A data analysis method and system based on vehicle-mounted terminal

Abstract

The present invention relates to the field of intelligent site selection technology, specifically disclosing a data analysis method and system based on an onboard terminal. The method comprises the following steps: obtaining the coordinates of parked vehicles within a preset after-sales area using an onboard terminal, clustering the coordinates to obtain clusters; obtaining the minimum circumscribed circle of the clusters, denoted as a screening circle, obtaining the locations of after-sales service stations within the screening circle, and modifying the clusters based on the locations of the after-sales service stations to obtain a target cluster; obtaining candidate locations and selecting new after-sales service station locations based on the theoretical center of gravity of the target cluster. This invention can optimize the layout of after-sales service station locations.

Description

Technical Field

[0001] The present invention relates to the field of intelligent site selection technology, and in particular to a data analysis method and system based on a vehicle-mounted terminal. Background Art

[0002] The on-board terminal is a type of intelligent device installed inside the vehicle. It is used to collect, process and transmit vehicle and road information in real time, such as location, speed and vehicle status, and provide remote monitoring and other services to ensure driving safety and traffic efficiency.

[0003] When a vehicle undergoes maintenance or experiences a breakdown, the owner typically needs to visit a designated after-sales service station for repair or inspection. The location of an after-sales service station is crucial for vehicle owners. Choosing a suitable location for an after-sales service station can effectively shorten the journey time and distance for vehicle owners, significantly improving convenience for vehicle owners. This not only increases customer satisfaction with after-sales service, but also helps enhance the brand's overall reputation and market competitiveness.

[0004] Existing technology typically selects locations for after-sales service stations based on manual experience. This approach is subjective and lacks a scientifically sound evaluation basis. This can lead to an uneven distribution of stations, leaving some areas with idle after-sales service resources and others with insufficient service stations. This not only fails to effectively meet the actual needs of car owners, but also reduces customer experience and satisfaction, negatively impacting the company's brand image and market competitiveness. Summary of the Invention

[0005] The purpose of the present invention is to provide a data analysis method and system based on a vehicle-mounted terminal to solve the following technical problems:

[0006] Existing technology typically selects locations for after-sales service stations based on manual experience. This approach is subjective and lacks a scientifically sound evaluation basis. This can lead to an uneven distribution of stations, leaving some areas with idle after-sales service resources and others with insufficient service stations. This not only fails to effectively meet the actual needs of car owners, but also reduces customer experience and satisfaction, negatively impacting the company's brand image and market competitiveness.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A data analysis method based on a vehicle terminal comprises the following steps:

[0009] In a preset after-sales area, the coordinates of the vehicle when it is parked are obtained based on the vehicle terminal, and the coordinates are clustered to obtain clusters;

[0010] Obtaining the minimum circumscribed circle of the cluster, recorded as a screening circle, obtaining the location of the after-sales service station within the screening circle, and modifying the cluster according to the location of the after-sales service station to obtain a target cluster;

[0011] Obtain the candidate location of the after-sales service station within the target cluster, obtain the theoretical center of gravity x of the target cluster, obtain the candidate location X that is closest to the theoretical center of gravity x, and obtain the area of ​​the triangle formed by any two after-sales service stations and the candidate location X. If the maximum triangle area S ≥ 0.8Y, then use the candidate location X as the new after-sales service station location. Y = min(y1, y2), where y1 and y2 represent the minimum circumscribed circle area and the minimum circumscribed rectangle area of ​​the target cluster, respectively. Min(y1, y2) indicates selecting the minimum value of y1 and y2.

[0012] As a further solution of the present invention, the process of clustering the coordinates specifically includes:

[0013] Set a first radius d, take coordinate a as the center, calculate the coordinate density within the first radius d, set a first density threshold fys, if the coordinate density is greater than or equal to the first density threshold, then generate an initial cluster A with a cluster radius d with coordinate a as the center, obtain the theoretical center of gravity A1 of the initial cluster A, and use the coordinate density within the cluster radius d as the weight of the theoretical center of gravity A1;

[0014] Set the second radius E, take the theoretical center of gravity i as the center, and calculate the judgment density within the second radius , J represents the total number of theoretical centers of gravity within the second radius E, Cj represents the weight of the j-th theoretical center of gravity within the second radius E, if the judgment density H≥Fys, a cluster with a clustering radius of E is generated with the theoretical center of gravity i as the center, Fys represents the preset second density threshold, and Fys>fys.

[0015] As a further solution of the present invention, the process of generating clusters further includes the following steps:

[0016] If the judgment density H is less than Fys, the theoretical center of gravity within the second radius E is marked as the pending center of gravity;

[0017] Taking the undetermined center of gravity as the center, calculate the coordinate density within the first radius d, which is recorded as the undetermined density. In the embodiment, the first radius d is increased by a preset first radius correction value d1, η is a preset coefficient and η>1, and a new to-be-determined density is obtained after each increase of the first radius;

[0018] Obtain the maximum undetermined density of a single undetermined centroid. If the maximum undetermined density is greater than or equal to 3fys, obtain a first radius D corresponding to the maximum undetermined density, and generate a cluster with a clustering radius of D with the undetermined centroid as the center.

[0019] As a further solution of the present invention, the process of correcting the cluster according to the location of the after-sales service station to obtain the target cluster specifically includes:

[0020] Set a screening radius R, take the location of the after-sales service station as the center, and calculate the coordinate density P within the screening radius R;

[0021] A coordinate density threshold Pys is set. If the coordinate density P>Pys, the screening radius R is slowly increased at a preset speed until the coordinate density P1 in the corresponding screening circle is equal to Pys after a certain adjustment of the screening radius. If the coordinate density P<Pys, the screening radius R is slowly decreased at a predetermined speed until the coordinate density P1 in the corresponding screening circle is equal to Pys after a certain adjustment of the screening radius.

[0022] When the coordinate density within the screening circle is equal to Pys, the coordinates within the screening circle are removed to obtain a new cluster, which is recorded as the target cluster.

[0023] As a further solution of the present invention: after the candidate location X is used as the new after-sales service station location, the following steps are further included:

[0024] Set the maximum triangle area threshold Sys. If the maximum triangle area S ≥ Sys, perform the following steps:

[0025] Sort the triangle areas in descending order to obtain a first sort. Starting from the area of ​​the first triangle in the first sort, determine the first triangle whose area is smaller than Sys and record it as the target area. Use the triangle corresponding to the target area as the target triangle and remove the coordinate points in the target triangle to obtain a new target cluster Z.

[0026] The theoretical center of gravity z of the target cluster Z is obtained, and a new after-sales service station position is determined again based on the theoretical center of gravity z.

[0027] As a further solution of the present invention: if all the candidate locations are selected as new after-sales service station locations, information is sent for reporting and selection is performed manually.

[0028] As a further solution of the present invention: if there is no after-sales service station in a target cluster, the candidate position closest to the theoretical center of gravity of the target cluster is used as the new after-sales service station position.

[0029] A data analysis system based on a vehicle-mounted terminal, comprising:

[0030] Clustering module: In a preset after-sales area, the coordinates of the vehicle when it is parked are obtained based on the vehicle terminal, and the coordinates are clustered to obtain clusters;

[0031] Cluster optimization module: obtaining the minimum circumscribed circle of the cluster, recorded as the screening circle, obtaining the location of the after-sales service station within the screening circle, and modifying the cluster according to the location of the after-sales service station to obtain the target cluster;

[0032] Selection module: Obtain the candidate location of the after-sales service station in the target cluster, obtain the theoretical center of gravity x of the target cluster, obtain the candidate location X closest to the theoretical center of gravity x, and obtain the area of ​​the triangle formed by any two after-sales service stations and the candidate location X. If the maximum triangle area S ≥ 0.8Y, then the candidate location X is used as the new after-sales service station location. Y = min (y1, y2), y1 and y2 represent the minimum circumscribed circle area and the minimum circumscribed rectangle area of ​​the target cluster, respectively, and min (y1, y2) indicates selecting the minimum value of y1 and y2.

[0033] Beneficial effects of the present invention: Compared with the prior art, the present invention first performs preliminary clustering of vehicle parking coordinates by setting a first radius d and a first density threshold fys to form an initial cluster and calculate the theoretical center of gravity A1. The advantage of using coordinate density as the center of gravity weight is that it can identify and lock the hot spot area in advance when the data volume is large and the distribution is relatively dispersed, thereby greatly improving the efficiency and accuracy of subsequent clustering operations; then, based on the second radius E and the second density threshold Fys, the density H is calculated and compared with Fys. If H≥Fys, a new cluster is generated, which can effectively eliminate noise points and perform more precise scale control on the clustering radius; if H<Fys, these centers of gravity are marked as pending centers of gravity, and in the subsequent radius expansion range The first radius d is gradually increased, and it is repeatedly adjusted by the preset first radius correction value d1 to find the maximum undetermined density threshold that can make the undetermined center of gravity meet 3fys. Once the threshold is found, the gradient change of the actual data distribution can be used to divide the effective clustering clusters that best represent the local hotspots. This step-by-step correction approach reduces the risk of rough or excessive clustering due to a one-time fixed radius, thereby making the target cluster closer to the actual distribution of vehicles; then the minimum circumscribed circle of the cluster is obtained and called the screening circle, and the cluster is corrected in combination with the existing after-sales service station location. By setting the screening radius R and comparing it with the coordinate density threshold Pys, the strategy of slowly increasing or decreasing the screening radius R is determined. This on-demand adjustment mechanism can ensure that the extracted target cluster is kept at a balanced density level while avoiding the deviation caused by the subjective radius specified by humans, thereby ensuring that the eliminated coordinate points accurately point to those areas that are of little significance in the after-sales service layout; when the target cluster is In further analysis, based on the candidate locations of after-sales service stations within the target cluster and the candidate location X closest to the theoretical center of gravity x, the area of ​​the triangle formed by any two after-sales service stations related to this location is calculated. If the maximum triangle area S satisfies S ≥ 0.8Y, X is directly selected as the new after-sales service station location. This ensures that the site selection of the new station has higher stability and discrimination while meeting coverage requirements. Next, a maximum triangle area threshold Sys is introduced. By sorting all triangle areas in descending order and comparing them one by one, when the first area smaller than Sys is found, the coordinate point within the corresponding triangle is eliminated to obtain a new target cluster Z. The theoretical center of gravity z of target cluster Z is then obtained and used to modify or determine the new after-sales service station location. This helps to achieve a more optimal network layout for the entire after-sales area after eliminating coordinate points that do not contribute enough to the service layout or are distributed on the edge. This provides accurate data and algorithm support for ultimately reducing after-sales costs, improving service accessibility, and improving resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below with reference to the accompanying drawings.

[0035] Figure 1 It is a flow chart of a data analysis method based on a vehicle-mounted terminal of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0037] See also Figure 1 As shown, the present invention is a data analysis method based on a vehicle-mounted terminal, comprising the following steps:

[0038] In a preset after-sales area, the coordinates of the vehicle when it is parked are obtained based on the vehicle terminal, and the coordinates are clustered to obtain clusters;

[0039] In a preferred embodiment of the present invention, the process of clustering the coordinates specifically includes:

[0040] Set a first radius d, take coordinate a as the center, calculate the coordinate density within the first radius d, set a first density threshold fys, if the coordinate density is greater than or equal to the first density threshold, then generate an initial cluster A with a cluster radius d with coordinate a as the center, obtain the theoretical center of gravity A1 of the initial cluster A, and use the coordinate density within the cluster radius d as the weight of the theoretical center of gravity A1;

[0041] Set the second radius E, take the theoretical center of gravity i as the center, and calculate the judgment density within the second radius , J represents the total number of theoretical centers of gravity within the second radius E, Cj represents the weight of the j-th theoretical center of gravity within the second radius E, if the judgment density H≥Fys, a cluster with a clustering radius of E is generated with the theoretical center of gravity i as the center, Fys represents the preset second density threshold, and Fys>fys.

[0042] It can be understood that, with the user's authorization, the coordinates of the user when parking are obtained based on the vehicle terminal. The coordinates when parking reflect the user's living address and other commonly used locations, which serve as the data basis for the subsequent layout of service point locations; a first radius d is set based on demand or experience data to measure the distribution of a certain coordinate within a certain range around it, and then the coordinate density within the first radius d is calculated with coordinate a as the center. It can be represented by a simple counting method. For example, the total number of vehicles (or parking coordinates) falling within this circular area within a range not exceeding d from coordinate a is counted. This value can be called the coordinate density; a first density threshold fys is set: it is used to measure whether there are enough coordinate points in a small range. If this threshold is exceeded, it means that the area around coordinate a is relatively crowded, and an initial cluster can be generated here. For example, if fys=30, it means that if the number of cars within radius d is ≥30, the conditions for establishing an initial cluster are met;

[0043] Like the first radius d, the value of E is usually set according to project requirements, experience or test data. It is used to measure the degree of aggregation of a theoretical center of gravity in a larger range, and then calculate the judgment density within the second radius E with the theoretical center of gravity i as the center: Assuming that we currently regard the theoretical centers of gravity A1, A2, A3... of each initial cluster as a "center of gravity point set", then with any theoretical center of gravity i as the center, count how many other theoretical centers of gravity fall within the range of radius E; at the same time, each theoretical center of gravity corresponds to its own weight (for example, the sum of the coordinate density of the initial cluster corresponding to the center of gravity A1), and all the theoretical centers of gravity falling within the range of E will be counted. The sum of the weights of the centers of gravity is the judgment density H. Since the judgment object here becomes the theoretical center of gravity itself, the threshold Fys is generally higher than fys to ensure that only when the density of the centers of gravity is high enough, this area is considered to have the basis for merging to form a larger cluster; if the judgment density H ≥ Fys, a cluster with a cluster radius of E is generated with the theoretical center of gravity i as the center: this means that if enough other center of gravity weights have accumulated in the area of ​​radius E near the center of gravity i, then the coordinates within this range (not just the center of gravity) can be merged into a larger cluster and regarded as a candidate area or the final valid cluster;

[0044] In a preferred embodiment of the present invention, if the judgment density H is less than Fys, the theoretical center of gravity within the second radius E is marked as the pending center of gravity;

[0045] Taking the undetermined center of gravity as the center, calculate the coordinate density within the first radius d, which is recorded as the undetermined density. In the embodiment, the first radius d is increased by a preset first radius correction value d1, η is a preset coefficient and η>1, and a new to-be-determined density is obtained after each increase of the first radius;

[0046] Obtain the maximum undetermined density of a single undetermined centroid. If the maximum undetermined density is greater than or equal to 3fys, obtain a first radius D corresponding to the maximum undetermined density, and generate a cluster with a cluster radius of D with the undetermined centroid as the center.

[0047] It is worth noting that if the calculated judgment density H is less than Fys, clusters will not be generated immediately. Instead, all theoretical centers of gravity within the second radius E will be marked as pending centers of gravity. In this case, with any pending center of gravity as the center, the number of coordinates within the first radius d is counted again to calculate the pending density, and the clusters will be generated within the preset first radius range. Increase d multiple times within, with each increase amplitude being the first radius correction value d1. For example, when the initial d = 1 km, η = 2, and d1 = 0.2 km, the coordinate density statistics of the same undetermined centroid will be carried out at radii such as 1 km, 1.2 km, 1.4 km, 1.6 km, 1.8 km, 2 km, etc., so as to obtain a set of undetermined densities that continuously expand with the radius. Finally, select the maximum undetermined density value that a single undetermined centroid can reach. If this maximum undetermined density exceeds 3 fys, it means that at an appropriately expanded radius, the coordinate distribution around this undetermined centroid has a sufficiently high density. At this time, the first radius D corresponding to the generation of this maximum undetermined density can be obtained, and a new clustering cluster is generated with this undetermined centroid as the center and D as the clustering radius, which can further挖掘潜在的高密度区域,避免因单次判定而忽略局部存在的深度聚集现象;

[0048] Obtain the minimum circumscribed circle of the obtained clustering cluster, denoted as the screening circle, obtain the positions of after-sales service stations within the screening circle, and correct the clustering cluster according to the positions of after-sales service stations to obtain the target cluster;

[0049] A preferred case in this embodiment: The process of correcting the clustering cluster according to the positions of after-sales service stations to obtain the target cluster specifically includes:

[0050] Set the screening radius R, take the positions of the after-sales service stations as the center, and calculate the coordinate density P within the screening radius R;

[0051] Set the coordinate density threshold Pys, if the coordinate density P > Pys, then slowly increase the screening radius R at a preset speed until after a certain adjustment of the screening radius, the coordinate density P1 within the corresponding screening circle = Pys; if the coordinate density P < Pys, then slowly decrease the screening radius R at the speed until after a certain adjustment of the screening radius, the coordinate density P1 within the corresponding screening circle = Pys;

[0052] When the coordinate density within the screening circle is equal to Pys, remove the coordinates within the screening circle to obtain a new clustering cluster, denoted as the target cluster;

[0053] It should be noted that there is an unclear expression "挖掘潜在的高密度区域" in the translation of , which may need to be further clarified according to the specific context for a more accurate translation.It should be noted that an initial screening radius R is set and the coordinate density P within the circumference is calculated with the location of the after-sales service station as the center. Subsequently, in order to adapt to the balance between coordinate distribution and after-sales service needs, a coordinate density threshold Pys is introduced. If the current coordinate density P is greater than Pys, the boundary is gradually expanded outward by increasing the screening radius R in small increments and multiple times until the coordinate density P1 obtained after a certain radius adjustment just drops to Pys. Conversely, if P is less than Pys, the screening radius R is gradually reduced by the same means, so that the coordinate density P1 within the screening circle is gradually increased to Pys. When the coordinate density within the screening circle is equal to Pys, the coordinate points covered by the screening circle are eliminated to obtain a new, more focused cluster as the target cluster. This dynamic radius adjustment approach ensures that only coordinate points that match the predetermined density threshold are retained around the service station, thereby better filtering discrete data while providing a more reasonable regional division basis for the layout and site selection of subsequent after-sales service stations.

[0054] Obtain the candidate location of the after-sales service station within the target cluster, obtain the theoretical center of gravity x of the target cluster, obtain the candidate location X that is closest to the theoretical center of gravity x, and obtain the area of ​​the triangle formed by any two after-sales service stations and the candidate location X. If the maximum triangle area S ≥ 0.8Y, then use the candidate location X as the new after-sales service station location. Y = min(y1, y2), where y1 and y2 represent the minimum circumscribed circle area and the minimum circumscribed rectangle area of ​​the target cluster, respectively. min(y1, y2) indicates selecting the minimum value of y1 and y2.

[0055] It is worth noting that in order to further optimize the layout of service stations, the possible candidate locations of existing after-sales service stations in the target cluster will be obtained first, and the theoretical center of gravity x of the target cluster will be calculated. Then, the candidate location X that is closest to the theoretical center of gravity x will be selected; then, the candidate location X will be connected with any two existing after-sales service stations in the target cluster to form a triangle, and the areas of all possible triangles will be calculated through alternating combinations, and the largest triangle area S will be found among them; if the largest triangle area S satisfies the condition S ≥ 0.8Y, then the candidate location X will be directly connected to the target cluster. Once the new service station location is confirmed, it can be considered to balance coverage and service efficiency in the surrounding area. Y is the smaller of y1 and y2 (Y = min(y1, y2)), where y1 represents the minimum circumscribed circle area of ​​the target cluster, and y2 represents the minimum circumscribed rectangle area of ​​the target cluster. This is done to constrain the feasibility of the triangular layout of stations using the most compact outlying geometry, thereby avoiding the waste of resources caused by arbitrary selection of new stations or deviation from the cluster center. Ultimately, a service station network with good compatibility and a reasonable density distribution is formed.

[0056] In another preferred embodiment of the present invention, after the candidate location X is used as the new after-sales service station location, the following steps are further included:

[0057] Set the maximum triangle area threshold Sys. If the maximum triangle area S ≥ Sys, perform the following steps:

[0058] Sort the triangle areas in descending order to obtain a first sort. Starting from the area of ​​the first triangle in the first sort, determine the first triangle whose area is smaller than Sys and record it as the target area. Use the triangle corresponding to the target area as the target triangle and remove the coordinate points in the target triangle to obtain a new target cluster Z.

[0059] The theoretical center of gravity z of the target cluster Z is obtained, and a new after-sales service station position is determined again based on the theoretical center of gravity z.

[0060] It is understandable that if the candidate location X has been confirmed as the new after-sales service station location, but the triangle area is still large, further refinement can be made: first, a maximum triangle area threshold Sys is set. When the calculated maximum triangle area S is greater than or equal to Sys, all triangle areas are sorted in descending order. Starting from the first place in the sorted result, the first triangle with an area smaller than Sys is found as the target area. The triangle corresponding to the target area is then regarded as the target triangle, and the coordinate points within the target triangle are removed to form a new target cluster Z. Subsequently, the theoretical center of gravity z of this target cluster Z is further calculated, and the appropriate after-sales service station location is re-determined based on this center of gravity. This method can fully utilize the synergy between the existing site and the new site, and can also perform local splitting in the overly large area, ensuring that the overall service distribution is more reasonable and more in line with actual needs.

[0061] In a preferred embodiment of the present invention, if all candidate locations are selected as new after-sales service station locations, information is sent for reporting and selection is performed manually.

[0062] In another preferred embodiment of the present invention, if there is no after-sales service station in a target cluster, the candidate position closest to the theoretical center of gravity of the target cluster is used as the new after-sales service station position.

[0063] A data analysis system based on a vehicle-mounted terminal, comprising:

[0064] Clustering module: In a preset after-sales area, the coordinates of the vehicle when it is parked are obtained based on the vehicle terminal, and the coordinates are clustered to obtain clusters;

[0065] Cluster optimization module: obtaining the minimum circumscribed circle of the cluster, recorded as the screening circle, obtaining the location of the after-sales service station within the screening circle, and modifying the cluster according to the location of the after-sales service station to obtain the target cluster;

[0066] Selection module: Obtain the candidate location of the after-sales service station in the target cluster, obtain the theoretical center of gravity x of the target cluster, obtain the candidate location X closest to the theoretical center of gravity x, and obtain the area of ​​the triangle formed by any two after-sales service stations and the candidate location X. If the maximum triangle area S ≥ 0.8Y, then the candidate location X is used as the new after-sales service station location. Y = min (y1, y2), y1 and y2 represent the minimum circumscribed circle area and the minimum circumscribed rectangle area of ​​the target cluster, respectively, and min (y1, y2) indicates selecting the minimum value of y1 and y2.

[0067] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A data analysis method based on a vehicle-mounted terminal, characterized in that: The following steps are involved: In a preset after-sales area, the coordinates of the vehicle when it is parked are obtained based on the vehicle terminal, and the coordinates are clustered to obtain clusters; Obtaining the minimum circumscribed circle of the cluster, recorded as a screening circle, obtaining the location of the after-sales service station within the screening circle, and modifying the cluster according to the location of the after-sales service station to obtain a target cluster; Obtain the candidate location of the after-sales service station within the target cluster, obtain the theoretical center of gravity x of the target cluster, obtain the candidate location X that is closest to the theoretical center of gravity x, and obtain the area of ​​the triangle formed by any two after-sales service stations and the candidate location X. If the maximum triangle area S ≥ 0.8Y, then use the candidate location X as the new after-sales service station location. Y = min(y1, y2), where y1 and y2 represent the minimum circumscribed circle area and the minimum circumscribed rectangle area of ​​the target cluster, respectively. min(y1, y2) indicates selecting the minimum value of y1 and y2. After the candidate location X is used as the new after-sales service station location, the following steps are also included: Set the maximum triangle area threshold Sys. If the maximum triangle area S ≥ Sys, perform the following steps: Sort the triangle areas in descending order to obtain a first sort. Starting from the area of ​​the first triangle in the first sort, determine the first triangle whose area is smaller than Sys and record it as the target area. Use the triangle corresponding to the target area as the target triangle and remove the coordinate points in the target triangle to obtain a new target cluster Z. The theoretical center of gravity z of the target cluster Z is obtained, and a new after-sales service station position is determined again based on the theoretical center of gravity z.

2. The data analysis method based on the vehicle terminal according to claim 1, characterized in that: The process of clustering the coordinates specifically includes: Set a first radius d, take coordinate a as the center, calculate the coordinate density within the first radius d, set a first density threshold fys, if the coordinate density is greater than or equal to the first density threshold, then generate an initial cluster A with a cluster radius d with coordinate a as the center, obtain the theoretical center of gravity A1 of the initial cluster A, and use the coordinate density within the cluster radius d as the weight of the theoretical center of gravity A1; Set the second radius E, take the theoretical center of gravity i as the center, and calculate the judgment density within the second radius , J represents the total number of theoretical centers of gravity within the second radius E, Cj represents the weight of the j-th theoretical center of gravity within the second radius E, if the judgment density H≥Fys, a cluster with a clustering radius of E is generated with the theoretical center of gravity i as the center, Fys represents the preset second density threshold, and Fys>fys.

3. The data analysis method based on the vehicle terminal according to claim 2, characterized in that: The process of generating clusters also includes the following steps: If the judgment density H is less than Fys, the theoretical center of gravity within the second radius E is marked as the pending center of gravity; Taking the undetermined center of gravity as the center, calculate the coordinate density within the first radius d, which is recorded as the undetermined density. In the embodiment, the first radius d is increased by a preset first radius correction value d1, η is a preset coefficient and η>1, and a new to-be-determined density is obtained after each increase of the first radius; Obtain the maximum undetermined density of a single undetermined centroid. If the maximum undetermined density is greater than or equal to 3fys, obtain a first radius D corresponding to the maximum undetermined density, and generate a cluster with a clustering radius of D with the undetermined centroid as the center.

4. The data analysis method based on a vehicle terminal according to claim 1, characterized in that: The process of modifying the cluster according to the location of the after-sales service station and obtaining the target cluster includes: Set a screening radius R, take the location of the after-sales service station as the center, and calculate the coordinate density P within the screening radius R; A coordinate density threshold Pys is set. If the coordinate density P>Pys, the screening radius R is slowly increased at a preset speed until the coordinate density P1 in the corresponding screening circle is equal to Pys after a certain adjustment of the screening radius. If the coordinate density P<Pys, the screening radius R is slowly decreased at a predetermined speed until the coordinate density P1 in the corresponding screening circle is equal to Pys after a certain adjustment of the screening radius. When the coordinate density within the screening circle is equal to Pys, the coordinates within the screening circle are removed to obtain a new cluster, which is recorded as the target cluster.

5. The data analysis method based on a vehicle terminal according to claim 1, characterized in that: If all candidate locations are selected as new after-sales service station locations, information will be sent for reporting and manual selection will be made.

6. The data analysis method based on a vehicle terminal according to claim 1, characterized in that: If there is no after-sales service station in a target cluster, the candidate location closest to the theoretical center of gravity of the target cluster is selected as the new after-sales service station location.

7. A data analysis system based on a vehicle terminal, characterized in that: include: Clustering module: In a preset after-sales area, the coordinates of the vehicle when it is parked are obtained based on the vehicle terminal, and the coordinates are clustered to obtain clusters; Cluster optimization module: obtaining the minimum circumscribed circle of the cluster, recorded as the screening circle, obtaining the location of the after-sales service station within the screening circle, and modifying the cluster according to the location of the after-sales service station to obtain the target cluster; Selection module: Obtain the candidate location of the after-sales service station in the target cluster, obtain the theoretical center of gravity x of the target cluster, obtain the candidate location X that is closest to the theoretical center of gravity x, obtain the area of ​​the triangle formed by any two after-sales service stations and the candidate location X, and if the maximum triangle area S ≥ 0.8Y, then the candidate location X is used as the new after-sales service station location, Y = min (y1, y2), y1 and y2 represent the minimum circumscribed circle area and the minimum circumscribed rectangle area of ​​the target cluster, respectively, and min (y1, y2) means selecting the minimum value of y1 and y2; After the candidate location X is used as the new after-sales service station location, the following steps are also included: Set the maximum triangle area threshold Sys. If the maximum triangle area S ≥ Sys, perform the following steps: Sort the triangle areas in descending order to obtain a first sort. Starting from the area of ​​the first triangle in the first sort, determine the first triangle whose area is smaller than Sys and record it as the target area. Use the triangle corresponding to the target area as the target triangle and remove the coordinate points in the target triangle to obtain a new target cluster Z. The theoretical center of gravity z of the target cluster Z is obtained, and a new after-sales service station position is determined again based on the theoretical center of gravity z.

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