CN112990632A - Regional industry competitiveness analysis system and method based on big data - Google Patents
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Abstract
The invention relates to a regional industry competitiveness analysis system and method based on big data, which effectively replace manual data processing by utilizing a machine learning method, greatly improve the data processing efficiency and improve the data availability, further improve the applicability of industrial analysis by acquiring and analyzing dynamic information, master multi-latitude data such as industrial dynamic, industrial layout, industrial technical innovation and the like, form a visual analysis effect graph and provide comprehensive data support for the healthy development of regional industry.
Description
Technical Field
The invention relates to the technical field of industrial analysis, in particular to a regional industry competitiveness analysis system and method based on big data.
Background
Industry development is an important component of regional economy, and the development trend of industry directly affects regional development planning. The detailed analysis of the regional industry is helpful for making a targeted industry policy to give full play to the advantages of the regional industry, and the method has great practical significance for deeply adjusting the industrial structure and accelerating the regional economic construction.
The existing regional industry analysis is often directed to the analysis of a single industry, the analysis of a certain industry in a region or the deep analysis of a certain direction of the industry, so that the content of the industry analysis is incomplete, the development condition of the regional industry cannot be controlled integrally, the comprehensive comparative analysis of the regional key industry is not formed, and a decision basis cannot be provided for the regional economic development; in addition, the traditional industrial analysis forms mostly display industrial analysis data in a data table or simple bar graph, bar graph or pie graph mode, and the display forms are mostly single static forms, which cannot help users to visually acquire information well.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a regional industry competitiveness analysis system and method based on big data, which finds regional leading industries, pillar industries and cultivation industries through machine learning and manual correction, integrates economic data and enterprise data related to the industries, can be used for evaluating the economic strength and integrity of each regional economic industry, performs dynamic targeted analysis on the regional pillar industries, monitors the development condition of the industries, masters multi-latitude data such as industrial dynamics, industrial layout, industrial technical innovation and the like, forms a visual analysis effect diagram, and provides comprehensive data support for the healthy development of the regional industries.
In order to achieve the above purpose, the technical scheme adopted by the invention comprises the following steps:
a big data-based regional industry competitiveness analysis system comprises a regional industry data acquisition module, a regional key type industry identification module, a regional key enterprise dynamic information tracking module and a regional key type industry data analysis module which are in data communication with one another;
the regional industrial data acquisition module acquires and collects all industrial data in the arrangement region through data crawling and/or manual acquisition, wherein the industrial data comprises industrial economic data, industrial technical innovation data and enterprise data of enterprises under industry;
the area key type industry identification module comprises a key type industry evaluation sub-module and a key type industry chain analysis sub-module; the key type industry evaluation submodule establishes a key type industry evaluation model through a machine learning method, and evaluates all industrial data in an analysis area by using the key type industry evaluation model to obtain key type industry data; the key type industrial chain analysis submodule establishes a key type industrial chain model through a machine learning method, and analyzes key type industrial data by using the key type industrial chain model to obtain a key type industrial chain and a key type industrial chain associated enterprise list, and simultaneously forms a key type industrial map; the key type industry comprises a leading industry, a pillar industry and a cultivation industry of a region, and the key type industry chain comprises industry chains of the leading industry, the pillar industry and the cultivation industry in the corresponding region;
the area key enterprise identification module obtains key enterprises and/or leading enterprises in the key type industry by analyzing the enterprise data of each enterprise in the key type industry chain;
the dynamic information tracking module of the regional key enterprise establishes a dynamic information tracking model of the key type industry aiming at key enterprises and/or leading enterprises under the key type industry through a machine learning method, and uses the dynamic information tracking model of the key type industry to track the dynamic information of the key type industry and the dynamic information of the key enterprises and/or leading enterprises under the key type industry so as to obtain the dynamic information of the key type industry and the dynamic information of the key enterprises and/or leading enterprises under the key type industry;
the area key type industry data analysis module integrates and analyzes key type industry data, key type industry chains, key type industry dynamic information and key enterprise and/or leading enterprise dynamic information in the key type industry to obtain an area industry general situation analysis chart and specific industry operation situation analysis.
Further, the industrial economic data comprises an industrial total value, an industrial added value, a business income, an enterprise tax and a profit sum of the industry; the industrial technical innovation data comprises the number and the variation trend of innovation platforms, entrepreneurship hatching carriers, entrepreneurship institutions, scientific research institutions and universities relevant to the industry; the enterprise data includes enterprise business data, enterprise profiles, enterprise products and services, enterprise financing data, enterprise investment layout data, and enterprise intellectual property data.
Further, the machine learning method comprises the steps that a professional analyst sorts partial data to obtain a comparison group, program evaluation is continuously conducted through machine learning so that the error between a machine learning result and the comparison group is smaller than a preset threshold value, a learning model is built, and adjustment is conducted on the basis of the data to obtain an optimal model.
Further, the regional key enterprise identification module classifies the enterprises in the region into major enterprises, key enterprises, unicorn enterprises, gazelle enterprises, high-growth enterprises and potential innovation enterprises under the key type industries by analyzing the enterprise scale under the key type industries in the region and the investment layout, the investment amount, the investment frequency and the total amount of the enterprises in the region.
Further, the dynamic information of the key type industry comprises dynamic information captured based on key words related to key type industry and annual report of the industry; the dynamic information of the key enterprises and/or the leading enterprises comprises dynamic information captured by using enterprise names and key technical names as keywords.
Further, the system also comprises an early warning analysis module; the early warning analysis module analyzes the operation indexes, the number of personnel, the regional layout, the investment and financing and dynamic information data of the target enterprise to obtain the development score of the enterprise, monitors the development acceleration of the target enterprise by using the development score and carries out early warning on the target enterprise with lower development score; the target enterprises comprise faucet enterprises, key enterprises, unicorn enterprises, gazelle enterprises, high-growth enterprises and/or potential innovation enterprises.
Further, the area emphasis type industry data analysis module comprises data cleaning processing, wherein the data cleaning processing comprises deleting invalid data, filling blank data and/or de-duplicating repeated data.
Further, the regional industry general situation analysis chart comprises an analysis chart of industrial economic development conditions, an industrial main product sales analysis chart and/or a key enterprise business data analysis chart of a specific industrial field; the analysis chart of the industrial economic development condition comprises an industry calendar year total operation chart, an industry total value chart, an industry added value chart, an enterprise tax chart, a total profit current term chart and/or a comparation analysis chart; the key enterprise operation data analysis chart in the specific industrial field comprises an industrial total value chart, an industrial added value chart, a business income chart, an enterprise tax revenue chart, a total profit chart and/or a recruitment number analysis chart of an enterprise.
Further, the analysis of the operation situation of the specific industry comprises the analysis of economic data of the specific industry, the analysis of revenue indexes of leading enterprises and/or key enterprises and/or potential innovation enterprises of the specific industry, the analysis of investment layout, the analysis of technical innovation of the industry and the dynamic information of the specific industry; the revenue index analysis comprises a list of ten enterprises in the industry field and the change condition of the business index in the same proportion; the investment layout analysis comprises the investment situation and the layout situation of key enterprises and/or leading enterprises in the industrial field in the area; the technical innovation analysis of the industry comprises the analysis of technical transformation investment, technical research and development investment and patent quantity of the industry from the time dimension.
A regional industry competitiveness analysis method based on big data comprises the following steps:
s1, collecting and sorting all industrial data in the area through data crawling and/or manual acquisition;
s2, establishing a key type industry evaluation model and a key type industry chain model through a machine learning method, using the key type industry evaluation model to evaluate and analyze all industry data in the area to obtain key type industry data, using the key type industry chain model to analyze the key type industry data to obtain a key type industry chain and a key type industry chain associated enterprise list, and simultaneously forming a key type industry map;
s3, obtaining key enterprises and/or leading enterprises in the key type industry by analyzing the enterprise data of each enterprise in the key type industry chain;
s4, establishing a dynamic information tracking model of the key type industry aiming at key enterprises and/or leading enterprises in the key type industry through a machine learning method, and using the dynamic information tracking model of the key type industry to track the dynamic information of the key type industry and the dynamic information of the key enterprises and/or leading enterprises in the key type industry to obtain the dynamic information of the key type industry and the dynamic information of the key enterprises and/or leading enterprises in the key type industry;
and S5, integrating and analyzing the important type industry data, the important type industry chain, the important type industry dynamic information and the important enterprise and/or faucet enterprise dynamic information in the important type industry to obtain the regional industry general situation analysis chart and the specific industry operation situation analysis.
The invention has the beneficial effects that:
by adopting the regional industry competitiveness analysis system and method based on big data to process and evaluate the economic strength and integrity of each industry of regional economy, the system and method can realize the targeted comprehensive comparative analysis of key industries in a region while deeply analyzing a certain industry, and provide sufficient decision basis for regional economic development; the machine learning method is used for effectively replacing manual data processing, so that the data processing efficiency is greatly improved, the data availability is improved, the data volume and the data quality applied to industrial analysis are improved, and the accuracy of industrial analysis is improved; the applicability of industrial analysis is further improved through acquisition and analysis of dynamic information, a regional industry overall situation analysis chart and specific industry operation situation analysis are obtained through analysis of the dynamic information, a visual analysis effect chart is further formed, and regional industry development decision-making personnel can utilize data analysis results conveniently.
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FIG. 1 is a schematic diagram of a big data-based regional industry competitiveness analysis system according to the present invention.
FIG. 2 is a flow chart of a big data-based regional industry competitiveness analysis method according to the present invention.
Detailed Description
For a clearer understanding of the contents of the present invention, reference will be made to the accompanying drawings and examples.
Fig. 1 is a schematic structural diagram of a big data-based regional industry competitiveness analysis system according to the present invention, which includes a regional industry data acquisition module, a regional key type industry identification module, a regional key enterprise dynamic information tracking module, and a regional key type industry data analysis module, all of which are in data communication with each other; the regional industrial data acquisition module acquires and collects all industrial data in a tidying region through data crawling and/or manual work, wherein the industrial data comprises industrial economic data (comprising industrial total value, industrial added value, business income, enterprise tax and total profit of the industry, the industrial technical innovation data comprises the number and change trend of innovation platforms, entrepreneurship hatching carriers, entrepreneurship institutions, scientific research institutions and colleges related to the industry, and the enterprise data comprises enterprise business data, enterprise introduction, enterprise products and services, enterprise financing data, enterprise investment layout data and enterprise intellectual property data), industrial technical innovation data and enterprise data of each enterprise under the industry; the area key type industry identification module comprises a key type industry evaluation sub-module and a key type industry chain analysis sub-module; the key type industry evaluation submodule establishes a key type industry evaluation model through a machine learning method, all industry data in an evaluation analysis area are evaluated by using the key type industry evaluation model to obtain key type industry data, the key type industry chain analysis submodule establishes a key type industry chain model through a machine learning method, key type industry data are analyzed by using the key type industry chain model to obtain a key type industry chain and a key type industry chain associated enterprise list, and a key type industry map is formed at the same time, the machine learning method comprises the steps of arranging part of data by a professional analyst to obtain a contrast group, continuously evaluating a program through machine learning to enable the error between a machine learning result and the contrast group to be smaller than a preset threshold value, establishing a learning model, and adjusting based on the data to obtain an optimal model; the area key enterprise identification module obtains key enterprises and/or faucet enterprises under the key type industry by analyzing enterprise data of enterprises under the key type industry chain, and specifically comprises the steps of classifying the enterprises in the area into the faucet enterprises, key enterprises, independent animal enterprises, gazelle enterprises, high-growth enterprises and potential innovation-type enterprises under the key type industry by analyzing the enterprise scale under the key type industry in the area and the investment layout, the investment amount, the investment frequency and the total amount of the enterprises in the area; the dynamic information tracking module of the regional key enterprise establishes a dynamic information tracking model of the key type industry aiming at key enterprises and/or leading enterprises under the key type industry through a machine learning method, and uses the dynamic information tracking model of the key type industry to track the dynamic information of the key type industry and the dynamic information of the key enterprises and/or leading enterprises under the key type industry, so as to obtain the dynamic information of the key type industry (including the dynamic information captured by taking key words related to annual reports of the key type industry and the industry) and the dynamic information of the key enterprises and/or leading enterprises under the key type industry (including the dynamic information captured by taking the names of the enterprises and the names of key technologies as key words); the area important type industry data analysis module integrates and analyzes important type industry data, important type industry chains, important type industry dynamic information and important enterprises and/or leading enterprises dynamic information under the important type industry to obtain area industry general situation analysis charts (including analysis charts of industrial economic development conditions such as an industry past year total operation chart, an industry total value chart, an industry added value chart, an enterprise tax rate chart, a profit total amount current date chart and/or an identity analysis chart and an industry main product sales analysis chart and/or an important enterprise business data analysis chart in a specific industry field such as an industry total product chart, an industry added value chart, a business income chart, an enterprise tax chart, a profit total value chart and/or a worker number analysis chart) and specific industry operation situation analysis (including economic data analysis of a specific industry, the method comprises the steps of analyzing revenue indexes of leading enterprises and/or key enterprises and/or potential innovation-type enterprises of a specific industry, namely, the list of the first ten enterprises which are revenue for enterprises in the industry field and the change condition of the business indexes thereof in a same ratio, analyzing investment layout, namely, the investment condition and the layout condition of the key enterprises and/or leading enterprises in the industry field in the region, analyzing the technical innovation analysis of the industry, namely, analyzing the technical transformation investment, the technical research and development investment and the patent quantity of the industry in the time dimension and the dynamic information of the specific industry), and cleaning data in the analysis process, wherein the cleaning treatment of the data comprises the deletion of invalid data, the filling of blank data and/or the deduplication of duplicated data.
The system also comprises an early warning analysis module; the early warning analysis module analyzes the operation indexes, the number of personnel, the regional layout, the investment and financing and dynamic information data of the target enterprise to obtain the development score of the enterprise, monitors the development acceleration of the target enterprise by using the development score and carries out early warning on the target enterprise with lower development score; the target enterprises comprise faucet enterprises, key enterprises, unicorn enterprises, gazelle enterprises, high-growth enterprises and/or potential innovation enterprises.
The invention also relates to a regional industry competitiveness analysis method based on big data, which specifically comprises the following steps:
s1, collecting and sorting all industrial data in the area through data crawling and/or manual acquisition; a professional analyst can arrange partial industrial data of the area, program evaluation is continuously carried out through machine learning, a learning model is established, adjustment is continuously carried out on the basis of the industrial data, a learning result is optimized, all industrial overall situation analysis results of the area are obtained, and key industrial data are extracted according to all industrial overall situation analysis results.
S2, establishing a key type industry evaluation model and a key type industry chain model through a machine learning method, using the key type industry evaluation model to evaluate and analyze all industry data in the area to obtain key type industry data, using the key type industry chain model to analyze the key type industry data to obtain a key type industry chain and a key type industry chain associated enterprise list, and simultaneously forming a key type industry map; the key point is to comb the logic of an industrial chain of a key industry, and professionals sort all links of the industrial chain and corresponding enterprise data, form a learning model through machine learning, output results, judge results manually, continuously input data for adjustment, optimize the learning model and the results, finally obtain a complete chain of the industry and a related enterprise list, and form a regional space layout diagram and a strong chain complement analysis diagram of the industry. The industrial area spatial layout diagram refers to a thermodynamic diagram and a geographical position distribution diagram which are manufactured according to the number of associated enterprises of each chain link of the key industry; the strong chain link-supplementing diagram indicates the strong chain link and the link needing chain supplementing according to the number of associated enterprises in each link of the industrial chain and the total industrial value of each industrial link, and provides a basis for accurate industry recruitment.
S3, obtaining key enterprises and/or leading enterprises in the key type industry by analyzing the enterprise data of each enterprise in the key type industry chain; major enterprises refer to enterprises with an intra-industry top10 combed according to total enterprise revenue indexes, leading enterprises refer to top5 enterprises which carry out screening in the range of the major enterprises, and innovative enterprises can also be screened synchronously, namely potential innovative enterprises with high innovation and high growth speed, such as high-growth enterprises, gazelle enterprises, unicorn enterprises and the like. Meanwhile, relevant data of all enterprises of major industries can be crawled according to the obtained enterprise list, wherein the relevant data comprises but is not limited to data such as enterprise total revenue, enterprise profit, enterprise tax and the like, major enterprises and leaders of the major enterprises can be sorted according to the enterprise revenue and the industrial total value index data, the major enterprises and leaders of the major enterprises and the leaders of the innovative enterprises can be analyzed, the development conditions of the major enterprises and the leaders of the innovative enterprises can be monitored, enterprise early warning analysis can be formed, the enterprise early warning analysis comprises analyzing main operation indexes, regional layout, investment and financing and dynamic information data of the major enterprises, major enterprises with high enterprise total value reduction speed, and the development acceleration of other enterprises can be monitored, and the main operation indexes comprise but not limited to the industrial total value, business income, total profit, net profit and the; the regional layout means whether an enterprise has branch organization layout in the region; the dynamic information refers to the related news trends of enterprises, and refers to the fact that professionals grab and arrange dynamic data of the enterprises according to industrial fields and enterprise names, program evaluation is continuously carried out through machine learning, a learning model is built, adjustment is continuously carried out based on the data, learning results are optimized, and dynamic information tracking of the enterprises is formed.
S4, establishing a dynamic information tracking model of the key type industry aiming at key enterprises and/or leading enterprises in the key type industry through a machine learning method, and using the dynamic information tracking model of the key type industry to track the dynamic information of the key type industry and the dynamic information of the key enterprises and/or leading enterprises in the key type industry to obtain the dynamic information of the key type industry and the dynamic information of the key enterprises and/or leading enterprises in the key type industry; the industrial dynamic information data refers to that a business person combs an industrial dynamic data source website, technical personnel capture and sort the industrial dynamic data according to keywords such as industrial and industrial annual reports and the like to form industrial dynamic data, program evaluation is continuously carried out through machine learning, a learning model is established, adjustment is continuously carried out based on the data, a learning result is optimized, and industrial dynamic information tracking is formed. Similarly, technical innovations in a particular industry may be analyzed, including technical modification, technical research and development, and patent numbers in the industry in a time dimension.
S5, integrating and analyzing the key type industry data, the key type industry chain, the key type industry dynamic information and the key enterprise and/or the leading enterprise dynamic information under the key type industry to obtain a regional industry overall situation analysis chart and specific industry operation situation analysis (including economic data analysis aiming at the specific industry, revenue index analysis of leading enterprises, key enterprises and innovative enterprises of the specific industry, investment layout analysis, industrial technical innovation analysis and specific industry dynamic information). And crawling relevant data of the regional key industry, including regional industry economic data, national industry economic data, regional industry innovation resources, associated enterprises and the like, cleaning and processing the data, and making a regional industry general situation analysis chart. The relevant data of the area key industry refers to economic data of the key industry; relevant data associated with the enterprise, including but not limited to revenue, total industrial value; innovative resource data including but not limited to spatial distribution and quantity statistics of innovative platforms, entrepreneurship incubation carriers, colleges, scientific research institutions, entrepreneurship institutions and scientific service institutions corresponding to the key industrial fields; national layout data of regional key industries and national key industry development situations. The cleaning and processing work of the data refers to deletion of invalid data, filling of blank data, duplication removal of duplicated data and the like. The method comprises the steps of manufacturing a regional industry general situation analysis chart, analyzing general situations of regional key industries by a pointer, analyzing the economic data indexes of the regional key industries from the aspects of the same ratio, the ring ratio, the proportion and the like, and mastering the change trend of the key industries from the time dimension; manufacturing an innovation resource space distribution diagram of the regional key industry and an innovation resource change trend diagram of the regional key industry; the major industry is the national layout and the national development situation of the major industry. The national layout of key industries comprises the distribution of the number of enterprises of the industries across the country and an enterprise list of total industrial values top5 of all the regions, the development situation of the key industries across the country refers to the national range, the development trend analysis of economic indexes such as revenues and product yield of the key industries and the like reflects the overall development situation of the key industries across the country through the accumulated number and the same proportion acceleration.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A big data-based regional industry competitiveness analysis system comprises a regional industry data acquisition module, a regional key type industry identification module, a regional key enterprise dynamic information tracking module and a regional key type industry data analysis module which are in data communication with one another;
the regional industrial data acquisition module acquires and collects all industrial data in the arrangement region through data crawling and/or manual acquisition, wherein the industrial data comprises industrial economic data, industrial technical innovation data and enterprise data of enterprises under industry;
the area key type industry identification module comprises a key type industry evaluation sub-module and a key type industry chain analysis sub-module; the key type industry evaluation submodule establishes a key type industry evaluation model through a machine learning method, and evaluates all industrial data in an analysis area by using the key type industry evaluation model to obtain key type industry data; the key type industrial chain analysis submodule establishes a key type industrial chain model through a machine learning method, and analyzes key type industrial data by using the key type industrial chain model to obtain a key type industrial chain and a key type industrial chain associated enterprise list, and simultaneously forms a key type industrial map; the key type industry comprises a leading industry, a pillar industry and a cultivation industry of a region, and the key type industry chain comprises industry chains of the leading industry, the pillar industry and the cultivation industry in the corresponding region;
the area key enterprise identification module obtains key enterprises and/or leading enterprises in the key type industry by analyzing the enterprise data of each enterprise in the key type industry chain;
the dynamic information tracking module of the regional key enterprise establishes a dynamic information tracking model of the key type industry aiming at key enterprises and/or leading enterprises under the key type industry through a machine learning method, and uses the dynamic information tracking model of the key type industry to track the dynamic information of the key type industry and the dynamic information of the key enterprises and/or leading enterprises under the key type industry so as to obtain the dynamic information of the key type industry and the dynamic information of the key enterprises and/or leading enterprises under the key type industry;
the area key type industry data analysis module integrates and analyzes key type industry data, key type industry chains, key type industry dynamic information and key enterprise and/or leading enterprise dynamic information in the key type industry to obtain an area industry general situation analysis chart and specific industry operation situation analysis.
2. The system of claim 1, wherein the industry economic data includes industry gross value, industry added value, revenue, business tax, total profit for an industry; the industrial technical innovation data comprises the number and the variation trend of innovation platforms, entrepreneurship hatching carriers, entrepreneurship institutions, scientific research institutions and universities relevant to the industry; the enterprise data includes enterprise business data, enterprise profiles, enterprise products and services, enterprise financing data, enterprise investment layout data, and enterprise intellectual property data.
3. The system of claim 1, wherein the machine learning method comprises the steps of organizing partial data by a professional analyst to obtain a comparison group, continuously evaluating the program through machine learning so that the error between the machine learning result and the comparison group is smaller than a preset threshold value, establishing a learning model, and adjusting based on the data to obtain an optimal model.
4. The system of claim 1, wherein the regional key enterprise identification module classifies the regional enterprises into major enterprises, key enterprises, independent animals enterprises, gazelle enterprises, high-growth enterprises, and potential innovation enterprises within the key type industries by analyzing the enterprise size and investment layout, investment amount, investment frequency, and total number of the enterprises within the region.
5. The system of claim 1, wherein the dynamic information of the major type industry includes dynamic information captured based on keywords related to annual reports of the major type industry and the industry in which the major type industry is located; the dynamic information of the key enterprises and/or the leading enterprises comprises dynamic information captured by using enterprise names and key technical names as keywords.
6. The system of claim 1, further comprising an early warning analysis module; the early warning analysis module analyzes the operation indexes, the number of personnel, the regional layout, the investment and financing and dynamic information data of the target enterprise to obtain the development score of the enterprise, monitors the development acceleration of the target enterprise by using the development score and carries out early warning on the target enterprise with lower development score; the target enterprises comprise faucet enterprises, key enterprises, unicorn enterprises, gazelle enterprises, high-growth enterprises and/or potential innovation enterprises.
7. The system of claim 1, wherein the area-major type industry data analysis module includes a cleansing process on data, the cleansing process on data including deletion of invalid data, padding of blank data, and/or deduplication of duplicate data.
8. The system of claim 1, wherein the regional industry general situation analysis chart comprises an analysis chart of industrial economic development conditions, an industrial main product sales analysis chart and/or an enterprise-focused business management data analysis chart of a specific industrial field; the analysis chart of the industrial economic development condition comprises an industry calendar year total operation chart, an industry total value chart, an industry added value chart, an enterprise tax chart, a total profit current term chart and/or a comparation analysis chart; the key enterprise operation data analysis chart in the specific industrial field comprises an industrial total value chart, an industrial added value chart, a business income chart, an enterprise tax revenue chart, a total profit chart and/or a recruitment number analysis chart of an enterprise.
9. The system of claim 1, wherein the industry-specific operational situation analysis comprises economic data analysis of a specific industry, revenue index analysis of leading and/or key and/or potential creative enterprises of a specific industry, investment layout analysis, technical innovation analysis of an industry, and dynamic information of a specific industry; the revenue index analysis comprises a list of ten enterprises in the industry field and the change condition of the business index in the same proportion; the investment layout analysis comprises the investment situation and the layout situation of key enterprises and/or leading enterprises in the industrial field in the area; the technical innovation analysis of the industry comprises the analysis of technical transformation investment, technical research and development investment and patent quantity of the industry from the time dimension.
10. A regional industry competitiveness analysis method based on big data comprises the following steps:
s1, collecting and sorting all industrial data in the area through data crawling and/or manual acquisition;
s2, establishing a key type industry evaluation model and a key type industry chain model through a machine learning method, using the key type industry evaluation model to evaluate and analyze all industry data in the area to obtain key type industry data, using the key type industry chain model to analyze the key type industry data to obtain a key type industry chain and a key type industry chain associated enterprise list, and simultaneously forming a key type industry map;
s3, obtaining key enterprises and/or leading enterprises in the key type industry by analyzing the enterprise data of each enterprise in the key type industry chain;
s4, establishing a dynamic information tracking model of the key type industry aiming at key enterprises and/or leading enterprises in the key type industry through a machine learning method, and using the dynamic information tracking model of the key type industry to track the dynamic information of the key type industry and the dynamic information of the key enterprises and/or leading enterprises in the key type industry to obtain the dynamic information of the key type industry and the dynamic information of the key enterprises and/or leading enterprises in the key type industry;
and S5, integrating and analyzing the important type industry data, the important type industry chain, the important type industry dynamic information and the important enterprise and/or faucet enterprise dynamic information in the important type industry to obtain the regional industry general situation analysis chart and the specific industry operation situation analysis.
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