Data processing method for new energy vehicle distribution

Abstract

The invention discloses a data processing method and device for new energy vehicle distribution, and belongs to the technical field of vehicle distribution data, and the data processing method for new energy vehicle distribution comprises the following steps: collecting data information; the collected data information is arranged and stored; the sorted data information is analyzed and processed; protecting the analyzed and processed data information by using a protection wall; applying the data subjected to the separation processing to marketing and after-sales decision service; updating the latest data in time; according to the invention, the processed data is stored and counted, so that the data can be effectively protected, the possibility of leakage of customer information and vehicle information is effectively reduced, the problem that the customer information leakage and the vehicle information leakage are easy to bring economic loss to customers and dealers is effectively solved, the trust of the customers to the dealers is improved, and the customer experience is improved. The subsequent sales work can be conveniently carried out, and the subsequent transaction rate can be effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle distribution data processing, and more specifically, relates to a data processing method for new energy vehicle distribution. Background Art

[0002] With the increasing global attention to environmental protection and sustainable development, the new energy vehicle market has shown a booming development trend. The sales of new energy vehicles are no longer limited to traditional models, and digital transformation has become a key driving force for the industry's development. In this context, data processing plays a crucial role in new energy vehicle distribution.

[0003] The data processing methods in the prior art are relatively single. In the actual processing process, there may be certain hidden dangers in traditional data processing methods. Some enterprises do not adopt effective firewall technologies to protect data, and access control lacks fine management. It is impossible to perform accurate access allowance or rejection operations based on IP addresses and ports. In the face of suspicious intrusion behaviors, there is a lack of effective detection and blocking mechanisms, which may cause the leakage of customer information and vehicle information. On the one hand, it may bring troubles of being harassed to customers, and on the other hand, the customer identity information and vehicle identity information may be misappropriated, which may cause certain economic losses. Therefore, a data processing method with high security performance is particularly important. Summary of the Invention

[0004] Problems to be Solved

[0005] In view of the problems raised in the existing background art, the present invention provides a data processing method for new energy vehicle distribution.

[0006] Technical Solution

[0007] To solve the above problems, the present invention adopts the following technical solutions.

[0008] A data processing method for new energy vehicle distribution is as follows:

[0009] Step S1, collect data information;

[0010] Step S2, organize and store the collected data information;

[0011] Step S3, analyze and process the organized data information;

[0012] Step S4, use a firewall to protect the data information after analysis and processing;

[0013] Step S5, apply the data after separation processing to marketing and after-sales decision-making services;

[0014] Step S6, update the latest data in a timely manner.

[0015] Preferably, the main steps of collecting data information in step S1 include:

[0016] S101. Collect customer information: Obtain the customer's name, contact information, home address, and age information through showroom reception and online consultation;

[0017] S102. Collect customers' car purchase intentions and budget information: Obtain the customers' satisfactory vehicle models and budget information through conversations;

[0018] S103. Collect vehicle information: Record the basic information of each new energy vehicle in the warehouse, including vehicle brand, model, configuration, color, vehicle identification number, and inventory location;

[0019] S104. Collect market information: Obtain the prices, promotional activities, and product features of competitors' new energy vehicles through reports from market research companies, official websites of competitors, and offline showroom research.

[0020] Furthermore, when organizing and storing the collected data information in step S2, the specific steps include:

[0021] S201. Data cleaning: Check for incorrect data in customer information and vehicle information, and delete obvious incorrect information;

[0022] S202. Data classification and coding: Classify customers into high-intention high-budget customers, high-intention low-budget customers, and low-intention potential customers according to factors such as car purchase intentions and budgets in customer data;

[0023] S203. Vehicle classification: Classify and code vehicles according to brand, model, and price range;

[0024] S204. Data storage: Use a relational database to store data, and establish a data backup mechanism to back up data regularly to prevent data loss.

[0025] Preferably, the methods of analyzing and processing data information in step S3 include:

[0026] S301. Sales trend analysis: Analyze the changing trends of the sales volume and sales amount of new energy vehicles in different time periods;

[0027] S302. Customer purchase behavior analysis: Study the customer's purchase decision-making process, including the time span from learning about vehicle models to final car purchase, and the number of brands and models compared by customers;

[0028] S303. Sales channel analysis: Evaluate the sales contributions of different sales channels. Calculate the sales proportion and sales growth rate of each channel to determine the key sales channels for development;

[0029] S304. Inventory analysis: Calculate the turnover rate of vehicle inventory;

[0030] S305. Inventory structure analysis: Analyze the brand, model, configuration, and color structure of the inventory vehicles;

[0031] S306. Market analysis: Calculate the dealer's share in the local new energy vehicle market, compare with competitors, collect the total sales data of local new energy vehicles and the dealer's sales data, and calculate the market share.

[0032] Furthermore, when using a firewall to protect the analyzed and processed data in step S4, the specific methods include:

[0033] S401. Access control protection: Protect access control based on IP addresses and ports;

[0034] S402. Intrusion prevention: When detecting suspicious behavior, use an IDS to block the connection;

[0035] S403. Virtual private network support;

[0036] S404. Support for encrypted data transmission.

[0037] Still further, in step S401, the firewall can allow or deny access based on the source IP address and the destination IP address; different network services usually use specific ports.

[0038] Even further, the virtual private network support in step S403 is as follows:

[0039] Secure remote access support: For employees who need to remotely access new energy vehicle dealership data, the firewall supports VPN connections. The VPN establishes a private network connection over a public network through an encrypted channel. Employees use VPN client software to connect to the dealer's internal network, and the firewall authenticates and encrypts the VPN connection;

[0040] Multi-factor authentication enhances security: During the VPN connection process, the firewall cooperates with the multi-factor authentication mechanism to improve the security of remote access and ensure that only authorized personnel can access new energy vehicle dealership data through the VPN.

[0041] Furthermore, in step S204, the relational database is constructed and supported by a hash index algorithm.

[0042] Beneficial effects

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] (1) By storing and statistically analyzing the processed data through this method, the present invention can effectively protect the data, effectively reduce the possibility of leakage of customer information and vehicle information, effectively solve the problems of leakage of customer information and vehicle information, which are likely to cause economic losses to customers and dealers, improve customers' trust in the dealer, facilitate the subsequent sales work, and effectively increase the subsequent transaction rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the technical solutions in the embodiments or exemplifications of the present application, the following will briefly introduce the drawings required for use in the embodiments or exemplifications. Obviously, the drawings in the following description are only some embodiments of the present application, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the drawings shown.

[0046] Figure 1 It is the system flowchart of the present invention;

[0047] Figure 2 It is the flowchart of step S1 in the present invention;

[0048] Figure 3 It is the flowchart of step S2 in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Usually, the components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0050] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application to be protected, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0051] Embodiment 1

[0052] As Figure 1 shown, a data processing method for new energy vehicle distribution is as follows:

[0053] Step S1: Collect data information;

[0054] Step S2: Organize and store the collected data information;

[0055] Step S3: Analyze and process the sorted data information;

[0056] Step S4: Protect the analyzed and processed data information using a firewall;

[0057] Step S5: Apply the processed data to marketing and after-sales decision-making services;

[0058] Step S6: Update the latest data in a timely manner.

[0059] The main steps of collecting data information in step S1 include:

[0060] S101: Collect customer information: Obtain the customer's name, contact information, home address, and age information through showroom reception and online consultations;

[0061] S102: Collect the customer's car purchase intention and budget information: Obtain the customer's satisfactory car model and budget information through conversations;

[0062] S103: Collect vehicle information: Record the basic information of each new energy vehicle in the warehouse, including vehicle brand, model, configuration, color, vehicle identification number, and inventory location;

[0063] S104: Collect market information: Obtain the prices, promotional activities, and product features of competitors' new energy vehicles through reports from market research companies, official websites of competitors, and offline showroom research.

[0064] When collecting data information, it covers customer information, customer car purchase intention and budget, vehicle itself information, and market information. Comprehensive basic data collection provides rich materials for subsequent sorting, storage, and analysis, and helps enterprises comprehensively understand their own business conditions, customer needs, and market competition situations.

[0065] Moreover, the collection of customer car purchase intention and budget information enables enterprises to accurately grasp customer needs and provides a direct basis for subsequent customer classification and sales strategy formulation. For example, by understanding the customer's satisfactory car model and budget, enterprises can accurately recommend car models during the sales process and improve sales efficiency.

[0066] Recording the detailed vehicle information is beneficial for enterprises to clearly master the inventory status and provide basic data support for inventory management, sales allocation, etc. For example, clarifying the vehicle identification number and inventory location can quickly locate the vehicle and improve the delivery efficiency.

[0067] Collecting market information through multiple channels enables enterprises to timely understand the dynamics of competitors and provides a reference for formulating reasonable price strategies, promotional activities, etc. For example, by paying attention to the official websites of competitors to obtain their prices and promotional activities, enterprises can adjust their own strategies to maintain competitiveness.

[0068] When organizing and storing the collected data information in step S2, the specific steps include:

[0069] S201. Data cleaning: Check for incorrect data in customer information and vehicle information, and delete obvious incorrect information.

[0070] S202. Data classification and coding: Classify customers into high-intent high-budget customers, high-intent low-budget customers, and low-intent potential customers according to factors such as purchase intent and budget in customer data.

[0071] S203. Vehicle classification: Classify and code vehicles according to brand, model, and price range.

[0072] S204. Data storage: Use a relational database to store data, and establish a data backup mechanism to backup data regularly to prevent data loss.

[0073] Refer to Figure 3 , when cleaning the collected data, the data quality can be guaranteed, obvious incorrect information can be deleted, and incorrect data can be prevented from misleading subsequent analysis. For example, if there is incorrect data such as a negative age in customer information, the data analysis result can be more accurate after cleaning. In the inventory vehicle information, if there is an incorrect encoding of the vehicle identification number, the accuracy of inventory management can be guaranteed after cleaning.

[0074] When classifying and coding data, classifying customers according to purchase intent and budget helps the enterprise formulate differentiated marketing strategies for different types of customers.

[0075] When classifying vehicles, classifying and coding vehicles according to brand, model, and price range facilitates the enterprise's inventory management and sales analysis. For example, during inventory counting, the quantity of vehicles in different brands and price ranges can be quickly counted; in sales analysis, the sales situation of different models can be understood, providing a basis for model optimization and procurement decisions.

[0076] When storing data, using a relational database to store data is convenient for data query, update, and management. Establishing a data backup mechanism to backup data regularly can effectively prevent data loss and ensure the security of the enterprise's data assets.

[0077] The methods for analyzing and processing data information in step S3 include:

[0078] S301. Sales trend analysis: Analyze the changing trends of the sales volume and sales amount of new energy vehicles in different time periods.

[0079] S302. Customer Purchase Behavior Analysis: Study the customer purchase decision-making process, including the time span from learning about the vehicle models to the final purchase, and the number of brands and models compared by customers;

[0080] S303. Sales Channel Analysis: Evaluate the sales contributions of different sales channels. Calculate the sales percentage and sales growth rate of each channel to determine the key sales channels for development;

[0081] S304. Inventory Analysis: Calculate the turnover rate of vehicle inventory;

[0082] S305. Inventory Structure Analysis: Analyze the brand, model, configuration, and color structure of the inventory vehicles;

[0083] S306. Market Analysis: Calculate the dealer's share in the local new energy vehicle market, compare it with competitors, collect the total sales data of local new energy vehicles and the dealer's sales data, and calculate the market share.

[0084] Refer to Figure 3 , by analyzing the sales trends, it can help the enterprise understand the sales performance of new energy vehicles in different time periods and predict future sales trends.

[0085] In specific implementation, if it is found through analysis that the sales volume of a certain vehicle model increases significantly in a specific season, the enterprise can make advance inventory preparations and market promotions to seize the sales peak season and increase sales volume.

[0086] By analyzing the customer purchase behavior and deeply studying the customer purchase decision-making process, it helps the enterprise optimize the sales process and service strategy. Understanding the time span from when customers learn about the vehicle models to making a purchase, the enterprise can formulate targeted follow-up strategies; knowing the number of brands and models compared by customers can help understand the hesitation points of customers when purchasing a vehicle, so as to focus on breakthroughs in the sales process.

[0087] By analyzing the sales channels, the enterprise can determine the key sales channels for development and allocate resources reasonably.

[0088] In specific implementation, if the online channel has a high sales percentage and a fast growth rate, the enterprise can increase the investment in the online platform, improve the online sales ability, and enhance the overall sales performance.

[0089] When analyzing the inventory and the market, calculating the vehicle inventory turnover rate can help the enterprise understand the inventory management efficiency. If the inventory turnover rate is low, the enterprise can adjust the procurement strategy in a timely manner to reduce inventory backlog and lower operating costs; calculating the dealer's share in the local new energy vehicle market and comparing it with competitors can help the enterprise clarify its market position. Understanding the total sales data of local new energy vehicles and the dealer's sales data helps the enterprise formulate a reasonable market expansion strategy and increase the market share.

[0090] When using a firewall to protect the analyzed and processed data in step S4, the specific methods include:

[0091] S401. Access control protection: Protect access control based on IP addresses and ports;

[0092] S402. Intrusion prevention: When detecting suspicious behavior, use an IDS to block the connection;

[0093] S403. Virtual private network support;

[0094] S404. Support for encrypted data transmission.

[0095] In step S401, the firewall can allow or deny access based on the source IP address and the destination IP address; different network services usually use specific ports.

[0096] The virtual private network support in step S403 is as follows:

[0097] Secure remote access support: For employees who need to remotely access new energy vehicle dealership data, the firewall supports VPN connections. The VPN establishes a private network connection over a public network through an encrypted channel. Employees use VPN client software to connect to the dealership's internal network, and the firewall authenticates and encrypts the VPN connection;

[0098] Multi-factor authentication enhances security: During the VPN connection process, the firewall cooperates with the multi-factor authentication mechanism to improve the security of remote access and ensure that only authorized personnel can access new energy vehicle dealership data through the VPN.

[0099] In step S204, the relational database is constructed and supported by a hash index algorithm.

[0100] By storing and statistically analyzing the processed data through this method, it is possible to effectively protect the data, effectively reduce the possibility of customer information and vehicle information leakage, effectively solve the problem of customer information leakage and vehicle information leakage, which are likely to cause economic losses to customers and dealerships, improve customers' trust in the seller, facilitate the subsequent development of sales work, and effectively increase the subsequent conversion rate.

[0101] The above embodiments only represent the preferred implementation modes of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications, improvements and substitutions can be made, and these all belong to the protection scope of the present invention.

Claims

1. A data processing method for new energy vehicle distribution, characterized in that: Here are the steps: Step S1, collecting data information; Step S2: arranging and storing the collected data information; Step S3, analyzing and processing the sorted data information; Step S4: Use a protective wall to protect the analyzed and processed data information; Step S5: applying the separated and processed data to marketing and after-sales decision-making services; Step S6: Update the latest data in a timely manner.

2. A data processing method for new energy vehicle distribution according to claim 1, characterized in that: The main steps of collecting data information in step S1 include: S101. Collect customer information: obtain the customer's name, contact information, home address and age information through exhibition hall reception and online consultation; S102, collecting the customer's car purchase intention and budget information: obtaining the customer's satisfactory car model and budget information through dialogue; S103. Collect vehicle information: record the basic information of each new energy vehicle in the warehouse, including vehicle brand, model, configuration, color, frame number, and inventory location; S104. Collect market information: Obtain the prices, promotional activities, and product features of competitors’ new energy vehicles through market research company reports, competitors’ official websites, and offline showroom surveys.

3. A data processing method for new energy vehicle distribution according to claim 2, characterized in that: When the collected data information is sorted and stored in step S2, the specific steps include: S201, data cleaning: check the wrong data in the customer information and vehicle information, and delete the obvious wrong information; S202, data classification and coding: customers are classified into high-intention high-budget customers, high-intention low-budget customers, and low-intention potential customers according to factors such as car purchase intention and budget in customer data; S203, vehicle classification: classify and code vehicles according to brand, model, and price range; S204. Data storage: Use a relational database to store data and establish a data backup mechanism to back up data regularly to prevent data loss.

4. A data processing method for new energy vehicle distribution according to claim 1, characterized in that: The method of analyzing and processing the data information in step S3 includes: S301. Sales trend analysis: analyzing the sales volume and sales volume trends of new energy vehicles in different time periods; S302, Customer Purchase Behavior Analysis: Study the customer's purchase decision process, including the time span from understanding the car model to the final purchase, and the number of brands and models compared by the customer; S303, Sales channel analysis: Evaluate the sales contribution of different sales channels. Calculate the sales share and sales growth rate of each channel, and determine the sales channels to be developed; S304, inventory analysis: calculating the turnover rate of vehicle inventory; S305. Inventory structure analysis: analyzing the brand, model, configuration, and color structure of inventory vehicles; S306. Market analysis: Calculate the dealer’s share in the local new energy vehicle market, compare it with competitors, collect the total sales data of local new energy vehicles and the dealer’s sales data, and calculate the market share.

5. The data processing method for new energy vehicle distribution according to claim 1, characterized in that: When a firewall is used in step S4 to protect the analyzed and processed data, the specific method includes: S401, access control protection: protect access control based on IP address and port; S402. Intrusion includes: using IDS to block the connection when suspicious behavior is detected; S403, virtual private network support; S404, data encryption transmission support.

6. A data processing method for new energy vehicle sales according to claim 5, characterized in that: In step S401, the firewall can allow or deny access based on the source IP address and the destination IP address; different network services usually use specific ports.

7. A data processing method for new energy vehicle sales according to claim 5, characterized in that: The virtual private network support in step S403 is: Secure remote access support: For employees who need to remotely access new energy vehicle dealership data, the firewall supports VPN connections. VPN establishes a private network connection on the public network through an encrypted channel. Employees use VPN client software to connect to the dealer's internal network, and the firewall authenticates and encrypts the VPN connection. Multi-factor authentication enhances security: During the VPN connection process, the firewall cooperates with the multi-factor authentication mechanism to improve the security of remote access and ensure that only authorized personnel can access new energy vehicle dealership data through VPN.

8. A data processing method for new energy vehicle distribution according to claim 3, characterized in that: In step S204, the relational database is constructed using a hash index algorithm.

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