Visual analysis method and device for energy consumption demand of electric vehicle industry influenced by policy

Abstract

The invention provides a policy-affected electric vehicle industry energy demand visual analysis method and device, and the method comprises the following steps: collecting the data of related enterprises in the electric vehicle industry, and carrying out the data preprocessing; constructing a policy-driven electric vehicle industry upstream, middle and downstream energy consumption association model, and obtaining energy consumption conditions of different energy consumption indexes of enterprises influenced by policies based on the model; constructing an electric vehicle industry development visual analysis system, and displaying the obtained energy consumption condition; and configuring a user visual interaction interface, and calculating and visualizing a result according to the information selected by the user based on the electric vehicle industry development visual analysis system. According to the method, the global influence of policies on the energy consumption conditions of upstream, middle and downstream links of the industry can be presented on the macroscopic level, potential laws can be deeply mined through interactive analysis on the microscopic level, and constructive suggestions are provided for policy formulators and industry participants.

Description

Technical Field

[0001] The present invention relates to the field of enterprise risk assessment, and in particular to a method and device for visually analyzing energy demand in the electric vehicle industry under policy influence. Background Art

[0002] As an important means to deal with fossil fuel consumption and greenhouse gas emissions, electric vehicles have been rapidly expanded in recent years. Although the rapid development of the electric vehicle industry is remarkable, it still faces many challenges. On the one hand, the development of the electric vehicle industry also involves the transformation of the energy consumption structure. How to effectively evaluate the joint impact of policies on industry development and energy consumption patterns is a difficult point in current research. On the other hand, the impact of different policies varies significantly due to different targets and scopes of action. This differentiated effect leads to different effects of policies in various links of the industry, and it is challenging to comprehensively evaluate the actual impact of policies on energy consumption in various links of the industry and optimize their synergy. In addition, the diversity and complexity of industry development data also increase the difficulty of analysis. Traditional analysis methods are difficult to fully reveal the dynamic relationship between these factors. Therefore, a method for analyzing the energy demand of the electric vehicle industry that takes into account the impact of policies is needed to reveal the complex relationship between policies and energy demand for industry development. Summary of the invention

[0003] In order to solve the above problems, this application proposes a visual analysis method for the development of the electric vehicle industry under policy influence, including the following steps:

[0004] A visual analysis method for energy demand in the electric vehicle industry influenced by policies includes the following steps:

[0005] Collect data from companies related to the electric vehicle industry and perform data preprocessing;

[0006] Construct a policy-driven upstream, midstream and downstream energy consumption correlation model for the electric vehicle industry, and obtain the energy consumption of different energy consumption indicators of enterprises affected by the policy based on the model;

[0007] Build a visual analysis system for the development of the electric vehicle industry to display the energy consumption data;

[0008] A user visualization interactive interface is configured, and based on the electric vehicle industry development visual analysis system, the results are calculated and visualized according to the information selected by the user.

[0009] Furthermore, the collection of electric vehicle industry-related enterprise data and data preprocessing include:

[0010] Collect information about companies related to the electric vehicle industry chain from public websites or databases, including at least the company name, company code, company address, company size, and company business scope;

[0011] Fill in missing data through public information of related companies;

[0012] The enterprise code is used as the unique identifier of the enterprise data, the address conversion API is called to convert the enterprise address into longitude and latitude, and the enterprise size is normalized;

[0013] Conduct text analysis on the business scope of enterprises based on a large language model, extract key field information and classify enterprises into different links of the industrial chain;

[0014] Convert the result data into structured JSON format.

[0015] Furthermore, the construction of a policy-driven upstream, midstream and downstream energy consumption correlation model for the electric vehicle industry includes:

[0016] Set energy consumption indicators, including energy saving rate C, energy efficiency E, and energy structure S;

[0017] Construct a policy tree with policy factors as nodes. Parent and child nodes represent the inclusion relationship between policies. Set a policy impact matrix W for the policy tree. The element w in the policy impact matrix W is i,j represents the sensitivity of the jth policy factor to the ith energy consumption indicator;

[0018] Based on the preset scoring criteria, the direct impact of policy factors on energy consumption indicators is scored according to the regulatory intensity or numerical information of policy factors. Positive scores indicate promotion, and negative scores indicate inhibition. The policy intensity matrix F is obtained. The element f in the policy intensity matrix F is i,j Represents the impact of the j-th policy factor on the i-th energy consumption indicator;

[0019] Construct an energy consumption correlation model for the upstream, midstream and downstream of the electric vehicle industry, and calculate the following for the three categories of enterprises:

[0020]

[0021] Among them, Y i represents the energy consumption of the i-th energy consumption indicator of the k-th enterprise affected by the policy, s k is the normalized enterprise size of the kth enterprise.

[0022] Furthermore, the construction of the visual analysis system for the development of the electric vehicle industry includes:

[0023] Construct an electric vehicle industry development map, which includes a base map layer, a city area layer, an enterprise spatial location layer, and a connection line layer, to show the relationship between the enterprise itself and the upstream, midstream, and downstream of the enterprise;

[0024] Draw an industry distribution bar chart to show the number of companies in different industry chain links. The X-axis of the bar chart is the industry chain link, and the Y-axis is the number of companies or total revenue. Different geographical regions are distinguished based on color.

[0025] The policy tree is visualized, with each node representing a policy factor, and the child nodes representing the included sub-item policy factors. The sensitivity of the policy in the policy impact matrix to the three energy consumption indicators is represented based on the depth of three colors.

[0026] Furthermore, the map base map layer uses the map of China as a basis, draws map tiles by calling the Tiandi Map vector base map API, and configures corresponding vector annotations;

[0027] The city area layer draws the boundaries of each city in the map;

[0028] The enterprise spatial location layer uses dots to represent enterprises. The dot position is based on the enterprise's geographical location. The dot color is based on the industrial chain link where the enterprise is located. The dot size represents the scale of the enterprise.

[0029] The connection line layer draws the relationship between the upstream, midstream and downstream of the enterprises, showing the cooperation relationship between the upstream, midstream and downstream enterprises. The thickness of the line indicates the intensity of cooperation or the transaction amount.

[0030] Furthermore, the configuration user visualization interactive interface, based on the electric vehicle industry development visualization analysis system, calculates and visualizes the results according to the information selected by the user, including:

[0031] A layer selection tool is configured for the electric vehicle industry development map, and the layer selection tool is used to select and overlay layers in response to user instructions.

[0032] Furthermore, the configuration user visualization interactive interface, based on the electric vehicle industry development visualization analysis system, calculates and visualizes the results according to the information selected by the user, including:

[0033] A screening tool is configured for the electric vehicle industry development map, wherein the screening tool is used to screen dots on the enterprise spatial location layer based on industrial chain links, enterprise scale or region in response to user instructions;

[0034] A search tool is configured for the electric vehicle industry development map. The search tool is used to display the company name, scale, and industrial chain position information in response to the mouse hovering over the dot, or to highlight the relevant industrial chain link in response to the mouse clicking on the dot, and synchronously update the company details or trend data in the industry distribution bar chart.

[0035] Furthermore, the configuration user visualization interactive interface, based on the electric vehicle industry development visualization analysis system, calculates and visualizes the results according to the information selected by the user, including:

[0036] An evaluation interface is configured for the policy tree. The evaluation interface is used to respond to a user's click on a policy factor node, call the upstream, midstream and downstream energy consumption association model of the electric vehicle industry to calculate the energy consumption of all enterprises under the influence of the corresponding policy factors in terms of three energy consumption indicators, and display the results in the electric vehicle industry development map through the enterprise spatial location layer.

[0037] Furthermore, the configuration user visualization interactive interface, based on the electric vehicle industry development visualization analysis system, calculates and visualizes the results according to the information selected by the user, including:

[0038] An evaluation interface is configured for the electric vehicle industry development map. The evaluation interface is used to respond to a user's click on a certain enterprise, call the upstream, midstream and downstream energy consumption association model of the electric vehicle industry to calculate the comprehensive impact of all policy factors on the enterprise, and display the results in the industry development map through the enterprise spatial location layer.

[0039] The second aspect of the present invention further provides a visual analysis device for energy demand of the electric vehicle industry affected by policies, which is used to implement the method described in the first aspect, including:

[0040] Data collection module, used to collect data from companies related to the electric vehicle industry and perform data preprocessing;

[0041] The model building module is used to build a policy-driven upstream, midstream and downstream energy consumption correlation model for the electric vehicle industry, and obtain the energy consumption of different energy consumption indicators of enterprises affected by the policy based on the model;

[0042] Dynamic display module, used to build a visual analysis system for the development of the electric vehicle industry and display the acquired energy usage;

[0043] The interface configuration module is used to configure the user visualization interaction interface, based on the electric vehicle industry development visual analysis system, and calculate and visualize the results according to the information selected by the user.

[0044] The beneficial effects of the present invention are as follows:

[0045] The visual analysis method of the electric vehicle industry energy demand of the present invention constructs a correlation model between policy factors and key industry indicators by designing a multi-level visual analysis process, and realizes joint analysis and dynamic exploration through an interactive electric vehicle industry development map. Compared with traditional methods, the method of the present invention can not only present the overall impact of policies on the energy consumption of the upstream, midstream and downstream links of the industry at a macro level, but also deeply explore potential laws through interactive analysis at the micro level, and provide constructive suggestions for policy makers and industry participants. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The figure is a flow chart of an embodiment of a method for visually analyzing energy demand in the electric vehicle industry according to the present invention.

[0047] Figure 2 This is a schematic diagram of the display interface of the electric vehicle industry development visual analysis system constructed in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0049] See also Figure 1 The embodiment of the present invention shows a visual analysis method of energy demand in the electric vehicle industry affected by policies, including the following steps:

[0050] S1. Collect enterprise data related to the electric vehicle industry and perform data preprocessing.

[0051] In some examples, the step specifically includes:

[0052] Collect enterprise information related to the electric vehicle industry chain from public websites or databases, including at least enterprise name, enterprise code, enterprise address, enterprise scale and enterprise business scope, and other data can be added as needed;

[0053] Fill in missing data through public information of related companies, such as obtaining missing information through official websites or social media;

[0054] Use the enterprise code as the unique identifier of the enterprise data, call the address conversion API to convert the enterprise address into longitude and latitude, standardize the geographic location information, and normalize the enterprise size;

[0055] Conduct text analysis on the business scope of enterprises based on a large language model, extract key field information and classify enterprises into different links of the industrial chain;

[0056] Convert the result data into structured JSON format.

[0057] In an example, the text analysis of the business scope of an enterprise based on a large language model is performed in the following manner:

[0058] Configure a large language model and design inference prompt words for the electric vehicle industry chain. The prompt word template is as follows:

[0059] {

[0060] Task: Classify the enterprise into one or more links of the electric vehicle industry chain according to its business scope. The following are the industry chain classifications and definitions:

[0061] 1. Raw material suppliers: companies that provide battery raw materials (such as lithium, cobalt, nickel, etc.) and other key component raw materials or produce electric vehicle batteries.

[0062] 2. Electric vehicle manufacturers: companies that produce electric vehicles, including vehicle design, software development, etc.

[0063] 3. Electric vehicle sales: electric vehicle sales channels, after-sales services, used car market, etc.

[0064] Please analyze the business scope text of the following companies and classify them into one or more of the above categories. Output the category to which the company belongs in JSON format and briefly explain the reason.

[0065] Input: {Enterprise business scope text}

[0066] Output: class_json = {

[0067] / / Classification results

[0068] "class": <string>,

[0069] / / Classification basis

[0070] "reason": <string>

[0071] }

[0072] }

[0073] The prompt word and the enterprise ID and business scope fields in all collected enterprise data are input into the large language model to obtain the inference result of the large language model.

[0074] Cross-validate the enterprise's industry chain classification, and invite industry experts or use domain knowledge for manual review when necessary.

[0075] S2. Construct a policy-driven upstream, midstream and downstream energy consumption correlation model for the electric vehicle industry, and obtain the energy consumption status of different energy consumption indicators of enterprises affected by the policy based on the model.

[0076] In some examples, the step specifically includes:

[0077] Set energy consumption indicators, including energy saving rate C, energy efficiency E, and energy structure S;

[0078] Construct a policy tree with policy factors as nodes. Parent and child nodes represent the inclusion relationship between policies. Set a policy impact matrix W for the policy tree. The element w in the policy impact matrix W is i,j represents the sensitivity of the jth policy factor to the ith energy consumption indicator;

[0079] Based on the preset scoring criteria, the direct impact of the policy factors on the energy consumption indicators is scored (-10 to 10 points) according to the intensity of regulation or numerical information of the policy factors. Positive scores indicate promotion, and negative scores indicate inhibition. The policy intensity matrix F is obtained. The element f in the policy intensity matrix F is i,j Represents the impact of the j-th policy factor on the i-th energy consumption indicator;

[0080] Construct an energy consumption correlation model for the upstream, midstream and downstream of the electric vehicle industry, and calculate the following for the three categories of enterprises:

[0081]

[0082] Among them, Y i represents the energy consumption of the i-th energy consumption indicator of the k-th enterprise affected by the policy, s k is the normalized enterprise size of the kth enterprise.

[0083] S3. Build a visual analysis system for the development of the electric vehicle industry to display the acquired energy consumption situation.

[0084] See also Figure 2 In some examples, the step specifically includes:

[0085] Build an electric vehicle industry development map, which includes a base map layer, a city area layer, an enterprise spatial location layer, and a connection line layer to display the relationship between enterprises themselves and between upstream, midstream, and downstream enterprises.

[0086] Specifically, the map basemap layer uses the map of China as the basis, draws map tiles by calling the Tiandi Map vector basemap API, and configures the corresponding vector annotations;

[0087] The urban area layer plots the boundaries of each city in the map;

[0088] The enterprise spatial location layer uses dots to represent enterprises. The dot location is based on the enterprise's geographical location (latitude and longitude). The dot color is based on the industrial chain link where the enterprise is located. The dot size indicates the scale of the enterprise.

[0089] The connection line layer draws the upstream, midstream and downstream relationships between enterprises, showing the cooperative relationship between upstream, midstream and downstream enterprises. The thickness of the line indicates the intensity of cooperation or transaction amount.

[0090] Draw an industry distribution bar chart to show the distribution of the number of enterprises in different links of the industrial chain. The X-axis of the bar chart is the industrial chain link (such as raw materials, vehicle manufacturing, etc.), and the Y-axis is the number of enterprises or total revenue, and different geographical areas (cities or provinces) are distinguished based on color.

[0091] The policy tree is visualized, with each node representing a policy factor, and the child nodes representing the included sub-item policy factors. The sensitivity of the policy in the policy impact matrix to the three energy consumption indicators is represented based on the depth of three colors.

[0092] S4. Configure a user visualization interactive interface, based on the electric vehicle industry development visual analysis system, and calculate and visualize results according to the information selected by the user.

[0093] In some illustrative examples, the steps include:

[0094] A layer selection tool is configured for the electric vehicle industry development map to select and overlay layers in response to user instructions.

[0095] A filtering tool is configured for the electric vehicle industry development map, which is used to filter the dots on the enterprise spatial location layer based on the industrial chain link, enterprise size or region in response to user instructions;

[0096] A search tool is configured for the electric vehicle industry development map to display the company name, scale, and industrial chain position information in response to the mouse hovering over the dot, or to highlight the relevant industrial chain link in response to the mouse clicking on the dot, and to synchronously update the company details or trend data in the industry distribution bar chart.

[0097] An evaluation interface is configured for the policy tree to respond to user clicks on policy factor nodes, call the upstream, midstream and downstream energy consumption association model of the electric vehicle industry to calculate the energy consumption of all enterprises in the three energy consumption indicators under the influence of corresponding policy factors, and display the results on the electric vehicle industry development map through the enterprise spatial location layer.

[0098] An evaluation interface is configured for the electric vehicle industry development map to respond to a user's click on a certain enterprise, call the upstream, midstream and downstream energy consumption association model of the electric vehicle industry to calculate the comprehensive impact of all policy factors on the enterprise, and display the results on the industry development map through the enterprise spatial location layer.

[0099] Another embodiment of the present invention further shows a device for visually analyzing energy demand of the electric vehicle industry under policy influence, which is used to implement the method as described in the first aspect above, including:

[0100] Data collection module, used to collect data from companies related to the electric vehicle industry and perform data preprocessing;

[0101] The model building module is used to build a policy-driven upstream, midstream and downstream energy consumption correlation model for the electric vehicle industry, and obtain the energy consumption of different energy consumption indicators of enterprises affected by the policy based on the model;

[0102] Dynamic display module, used to build a visual analysis system for the development of the electric vehicle industry and display the acquired energy usage;

[0103] The interface configuration module is used to configure the user visualization interaction interface, based on the electric vehicle industry development visual analysis system, and calculate and visualize the results according to the information selected by the user.

[0104] The specific functions and implementation principles of each module of the above-mentioned device in this embodiment can be found in the various steps shown in the above-mentioned method embodiment, and will not be elaborated here.

[0105] It should be noted that the method of the embodiment of the present invention can be performed by a single device, such as a computer or a server. The method of this embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present invention, and the multiple devices will interact with each other to complete the described method.

[0106] It should be noted that some embodiments of the present invention are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0107] The embodiments of the present invention are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the present invention.< / string> < / string>

Claims

1. A visual analysis method for energy demand in the electric vehicle industry affected by policies, characterized in that: The steps include: Collect data from companies related to the electric vehicle industry and perform data preprocessing; Construct a policy-driven upstream, midstream and downstream energy consumption correlation model for the electric vehicle industry, and obtain the energy consumption of different energy consumption indicators of enterprises affected by the policy based on the model; Build a visual analysis system for the development of the electric vehicle industry to display the energy consumption data; A user visualization interactive interface is configured, and based on the electric vehicle industry development visual analysis system, the results are calculated and visualized according to the information selected by the user.

2. The method for visually analyzing the energy demand of the electric vehicle industry under the influence of policies as claimed in claim 1, characterized in that: The collection of electric vehicle industry-related enterprise data and data preprocessing include: Collect information about companies related to the electric vehicle industry chain from public websites or databases, including at least the company name, company code, company address, company size, and company business scope; Fill in missing data through public information of related companies; The enterprise code is used as the unique identifier of the enterprise data, the address conversion API is called to convert the enterprise address into longitude and latitude, and the enterprise size is normalized; Conduct text analysis on the business scope of enterprises based on a large language model, extract key field information and classify enterprises into different links of the industrial chain; Convert the result data into structured JSON format.

3. The visual analysis method of energy demand in the electric vehicle industry affected by policies as claimed in claim 2, characterized in that: The construction of the policy-driven upstream, midstream and downstream energy consumption correlation model of the electric vehicle industry includes: Set energy consumption indicators, including energy saving rate C, energy efficiency E, and energy structure S; Construct a policy tree with policy factors as nodes. Parent and child nodes represent the inclusion relationship between policies. Set a policy impact matrix W for the policy tree. The element w in the policy impact matrix W is i,j represents the sensitivity of the jth policy factor to the ith energy consumption indicator; Based on the preset scoring criteria, the direct impact of policy factors on energy consumption indicators is scored according to the regulatory intensity or numerical information of policy factors. Positive scores indicate promotion, and negative scores indicate inhibition. The policy intensity matrix F is obtained. The element f in the policy intensity matrix F is i,j Represents the impact of the j-th policy factor on the i-th energy consumption indicator; Construct an energy consumption correlation model for the upstream, midstream and downstream of the electric vehicle industry, and calculate the following for the three categories of enterprises: Among them, Y i represents the energy consumption of the i-th energy consumption indicator of the k-th enterprise affected by the policy, s k is the normalized enterprise size of the kth enterprise.

4. The visual analysis method of energy demand in the electric vehicle industry affected by policies as claimed in claim 3, characterized in that: The construction of the electric vehicle industry development visual analysis system includes: Construct an electric vehicle industry development map, which includes a base map layer, a city area layer, an enterprise spatial location layer, and a connection line layer, to show the relationship between the enterprise itself and the upstream, midstream, and downstream of the enterprise; Draw an industry distribution bar chart to show the number of companies in different industry chain links. The X-axis of the bar chart is the industry chain link, and the Y-axis is the number of companies or total revenue. Different geographical regions are distinguished based on color. The policy tree is visualized, with each node representing a policy factor, and the child nodes representing the included sub-item policy factors. The sensitivity of the policy in the policy impact matrix to the three energy consumption indicators is represented based on the depth of three colors.

5. The visual analysis method of energy demand in the electric vehicle industry affected by policies as claimed in claim 4, characterized in that: The map base map layer uses the map of China as the basis, draws map tiles by calling the Tiandi Map vector base map API, and configures corresponding vector annotations; The city area layer draws the boundaries of each city in the map; The enterprise spatial location layer uses dots to represent enterprises. The dot position is based on the enterprise's geographical location. The dot color is based on the industrial chain link where the enterprise is located. The dot size represents the scale of the enterprise. The connection line layer draws the relationship between the upstream, midstream and downstream of the enterprises, showing the cooperation relationship between the upstream, midstream and downstream enterprises. The thickness of the line indicates the intensity of cooperation or the transaction amount.

6. The visual analysis method of energy demand in the electric vehicle industry affected by policies as claimed in claim 4, characterized in that: The configuration user visualization interactive interface, based on the electric vehicle industry development visualization analysis system, calculates and visualizes the results according to the information selected by the user, including: A layer selection tool is configured for the electric vehicle industry development map, and the layer selection tool is used to select and overlay layers in response to user instructions.

7. The visual analysis method of energy demand in the electric vehicle industry affected by policies as claimed in claim 5, characterized in that: The configuration user visualization interactive interface, based on the electric vehicle industry development visualization analysis system, calculates and visualizes the results according to the information selected by the user, including: A screening tool is configured for the electric vehicle industry development map, wherein the screening tool is used to screen dots on the enterprise spatial location layer based on industrial chain links, enterprise scale or region in response to user instructions; A search tool is configured for the electric vehicle industry development map. The search tool is used to display the company name, scale, and industrial chain position information in response to the mouse hovering over the dot, or to highlight the relevant industrial chain link in response to the mouse clicking on the dot, and synchronously update the company details or trend data in the industry distribution bar chart.

8. The visual analysis method of energy demand in the electric vehicle industry affected by policies as claimed in claim 5, characterized in that: The configuration user visualization interactive interface, based on the electric vehicle industry development visualization analysis system, calculates and visualizes the results according to the information selected by the user, including: An evaluation interface is configured for the policy tree. The evaluation interface is used to respond to a user's click on a policy factor node, call the upstream, midstream and downstream energy consumption association model of the electric vehicle industry to calculate the energy consumption of all enterprises under the influence of the corresponding policy factors in terms of three energy consumption indicators, and display the results in the electric vehicle industry development map through the enterprise spatial location layer.

9. The visual analysis method of energy demand in the electric vehicle industry affected by policies as claimed in claim 5, characterized in that: The configuration user visualization interactive interface, based on the electric vehicle industry development visualization analysis system, calculates and visualizes the results according to the information selected by the user, including: An evaluation interface is configured for the electric vehicle industry development map. The evaluation interface is used to respond to a user's click on a certain enterprise, call the upstream, midstream and downstream energy consumption association model of the electric vehicle industry to calculate the comprehensive impact of all policy factors on the enterprise, and display the results in the industry development map through the enterprise spatial location layer.

10. A visual analysis device for energy demand in the electric vehicle industry affected by policies, used to implement the method according to any one of claims 1 to 9, characterized in that: include: Data collection module, used to collect data from companies related to the electric vehicle industry and perform data preprocessing; The model building module is used to build a policy-driven upstream, midstream and downstream energy consumption correlation model for the electric vehicle industry, and obtain the energy consumption of different energy consumption indicators of enterprises affected by the policy based on the model; Dynamic display module, used to build a visual analysis system for the development of the electric vehicle industry and display the acquired energy usage; The interface configuration module is used to configure the user visualization interaction interface, based on the electric vehicle industry development visual analysis system, and calculate and visualize the results according to the information selected by the user.

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