Automobile cross-regional sales detection method based on GPS anti-coding

Abstract

The invention provides an automobile cross-regional sales detection method based on GPS anti-coding, and the method comprises the following steps: S1, obtaining vehicle management information, and defining a vehicle saleable region according to the vehicle management information, wherein the vehicle management information comprises vehicle basic information, vehicle state maintenance information, vehicle affiliation information and vehicle transaction reporting information; the system not only can detect the cross-regional state of the sold vehicle in real time and send alarm information, but also can provide a visual summarized report for analysis, thereby helping a main engine plant to better manage subordinate dealer groups and standardize the automobile sales market, and promoting the healthy development of the automobile sales industry. In addition, the GPS anti-coding model can greatly reduce the cost of purchasing external coding APIs, and the model has flexible transportability, supports localization and cloud deployment, and improves the recognition efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, to a method for detecting cross-region sales of automobiles based on GPS anti-coding. Background Art

[0002] With the acceleration of global economic integration, the competition in the automotive market has become increasingly fierce. Due to differences in laws, regulatory requirements, and market strategies in different regions, there are significant differences in the automotive sales models and price systems within each region. In order to maintain the stability of the regional market and the healthy development of the dealer network, the original equipment manufacturer (OEM) usually establishes an exclusive cooperation relationship with dealers within a specific region and restricts dealers in other regions from conducting sales activities in that region through various agreements. This mechanism not only helps to protect the interests of local dealers but also ensures that the OEM can effectively manage its brand influence and market share.

[0003] However, in practice, this sales strategy based on geographical region division faces challenges brought about by cross-region sales behavior. Some unauthorized dealers or individuals may purchase vehicles in one region at a low price and transfer them to other regions with higher prices for illegal sales to make differential profits. Such behavior seriously disrupts the normal market order, weakens the competitive advantage of legitimate dealers, damages the OEM's effective control ability over the sales channel, and ultimately affects the healthy and stable development of the entire automotive industry.

[0004] Currently, in response to the problem of cross-region sales of automobiles, the industry has not yet formed a complete and effective proactive warning system. In most cases, it is only when the cross-region sales behavior has caused a certain degree of impact on the local market that it will be discovered and dealt with. This not only increases the costs for the OEM and its authorized dealers to identify and respond to violations but also reflects the deficiencies in the preventive, real-time, and accuracy aspects of existing monitoring means and technical solutions. Therefore, developing a technical solution that can detect and warn potential cross-region sales behavior in a timely and accurate manner is of great significance for maintaining a fair competition environment in the automotive market and protecting the interests of all parties involved. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide, in view of the deficiencies in the above technical solutions, a method for detecting cross-region sales of automobiles based on GPS anti-coding that can not only detect the cross-region status of sold vehicles in real time but also send warning messages.

[0006] The present invention provides a method for detecting cross-region sales of automobiles based on GPS anti-coding, the method comprising the following steps:

[0007] S1. Obtain vehicle management information, and define the vehicle's salable area according to the vehicle management information, where the vehicle management information includes the vehicle's basic information, vehicle status maintenance information, vehicle ownership information, and vehicle movement reporting information;

[0008] S2. Obtain the list of vehicles to be detected currently based on the vehicle's basic information, vehicle status maintenance information, vehicle ownership information, and vehicle movement reporting information, and trigger the vehicle-mounted positioning system to collect and upload the real-time longitude and latitude data of the vehicle after judging the current status of the vehicle according to the vehicle list. Convert the longitude and latitude data into specific geographical locations through the GPS reverse coding model for the real-time longitude and latitude data of the vehicle;

[0009] S3. Compare the geographical location information obtained in step S2 with the vehicle's salable area in step S1, calculate the mismatch rate, judge whether the vehicle is in a cross-region sales state by comparing the mismatch rate with the threshold K set by the system, and record the information of vehicles with cross-region sales anomalies;

[0010] S4. Based on the information of vehicles with cross-region sales anomalies, send alarm information to the designated personnel in real time according to the maintained role permissions, and summarize all the detailed information of cross-region sales vehicles in the form of a report for managers at all levels to view.

[0011] In the method for detecting cross-region sales of automobiles based on GPS reverse coding according to the present invention; in step S1, the vehicle status maintenance information includes, but is not limited to, information on the vehicle's inventory, logistics, franchise store, and after-sales record of the vehicle's transfer status. The vehicle's basic information includes, but is not limited to, the vehicle's unique identification number VIN, vehicle off-line date, vehicle model, vehicle-owned franchise store, and vehicle sales area.

[0012] In the method for detecting cross-region sales of automobiles based on GPS reverse coding according to the present invention; step S2 includes the following steps:

[0013] S21. Obtain the vehicle's unique identification number VIN, vehicle off-line date, vehicle model, vehicle-owned franchise store, and vehicle sales area of the vehicle that has been assigned to the franchise store but not delivered according to the vehicle's basic information and vehicle movement reporting information; and judge the current sleep state of the vehicle according to the vehicle's unique identification number VIN, vehicle off-line date, vehicle model, vehicle-owned franchise store, and vehicle sales area. If the vehicle is currently in an awake state, do not actively execute the instruction, and only receive at least m pieces of longitude and latitude data C uploaded by the vehicle-mounted positioning system through the background program n (L)={L n，1 , L n，2 ...L n，b} If the current vehicle is in a sleep state, after executing the active instruction, still receive at least m pieces of longitude and latitude data C uploaded by the vehicle-mounted positioning system through the background programn (L) = {L n，1 , L n，2 ...L n，b}, where L n,b represents the b-th longitude and latitude point of the n-th vehicle, and C n (L) is the longitude and latitude set of the n-th vehicle.

[0014] In the method for detecting cross-region sales of automobiles based on GPS anti-coding according to the present invention; the step S2 further includes the following steps:

[0015] S22, perform longitude and latitude coordinate conversion on the longitude and latitude set C n (L) = {L n，1 , L n，2 ...L n，b} to obtain a new longitude and latitude set C new_n (L) = {L new_n，1 , L new_n，2 ...L new_n,b};

[0016] where L new_n，b is the longitude and latitude point after the coordinate system conversion of the b-th original longitude and latitude of the n-th vehicle, and C new_n (L) is the converted longitude and latitude set of the n-th vehicle.

[0017] In the method for detecting cross-region sales of automobiles based on GPS anti-coding according to the present invention; the step S2 further includes the following steps:

[0018] S23, obtain the longitude and latitude data set R i，j，f = {(l1), (l2), (l3),...(l x )} of the urban boundary of our country, where R i，j，f is the longitude and latitude set of districts and counties, i represents the province, j represents the city, f represents the district and county, and {(l1), (l2), (l3),...(l x )} represents all x longitude and latitude points on the boundary of the district and county. Split the longitude and latitude data set R i，j，f = {(l1), (l2), (l3),...(l x )} of the urban boundary to obtain a one-to-one correspondence set {l1 = R i，j，f , l2 = R i，j，f ,..., l x = R i，j，f} of single longitude and latitude points and districts and counties.

[0019] In the method for detecting cross-region sales of automobiles based on GPS anti-coding according to the present invention; the step S2 further includes the following steps:

[0020] S24. Select m longitude and latitude points closest to point H through KNN. The set of longitude and latitude of these m H points is represented as {l m1 , l m2 , l mm}, and the corresponding vehicle salable area is represented as R = {R1, R2,..., R m}. Traverse the geographical locations R m in R, and judge one by one whether point H is within the range of the geographical location R m .

[0021] In the method for detecting cross-region vehicle sales based on GPS reverse coding according to the present invention; in the step S24, first find all the boundary points of the geographical location R m , draw them into a polygon, calculate the area S1 of the polygon, then connect point H with each boundary point of the geographical location R m , calculate the sum of the areas S2 of the multiple polygons formed by point H and the boundary points. If S1 = S2, it means that point H is within the range of the geographical location R m , and the plaintext geographical location of point H is R m , otherwise, continue to traverse downwards until a geographical location R m within its range is found, and then stop traversing; where m is a configurable parameter.

[0022] In the method for detecting cross-region vehicle sales based on GPS reverse coding according to the present invention; the step S2 further includes the following steps:

[0023] S25. Input the new longitude and latitude set C new_n (L) = {L new_n，1 , L new_n,2 ... L new_n,b} into the GPS reverse coding model in sequence to obtain the corresponding specific geographical location set R d = {R1, R2... R b}, where b is the number of longitude and latitude data points obtained, and d is the vehicle number.

[0024] In the method for detecting cross-region vehicle sales based on GPS reverse coding according to the present invention; the step S3 includes the following steps:

[0025] S31. Compare the specific geographical location set R d = {R1, R2... R b} with the vehicle salable area R one by one, and calculate the mismatch rate r n , If r n is greater than the system-set threshold K, then judge that vehicle n is in a cross-region sales state, and record the information of cross-region sales abnormal vehicles.

[0026] The method for detecting cross-region sales of vehicles based on GPS anti-coding of the present invention can not only detect the cross-region status of sold vehicles in real time and send warning messages, but also provide visual summary reports for analysis, helping the vehicle manufacturer better manage its subordinate dealer groups and standardize the vehicle sales market, and promoting the healthy development of the vehicle sales industry. In addition, the GPS anti-coding model can greatly reduce the cost of purchasing external coding APIs, and the model has flexible portability, supporting local and cloud deployments, and increasing the recognition efficiency. Brief Description of the Drawings

[0027] Figure 1 It is a schematic flowchart of an embodiment of the method for detecting cross-region sales of vehicles based on GPS anti-coding of the present invention. Detailed Embodiment

[0028] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] As Figure 1 shown, it is a schematic flowchart of an embodiment of a method for detecting cross-region sales of vehicles based on GPS anti-coding of the present invention. A method for detecting cross-region sales of vehicles based on GPS anti-coding is provided, and the method is applied to an automobile production line. The method includes the following steps:

[0031] In step S1, vehicle management information is obtained, and the vehicle's salable area is defined according to the vehicle management information, where the vehicle management information includes the vehicle's basic information, vehicle status maintenance information, vehicle ownership information, and vehicle movement reporting information;

[0032] In step S2, obtain the current list of vehicles to be detected based on the basic information of the vehicle, vehicle status maintenance information, vehicle ownership information, and vehicle movement reporting information, and trigger the vehicle-mounted positioning system to collect and upload the real-time longitude and latitude data of the vehicle according to the vehicle list. Convert the real-time longitude and latitude data of the vehicle into specific geographical locations through the GPS reverse coding model;

[0033] In step S3, compare the geographical location information obtained in step S2 with the vehicle's salable area in step S1, calculate the mismatch rate, and determine whether the vehicle is in a cross-region sales state by comparing the mismatch rate with the system-set threshold K, and record the information of vehicles with cross-region sales anomalies;

[0034] In step S4, based on the information of vehicles with cross-region sales anomalies, send an alarm message to the designated personnel in real time according to the maintained role permissions, and summarize all the detailed information of cross-region sales vehicles in the form of a report for viewing by management personnel at all levels.

[0035] In one embodiment, in step S1, the vehicle status maintenance information includes but is not limited to information on the vehicle's status in stock, in logistics, in the franchise store, and after-sales record of vehicle transfer. The basic information of the vehicle includes but is not limited to the vehicle's unique identification number VIN, vehicle off-line date, vehicle model, vehicle-owned franchise store, and vehicle sales area.

[0036] In one embodiment, step S2 includes the following steps:

[0037] In step S21, obtain the vehicle's unique identification number VIN, vehicle off-line date, vehicle model, vehicle-owned franchise store, and vehicle sales area of the vehicles that have been assigned to the franchise store but not delivered based on the basic information of the vehicle and the vehicle movement reporting information; and determine the current sleep state of the vehicle according to the vehicle's unique identification number VIN, vehicle off-line date, vehicle model, vehicle-owned franchise store, and vehicle sales area. If the vehicle is currently in an active state, do not actively execute the instruction, and only receive at least m pieces of longitude and latitude data C uploaded by the vehicle-mounted positioning system through the background program n (L) = {L n，1 , L n，2 ...L n，b}, if the current vehicle is in a sleep state, after executing the active instruction, still receive at least m pieces of longitude and latitude data C uploaded by the vehicle-mounted positioning system through the background program n (L) = {L n，1 , L n，2 ...L n，b}, where L n，b represents the b-th longitude and latitude point of the n-th vehicle, and C n (L) is the longitude and latitude set of the n-th vehicle.

[0038] In one embodiment, step S2 further includes the following steps:

[0039] In step S22, the longitude and latitude set C n (L)={L n，1 , L n，2 ...L n,b}Convert the longitude and latitude coordinates to obtain the new longitude and latitude set C new_n (L)={L new_n，1 , L new_n，2 ...L new_n，b};

[0040] Among them, L new_n，b C is the latitude and longitude point of the nth vehicle after the bth original latitude and longitude is converted to the coordinate system. new_n (L) is the converted longitude and latitude set of the nth vehicle.

[0041] In one embodiment, step S2 further includes the following steps:

[0042] In step S23, obtain the longitude and latitude data set R of the city boundaries in my country. i，j，f ={(l1), (l2), (l3),...(l x )}, where R i，j，f is the set of longitude and latitude of counties, i represents province, j represents city, f represents county, {(l1), (l2), (l3), ... (l x )} represents all x longitude and latitude points on the county boundary, and the city boundary longitude and latitude data set R i，j,f ={(l1), (l2), (l3),...(l x )} to split and obtain a one-to-one correspondence set between a single latitude and longitude point and a district or county {l1=R i，j，f , l2=R i，j，f ,...,l x =R i，j，f}.

[0043] In one embodiment, step S2 further includes the following steps:

[0044] In step S24, the m latitude and longitude points closest to point H are screened by KNN. The latitude and longitude set of the m points H is represented as {l m1 , l m2 , l mm}, and its corresponding vehicle sales area is expressed as R = {R1, R2, ..., R m}, traverse the geographic locations R in R m , and judge whether point H is located at geographic location R one by one mwithin the range.

[0045] In one embodiment, in step S24, first find the geographical location R m all boundary points, draw them into a polygon, calculate the area S1 of the polygon, and then connect point H to each geographical location R m boundary points, calculate the sum of the areas S2 of the multiple polygons formed by point H and the boundary points. If S1 = S2, it indicates that point H is within the geographical location R m range, and the plaintext geographical location of point H is R m , otherwise, continue to traverse downwards until a geographical location R within its range is found m , then stop traversing; where m is a configurable parameter.

[0046] In one embodiment, step S2 further includes the following steps:

[0047] In step S25, the new set of longitude and latitude C new_n (L) = {L new_n，1 , L new_n，2 ... L new_n，b} is input into the GPS inverse coding model in sequence to obtain the corresponding specific geographical location set R d = {R1, R2... R b}, where b is the number of longitude and latitude data points obtained, and d is the vehicle number.

[0048] In one embodiment, step S3 includes the following steps:

[0049] In step S31, the specific geographical location set R d = {R1, R2... R b} is compared with the vehicle's salable area R one by one, and the mismatch rate r n is calculated, If r n is greater than the system-set threshold K, it is determined that vehicle n is in a cross-region sales state, and the cross-region sales abnormal vehicle information is recorded. It should be noted that the system-set threshold K for the mismatch rate in this embodiment is 0.9.

[0050] In the implementation in this city, vehicle management information includes basic vehicle information, vehicle status maintenance information, vehicle ownership information, vehicle movement reporting information, role management information, etc. Vehicle status maintenance information is used to maintain the real-time status of vehicles, such as information recording the vehicle transfer status in inventory, in logistics, at the franchise store, after-sales, etc. This information is used to determine whether the vehicle belongs to the scope of cross-region monitored vehicles; the basic vehicle information is used to enter the basic vehicle information, including but not limited to the vehicle's unique code VIN, the vehicle off-line date, the vehicle model, the franchise store to which the vehicle belongs, the vehicle sales area, etc.; vehicle movement reporting information is used to report vehicle movement situations in advance and distribute them to the superior supervisors for approval. Abnormal cross-region vehicles that have passed the reporting review are not within the monitoring scope. Role management information is used to configure different cross-region monitoring roles and assign different viewing and operating permissions. For example, supervisors, regional commissioners, sub-regional commissioners, and dealers are respectively configured with different permissions.

[0051] Specifically, in step S21, the background program determines the current sleep state of the vehicle and decides whether to execute the active instruction. The background program first determines the current sleep state of the vehicle. If the vehicle is currently in an awake state, it does not actively execute the instruction and only receives at least m longitude and latitude coordinates C continuously uploaded by the in-vehicle positioning system in real time through the background program such as KAFKA n (L) = {L n，1 , L n，2 ... L n，b}, where L n，b represents the b-th longitude and latitude point of the n-th vehicle, and C n (L) is the longitude and latitude set of the n-th vehicle; if the current vehicle is in a sleep state, after executing the active instruction, it still receives at least m longitude and latitude coordinates C uploaded by the in-vehicle positioning system in real time through the background program n (L) = {L n，1 , L n，2 ... L n，b}.

[0052] Specifically, for the longitude and latitude set C n (L) = {L n，1 , L n，2 ... L n，bPerform longitude and latitude coordinate conversion. The main purpose of the conversion is to address the issue of inconsistent coordinate systems between the longitude and latitude coordinates uploaded by the vehicle head unit for positioning and the longitude and latitude coordinate system required by the GPS reverse coding model. Through the conversion between coordinate systems, they are made to be in the same coordinate system. It mainly involves the conversion between commonly used coordinate systems such as the WGS84 coordinate system (geodetic coordinate system), GCJ02 coordinate system (Mars coordinate system), and BD09 coordinate system (Baidu coordinate system). Currently, the longitude and latitude coordinate system generally uploaded by the vehicle head unit positioning system is WGS84, and the standard coordinate system used later is generally the GCJ02 coordinate system. Therefore, taking the conversion from WGS84 to the GCJ02 coordinate system as an example to illustrate the conversion process, the coordinate to be converted is represented by L n，1 =(lat, lng) for example, which mainly includes two parts. The first part is to determine whether the longitude and latitude are within China. Coordinates outside China are not converted. The judgment method is whether the following conditions 1 (condl) are simultaneously satisfied. condl = (lng > 73.66) & (lng < 135.05) & (lat > 3.86) & (lat < 53.55). If satisfied, then proceed to the second part, the actual conversion between longitude and latitude.

[0053] First, calculate the deviation of longitude and latitude from the geodetic origin of China:

[0054] Δlng0 = lng - 105, Δlat0 = lat - 35,

[0055] Then, perform sine and cosine polynomial calculations on longitude and latitude respectively:

[0056]

[0057] Finally, after the conversion is completed, the longitude and latitude under the GCJ02 coordinate system are, lngne w = lng + Δlng2; lat new = lat + Δlat2; where, π is the circumference ratio π = 3.1415926535897932384626, a is the semi-major axis, a = 6378245, e 2 is the flattening, e 2 = 0.00669342162296594323.

[0058] Sequentially pass the WGS84 longitude and latitude set uploaded by the vehicle head unit positioning through the above conversion to obtain the new longitude and latitude set C new_n (L) = {L new_n，1 , L new_n，2 ...L new_n，b};

[0059] where, L new_n，b is the longitude and latitude point after the conversion of the b-th original longitude and latitude of the n-th vehicle, and C new_n(L) is the set of transformed longitude and latitude of the nth vehicle.

[0060] In step S23, the acquisition of the longitude and latitude dataset of China's urban boundaries is through the official national website or publicly downloadable longitude and latitude data points of urban boundaries provided by map service providers such as AutoNavi and Baidu. Taking AutoNavi as an example, individual or enterprise developers can use the administrative region query API provided by AutoNavi to retrieve the set of longitude and latitude of the boundaries of all provinces - cities - districts and counties in China at one time. Obtain such as R i，j,f ={(l1), (l2), (l3),...(l x )} of the longitude and latitude set of districts and counties, where R i，j，f is the set of longitude and latitude of the boundaries of provinces - cities - districts and counties, the province is represented as i, the city is represented as j, the district and county is represented as f, {(l1), (l2), (l3),...(l x )} are all x longitude and latitude points on the boundary of the district and county. At the same time, the level of the dataset to be obtained can be determined according to actual needs. For example, if only the city level needs to be judged, only the longitude and latitude of the city boundary can be obtained. If only the province level needs to be judged, only the longitude and latitude of the province boundary can be obtained.

[0061] Preferably, after obtaining this part of the data, it can be stored and directly read when used. In particular, attention should be paid to the category of the longitude and latitude coordinate system obtained through various channels. For example, the longitude and latitude points obtained through the AutoNavi API above have a unified coordinate system of GCJ02.

[0062] In step S24, the dataset of multiple boundary points corresponding to one district and county obtained in step S23 is split to form a one-to-one correspondence set of single longitude and latitude points and districts and counties, such as {l1 = R i，j，f , l2 = R i，j，f ,..., l x = R i，j，f}; the clustering model determines the m nearest clustering points of the point to be encoded. Taking KNN (K-Nearest Neighbor algorithm) as an example, for the point to be encoded H, KNN can screen m longitude and latitude points closest to point H, and these longitude and latitude points all have clear label classifications belonging to which district and county. The set of longitude and latitude of these m points is expressed as: {l m1 , l m2 , l mm}, and their corresponding geographical locations can be correspondingly expressed as R = {R1, R2,..., R m}, where the value of m here is a configurable parameter of KNN, the number of nearest neighbors. This value should not be too large to prevent the difficulty of traversing and judging within each subsequent area and the long time consumption, nor should it be too small to prevent no correct geographical location from being selected.

[0063] Through step S23, the geographical location of point H can be basically determined as {R1, R2,..., Rm One of those in R. Traverse the geographical locations R in R m and judge one by one whether point H is located in the geographical location R m range. The main steps are as follows: First, find all the boundary points of R m and draw them into a polygon, calculate the area S1 of the polygon. Secondly, connect X to each boundary point of R m and calculate the sum of the areas S2 of the multiple polygons formed by point X and the boundary points. If S1 = S2, it means that X is located in R m range, and the plaintext geographical location encoded by X is R m , otherwise, continue to traverse downwards until a geographical location R within its range is found m , and then stop traversing.

[0064] Through the above steps, the conversion of a specific longitude and latitude to a detailed district or city can be quickly realized. The main function of step S23 is to reduce the computational complexity of the traversal operation in step S24 and speed up the encoding speed.

[0065] Specifically, this application sends real-time cross-region sales vehicle warning information in the form of email or text message. The information content sent can include: the vin code of the vehicle in the cross-region sales state, the franchise store to which the vehicle belongs and its franchise store information, the franchise store in the current area where the vehicle is located and its franchise store information, etc. These information can assist managers to quickly locate the vehicle position and details and contact the relevant responsible persons. At the same time, the real-time warning information will be automatically stored in the database. The purpose is to provide statistical data with flexible cycles and dimensions for managers at all levels to review problems and formulate management strategies.

[0066] The beneficial effects of a method for detecting cross-region sales of automobiles based on GPS reverse coding provided by an embodiment of the present invention are at least as follows:

[0067] 1) Through the clustering method, this application can greatly reduce the judgment complexity in irregular polygon areas of cities or districts, significantly improve the program operation efficiency, effectively reduce the occupation of computing resources, and optimize the system performance.

[0068] 2) This invention closely conforms to the actual cross-region sales scenario of automobiles, supports real-time status detection and information distribution, realizes the leap from "post-event management" to "real-time control", and can discover and handle cross-region sales problems in a timely manner.

[0069] 3) The GPS reverse coding module of this application has high portability and can flexibly select the local or cloud deployment method according to the actual application scenario requirements and response requirements, providing users with a more personalized and convenient use experience.

[0070] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0071] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0072] Therefore, as described above, the above are only the preferred specific embodiments of the present invention, and the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. The protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for detecting cross-region sales of automobiles based on GPS anti-coding, characterized in that, The method includes the following steps: S1. Obtain vehicle management information, and define the vehicle's salable area based on the vehicle management information, where the vehicle management information includes the vehicle's basic information, vehicle status maintenance information, vehicle ownership information, and vehicle movement reporting information; S2. Obtain the list of vehicles to be detected currently based on the vehicle's basic information, vehicle status maintenance information, vehicle ownership information, and vehicle movement reporting information, and trigger the vehicle-mounted positioning system to collect and upload the real-time longitude and latitude data of the vehicle after judging the current status of the vehicle according to the vehicle list, and convert the longitude and latitude data into specific geographical locations through the GPS inverse coding model for the real-time longitude and latitude data of the vehicle; S3. Compare the geographical location information obtained in step S2 with the vehicle's salable area in step S1, calculate the mismatch rate, judge whether the vehicle is in a cross-region sales state by comparing the mismatch rate with the threshold K set by the system, and record the information of vehicles with cross-region sales anomalies; S4. Based on the information of vehicles with cross-region sales anomalies, send alarm information to the designated personnel in real time according to the maintained role permissions, and summarize all the detailed information of cross-region sales vehicles for viewing by management personnel at all levels in the form of a report.

2. The method for detecting cross-region sales of automobiles based on GPS anti-coding according to claim 1, wherein In step S1, the vehicle status maintenance information includes, but is not limited to, information on the vehicle's status during inventory, logistics, franchise store, and after-sales record of vehicle transfer, and the vehicle's basic information includes, but is not limited to, the vehicle's unique identification number VIN code, vehicle off-line date, vehicle model, vehicle-owned franchise store, and vehicle sales area.

3. The method for detecting cross-region sales of automobiles based on GPS anti-coding according to claim 2, wherein, Step S2 includes the following steps: S21. Obtain the vehicle unique identification number (VIN), vehicle off-line date, vehicle model, vehicle-assigned franchise store, and vehicle sales area of the vehicles that have been allocated to the franchise store but not yet delivered based on the basic information of the vehicle and the vehicle change reporting information; and determine the current sleep state of the vehicle according to the VIN, vehicle off-line date, vehicle model, vehicle-assigned franchise store, and vehicle sales area. If the vehicle is currently in an awake state, do not actively execute the instruction, but only receive at least m pieces of longitude and latitude data C uploaded by the in-vehicle positioning system through the background program n (L) = {L n,1 , L n,2 … L n,b}, if the current vehicle is in a sleep state, after executing the active instruction, still receive at least m pieces of longitude and latitude data C uploaded by the in-vehicle positioning system through the background program n (L) = {L n,1 , L n,2 … L n,b}, where L n,b represents the b-th longitude and latitude point of the n-th vehicle, and C n (L) is the longitude and latitude set of the n-th vehicle.

4. The method for detecting cross-region sales of automobiles based on GPS anti-coding according to claim 3, wherein, Step S2 further includes the following steps: S22, perform longitude and latitude coordinate conversion on the longitude and latitude set C n (L) = {L n,1 , L n,2 … L n,b} to obtain a new longitude and latitude set C new_n (L) = {L new_n,1 , L new_n,2 … L new_n,b}; Among them, L new_n,b is the longitude and latitude point after the original longitude and latitude of the b-th of the n-th vehicle is converted to the coordinate system, and C new_n (L) is the set of converted longitudes and latitudes of the n-th vehicle.

5. The method for detecting cross-region sales of automobiles based on GPS anti-coding according to claim 4, wherein, Step S2 further includes the following steps: S23. Obtain the latitude and longitude data set R of the boundaries of Chinese cities i,j,f ={(l1),(l2),(l3),…(l x )}, where R i,j,f is the set of district / county latitudes and longitudes. i represents the province, j represents the city, f represents the district / county, and {(l1),(l2),(l3),…(l x )} represents all x latitude and longitude points on the boundary of the district / county. Split the latitude and longitude data set R i,j,f ={(l1),(l2),(l3),…(l x )} to obtain the one-to-one correspondence set between individual latitude and longitude points and districts / counties {l1 = R i,j,f , l2 = R i,j,f , …, l x = R i,j,f}.

6. The method for detecting cross-region sales of automobiles based on GPS anti-coding according to claim 5, characterized in that, Step S2 further includes the following steps: S24, screen m longitude and latitude points closest to point H through KNN. The longitude and latitude set of these m H points is represented as {l m1 , l m2 , l mm}, and the corresponding vehicle salable area is represented as R = {R1, R2, …, R m}. Traverse the geographical locations R m in R, and judge one by one whether point H is within the range of the geographical location R m .

7. The method for detecting cross-region sales of automobiles based on GPS anti-coding according to claim 6, characterized in that In the step S24, first find the geographical location R m all the boundary points, draw them into a polygon, calculate the area S1 of the polygon, and then connect point H to each boundary point of the geographical location R m to calculate the sum of the areas S2 of the multiple polygons formed by point H and the boundary points. If S1 = S2, it means that point H is located within the geographical location R m and the plaintext geographical location of point H is R m , otherwise, continue to traverse downward until a geographical location R within its range is found m , and then stop traversing; where m is a configurable parameter.

8. The method for detecting cross-region sales of automobiles based on GPS anti-coding according to claim 7, wherein Step S2 further includes the following steps: S25, the new set of longitude and latitude C new_n (L) = {L new_n,1 , L new_n,2 … L new_n,b} is input into the GPS reverse coding model in sequence to obtain the corresponding specific set of geographical locations R d = {R1, R2… R b}, where b is the number of longitude and latitude data points obtained, and d is the vehicle number.

9. The method for detecting cross-region sales of automobiles based on GPS anti-coding according to claim 8, characterized in that, Step S3 includes the following steps: S31. Compare the specific geographical location set R d ={R1, R2…R b} with the vehicle's salable area R one by one, and calculate the mismatch rate If r n is greater than the system-set threshold K, then determine that vehicle n is in a cross-region sales status and record the information of cross-region sales abnormal vehicles.

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