards support.

[0116] This indicates the probability that the corresponding company is inclined to support it.

[0117] The bias selection is a discrete category (support / neutral / resist), and it is necessary to know "which business characteristic drives the bias".

[0118] Based on the calculation results, the matching degree between the business operations of enterprises in the target region and the preference selection is obtained, as shown below:

[0119] ;

[0120] In the formula, Indicates the first The degree of matching between a company's business operations and its preferred choices (overall correlation score).

[0121] Indicates the first The weight of each business feature;

[0122] Indicates the first Normalized values ​​of each business feature (dimension eliminated).

[0123] Indicates the first The score of each enterprise's preference for changes in industrial planning;

[0124] This represents the total number of business features.

[0125] The score obtained by enterprises for "matching degree between business operations and preferred selection" can be used to optimize the ranking of investment resources.

[0126] Within the acquisition unit, relevant monitoring includes news about the target company's expansion, relocation, financing, executive changes, litigation disputes, and negative events.

[0127] By conducting relevant monitoring, we can promptly capture the dynamics of enterprises and assess their impact on business operations and the investment environment.

[0128] This kind of monitoring of enterprise dynamics can promptly identify potential risks and provide risk warnings to the investment promotion department. At the same time, understanding enterprise dynamics helps to assess changes in the investment promotion environment and provides a basis for adjusting investment promotion strategies.

[0129] Within the analysis unit, changes in positioning and objectives include adjustments to functional positioning (e.g., a region shifting from a "manufacturing base" to an "innovation source," or from "comprehensive" to "specialized"), replacement of leading industries (e.g., the original focus on equipment manufacturing is changed to new energy / digital economy in the new plan), revision of quantitative targets (e.g., the output value target is adjusted from 50 billion to 80 billion, or the timeline is brought forward / postponed), and addition / removal of industrial categories (e.g., the addition of strategic industries such as digital creativity, aerospace and marine industries from the "13th Five-Year Plan" to the "14th Five-Year Plan").

[0130] Changes in industrial direction include adjustments to the direction of industrial integration (such as shifting from single manufacturing to a combination of "manufacturing + services" or "digital economy + real economy").

[0131] Changes in spatial layout include adjustments to functional zoning (e.g., changing from "production and manufacturing zone" to "research and innovation zone", or adding "supporting service zone"), adjustments to land use standards (e.g., increasing the plot ratio from ≥1.0 to ≥2.5, raising the investment / tax threshold per mu), and the direction of industrial transfer (some industries relocate from the core area to the peripheral area, or are coordinated across regions).

[0132] Changes in policies and safeguard mechanisms include changes in fiscal support and adjustments to land policies (such as shifting from "bidding, auction, and listing" to "standard land" supply, or policies for the redevelopment of inefficient land).

[0133] A comprehensive analysis of changes in industrial planning helps to gain a deeper understanding of their impact on investment promotion efforts; at the same time, based on detailed analysis results, more precise investment promotion strategies can be formulated to improve the effectiveness of investment promotion.

[0134] Based on the calculation results, relevant statistics are conducted. These statistics are based on changes in industrial planning, including the proportion of matching degree between different business operations and corresponding preference choices. The proportions are then sorted in descending order, and the investment promotion resources are updated and optimized based on the sorting results.

[0135] In this embodiment, for example, in the change of industrial planning, the 1 to 3 with the largest change in bias selection are regarded as the first objects of update and optimization, the 4 to 6 with the largest change in bias selection are regarded as the second objects of update and optimization, and so on.

[0136] The relevant statistics also include information obtained from businesses in the target region, as shown below:

[0137] The business operations of enterprises in the target region are categorized, and the number of enterprises corresponding to each business operation is obtained based on the categorization results.

[0138] The relevant processing is based on the number of enterprises, including classifying the business operations of more than 50% of the enterprises as Class I business operations, more than 30% of the enterprises as Class II business operations, and more than 20% of the enterprises as Class III business operations;

[0139] Based on the changes in industrial planning, the preference for enterprises corresponding to Category I business operations will be updated and optimized as the primary investment attraction resource, the preference for enterprises corresponding to Category II business operations will be updated and optimized as the secondary investment attraction resource, and the preference for enterprises corresponding to Category III business operations will be updated and optimized as the even more important investment attraction resource.

[0140] This data-driven approach to optimizing investment resources improves the scientific rigor and accuracy of decision-making.

[0141] At the same time, by differentiating between business operation types and quantities, priority management of investment resources was achieved, thereby improving resource utilization efficiency.

[0142] Based on the preference selection for changes in industrial planning, a response rate to changes in industrial planning is preset among enterprises corresponding to Category I, Category II, and Category III business operations. If the response rate of enterprises corresponding to Category I, Category II, and Category III business operations is lower than the preset response rate, then when updating and optimizing investment resources in the future, the preference selection of enterprises with a response rate lower than the preset response rate will be eliminated.

[0143] By setting preset response rates, the system can flexibly respond to changes in the market and the needs of enterprises. Furthermore, it can be dynamically adjusted based on actual response data, ensuring the continuous optimization and adaptability of the investment promotion strategy.

[0144] In summary, this application integrates and tracks multi-source data, combined with in-depth policy and market insights and quantitative evaluation analysis, to achieve dynamic optimization and precise allocation of investment promotion resources. Furthermore, the system can sensitively capture policy and market changes, deeply analyze changes in industrial planning, and scientifically assess the correlation between enterprise business operations and changes in industrial planning, guided by enterprise needs and preferences. This allows for dynamic adjustment of investment promotion resource priorities, improving investment promotion efficiency and decision-making quality, and contributing to the healthy development of the regional economy.

[0145] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A regional industrial investment promotion resource dynamic updating and optimization system based on an artificial intelligence platform, characterized in that: include: The information acquisition module is used to acquire multi-source data of the target region. The multi-source data includes enterprise business information, local government public policy documents, land / factory resource data, upstream and downstream relationships of the industrial chain, historical investment signing data, public opinion and enterprise dynamics, investment data of competing regions, and talent and university resources. The module also acquires updated data from the acquired multi-source data. The data processing module includes an acquisition unit and an analysis unit; The acquisition unit is used to acquire relevant changes, which are acquired based on the publicly available policy documents of the local government, as shown below: Based on publicly available policy documents from local governments, relevant changes in land / factory resource data, public opinion and enterprise dynamics, and investment promotion data from competing regions are obtained; among which, the relevant changes in land / factory resource data include changes in the location, area, use, and starting price of land parcels; The relevant changes in public opinion and corporate dynamics include changes in public opinion and corporate dynamics on news websites and social forums, and relevant monitoring is carried out based on the changes in public opinion and corporate dynamics; The relevant changes in the investment promotion data of the competing regions include collecting investment promotion policy documents, industrial plans and preferential measures issued by the governments of the competing regions. The analysis unit responds to the acquisition unit to perform relevant analysis, including relevant analysis based on the industry plan, as shown below: Obtain changes in industrial planning, including changes in positioning and objectives, industrial direction, spatial layout, and policies and support mechanisms. Based on the changes in the acquired industrial planning, analyze the differences between the current industrial planning of the target region and the industrial planning of the competing regions; A data fusion and update module, comprising an extraction unit, a calculation unit, and an update unit; The extraction unit is used to perform evaluation extraction based on the differences. The evaluation extraction is based on the ratings given by enterprises in the target region to the differences. Based on these ratings, the bias of enterprises in the target region towards changes in industrial planning is obtained. The calculation unit is used to perform relevant calculations, including calculating the correlation between the business operations of enterprises in the target region and the bias selection; The updating unit is used to dynamically update and optimize investment resources based on the correlation, including prioritizing the changes in industrial planning corresponding to the direction with high correlation as the primary investment resources for updating and optimization.

2. The regional industrial investment promotion resource dynamic updating and optimization system based on an artificial intelligence platform as described in claim 1, characterized in that, In the calculation unit, the correlation between the business operations of enterprises in the target region and the bias selection is calculated according to the following formula: ； In the formula, Indicates the first Business operation characteristic values ​​of an individual enterprise; Represents all enterprises The mean; Indicates the first The score of each enterprise's preference for changes in industrial planning; Represents all enterprises The mean; This indicates the total number of businesses in the target region; This represents the correlation coefficient.

3. The regional industrial investment promotion resource dynamic updating and optimization system based on an artificial intelligence platform as described in claim 2, characterized in that, It also includes calculations based on the following formula: ； In the formula, Indicates the first Individual companies tend to "support" changes in industry planning; Indicates the first The first business characteristic of an enterprise; Indicates the first The second business characteristic of an enterprise; Indicates the first The first company One business characteristic; Represents the intercept term; Indicates the first Regression coefficients for each business characteristic; This indicates the probability that the corresponding company is inclined to support it.

4. The regional industrial investment promotion resource dynamic updating and optimization system based on an artificial intelligence platform as described in any one of claims 2 to 3, characterized in that, Based on the calculation results, the matching degree between the business operations and preference selection of enterprises in the target region is obtained, as shown below: ； In the formula, Indicates the first The degree of alignment between an enterprise's business operations and its preferred choices; Indicates the first The weight of each business feature; Indicates the first Normalized values ​​of each business feature; Indicates the first The score of each enterprise's preference for changes in industrial planning; This represents the total number of business features.

5. The regional industrial investment promotion resource dynamic updating and optimization system based on an artificial intelligence platform as described in claim 1, characterized in that, In the acquisition unit, the relevant monitoring includes news about the target company's expansion, relocation, financing, senior management changes, litigation disputes, and negative events.

6. The regional industrial investment promotion resource dynamic updating and optimization system based on an artificial intelligence platform as described in claim 1, characterized in that, In the analysis unit, the changes in positioning and objectives include adjustments to functional positioning, replacement of leading industries, revision of quantitative objectives, and addition / removal of industry categories; The changes in the industrial direction include adjustments to the direction of industrial integration; The changes in the spatial layout include adjustments to functional zoning, land use standards, and the direction of industrial transfer; The changes in the aforementioned policies and safeguard mechanisms include changes in fiscal support and adjustments to land policies.

7. The regional industrial investment promotion resource dynamic updating and optimization system based on an artificial intelligence platform as described in claim 6, characterized in that, Based on the calculation results, relevant statistics are performed. These statistics are based on changes in the industrial plan, including the proportion of matching degree between different business operations and corresponding preference choices. The proportions are sorted in descending order, and the investment promotion resources are updated and optimized based on the sorting results.

8. The regional industrial investment promotion resource dynamic updating and optimization system based on an artificial intelligence platform as described in claim 7, characterized in that, The relevant statistics also include relevant information obtained from enterprises in the target region, as shown below: The business operations of enterprises in the target region are categorized, and the number of enterprises corresponding to each business operation is obtained based on the categorization results. The relevant processing is based on the number of enterprises, including classifying the business operations of more than 50% of the enterprises as Class I business operations, more than 30% of the enterprises as Class II business operations, and more than 20% of the enterprises as Class III business operations; Based on the changes in the aforementioned industry plan, the preference for enterprises corresponding to Category I business operations will be updated and optimized as the primary investment attraction resource, the preference for enterprises corresponding to Category II business operations will be updated and optimized as the secondary investment attraction resource, and the preference for enterprises corresponding to Category III business operations will be updated and optimized as the secondary investment attraction resource.

9. The regional industrial investment promotion resource dynamic updating and optimization system based on an artificial intelligence platform as described in claim 8, characterized in that, Based on the preference for changes in industrial planning, a response rate to changes in industrial planning is preset among enterprises corresponding to the aforementioned Class I, Class II, and Class III business operations; If the response rate of enterprises corresponding to Category I, Category II, and Category III business operations is lower than the preset response rate, then when updating and optimizing investment resources in the future, enterprises with response rates lower than the preset response rate will be selectively removed.

Images