A data analysis method and system for regional industry evaluation

Abstract

The present invention relates to the technical field of industrial evaluation, and in particular to a data analysis method and system for regional industrial evaluation. The method comprises: obtaining basic information of all enterprises in a to-be-evaluated region within a set time period from a database, and collecting industrial development planning information within the same time period; constructing a characteristic time series of industrial development planning; constructing an enterprise characteristic time series to reflect changes in enterprise information during the execution of each industrial development plan; extracting a keyword one and a keyword two from the basic information of the enterprise and the industrial development planning information respectively to form an enterprise keyword set and an industry keyword set; setting a weight value one by comparing keywords in the enterprise keyword set and the industry keyword set through a similarity measurement method; constructing a set of weight values, and constructing multiple heat maps in combination with enterprise latitude and longitude information; obtaining the business data information of the enterprise, establishing a correlation with the weight value one, and evaluating the development trend of the regional economy and the health status of the industry.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial evaluation, and in particular to a data analysis method and system for regional industrial evaluation. Background Art

[0002] The development of regional economy is inseparable from the development of industries within the region. It is an effective measure to promote the healthy development of regional economy by formulating regional industrial development plans to guide the development of regional industries. During the implementation of industrial development plans, it is necessary to continuously evaluate the development trends and quality of industries within the region so as to make adjustments to the deficiencies in the development plans.

[0003] In the existing technology, heat maps are often used to reflect the degree of agglomeration of enterprises in different regions. However, relying solely on the degree of agglomeration cannot fully reflect the quality and trend of regional industrial development. To this end, we propose a method: based on heat maps, combined with industrial development plans and enterprise development data, to establish a connection between enterprises and development plans. This method can not only evaluate the quality and trend of industrial development in a region, but also adjust the industrial development plan in a timely manner according to the trend of industrial development. Summary of the invention

[0004] The present invention simultaneously considers enterprise characteristics, industrial policies and economic performance, providing a comprehensive perspective for regional industrial assessment.

[0005] The technical solution proposed by the present invention is: a data analysis method for regional industry evaluation, the method comprising: Obtain basic information of all enterprises in the area to be evaluated within a set time period from the database, and collect industrial development planning information within the same time period; Construct a characteristic time series of industrial development planning, where the information set at each time point includes the start and end time and specific content of the industrial development plan; construct an enterprise characteristic time series to reflect the changes in enterprise information during the implementation of each industrial development plan; Extract keyword 1 and keyword 2 from the basic information of the enterprise and the information of industrial development plan respectively to form the enterprise keyword set and the industry keyword set; By comparing the keywords in the enterprise keyword set and the industry keyword set through a similarity measurement method, the degree of relevance of each enterprise to the specific industry development planning needs is quantified to set a weight value of one; Construct a set of weight values, and construct multiple heat maps based on the longitude and latitude information of enterprises to represent the distribution of enterprises in geographical space under different industrial development plans and their correlation with industrial development needs; Obtain the business data information of the enterprise, establish an association with the weight value of one, and evaluate the development trend of the regional economy and the health of the industry by analyzing the changes in business data during the implementation of each industrial development plan.

[0006] Preferably, the basic information of the enterprise includes the name, latitude and longitude, scale information, main business, honors, establishment date and operating status of the enterprise; The construction of the characteristic time series of industrial development planning is specifically as follows: Construct characteristic time series of industrial development planning: ,in, Indicates an industrial development planning information set; The industrial development planning information set includes The start and end time information and planning information of the implementation of the industrial development plan, Indicates the number of industrial development plans; Constructing enterprise characteristic time series ,in, Indicates The basic information set of enterprises in the area to be evaluated during the implementation of each industrial development plan; the changes in enterprise information in the area to be evaluated during the implementation of each industrial development plan are reflected through enterprise characteristic time series; The keyword 1 and keyword 2 are extracted from the basic information of the enterprise and the industrial development planning information respectively to form the enterprise keyword set and the industry keyword set, including: from Extract multiple keywords to form a corporate keyword set from Extract multiple keywords from the data to form an industry keyword set; After setting the weight value to one according to the degree of association, the following steps are also included: Construct a set of weight values ,in, Indicates The first The weight of an enterprise is one. Indicates the number of enterprises.

[0007] Preferably, the weight values ​​in the weight value set are arranged in ascending order, and the first The weight value is one, forming a key correlation value set , , ;According to the key correlation value set, obtain the name information of the corresponding enterprise to form a key enterprise information set , ,in, Respectively expressed in During the implementation period of the industrial development plan from the 1st to the 2nd The name of the enterprise.

[0008] Preferably, the heat maps during the multiple industrial development plans are arranged in chronological order to form a heat map sequence; Each heat map is divided into multiple annual layers, and each annual layer represents the heat regional distribution of one year during the implementation of an industrial development plan. Specifically, the average value of the weight value of each enterprise in one year during an industrial development plan is calculated; based on the average value of the weight value of each enterprise, the corresponding annual layer is drawn; After superimposing multiple annual layers in chronological order, a thermal regional distribution during the implementation of an industrial development plan is formed; According to the key enterprise information set, the weight value corresponding to the key enterprise information is extracted from an annual layer, and a key enterprise heat map is drawn; Identify the boundaries of the heat areas in each key enterprise heat map, display the boundary lines, and set the corresponding display color for each boundary line; Calculate the area of ​​the thermal zone of the key enterprises in each annual layer, form a time series of the thermal zone area, and set a risk threshold. If the area of ​​the thermal zone of the key enterprise is smaller than the risk threshold, it is judged that many enterprises in the region have changed their main business direction, and the industrial development potential of the region is declining, and corresponding measures need to be taken.

[0009] Preferably, multiple first-level classification conditions are set, and the first-level classification conditions include but are not limited to enterprise scale, enterprise density, enterprise growth, innovation results, and R&D strength; the first-level classification conditions include multiple second-level classification conditions, and the second-level classification conditions include but are not limited to strategic emerging industry enterprises, enterprises above designated size, Fortune 500 companies, national high-tech enterprises, and specialized, sophisticated, and innovative small and medium-sized enterprises; The enterprises in each annual layer are divided into different types of type sets through the secondary classification conditions; specifically: The type set for the annual layer is ,in, Indicates During the industrial development planning period The annual layer A set of types, ; ,in, Indicates that it belongs to The set of enterprise names of the type set, Indicates that it belongs to The number of enterprises in each type set; Assign a weight value of two to each type set , calculate the weight value three , Indicates the total number of type sets; Obtain the latitude and longitude information of the enterprises in each type of set, and combine the weight value of each type to generate a secondary indicator heat map that reflects the distribution of enterprises under the secondary classification conditions; Obtain the latitude and longitude information of the enterprises in each type of set, and combine it with the weight value three to generate a first-level indicator heat map that reflects the distribution of enterprises under the first-level classification conditions; Analyze regional R&D strength and development potential through primary indicator heat maps and secondary indicator heat maps.

[0010] Preferably, the enterprise keyword set and the industry keyword set are compared to determine the similarity between each enterprise and the first The correlation degree of the development planning needs of the industries is determined, and the weight value is set according to the correlation degree, including the following steps: Obtain basic information about the enterprise and information on industrial development plans; Using the TF-IDF vector method to extract enterprise keywords and industry keywords from the basic information of the enterprise and the information of the industrial development plan, respectively, to construct an enterprise keyword set and an industry keyword set; the enterprise keyword set includes but is not limited to the name of the enterprise, the name of the main business of the enterprise, the scale of the enterprise, and the type of industry to which the enterprise belongs; the industry keyword set includes but is not limited to the types of key development industries, the types of key development business of the enterprise, and the types of enterprises to be cultivated; The similarity between enterprise keywords and industry keywords is calculated using the cosine similarity algorithm; Construct a similarity matrix and set a weight value of one according to the similarity matrix. The greater the similarity, the greater the weight value of one.

[0011] Preferably, constructing multiple heat maps of the correlation between enterprises and industrial development needs in the area to be evaluated based on a set of weight values ​​and the longitude and latitude of the enterprises includes the following steps: Import the data visualization library Folium; Get the name, longitude and latitude of the enterprise and the weight value of each enterprise under an industrial development plan; Creating a base map using Folium Figure 1 , the center point is set to the average latitude and longitude position of all enterprises; Use Folium's HeatMap plugin to add a thermal layer. Repeat the above steps to construct a thermal map layer corresponding to different industrial development plans.

[0012] Preferably, obtaining the business data information of the enterprise and establishing the associated information with the weight value 1 includes the following steps: Obtaining the name, longitude and latitude of the enterprise, weight value 1, and business data information, wherein the business data includes revenue, profit, and market share; Normalize the operating data; Assign a weight to each item of the operating data, then calculate a weighted average, and use the weighted average as a comprehensive score; Creating a base map using Folium Figure 2 ; Combine the company's name, longitude and latitude, weight value 1 and comprehensive score, and use Folium's HeatMap plug-in to add thermal layer 2; The HeatMap plug-in is used to overlay the thermal layer 1 and the thermal layer 2 to obtain a heat map, which is used to show the correlation between the enterprise and the industrial development plan and reflect the economic benefits of the enterprise.

[0013] The present invention also provides a data analysis system for regional industry evaluation, wherein the system is used to execute the data analysis method for regional industry evaluation.

[0014] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the data analysis method for regional industry assessment.

[0015] Beneficial effects of the present invention: 1. The present invention first obtains the degree of association between each enterprise and the industrial development plan, that is, the weight value 1 (thermal value), and draws the thermal Figure 1 ; Then, obtain the business data of the enterprise including revenue, profit and market share; normalize the business data; assign weights to each item in the business data, and then calculate the weighted average, and use the weighted average as the comprehensive score. Combine the name, longitude and latitude of the enterprise, weight value one and the comprehensive score, use Folium's HeatMap plug-in to add thermal layer two, and superimpose thermal layer one and thermal layer two to obtain a heat map corresponding to the corresponding industrial development plan. The heat map shows the correlation between the enterprise and the industrial development plan, as well as the relationship between the economic benefits of enterprises in the region and weight value one.

[0016] 2. The present invention arranges the heat maps of multiple industrial development planning periods in chronological order to form a heat map sequence. By observing the heat maps of different years, we can clearly see the expansion or contraction trend of the key enterprise heat areas over time. This helps to identify the growth trajectory and market influence changes of enterprises in the region.

[0017] 3. In the present invention, by calculating the average value of the weight value one of each enterprise in a year during an industrial development plan; then drawing the corresponding annual layer based on the average value of the weight value one of each enterprise; through the annual layer, the development changes of the enterprise in different time periods can be observed in more detail, and by comparing the heat maps of different years, the changing trends of enterprise distribution and potential development opportunities can be identified. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flow chart of a data analysis method for regional industry evaluation.

[0019] Figure 2 Schematic diagram of heat map construction in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0021] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0022] refer to Figure 1 The technical solution provided by the present invention is: a data analysis method for regional industry evaluation, comprising the following steps: 1. Obtain from the database the basic information of all enterprises in the area to be evaluated within the set time period; the basic information of the enterprises includes the name, longitude and latitude, scale information, main business, honors, establishment date and operating status of the enterprises.

[0023] 2. Obtain all industrial development planning information for the area to be evaluated within the set time period.

[0024] 3. Constructing characteristic time series of industrial development planning ,in, Indicates an industrial development planning information set; The industrial development planning information set includes The start and end time information and planning information of the implementation of the industrial development plan, Indicates the number of industrial development plans.

[0025] 4. Constructing Enterprise Characteristic Time Series ,in, Indicates The basic information set of enterprises in the area to be evaluated during the implementation of each industrial development plan; the changes in enterprise information in the area to be evaluated during the implementation of each industrial development plan are reflected through enterprise characteristic time series.

[0026] 5. From Extract multiple keywords to form a corporate keyword set from A plurality of keywords are extracted from the industry to form an industry keyword set.

[0027] 6. Compare the enterprise keyword set and the industry keyword set, and use the similarity measurement method to determine whether each enterprise is similar to the first The degree of correlation between the development planning needs of each industry is determined by setting a weight value according to the degree of correlation, including: Obtain basic information about the enterprise and information on industrial development plans; Using the TF-IDF vector method to extract enterprise keywords and industry keywords from the basic information of the enterprise and the information of the industrial development plan, respectively, to construct an enterprise keyword set and an industry keyword set; the enterprise keyword set includes but is not limited to the name of the enterprise, the name of the main business of the enterprise, the scale of the enterprise, and the type of industry to which the enterprise belongs; the industry keyword set includes but is not limited to the types of key development industries, the types of key development business of the enterprise, and the types of enterprises to be cultivated; The similarity between enterprise keywords and industry keywords is calculated using the cosine similarity algorithm; Construct a similarity matrix and set a weight value of one according to the similarity matrix. The greater the similarity, the greater the weight value of one.

[0028] 7. Construct a set of weight values ,in, Indicates The first The weight of an enterprise is one. Indicates the number of enterprises.

[0029] 8. Based on a set of weight values ​​and the longitude and latitude of the enterprise, multiple heat maps of the correlation between the enterprises and the industrial development needs in the area to be evaluated are constructed, including the steps of: importing the data visualization library Folium; Get the name, longitude and latitude of the enterprise and the weight value of each enterprise under an industrial development plan; Creating a base map using Folium Figure 1 , the center point is set to the average latitude and longitude position of all enterprises; Use Folium's HeatMap plugin to add a thermal layer. Repeat the above steps to construct a thermal map layer corresponding to different industrial development plans.

[0030] Add corresponding planning information tags to each heat map to help decision makers identify enterprises that play an important role in each industrial development plan; IX. Based on the basic information of each enterprise, obtain the enterprise's operating data information, establish an association with the operating data information weight value of one, and add the enterprise's operating data information to the corresponding heat map to analyze the regional economic development trend during the implementation of each industrial development plan to evaluate the development status of the regional industry, including: Obtaining the name, longitude and latitude of the enterprise, a weight value, and operating data information, wherein the operating data includes revenue, profit, and market share; normalizing the operating data; assigning a weight to each item in the operating data, and then calculating a weighted average, and using the weighted average as a comprehensive score; Creating a base map using Folium Figure 2 Combine the company's name, longitude and latitude, weight value 1 and comprehensive score, and use Folium's HeatMap plug-in to add thermal layer 2; The HeatMap plug-in is used to overlay the thermal layer 1 and the thermal layer 2 to obtain a heat map corresponding to the corresponding industrial development plan. The heat map is used to show the correlation between the enterprise and the industrial development plan and reflect the economic benefits of the enterprise.

[0031] Arrange the weight values ​​in the weight value set in ascending order, and extract the previous The weight value is one, forming a key correlation value set , , ;According to the key correlation value set, obtain the name information of the corresponding enterprise to form a key enterprise information set , ,in, Respectively expressed in During the implementation period of the industrial development plan from the 1st to the 2nd Arrange the heat maps of multiple industrial development plans in chronological order to form a heat map sequence.

[0032] like Figure 2In this embodiment, the preset time period is 10 years. If an industrial development plan is formulated every 5 years, there are two heat maps corresponding to each industrial development plan in the preset time period, and each heat map has 5 annual layers. Each annual layer represents the heat regional distribution of an industrial development plan during one year, specifically: calculate the average value of the weight value of each enterprise in one year during an industrial development plan; draw the corresponding annual layer based on the average value of the weight value of each enterprise; the development and changes of enterprises in different time periods can be observed in more detail, and by comparing the heat maps of different years, the changing trend of enterprise distribution and potential development opportunities can be identified.

[0033] After overlaying multiple annual layers in chronological order, a heat map distribution during the implementation of an industrial development plan is formed. By observing the heat maps of different years, we can clearly see the expansion or contraction trend of key enterprise heat maps over time. This helps to identify the growth trajectory and market influence changes of enterprises in the region.

[0034] According to the key enterprise information set, the weight value corresponding to the key enterprise information is extracted from an annual layer, and a key enterprise heat map is drawn; Identify the boundaries of the thermal areas in the heat map of each key enterprise, display the boundary lines, and set the corresponding display color for each boundary line. Specifically, it includes: obtaining key enterprise information, identifying the thermal areas of key enterprises based on the key enterprise information, creating buffers for each key enterprise, using the unary_union method to merge the buffers to form a complete thermal area boundary, and using the area attribute of Geopandas to obtain the area of ​​the area contained in the boundary.

[0035] Calculate the area of ​​the thermal zone of key enterprises in each annual layer to form a time series of thermal zone area , in order to quantify the changes in the distribution of enterprises. By drawing a heat map of key enterprises, it is helpful to focus on key enterprises and analyze their development status. Real-time monitoring of the development of key enterprises in different time periods, timely detection of abnormal changes, and providing a basis for decision-making.

[0036] in, Indicates During the industrial development planning period The area of ​​the key enterprise thermal zone in each annual layer.

[0037] Then, a risk threshold is set. If the area of ​​the thermal zone of a key enterprise is smaller than the risk threshold, it is judged that many enterprises in the region have changed their main business direction, the industrial development potential of the region is declining, and corresponding measures need to be taken.

[0038] By comparing heat maps from different years, we can identify the movement direction of key enterprise heat areas, which is of great significance for understanding the market strategy, resource allocation and future development direction of enterprises.

[0039] At the same time, combined with geographic information system (GIS) technology, the movement paths of key enterprise thermal areas can be tracked to further analyze the driving and influencing factors behind them.

[0040] Set multiple first-level classification conditions, including but not limited to enterprise scale, enterprise density, enterprise growth, innovation achievements, and R&D strength; the first-level classification conditions include multiple second-level classification conditions, including but not limited to strategic emerging industry enterprises, large-scale enterprises, Fortune 500 companies, national high-tech enterprises, and specialized, sophisticated and innovative small and medium-sized enterprises.

[0041] According to the company name, information related to the above two classification conditions can be obtained from the Internet through web crawler technology.

[0042] The enterprises in each annual layer are divided into different types of type sets through the secondary classification conditions; specifically: Set up The type set for the annual layer is ,in, Indicates During the industrial development planning period The annual layer A set of types, ; ,in, Indicates that it belongs to The set of enterprise names of the type set, Indicates that it belongs to The number of enterprises in each type set; In this embodiment, referring to Table 1, if the enterprises belonging to the strategic information industry are: Enterprise 1, Enterprise 2 and Enterprise 3. The enterprise name set representing the first type set, Represents the number of enterprises belonging to the first type set, that is: , =3.

[0043] Assign a weight value of two to each type set , calculate the weight value three , Represents the total number of type sets, i.e., weight value three is obtained by weight value two.

[0044] The latitude and longitude information of enterprises in each type concentration is obtained, and combined with the weight value of each type, a secondary indicator heat map is generated to reflect the distribution of enterprises under the secondary classification conditions; the latitude and longitude information of enterprises in each type concentration is obtained, and combined with the weight value of each type, a primary indicator heat map is generated to reflect the distribution of enterprises under the primary classification conditions; the distribution characteristics of different types of enterprises can be obtained through the primary indicator heat map and the secondary indicator heat map, and the R&D strength and economic strength of the industry in the region can be reflected through different enterprise heat areas, which is convenient for decision makers to evaluate the development trend and quality of the regional industry.

[0045] In this embodiment, the first-level classification conditions and the second-level classification conditions are obtained by importing a preset classification table, see Table 1.

[0046] Table 1 Classification table

[0047] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the data analysis method for regional industry assessment.

[0048] The embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. The embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by the central processing unit (CPU), the above-mentioned functions defined in the method of the present application are executed. It should be noted that the above-mentioned computer-readable medium of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of an electrical, magnetic, optical, electromagnetic, infrared segment, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wire segments, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, electrical wire, optical cable, RF, etc., or any suitable combination of the foregoing.

[0049] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0050] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be deformed or modified in any way.

Claims

1. A data analysis method for regional industry evaluation, characterized in that: The method comprises: Obtain basic information of all enterprises in the area to be evaluated within a set time period from the database, and collect industrial development planning information within the same time period; Construct a characteristic time series of industrial development planning, where the information set at each time point includes the start and end time and specific content of the industrial development plan; construct an enterprise characteristic time series to reflect the changes in enterprise information during the implementation of each industrial development plan; Extract keyword 1 and keyword 2 from the basic information of the enterprise and the information of industrial development plan respectively to form the enterprise keyword set and the industry keyword set; By comparing the keywords in the enterprise keyword set and the industry keyword set through a similarity measurement method, the degree of relevance of each enterprise to the specific industry development planning needs is quantified to set a weight value of one; Construct a set of weight values, and construct multiple heat maps based on the longitude and latitude information of enterprises to represent the distribution of enterprises in geographical space under different industrial development plans and their correlation with industrial development needs; Obtain the business data information of the enterprise, establish an association with the weight value of one, and evaluate the development trend of the regional economy and the health of the industry by analyzing the changes in business data during the implementation of each industrial development plan.

2. A data analysis method for regional industry evaluation according to claim 1, characterized in that: The basic information of the enterprise includes the name, latitude and longitude, scale information, main business, honors, establishment date and operating status of the enterprise; The construction of the characteristic time series of industrial development planning is specifically as follows: Construct characteristic time series of industrial development planning: ,in, Indicates an industrial development planning information set; The industrial development planning information set includes The start and end time information and planning information of the implementation of the industrial development plan, Indicates the number of industrial development plans; Constructing enterprise characteristic time series ,in, Indicates The basic information set of enterprises in the area to be evaluated during the implementation of each industrial development plan; the changes in enterprise information in the area to be evaluated during the implementation of each industrial development plan are reflected through enterprise characteristic time series; The keyword 1 and keyword 2 are extracted from the basic information of the enterprise and the industrial development planning information respectively to form the enterprise keyword set and the industry keyword set, including: from Extract multiple keywords to form a corporate keyword set from Extract multiple keywords from the data to form an industry keyword set; After setting the weight value to one according to the degree of association, the following steps are also included: Construct a set of weight values ,in, Indicates The first The weight of an enterprise is one. Indicates the number of enterprises.

3. A data analysis method for regional industry evaluation according to claim 2, characterized in that: Arrange the weight values ​​in the weight value set in ascending order, and extract the The weight value is one, forming a key correlation value set , , ; Obtain the name information of the corresponding enterprise according to the key correlation value set to form a key enterprise information set , ,in, Respectively expressed in During the implementation period of the industrial development plan from the 1st to the 2nd The name of the enterprise.

4. A data analysis method for regional industry evaluation according to claim 1, characterized in that: Arrange the heat maps of multiple industrial development planning periods in chronological order to form a heat map sequence; Each heat map is divided into multiple annual layers, and each annual layer represents the heat regional distribution of one year during the implementation of an industrial development plan. Specifically, the average value of the weight value of each enterprise in one year during an industrial development plan is calculated; based on the average value of the weight value of each enterprise, the corresponding annual layer is drawn; After superimposing multiple annual layers in chronological order, a thermal regional distribution during the implementation of an industrial development plan is formed; According to the key enterprise information set, the weight value corresponding to the key enterprise information is extracted from an annual layer, and a key enterprise heat map is drawn; Identify the boundaries of the heat areas in each key enterprise heat map, display the boundary lines, and set the corresponding display color for each boundary line; Calculate the area of ​​the thermal zone of the key enterprises in each annual layer, form a time series of the thermal zone area, and set a risk threshold. If the area of ​​the thermal zone of the key enterprise is smaller than the risk threshold, it is judged that many enterprises in the region have changed their main business direction, and the industrial development potential of the region is declining, and corresponding measures need to be taken.

5. A data analysis method for regional industry evaluation according to claim 4, characterized in that: Set multiple first-level classification conditions, including but not limited to enterprise scale, enterprise density, enterprise growth, innovation achievements, and R&D strength; the first-level classification conditions include multiple second-level classification conditions, including but not limited to strategic emerging industry enterprises, enterprises above designated size, Fortune 500 companies, national high-tech enterprises, and specialized, sophisticated, and innovative small and medium-sized enterprises; The enterprises in each annual layer are divided into different types of type sets through the secondary classification conditions; specifically: Set up The type set for the annual layer is ,in, Indicates During the industrial development planning period The annual layer A set of types, ; ,in, Indicates that it belongs to The set of enterprise names of the type set, Indicates that it belongs to The number of enterprises in each type set; Assign a weight value of two to each type set , calculate the weight value three ,in, Indicates the total number of type sets; Obtain the latitude and longitude information of the enterprises in each type of set, and combine the weight value of each type to generate a secondary indicator heat map that reflects the distribution of enterprises under the secondary classification conditions; Obtain the latitude and longitude information of the enterprises in each type of set, and combine it with the weight value three to generate a first-level indicator heat map that reflects the distribution of enterprises under the first-level classification conditions; Analyze regional R&D strength and development potential through primary indicator heat maps and secondary indicator heat maps.

6. A data analysis method for regional industry evaluation according to claim 1, characterized in that: The comparison of the enterprise keyword set and the industry keyword set is performed by using a similarity measurement method to determine whether each enterprise is similar to the first The correlation degree of the development planning needs of the industries is determined, and the weight value is set according to the correlation degree, including the following steps: Obtain basic information about the enterprise and information on industrial development plans; Using the TF-IDF vector method to extract enterprise keywords and industry keywords from the basic information of the enterprise and the information of the industrial development plan, respectively, to construct an enterprise keyword set and an industry keyword set; the enterprise keyword set includes but is not limited to the name of the enterprise, the name of the main business of the enterprise, the scale of the enterprise, and the type of industry to which the enterprise belongs; the industry keyword set includes but is not limited to the types of key development industries, the types of key development business of the enterprise, and the types of key cultivated enterprises; The similarity between enterprise keywords and industry keywords is calculated using the cosine similarity algorithm; Construct a similarity matrix and set a weight value of one according to the similarity matrix. The greater the similarity, the greater the weight value of one.

7. A data analysis method for regional industry evaluation according to claim 6, characterized in that: The step of constructing multiple heat maps of the correlation between enterprises and industrial development needs in the area to be evaluated based on a set of weight values ​​and the longitude and latitude of the enterprises includes the following steps: Import the data visualization library Folium; Get the name, longitude and latitude of the enterprise and the weight value of each enterprise under an industrial development plan; Use Folium to create a basic map. The center point is set to the average latitude and longitude of all enterprises. Use Folium's HeatMap plugin to add a thermal layer. Repeat the above steps to construct a thermal map layer corresponding to different industrial development plans.

8. A data analysis method for regional industry evaluation according to claim 7, characterized in that: The step of obtaining the business data information of the enterprise and establishing an association with the weight value 1 includes the following steps: Obtaining the name, longitude and latitude of the enterprise, weight value 1, and business data information, wherein the business data includes revenue, profit, and market share; Normalize the operating data; Assign a weight to each item of the operating data, then calculate a weighted average, and use the weighted average as a comprehensive score; Use Folium to create base map 2; Combine the company's name, longitude and latitude, weight value 1 and comprehensive score, and use Folium's HeatMap plug-in to add thermal layer 2; The HeatMap plug-in is used to overlay the thermal layer 1 and the thermal layer 2 to obtain a heat map, which is used to show the correlation between the enterprise and the industrial development plan and reflect the economic benefits of the enterprise.

9. A data analysis system for regional industry evaluation, characterized in that: The system is used to execute a data analysis method for regional industry assessment as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement a data analysis method for regional industry assessment as described in any one of claims 1 to 8.

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