Automobile key user selection method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of electric digital data processing, in particular to an automobile key user selection method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the actual driving data of a plurality of automobile users; according to the actual driving data, obtaining driving characteristics of the plurality of automobile users, and performing user screening by using the driving characteristics to obtain initial automobile users meeting a preset screening condition; extracting key road data meeting a preset path condition from the actual driving data of the initial automobile users, and calculating the key road driving mileage of each initial automobile user based on the key road data, so as to obtain the key road driving mileage of each initial automobile user based on the key road driving mileage; and extracting automobile key users meeting a preset driving characteristic condition from the at least one initial automobile user. Therefore, the technical problem that the test data is one-sided and cannot be applied to improving the vehicle performance and the user experience due to the fact that the actual road test condition of the user group is difficult to accurately reflect in the related technology is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a method, device, electronic equipment and storage medium for selecting key automobile users. Background Art

[0002] The collection of actual road test data for automobiles is crucial to improving vehicle performance and user experience. In the process of forming a test fleet, relevant technologies can select users for test vehicles by random sampling. However, although the relevant technologies are simple and fast to execute, they cannot guarantee the representativeness of the test users. In other words, the test data of the selected test users cannot represent the entire user group, making it difficult to accurately reflect the actual road test conditions of the user group, making the test data relatively one-sided and difficult to apply to improve vehicle performance and user experience, which needs to be improved. Summary of the invention

[0003] The present invention provides a method, device, electronic device and storage medium for selecting key automobile users, so as to solve the technical problem in the related art that it is difficult to accurately reflect the actual road test conditions of the user group, so that the test data is relatively one-sided and cannot be used to improve vehicle performance and user experience.

[0004] A first aspect of the present invention provides a method for selecting key automobile users, comprising the following steps: obtaining actual driving data of multiple automobile users; obtaining driving characteristics of the multiple automobile users based on the actual driving data, and using the driving characteristics to screen users to obtain at least one initial automobile user who meets preset screening conditions; extracting key road data that meets preset path conditions from the actual driving data of the at least one initial automobile user, and calculating the key road mileage of each initial automobile user based on the key road data, so as to extract key automobile users that meet preset driving characteristic conditions from the at least one initial automobile user based on the key road mileage.

[0005] Optionally, in one embodiment of the present invention, obtaining the driving characteristics of the multiple automobile users based on the actual driving data includes: calculating motion characteristics in the driving characteristics based on the actual driving data, wherein the motion characteristics include at least one of an average speed characteristic, an average acceleration characteristic, an average deceleration characteristic, a speed distribution characteristic and an acceleration distribution characteristic.

[0006] Optionally, in one embodiment of the present invention, obtaining the driving characteristics of the multiple automobile users based on the actual driving data includes: matching the latitude and longitude data in the actual driving data with roads in a road network to determine the road type of each road involved in the actual driving data; respectively calculating the personal driving mileage of each automobile user on roads of different road types, and obtaining driving proportion characteristics of different road types in the driving characteristics based on the personal driving mileage.

[0007] Optionally, in one embodiment of the present invention, extracting key road data that meets the preset path condition from the actual driving data of the at least one initial automobile user includes: extracting longitude and latitude data from the actual driving data of the initial automobile user; combining the spatial clustering algorithm and the longitude and latitude data of the at least one initial automobile user to obtain at least one dense driving area; eliminating duplicate longitude and latitude points in all dense driving areas, and using the remaining longitude and latitude points as nodes; calculating the paths of all nodes that meet the preset length condition, and marking the roads involved in the paths that meet the preset length condition as the key roads; calculating the mileage on the key roads, and determining whether the mileage on the key roads meets the preset mileage condition; if the mileage on the key roads does not meet the preset mileage condition, adjusting the parameters of the spatial clustering algorithm until the mileage on the key roads meets the preset mileage condition to obtain all the key roads.

[0008] Optionally, in one embodiment of the present invention, extracting key automobile users who meet preset driving characteristic conditions from the at least one initial automobile user based on the key road mileage includes: calculating the key road mileage ratio of each initial automobile user based on the key road mileage; arranging the initial automobile users in descending order of the key road mileage ratio, and selecting the initial automobile users within a preset ranking threshold as the key users.

[0009] The second aspect of the present invention provides a device for selecting key automobile users, comprising: an acquisition module for acquiring actual driving data of multiple automobile users; a screening module for obtaining driving characteristics of the multiple automobile users based on the actual driving data, and using the driving characteristics to screen users to obtain at least one initial automobile user who meets preset screening conditions; an extraction module for extracting key road data that meets preset path conditions from the actual driving data of the at least one initial automobile user, and calculating the key road mileage of each initial automobile user based on the key road data, so as to extract key automobile users that meet preset driving characteristic conditions from the at least one initial automobile user based on the key road mileage.

[0010] Optionally, in one embodiment of the present invention, the screening module includes: a first calculation unit, used to calculate the motion characteristics in the driving characteristics based on the actual driving data, wherein the motion characteristics include at least one of the average speed characteristics, the average acceleration characteristics, the average deceleration characteristics, the speed distribution characteristics and the acceleration distribution characteristics.

[0011] Optionally, in one embodiment of the present invention, the screening module includes: a matching unit, used to match the latitude and longitude data in the actual driving data with roads in the road network to determine the road type of each road involved in the actual driving data; a second calculation unit, used to respectively calculate the personal mileage of each car user on roads of different road types, and obtain the driving proportion characteristics of different road types in the driving characteristics based on the personal driving mileage.

[0012] Optionally, in one embodiment of the present invention, the extraction module includes: an extraction unit, used to extract longitude and latitude data from the actual driving data of the initial automobile user; a third calculation unit, used to combine the spatial clustering algorithm and the longitude and latitude data of the at least one initial automobile user to obtain at least one dense driving area; a removal unit, used to remove duplicate longitude and latitude points in all dense driving areas, and use the remaining longitude and latitude points as nodes; a fourth calculation unit, used to calculate the paths of all nodes that meet the preset length conditions, and mark the roads involved in the paths that meet the preset length conditions as the key roads; a fifth calculation unit, used to calculate the mileage on the key roads, and determine whether the mileage on the key roads meets the preset mileage conditions; an adjustment unit, used to adjust the parameters of the spatial clustering algorithm when the mileage on the key roads does not meet the preset mileage conditions, until the mileage on the key roads meets the preset mileage conditions, so as to obtain all the key roads.

[0013] Optionally, in one embodiment of the present invention, the extraction module includes: a sixth calculation unit, used to calculate the key road mileage ratio of each initial automobile user based on the key road mileage; a selection unit, used to arrange the initial automobile users in descending order of the key road mileage ratio, and select the initial automobile users within a preset ranking threshold as the key users.

[0014] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for selecting key car users as described in the above embodiment.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for selecting key automobile users as described in the above embodiment.

[0016] A fifth aspect of the present invention provides a computer program product, including a computer program, which, when executed, is used to implement the above method for selecting key automobile users.

[0017] The embodiment of the present invention can, on the one hand, obtain the driving characteristics of multiple car users based on their actual driving data, and use the driving characteristics to screen users, and obtain at least one initial car user that meets the preset screening conditions. On the other hand, the embodiment of the present invention can extract key road data that meets the preset path conditions from the actual driving data of at least one initial car user, and calculate the key road driving mileage of each initial car user based on the key road data, thereby extracting key car users that meet the preset driving characteristic conditions based on the key road driving mileage, and based on the user's historical actual driving data, comprehensively analyze the user's travel characteristics, road proportion characteristics, and driving road factors, and extract key car users that can be used to characterize the user group, so that the test data can be used to improve vehicle performance and user experience. Thus, the technical problem in the related art that it is difficult to accurately reflect the actual road test conditions of the user group, making the test data relatively one-sided and unable to be used to improve vehicle performance and user experience is solved.

[0018] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a method for selecting key automobile users according to an embodiment of the present invention; Figure 2 A flowchart of a method for selecting key automobile users according to an embodiment of the present invention; Figure 3 is a schematic diagram of a road structure of a key road according to an embodiment of the present invention; Figure 4 A schematic diagram of the structure of a device for selecting key automobile users according to an embodiment of the present invention; Figure 5 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0021] The following describes the method, device, electronic device and storage medium for selecting key automobile users of the embodiment of the present invention with reference to the accompanying drawings. In view of the technical problem that it is difficult to accurately reflect the actual road test situation of the user group in the related art mentioned in the above background technology, the test data is relatively one-sided and cannot be applied to improve vehicle performance and user experience, the present invention provides a method for selecting key automobile users, in which, on the one hand, the driving characteristics of multiple automobile users can be obtained according to the actual driving data of multiple automobile users, and the driving characteristics can be used to screen users to obtain at least one initial automobile user who meets the preset screening conditions, on the other hand, the embodiment of the present invention can extract key road data that meets the preset path conditions from the actual driving data of at least one initial automobile user, and calculate the key road driving mileage of each initial automobile user based on the key road data, so as to extract the key automobile users that meet the preset driving characteristic conditions based on the key road driving mileage, and based on the historical actual driving data of the user, the user's travel characteristics, road ratio characteristics and driving road multiple factors are comprehensively analyzed to extract the key automobile users that can be used to characterize the user group, so that the test data can be applied to improve vehicle performance and user experience. Thus, the technical problem that it is difficult to accurately reflect the actual road test situation of the user group in the related art, so that the test data is relatively one-sided and cannot be applied to improve vehicle performance and user experience is solved.

[0022] Specifically, Figure 1 The present invention provides a flowchart of a method for selecting key automobile users according to an embodiment of the present invention.

[0023] like Figure 1 As shown, the method for selecting key automobile users includes the following steps: In step S101 , actual driving data of a plurality of car users is obtained.

[0024] In the actual implementation process, the embodiment of the present invention can extract the actual driving data of multiple car users through the remote monitoring platform, and the data includes time, longitude, latitude, and vehicle speed.

[0025] In step S102, driving characteristics of a plurality of automobile users are obtained according to actual driving data, and users are screened using the driving characteristics to obtain at least one initial automobile user that meets a preset screening condition.

[0026] Furthermore, the embodiment of the present invention can calculate the driving characteristics of each car user and a plurality of car users as a whole based on the actual driving data, such as movement characteristics and driving proportion characteristics on different types of roads.

[0027] According to the driving characteristics of each car user and a plurality of car users as a whole, the embodiment of the present invention can perform a preliminary screening of car users.

[0028] Optionally, in one embodiment of the present invention, driving characteristics of multiple automobile users are obtained according to actual driving data, including: calculating motion characteristics in the driving characteristics based on the actual driving data, wherein the motion characteristics include at least one of average speed characteristics, average acceleration characteristics, average deceleration characteristics, speed distribution characteristics and acceleration distribution characteristics.

[0029] In some embodiments, the embodiments of the present invention can calculate the motion characteristics of each car user and the overall motion characteristics of multiple car users based on actual driving data, including average speed, average acceleration, average deceleration, speed distribution, and acceleration distribution. The acceleration is calculated by the vehicle speed.

[0030] Optionally, in one embodiment of the present invention, driving characteristics of multiple automobile users are obtained based on actual driving data, including: matching the latitude and longitude data in the actual driving data with roads in the road network to determine the road type of each road involved in the actual driving data; calculating the personal mileage of each automobile user on roads of different road types, and obtaining driving proportion characteristics of different road types in the driving characteristics based on the personal driving mileage.

[0031] In other embodiments, the embodiments of the present invention can match the latitude and longitude data in the actual driving data of the user with the roads in the road network through the geographic inverse coding function, and determine the type of each road (urban road, suburban road, expressway) based on the road network information. Based on the matching results, the driving mileage of each car user and multiple car users as a whole on different types of roads is calculated, and the driving proportion characteristics of each user and the entire urban road, suburban road and expressway are further obtained.

[0032] For example, based on the above motion characteristics and driving proportion characteristics, the embodiment of the present invention can compare the motion characteristics and road driving proportion characteristics of each car user with the overall motion characteristics and road driving proportion characteristics of multiple car users to screen initial car users that meet the following conditions: The relative deviations of the average speed, average acceleration, and average deceleration characteristics are all less than 10%; The correlation coefficients of velocity distribution and acceleration distribution are both greater than 0.9; The absolute value of the driving ratio deviation on urban roads, suburban roads and expressways is less than 5%.

[0033] In step S103, key road data satisfying preset path conditions are extracted from actual driving data of at least one initial automobile user, and the key road mileage of each initial automobile user is calculated based on the key road data, so as to extract key automobile users satisfying preset driving characteristic conditions from at least one initial automobile user based on the key road mileage.

[0034] In the actual implementation process, the embodiment of the present invention can further screen the initial car users, so that the car key users obtained after the screening can represent the user group.

[0035] The embodiment of the present invention can extract key road data from the actual driving data of at least one initial automobile user, that is, extract road data where cars appear more frequently, and calculate the mileage of each initial automobile user on key roads, that is, the key road mileage, so as to perform secondary screening of automobile users according to the ratio of the user's key road mileage to the total mileage.

[0036] Among them, the preset path conditions and the preset driving characteristic conditions can be set accordingly by technical personnel in this field according to actual conditions, and no specific restrictions are made here.

[0037] Optionally, in one embodiment of the present invention, key road data that meets preset path conditions is extracted from actual driving data of at least one initial automobile user, including: extracting longitude and latitude data from the actual driving data of the initial automobile user; combining the spatial clustering algorithm and the longitude and latitude data of at least one initial automobile user to obtain at least one dense driving area; eliminating duplicate longitude and latitude points in all dense driving areas, and using the remaining longitude and latitude points as nodes; calculating the paths of all nodes that meet preset length conditions, and marking the roads involved in the paths that meet the preset length conditions as key roads; calculating the mileage on the key roads, and determining whether the mileage on the key roads meets the preset mileage conditions; if the mileage on the key roads does not meet the preset mileage conditions, adjusting the parameters of the spatial clustering algorithm until the mileage on the key roads meets the preset mileage conditions to obtain all the key roads.

[0038] As a possible implementation method, an embodiment of the present invention can extract the longitude and latitude data from the actual driving data of the initial car user, and use the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) spatial clustering algorithm to cluster the vehicle's longitude and latitude points. Based on the minimum sample point parameter in the DBSCAN algorithm, the location points with low frequency of car appearance are identified and filtered out, and only the clusters of location points with high frequency of car appearance (i.e., meeting the minimum sample point parameter requirements) are retained, and each retained cluster represents a dense driving area.

[0039] The duplicate longitude and latitude points in the cluster are removed, and the deduplicated longitude and latitude points are used as nodes. The paths connecting all nodes in the cluster are calculated based on the Kruskal algorithm, and the shortest path is determined as the path that meets the preset length condition, that is, the shortest path, and the roads involved in the shortest path are recorded as key roads. Among them, the Kruskal algorithm is an algorithm for solving the minimum spanning tree of a weighted undirected graph. The minimum spanning tree refers to finding a tree in a weighted undirected graph that contains all the nodes in the graph and has the smallest sum of edge weights. The core idea of ​​the Kruskal algorithm is to make greedy selection based on edge weights.

[0040] Among them, the weight of the edge in Kruskal algorithm w is the distance between two longitude and latitude points. The calculation formula is as follows.

[0041] , , in, , are the latitudes of any two nodes among all nodes, expressed in radians, is the difference between the latitudes of the two nodes, is the difference between the longitudes of the two nodes, R is the radius of the Earth, c is the angular distance between two nodes, w is the weight of the edge.

[0042] After completing the above calculation, the embodiment of the present invention can adjust the minimum sample point parameter in the DBSCAN algorithm and re-execute the spatial clustering and calculation until the mileage on the key roads accounts for a certain threshold of the total mileage of the initial car users, such as more than 50%, and finally complete the key road extraction, and calculate the mileage ratio of each initial car user on the key roads.

[0043] The calculation method of mileage on key roads is as follows: First, the distance between each latitude and longitude point in the initial car user's actual driving data and the key road is calculated one by one. If the distance is less than the preset threshold, it is determined that the data point is located on the key road. Secondly, the vehicle mileage on the key road is calculated based on the vehicle speed data, and finally the mileage on the key road is obtained.

[0044] Optionally, in one embodiment of the present invention, based on the key road mileage, key automobile users who meet preset driving characteristic conditions are extracted from at least one initial automobile user, including: calculating the key road mileage ratio of each initial automobile user based on the key road mileage; arranging the initial automobile users in descending order of the key road mileage ratio, and selecting the initial automobile users within a preset ranking threshold as key users.

[0045] Furthermore, the embodiment of the present invention may arrange the screened initial automobile users in descending order according to the proportion of mileage on key roads, and select the top-ranked users, such as selecting the top 10% of users as key automobile users.

[0046] Combination Figure 2 and Figure 3 As shown, the working principle of the method for selecting key automobile users according to an embodiment of the present invention is described in detail with an embodiment.

[0047] like Figure 2 As shown, taking the actual driving data of 100 heavy-duty vehicles in Nanning City, Guangxi Zhuang Autonomous Region for 6 months as an example, the embodiment of the present invention may include the following steps: Step S201: Acquire actual driving data.

[0048] The embodiment of the present invention can extract actual driving data of multiple car users through a remote monitoring platform, wherein the actual driving data includes time, longitude, latitude, and vehicle speed.

[0049] Step S202: motion feature calculation.

[0050] Based on the actual driving data, the embodiment of the present invention can calculate the motion characteristics of each car user and multiple car users as a whole, including average speed, average acceleration, average deceleration, speed distribution, and acceleration distribution. Among them, the acceleration is calculated by the vehicle speed. The user motion characteristics can be shown in Table 1.

[0051] Table 1

[0052] Step S203: Calculating the driving ratio characteristics of different types of roads.

[0053] Through the geographic inverse coding function, the embodiment of the present invention can match the latitude and longitude data in the actual driving data of the car user with the roads in the road network, and determine the type of each road (urban road, suburban road, expressway) based on the road network information. Based on the matching results, the mileage of each car user and multiple car users as a whole on different types of roads is calculated, and the driving proportion characteristics of urban roads, suburban roads and expressways for each car user and multiple car users as a whole are further obtained.

[0054] Step S204: Driving feature screening.

[0055] Compare each user's motion characteristics and road travel ratio with the overall characteristics and ratio, and select users who meet the following conditions to obtain initial car users: (1) The relative deviations of the average speed, average acceleration, and average deceleration characteristics are all less than 10%.

[0056] (2) The correlation coefficients of velocity distribution and acceleration distribution are both greater than 0.9.

[0057] (3) The absolute value of the driving ratio deviation on urban roads, suburban roads and expressways is less than 5%.

[0058] Step S205: Extracting key roads based on spatial clustering algorithm.

[0059] like Figure 3 As shown, the embodiment of the present invention can extract the longitude and latitude data from the actual driving data of the initial car user, cluster the longitude and latitude points of the vehicle using the DBSCAN spatial clustering algorithm, identify and filter out the location points with low frequency of car appearance based on the minimum sample point parameter in the DBSCAN algorithm, and only retain the clusters of location points with high frequency of car appearance (i.e., meeting the minimum sample point parameter requirements), and each retained cluster represents a dense driving area.

[0060] The duplicate longitude and latitude points in the cluster are removed, and the deduplicated longitude and latitude points are used as nodes. The paths that meet the preset length conditions and connect all the nodes in the cluster are calculated based on the Kruskal algorithm, and the roads involved in the paths that meet the preset length conditions are recorded as key roads. Among them, the Kruskal algorithm is an algorithm for solving the minimum spanning tree of a weighted undirected graph. The minimum spanning tree refers to a tree in a weighted undirected graph that contains all the nodes in the graph and has the smallest sum of edge weights. The core idea of ​​the Kruskal algorithm is to make greedy selections based on edge weights.

[0061] Among them, the weight of the edge in Kruskal algorithm w is the distance between two longitude and latitude points. The calculation formula is as follows.

[0062] , , in, , are the latitudes of any two nodes among all nodes, expressed in radians, is the difference between the latitudes of the two nodes, is the difference between the longitudes of the two nodes, R is the radius of the Earth, c is the angular distance between two nodes, w is the weight of the edge.

[0063] After completing the above calculation, the embodiment of the present invention can adjust the minimum sample point parameter in the DBSCAN algorithm and re-execute the spatial clustering and calculation until the mileage on the key roads accounts for more than 50% of the total mileage of the initial automobile users, and finally complete the extraction of key roads.

[0064] The calculation method of mileage on key roads is as follows: First, the distance between each latitude and longitude point in the initial car user's actual driving data and the key road is calculated one by one. If the distance is less than the preset threshold, it is determined that the data point is located on the key road. Secondly, the vehicle mileage on the key road is calculated based on the vehicle speed data, and finally the mileage on the key road is obtained.

[0065] Step S206: Calculate the key road mileage ratio.

[0066] The embodiment of the present invention can calculate the key road mileage ratio based on the key road mileage and total mileage of each initial automobile user, wherein the key road mileage ratios of different users can be shown in Table 2.

[0067] Table 2

[0068] Step S207: Output of key automobile users.

[0069] The embodiment of the present invention can arrange the screened initial automobile users in descending order according to the proportion of mileage on key roads, and select the top 10% of users as key automobile users.

[0070] According to the method for selecting key automobile users proposed in the embodiment of the present invention, on the one hand, the driving characteristics of multiple automobile users can be obtained based on the actual driving data of multiple automobile users, and the driving characteristics can be used to screen users to obtain at least one initial automobile user that meets the preset screening conditions. On the other hand, the embodiment of the present invention can extract key road data that meets the preset path conditions from the actual driving data of at least one initial automobile user, and calculate the key road driving mileage of each initial automobile user based on the key road data, thereby extracting key automobile users that meet the preset driving characteristic conditions based on the key road driving mileage, and based on the historical actual driving data of the user, comprehensively analyzing the user's travel characteristics, road proportion characteristics and driving road factors, extracting key automobile users that can be used to characterize the user group, so that the test data can be used to improve vehicle performance and user experience. Therefore, the technical problem in the related art that it is difficult to accurately reflect the actual road test conditions of the user group, making the test data relatively one-sided and unable to be used to improve vehicle performance and user experience is solved.

[0071] Next, the device for selecting key automobile users according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0072] Figure 4 It is a block diagram of a device for selecting key automobile users according to an embodiment of the present invention.

[0073] like Figure 4 As shown, the device 10 for selecting key automobile users includes: an acquisition module 100 , a screening module 200 and an extraction module 300 .

[0074] Specifically, the acquisition module 100 is used to acquire actual driving data of multiple car users; A screening module 200 is used to obtain driving characteristics of multiple car users according to actual driving data, and to screen users using the driving characteristics to obtain at least one initial car user that meets a preset screening condition; The extraction module 300 is used to extract key road data that meets preset path conditions from the actual driving data of at least one initial automobile user, and calculate the key road mileage of each initial automobile user based on the key road data, so as to extract automobile key users that meet preset driving characteristic conditions from at least one initial automobile user based on the key road mileage.

[0075] Optionally, in one embodiment of the present invention, the screening module 200 includes: a first calculation unit.

[0076] The first calculation unit is used to calculate the motion characteristics in the driving characteristics based on the actual driving data, wherein the motion characteristics include at least one of the average speed characteristics, the average acceleration characteristics, the average deceleration characteristics, the speed distribution characteristics and the acceleration distribution characteristics.

[0077] Optionally, in one embodiment of the present invention, the screening module 200 includes: a matching unit and a second calculation unit.

[0078] The matching unit is used to match the latitude and longitude data in the actual driving data with the roads in the road network to determine the road type of each road involved in the actual driving data; The second calculation unit is used to calculate the personal driving mileage of each automobile user on roads of different road types, and obtain driving proportion characteristics of different road types in the driving characteristics based on the personal driving mileage.

[0079] Optionally, in one embodiment of the present invention, the extraction module 300 includes: an extraction unit, a third calculation unit, a elimination unit, a fourth calculation unit, a fifth calculation unit and an adjustment unit.

[0080] The extraction unit is used to extract the latitude and longitude data from the actual driving data of the initial automobile user; A third calculation unit, configured to obtain at least one dense driving area by combining a spatial clustering algorithm with latitude and longitude data of at least one initial automobile user; A removal unit is used to remove duplicate longitude and latitude points in all dense driving areas and use the remaining longitude and latitude points as nodes; A fourth calculation unit, used to calculate paths of all nodes that meet a preset length condition, and mark roads involved in the paths that meet the preset length condition as key roads; a fifth calculation unit, used to calculate the mileage on the key road and determine whether the mileage on the key road meets the preset mileage condition; The adjustment unit is used to adjust the parameters of the spatial clustering algorithm when the mileage on the key roads does not meet the preset mileage conditions, until the mileage on the key roads meets the preset mileage conditions, so as to obtain all the key roads.

[0081] Optionally, in one embodiment of the present invention, the extraction module 300 includes: a sixth calculation unit and a selection unit.

[0082] Wherein, the sixth calculation unit is used to calculate the key road mileage ratio of each initial automobile user based on the key road mileage; The selection unit is used to arrange the initial automobile users in descending order according to the proportion of mileage on key roads, and select the initial automobile users within a preset ranking threshold as key users.

[0083] It should be noted that the above explanation of the embodiment of the method for selecting key automobile users is also applicable to the device for selecting key automobile users of this embodiment, and will not be repeated here.

[0084] According to the device for selecting key automobile users proposed in the embodiment of the present invention, on the one hand, the driving characteristics of multiple automobile users can be obtained based on the actual driving data of multiple automobile users, and the driving characteristics can be used to screen users to obtain at least one initial automobile user that meets the preset screening conditions. On the other hand, the embodiment of the present invention can extract key road data that meets the preset path conditions from the actual driving data of at least one initial automobile user, and calculate the key road driving mileage of each initial automobile user based on the key road data, thereby extracting key automobile users that meet the preset driving characteristic conditions based on the key road driving mileage, and based on the historical actual driving data of the user, comprehensively analyzing the user's travel characteristics, road proportion characteristics and driving road factors, extracting key automobile users that can be used to characterize the user group, so that the test data can be used to improve vehicle performance and user experience. Thus, the technical problem in the related art that it is difficult to accurately reflect the actual road test conditions of the user group, making the test data relatively one-sided and unable to be used to improve vehicle performance and user experience is solved.

[0085] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include: A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0086] When the processor 502 executes the program, the method for selecting key automobile users provided in the above embodiment is implemented.

[0087] Furthermore, the electronic device further comprises: The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0088] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0089] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0090] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0091] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0092] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0093] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above method for selecting key automobile users is implemented.

[0094] The embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for selecting key automobile users provided by the embodiment of the present invention.

[0095] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0096] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0097] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0098] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0099] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0100] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0101] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0102] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for selecting key automobile users, characterized in that: The following steps are involved: Obtain actual driving data of multiple car users; Obtaining driving characteristics of the plurality of automobile users according to the actual driving data, and using the driving characteristics to perform user screening to obtain at least one initial automobile user that meets a preset screening condition; Key road data satisfying preset path conditions are extracted from the actual driving data of the at least one initial automobile user, and the key road mileage of each initial automobile user is calculated based on the key road data, so as to extract automobile key users satisfying preset driving characteristic conditions from the at least one initial automobile user based on the key road mileage.

2. The method for selecting key automobile users according to claim 1, characterized in that: The obtaining the driving characteristics of the plurality of automobile users according to the actual driving data includes: Based on the actual driving data, a motion feature in the driving feature is calculated, wherein the motion feature includes at least one of an average speed feature, an average acceleration feature, an average deceleration feature, a speed distribution feature, and an acceleration distribution feature.

3. The method for selecting key automobile users according to claim 1, characterized in that: The obtaining the driving characteristics of the plurality of automobile users according to the actual driving data includes: Matching the latitude and longitude data in the actual driving data with roads in the road network to determine the road type of each road involved in the actual driving data; The personal driving mileage of each automobile user on roads of different road types is calculated respectively, and the driving proportion characteristics of different road types in the driving characteristics are obtained based on the personal driving mileage.

4. The method for selecting key automobile users according to claim 3, characterized in that: The step of extracting key road data satisfying a preset path condition from the actual driving data of the at least one initial automobile user comprises: Extracting longitude and latitude data from actual driving data of the initial automobile user; Combining a spatial clustering algorithm with the latitude and longitude data of the at least one initial automobile user to obtain at least one dense driving area; Eliminate all duplicate longitude and latitude points in dense driving areas and use the remaining longitude and latitude points as nodes; Calculating paths of all nodes that meet a preset length condition, and marking roads involved in the paths that meet the preset length condition as key roads; Calculating the mileage on the key road, and determining whether the mileage on the key road meets a preset mileage condition; If the mileage on the key roads does not meet the preset mileage condition, the parameters of the spatial clustering algorithm are adjusted until the mileage on the key roads meets the preset mileage condition to obtain all the key roads.

5. The method for selecting key automobile users according to claim 1, characterized in that: The extracting, based on the key road mileage, a key automobile user that meets a preset driving characteristic condition from the at least one initial automobile user comprises: Calculating the key road mileage ratio of each initial automobile user based on the key road mileage; The initial automobile users are arranged in descending order according to the ratio of the key road mileage, and the initial automobile users within a preset ranking threshold are selected as the key users.

6. A device for selecting key automobile users, characterized in that: include: An acquisition module, used to acquire actual driving data of multiple car users; a screening module, configured to obtain the driving characteristics of the plurality of automobile users according to the actual driving data, and perform user screening using the driving characteristics to obtain at least one initial automobile user that meets a preset screening condition; An extraction module is used to extract key road data that meets preset path conditions from the actual driving data of the at least one initial automobile user, and calculate the key road mileage of each initial automobile user based on the key road data, so as to extract automobile key users that meet preset driving characteristic conditions from the at least one initial automobile user based on the key road mileage.

7. The device for selecting key automobile users according to claim 6, characterized in that: The screening module comprises: The first calculation unit is used to calculate the motion characteristics in the driving characteristics based on the actual driving data, wherein the motion characteristics include at least one of an average speed characteristic, an average acceleration characteristic, an average deceleration characteristic, a speed distribution characteristic, and an acceleration distribution characteristic.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for selecting key automobile users as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for selecting key automobile users as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed, it is used to implement the method for selecting key automobile users as described in any one of claims 1-5.

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