Method for identifying spatial clustering characteristics of regional new energy automobile industry

Abstract

The present application relates to a kind of regional new energy automobile industry space cluster feature identification method. It is applicable to new energy automobile industry planning field. The method comprises: obtaining the business registration information of new energy automobile related enterprises in target area, and determining the main industry type of each related enterprise registration based on the business information in business registration information;Determine the industry type corresponding to each related enterprise;Construct regional new energy automobile enterprise economic and spatial information dataset, including basic attribute information and geographic spatial data, wherein the basic attribute information includes registered capital and industry type;Based on economic and spatial information dataset, the industrial development level and the development level of the type of industry of each sub-region in target area are calculated by comprehensively considering the number of enterprises, geographic spatial data, registered capital and industry type;Based on the industrial development level and the development level of the type of industry, the new energy automobile industry cluster feature is identified using spatial clustering method.

Description

Technical Field

[0001] This invention relates to a method for identifying the spatial cluster characteristics of a regional new energy vehicle industry. It is applicable to the field of new energy vehicle industry planning. Background Technology

[0002] The new energy vehicle industry is an important strategic emerging industry in my country, and its regional development level has attracted great attention from local governments and related enterprises. Existing technologies for identifying regional new energy vehicle industry spatial clusters mainly rely on data from representative regional enterprises, the distribution of production bases, and industry statistics. They comprehensively utilize methods such as theoretical deduction, descriptive statistics, and statistical indicator construction to identify the spatial distribution and agglomeration characteristics of the regional new energy vehicle industry.

[0003] However, on the one hand, the identification of existing industrial clusters is mostly based on calculations from existing industrial statistical data, which is not easy to obtain. At the same time, due to the increasingly deep connections between existing industries, statistics based solely on industry classification standards inevitably overlook some related industries (for example, Shanghai NIO Automobile Co., Ltd. is a typical new energy vehicle company, but its industry classification belongs to technology promotion and application services). On the other hand, since new energy vehicle-related data is mostly national or representative enterprise data, it is difficult to achieve cluster analysis at a local spatial scale. The regional spatial cluster characteristics obtained by related methods (such as provincial and municipal scales) are difficult to apply to local spatial scales (such as district and county scales). Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method for identifying the spatial cluster characteristics of regional new energy vehicle industry, in order to address the problems mentioned above.

[0005] The technical solution adopted in this invention is: a method for identifying the spatial cluster characteristics of a regional new energy vehicle industry, comprising: Obtain enterprise registration information of new energy vehicle-related enterprises within the target area, and determine the main industry type of each related enterprise based on the business information in the enterprise registration information; Based on the main industry types of new energy vehicle-related enterprises in the target area, determine the industry type corresponding to each related enterprise; Based on the enterprise registration information and corresponding industry types of new energy vehicle-related enterprises in the target area, a regional new energy vehicle enterprise economic and spatial information dataset is constructed, including basic attribute information and geospatial data. The basic attribute information includes enterprise registered capital and industry type. Based on economic and spatial information datasets, and by combining the number of enterprises, geospatial data, enterprise registered capital, and industry type, the industrial development level of each sub-region and the industrial development level of each sub-type within the target area are calculated. Based on the level of industrial development and the level of industrial development of sub-types, spatial clustering methods are used to identify the characteristics of new energy vehicle industry clusters.

[0006] The determination of the main industry type for each relevant enterprise registration based on business information in the enterprise registration information includes: Based on the enterprise registration information of each relevant enterprise, the number of enterprises corresponding to each business information is counted, and then the proportion of each business information is determined by combining the number of relevant enterprises in the target area. The business information is sorted by percentage, and the top 90% of businesses are identified as the main industry types for new energy vehicle-related companies.

[0007] The determination of the industry type corresponding to each new energy vehicle-related enterprise based on its main industry type within the target region includes: Based on the main industry types of each relevant enterprise, and referring to the industry type classification table, determine the corresponding industry type of each relevant enterprise; The industry types in the industry type classification table include R&D services, manufacturing, information technology services, sales and other services, as well as the main industry types corresponding to each industry type.

[0008] The comprehensive data on the number of enterprises, geospatial data, registered capital, and industry type are used to calculate the industrial development level of each sub-region and the industrial development level of its sub-types within the target area, including: Transforming corporate registered capital into a corporate size weighting indicator will allow large-scale enterprises to demonstrate a stronger contribution in industry-level calculations. By integrating the weights of the number of enterprises and their size, the specific development level of each sub-region in each sub-industry type is calculated, achieving an assessment that emphasizes both scale and quantity. The industrial development level of a sub-region is obtained by superimposing the development levels of each sub-industry type within that sub-region.

[0009] The comprehensive data on the number of enterprises, geospatial data, registered capital, and industry type are used to calculate the industrial development level of each sub-region and the industrial development level of its sub-types within the target area, including: ; ; ; in, sub-region The industrial level of the new energy vehicle sub-type. sub-region The overall development level of the new energy vehicle industry For enterprises The weight, It is the absolute value of the minimum value among the enterprise weights. Representative companies Registered capital, This represents the total number of enterprises.

[0010] The method employs spatial clustering, using the Getis-Ord Gi* index to identify spatial clusters with statistically significant high values ​​in the dataset.

[0011] The method of using the Getis-Ord Gi* index to identify spatial clusters with statistically significant high values ​​in the dataset includes: ; ; ; in, Representative sub-region Getis-Ord Gi* index, sub-region and subregions Spatial weights between them sub-region The level of the new energy vehicle industry This represents the total number of sub-regions.

[0012] A device for identifying the spatial cluster characteristics of a regional new energy vehicle industry, comprising: The main industry identification module is used to obtain the enterprise registration information of new energy vehicle-related enterprises in the target area, and to determine the main industry type of each relevant enterprise based on the business information in the enterprise registration information. The industry type determination module is used to determine the industry type of each new energy vehicle-related enterprise based on the main industry type of each enterprise in the target area. The dataset construction module is used to construct a regional new energy vehicle enterprise economic and spatial information dataset based on the enterprise registration information and corresponding industry type of each new energy vehicle-related enterprise in the target area. The dataset includes basic attribute information and geospatial data, where the basic attribute information includes enterprise registered capital and industry type. The development level calculation module is used to calculate the industrial development level of each sub-region and the industrial development level of each sub-type within the target area based on economic and spatial information datasets, and by integrating the number of enterprises, geospatial data, enterprise registered capital and industry type. The cluster feature identification module is used to identify the characteristics of the new energy vehicle industry cluster based on the level of industrial development and the level of industrial development of sub-types, using spatial clustering methods.

[0013] A storage medium storing a computer program executable by a processor, wherein the computer program, when executed, implements the steps of the method for identifying the spatial cluster characteristics of the new energy vehicle industry in the region.

[0014] A device for identifying the spatial cluster characteristics of a regional new energy vehicle industry has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the method for identifying the spatial cluster characteristics of a regional new energy vehicle industry.

[0015] The beneficial effects of this invention are as follows: This invention determines the main industry type by comparing the business information in the enterprise registration information, and then determines the industry type of the enterprise. Combined with geospatial data, etc., it obtains a dataset of regional new energy vehicle-related enterprises divided into various sub-industry types. Using the sub-regional scale as the statistical unit, it measures the comprehensive development level of the industry and the sub-type industry level at the sub-regional scale. Furthermore, it uses spatial clustering method to identify the characteristics of regional new energy vehicle industry clusters.

[0016] This invention proposes a classification system for the new energy vehicle industry based on quantity proportions and industry type classification tables, effectively solving the positioning problem for enterprises operating across different segments of the industrial chain. In addition to considering the number of enterprises in a region, this invention also takes into account the existing scale differences among enterprises, comprehensively forming the regional level of the new energy vehicle industry. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a framework for a method to identify the spatial cluster characteristics of a regional new energy vehicle industry based on registration information, provided by a specific example of the present invention.

[0018] Figure 2 This is a schematic diagram showing the main industry affiliation of new energy vehicle enterprises in the selected region, provided by a specific example of the present invention.

[0019] Figure 3 This is a schematic diagram of the "socioeconomic and spatial information" dataset of regional new energy vehicle enterprises provided by a specific example of the present invention.

[0020] Figure 4 This is a schematic diagram illustrating the spatial characteristics of a regional new energy vehicle industry cluster identified using spatial clustering methods, provided by a specific example of the present invention. Detailed Implementation

[0021] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0022] In the description of this invention, "multiple" means two or more. The use of "first" and "second" is for distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0023] Example 1: As Figure 1 As shown in the figure, this embodiment is a method for identifying the spatial cluster characteristics of a regional new energy vehicle industry, which specifically includes the following steps:

[0024] S1. Obtain the enterprise registration information of new energy vehicle-related enterprises in the target area, and determine the main industry type of each related enterprise based on the business information in the enterprise registration information.

[0025] S1-1. Based on the enterprise registration information of each relevant enterprise, count the number of enterprises corresponding to each business information, and then determine the proportion of each business information by combining the number of relevant enterprises in the target area.

[0026] On a business registration information query website, using "automobiles, new energy" as keywords and "Zhejiang Province" as the case study area, the search scope was set to "company name / registration number, business scope." Additional filters were added to streamline the data, including establishment year "before 2024," registered capital "over 1 million," and company status "open." Based on the filtered results, the main industry types of Zhejiang's new energy vehicle companies were determined.

[0027] S1-2. Sort the information of each business according to its proportion, and take the top 90% of the businesses as the main industry types of new energy vehicle related enterprises.

[0028] like Figure 2As shown, among the industries that account for the top 90% of the number of enterprises, the main business of Zhejiang's new energy vehicle enterprises in their business registration is mainly concentrated in "wholesale", "retail", "technology promotion and application services", "software and information technology services", "business services", "motor vehicle, electronic product and daily product repair", "leasing", "research and experimental development", "electricity and heat production and supply", "professional and technical services" and "automobile manufacturing".

[0029] S2. Based on the main industry types of new energy vehicle-related enterprises in the target area, determine the industry type corresponding to each related enterprise.

[0030] In this embodiment, the industry type corresponding to each relevant enterprise is determined by referring to the industry type classification table based on the main industry type of each relevant enterprise.

[0031] Based on the industry information registered by enterprises, the new energy vehicle industry is divided into various sub-industry types, including four main industry types: "R&D services", "production and manufacturing", "information technology services" and "sales and other services". The industry type classification table is shown in Table 1.

[0032] Table 1. Industry Type Classification Table

[0033] Among them, enterprises in the "R&D services" industry type refer to those whose main business is "research and experimental development" (M73), "professional and technical services" (M74), and "science and technology promotion and application services" (M75) in business registration. Enterprises in the "manufacturing" industry category refer to those whose main business is "automobile manufacturing" (C36) according to business registration. Enterprises in the "Information Technology Services" industry category refer to those whose main business is "Software and Information Technology Services" (I65) as registered with the industry and commerce authorities. Enterprises in the "Sales and Other Services" industry category refer to those whose main business, as registered with the industry and commerce authorities, falls under the categories of "Electricity and Heat Production and Supply" (D44), "Wholesale" (F51), "Retail" (F52), "Leasing" (L71), "Business Services" (L72), and "Repair of Motor Vehicle Electronic Products and Daily Necessities" (O81).

[0034] S3. Based on the enterprise registration information and corresponding industry types of new energy vehicle-related enterprises in the target area, construct a regional new energy vehicle enterprise economic and spatial information dataset.

[0035] In this example, the economic and spatial information dataset includes basic attribute information and geospatial data. For example... Figure 3As shown, the basic attribute fields include business registration information such as company name, industry type, registration time, registered capital, registered address, province, city, and district / county; geospatial data refers to the WGS84 coordinate system vector point data (including latitude and longitude) corresponding to the registered address. The dataset realizes the association mapping between the company's business registration information and spatial geographic location through structured storage.

[0036] S4. Based on economic and spatial information datasets, and by combining the number of enterprises, geospatial data, registered capital of enterprises, and industry type, the industrial development level of each sub-region and the industrial development level of each sub-type within the target area are calculated.

[0037] The weighted average of registered capital (normalized by mean and variance) is used to represent the level of the new energy vehicle industry in a district or county. The calculation method is as follows: ① ② ② in, For districts and counties The industrial level of the new energy vehicle sub-type. For districts and counties The overall development level of the new energy vehicle industry For enterprises The weight, It is the absolute value of the minimum value among the enterprise weights. Representative companies Registered capital, This represents the total number of enterprises.

[0038] Formula ① addresses the pain point of "ignoring differences in enterprise size" in traditional industry level assessment by transforming enterprise registered capital into a quantifiable and comparable weighted indicator, enabling large-scale enterprises (with high registered capital) to demonstrate a stronger contribution in industry level calculation.

[0039] Formula ① uses mean-variance normalization to transform the data into a standard distribution with a mean of 0 and a standard deviation of 1, avoiding excessive interference from extreme values ​​(such as the high registered capital of very large enterprises) on the overall weights and ensuring the rationality of the weight distribution; the numerator " The essence is that the deviation between the registered capital of a single enterprise and the average registered capital of all enterprises is magnified (multiplied by the total number of enterprises). The percentage () directly reflects the degree of deviation of a company's size from the industry average; the denominator is the standard deviation of the registered capital of all companies multiplied by the average. It is used to standardize the deviation value, so that the weights of enterprises of different sizes and numbers are comparable horizontally.

[0040] In response to the characteristic of "large range of registered capital (from millions to billions)" in the new energy vehicle industry, normalization processing is used to allow the weights of micro and small enterprises and leading enterprises to be superimposed on the same dimension, laying the foundation for subsequent industry level calculations.

[0041] Formula ② integrates "number of enterprises" and "enterprise size weight" to calculate the weight of a certain district / county. In subdivided industry types The specific development level of (such as R&D services and manufacturing) should be assessed to achieve an evaluation that emphasizes both quality (scale) and quantity.

[0042] Constant term "1": Assigns a base score to each enterprise to ensure that the enterprise's weight is balanced. Even if the registered capital is 0 (i.e., the registered capital is equal to the industry average) or low (small-scale enterprises), the "1" can still reflect its basic value in the industrial layout and avoid ignoring the contribution of micro and small enterprises;

[0043] Weighting item ": Formula ① is introduced to calculate the scale weight, which reflects the impact of differences in firm size on the level of industry."

[0044] Correction item " ": after mean and variance normalization" Negative values ​​may occur (for companies with registered capital lower than the industry average). Adding the "absolute value of the minimum weight" can correct the contribution value of all companies to a non-negative number, avoiding the distortion of results caused by negative weights offsetting positive weights, and ensuring that the level of the subdivided industry is a positive cumulative value.

[0045] Formula ② supports the key point of "calculating the industrial development level by combining the number of enterprises and the registered capital of enterprises", so that the level of subdivided industries reflects both the density of enterprise agglomeration and the scale and strength of the industry.

[0046] S5. Based on the level of industrial development and the level of industrial development of sub-types, spatial clustering methods are used to identify the characteristics of new energy vehicle industry clusters.

[0047] This embodiment uses the Getis-Ord Gi* index to identify spatial clusters of statistically significant high-value (hotspot) clusters in a dataset, and determines the location and significance level of the clusters by calculating the Z-score and P-value of each feature. A higher Z-score indicates a denser clustering of high-value attributes of the target object. The calculation method is as follows: ④ ⑤ ⑥ in, The average level of industrial development in representative districts and counties, The standard difference in industrial level of representative districts and counties Representative of districts and counties Getis-Ord Gi* index, For districts and counties and districts and counties Spatial weights between them For districts and counties The level of the new energy vehicle industry This represents the total number of districts and counties.

[0048] This example uses spatial statistics tools in ArcGIS Pro 3.0 to perform spatial autocorrelation analysis to obtain high-value clustered regions and completes the corresponding map visualization. Figure 4 .

[0049] Example 2: This example is a device for identifying the spatial cluster characteristics of a regional new energy vehicle industry, specifically including: The main industry identification module is used to obtain the enterprise registration information of new energy vehicle-related enterprises in the target area, and to determine the main industry type of each relevant enterprise based on the business information in the enterprise registration information. The industry type determination module is used to determine the industry type of each new energy vehicle-related enterprise based on the main industry type of each enterprise in the target area. The dataset construction module is used to construct a regional new energy vehicle enterprise economic and spatial information dataset based on the enterprise registration information and corresponding industry type of each new energy vehicle-related enterprise in the target area. The dataset includes basic attribute information and geospatial data, where the basic attribute information includes enterprise registered capital and industry type. The development level calculation module is used to calculate the industrial development level of each sub-region and the industrial development level of each sub-type within the target area based on economic and spatial information datasets, and by integrating the number of enterprises, geospatial data, enterprise registered capital and industry type. The cluster feature identification module is used to identify the characteristics of the new energy vehicle industry cluster based on the level of industrial development and the level of industrial development of sub-types, using spatial clustering methods.

[0050] Example 3: This example is a storage medium that stores a computer program that can be executed by a processor. When the computer program is executed, it implements the steps of the regional new energy vehicle industry spatial cluster feature identification method described in Example 1.

[0051] Example 4: This example is a regional new energy vehicle industry spatial cluster feature identification device, which has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the regional new energy vehicle industry spatial cluster feature identification method described in Example 1.

[0052] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0053] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0054] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0055] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0056] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0057] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0058] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for identifying the spatial cluster characteristics of a regional new energy vehicle industry, characterized in that, include: Obtain enterprise registration information of new energy vehicle-related enterprises within the target area, and determine the main industry type of each related enterprise based on the business information in the enterprise registration information; Based on the main industry types of new energy vehicle-related enterprises in the target area, determine the industry type corresponding to each related enterprise; Based on the enterprise registration information and corresponding industry types of new energy vehicle-related enterprises in the target area, a regional new energy vehicle enterprise economic and spatial information dataset is constructed, including basic attribute information and geospatial data. The basic attribute information includes enterprise registered capital and industry type. Based on economic and spatial information datasets, and by combining the number of enterprises, geospatial data, enterprise registered capital, and industry type, the industrial development level of each sub-region and the industrial development level of each sub-type within the target area are calculated. Based on the level of industrial development and the level of industrial development of sub-types, spatial clustering methods are used to identify the characteristics of new energy vehicle industry clusters.

2. The method for identifying the spatial cluster characteristics of the regional new energy vehicle industry according to claim 1, characterized in that, The determination of the main industry type for each relevant enterprise registration based on business information in the enterprise registration information includes: Based on the enterprise registration information of each relevant enterprise, the number of enterprises corresponding to each business information is counted, and then the proportion of each business information is determined by combining the number of relevant enterprises in the target area. The business information is sorted by percentage, and the top 90% of businesses are identified as the main industry types for new energy vehicle-related companies.

3. The method for identifying the spatial cluster characteristics of the regional new energy vehicle industry according to claim 1, characterized in that, The determination of the industry type corresponding to each new energy vehicle-related enterprise based on its main industry type within the target region includes: Based on the main industry types of each relevant enterprise, and referring to the industry type classification table, determine the corresponding industry type of each relevant enterprise; The industry types in the industry type classification table include R&D services, manufacturing, information technology services, sales and other services, as well as the main industry types corresponding to each industry type.

4. The method for identifying the spatial cluster characteristics of the regional new energy vehicle industry according to claim 1, characterized in that, The comprehensive data on the number of enterprises, geospatial data, registered capital, and industry type are used to calculate the industrial development level of each sub-region and the industrial development level of its sub-types within the target area, including: Transforming corporate registered capital into a corporate size weighting indicator will enable large-scale enterprises to demonstrate a stronger contribution in industry-level calculations. By integrating the weights of the number of enterprises and their size, the specific development level of each sub-region in each sub-industry type is calculated, achieving an assessment that emphasizes both scale and quantity. The industrial development level of a sub-region is obtained by superimposing the development levels of each sub-industry type within that sub-region.

5. The method for identifying the spatial cluster characteristics of the regional new energy vehicle industry according to claim 4, characterized in that, The comprehensive data on the number of enterprises, geospatial data, registered capital, and industry type are used to calculate the industrial development level of each sub-region and the industrial development level of its sub-types within the target area, including: ； ； ； in, sub-region The industrial level of the new energy vehicle sub-type. sub-region The overall development level of the new energy vehicle industry For enterprises The weight, It is the absolute value of the minimum value among the enterprise weights. Representative companies Registered capital, This represents the total number of enterprises.

6. The method for identifying the spatial cluster characteristics of the regional new energy vehicle industry according to claim 1, characterized in that, The method employs spatial clustering, using the Getis-Ord Gi* index to identify spatial clusters with statistically significant high values ​​in the dataset.

7. The method for identifying the spatial cluster characteristics of the regional new energy vehicle industry according to claim 6, characterized in that, The method of using the Getis-Ord Gi* index to identify spatial clusters with statistically significant high values ​​in the dataset includes: ； ； ； in, Representative sub-region Getis-Ord Gi* index, sub-region and subregions Spatial weights between them sub-region The level of the new energy vehicle industry This represents the total number of sub-regions.

8. A device for identifying the spatial cluster characteristics of a regional new energy vehicle industry, characterized in that, include: The main industry identification module is used to obtain the enterprise registration information of new energy vehicle-related enterprises in the target area, and to determine the main industry type of each relevant enterprise based on the business information in the enterprise registration information. The industry type determination module is used to determine the industry type of each new energy vehicle-related enterprise based on the main industry type of each enterprise in the target area. The dataset construction module is used to construct a regional new energy vehicle enterprise economic and spatial information dataset based on the enterprise registration information and corresponding industry type of each new energy vehicle-related enterprise in the target area. The dataset includes basic attribute information and geospatial data, where the basic attribute information includes enterprise registered capital and industry type. The development level calculation module is used to calculate the industrial development level of each sub-region and the industrial development level of each sub-type within the target area based on economic and spatial information datasets, and by integrating the number of enterprises, geospatial data, enterprise registered capital and industry type. The cluster feature identification module is used to identify the characteristics of the new energy vehicle industry cluster based on the level of industrial development and the level of industrial development of sub-types, using spatial clustering methods.

9. A storage medium having a computer program stored thereon that can be executed by a processor, characterized in that, When the computer program is executed, it implements the steps of the method for identifying the spatial cluster characteristics of the regional new energy vehicle industry as described in any one of claims 1 to 7.

10. A regional new energy vehicle industry spatial cluster feature identification device, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, characterized in that, When the computer program is executed, it implements the steps of the method for identifying the spatial cluster characteristics of the regional new energy vehicle industry as described in any one of claims 1 to 7.

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