Road section recognition processing method and device, vehicle terminal, storage medium and program product

Abstract

The embodiment of the invention provides a road section recognition processing method and device, a vehicle terminal, a storage medium and a program product, and relates to the technical field of road safety. The method comprises the following steps: acquiring a road section identification request, and acquiring target data of a vehicle terminal according to the road section identification request; the target data represents at least one preset type of data in a preset range area in front of the real-time position of the vehicle terminal. And determining a core region in the preset range region according to the target data. And according to the core area, determining a village-passing town-passing road section in the preset range area. The method achieves the effect of improving the recognition accuracy of village-crossing and town-crossing roads.

Description

Technical Field

[0001] The present application relates to the field of road safety technology, and in particular to a road section identification and processing method, device, vehicle terminal, storage medium and program product. Background Art

[0002] At present, roads through villages and towns are roads that pass through villages, towns, suburbs and other areas outside the urban built-up area. Along the roads, there are a large number of residential and commercial functional areas, and there may also be schools, hospitals, transportation stations and other essential facilities. It is the area with the highest social and economic activities in villages, towns and suburbs. There are a large number of vehicles, pedestrians, and non-motor vehicles mixed and crossing each other, and the risk of traffic accidents is also increased accordingly. Therefore, effective management of traffic safety on roads through villages and towns can not only protect the lives of drivers and pedestrians, but also promote the stable development of social economy and the smooth flow of transportation.

[0003] In the prior art, driving videos are collected by the on-board forward-looking camera, and AI and other algorithms are used to identify the structures on both sides of the road in the video to determine whether they are houses. Based on the distance between consecutive houses, it is determined whether the road is a section that passes through a village or a town.

[0004] However, in the existing technology, since there is basically no difference in shape between different types of houses, the video recognition method cannot accurately identify the type of house when identifying roads through villages and towns, and cannot distinguish the type of house according to the characteristics of village and town sections. In addition, the video recognition range is limited, and it is difficult to accurately identify houses that are farther away, resulting in low accuracy in identifying roads through villages and towns. Summary of the invention

[0005] The embodiments of the present application provide a road section identification processing method, device, vehicle terminal, storage medium and program product to achieve the effect of improving the identification accuracy of roads passing through villages and towns.

[0006] In a first aspect, an embodiment of the present application provides a road segment identification processing method, including:

[0007] Obtaining a road segment identification request, and obtaining target data of the vehicle terminal according to the road segment identification request; wherein the target data represents at least one type of preset data within a preset range area in front of the real-time position of the vehicle terminal;

[0008] Determine a core area in the preset range according to the target data;

[0009] According to the core area, the road sections passing through villages and towns in the preset range are determined.

[0010] In one possible implementation, the target data includes navigation map data and signaling data; wherein the navigation map data includes building attribute information and road attribute information of the preset range area; the signaling data represents the communication data generated by the terminal device within the preset range area, and the terminal device represents the device that generates mobile communication within the range area.

[0011] In a possible implementation manner, the building attribute information includes a plurality of preset types of point of interest data; and determining the core area in the preset range area according to the target data includes:

[0012] Determining the density distribution of static interest points according to the interest point data of each preset type in the building attribute information;

[0013] Determining a dynamic activity density distribution according to the signaling data in the target data;

[0014] A core area is determined in the preset range area according to the static interest point density distribution and the dynamic activity density distribution.

[0015] In a possible implementation manner, determining the density distribution of static points of interest according to the data of points of interest of each preset type in the building attribute information includes:

[0016] For each preset type of interest point data, determining a preliminary interest point density distribution of the interest point data according to a preset static density formula;

[0017] The static interest point density distribution of the interest point data is determined according to the preliminary interest point density distribution, a preset superposition formula, and a first preset weight of each preset type of interest point data.

[0018] In a possible implementation, determining a core area in the preset range area according to the static interest point density distribution and the dynamic activity density distribution includes:

[0019] Determining the comprehensive thermal density of villages and towns in the preset range area according to the third preset weight corresponding to the static interest point density distribution and the fourth preset weight corresponding to the dynamic vitality density distribution;

[0020] According to a preset natural breakpoint method, the data represented by the comprehensive thermal density is divided into a plurality of groups;

[0021] The thermal value of each group is determined, and the group with the highest thermal value is determined as the core area of ​​the village or town.

[0022] In a possible implementation manner, the road attribute information includes urban built-up area data and the road grade of the road where the vehicle terminal is located;

[0023] Among them, the urban built-up area data is the area data within a preset edge range of the city where the vehicle terminal is located on the corresponding map; and the road grade includes multiple grades.

[0024] In a possible implementation manner, determining the road section passing through villages and towns in the preset range area according to the core area includes:

[0025] Extracting a target road corresponding to the road grade in the road attribute information from the urban built-up area data;

[0026] The core area and the target road are spatially superimposed to determine that the overlapping area is a road section passing through villages and towns in the preset range area.

[0027] In a second aspect, an embodiment of the present application provides a road segment identification processing device, including:

[0028] A first acquisition module is used to acquire a road segment identification request, and acquire target data of the vehicle terminal according to the road segment identification request; wherein the target data represents at least one type of preset data within a preset range area in front of the real-time position of the vehicle terminal;

[0029] A first determining module, configured to determine a core area in the preset range area according to the target data;

[0030] The second determination module is used to determine the road section passing through villages and towns in the preset range area according to the core area.

[0031] In one possible implementation, the target data includes navigation map data and signaling data; wherein the navigation map data includes building attribute information and road attribute information of the preset range area; the signaling data represents the communication data generated by the terminal device within the preset range area, and the terminal device represents the device that generates mobile communication within the range area.

[0032] In a possible implementation manner, the building attribute information includes a plurality of preset types of point of interest data; the first determination module includes:

[0033] A first determining unit, configured to determine a static interest point density distribution according to interest point data of each preset type in the building attribute information;

[0034] A second determining unit, configured to determine a dynamic activity density distribution according to the signaling data in the target data;

[0035] A third determining unit is used to determine a core area in the preset range area according to the static interest point density distribution and the dynamic activity density distribution.

[0036] In a possible implementation manner, the first determining unit is specifically configured to:

[0037] For each preset type of interest point data, determining a preliminary interest point density distribution of the interest point data according to a preset static density formula;

[0038] The static interest point density distribution of the interest point data is determined according to the preliminary interest point density distribution, a preset superposition formula, and a first preset weight of each preset type of interest point data.

[0039] In a possible implementation manner, the third determining unit is specifically configured to:

[0040] Determining the comprehensive thermal density of villages and towns in the preset range area according to the third preset weight corresponding to the static interest point density distribution and the fourth preset weight corresponding to the dynamic vitality density distribution;

[0041] According to a preset natural breakpoint method, the data represented by the comprehensive thermal density is divided into a plurality of groups;

[0042] The thermal value of each group is determined, and the group with the highest thermal value is determined as the core area of ​​the village or town.

[0043] In a possible implementation manner, the road attribute information includes urban built-up area data and the road grade of the road where the vehicle terminal is located;

[0044] Among them, the urban built-up area data is the area data within a preset edge range of the city where the vehicle terminal is located on the corresponding map; and the road grade includes multiple grades.

[0045] In a possible implementation manner, the second determining module is specifically configured to:

[0046] Extracting a target road corresponding to the road grade in the road attribute information from the urban built-up area data;

[0047] The core area and the target road are spatially superimposed to determine that the overlapping area is a road section passing through villages and towns in the preset range area.

[0048] In a third aspect, an embodiment of the present application provides a vehicle terminal, including: a memory, a processor;

[0049] The memory stores computer-executable instructions;

[0050] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0051] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0052] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0053] The road section identification processing method, device, vehicle terminal, storage medium and program product provided in the embodiment of the present application obtain a road section identification request, and obtain the target data of the vehicle terminal according to the road section identification request; wherein the target data represents at least one preset type of data located in a preset range area in front of the real-time position of the vehicle terminal. According to the target data, the core area in the preset range area is determined. According to the core area, the road section passing through the village and town in the preset range area is determined. In this scheme, in the process of identifying the road passing through the village and town, the reliable static preset type data of multiple sources obtained can be used to obtain the road section passing through the village and town through analysis and calculation. Therefore, the present application is based on multi-source data for identification, and is deeply combined with the characteristics of the road passing through the village and town, so as to effectively identify the road section passing through the village and town with a high level of road traffic safety risk, with high recognition accuracy, low operation difficulty, high recognition efficiency, no dependence on hardware equipment, low calculation cost, and provides basic data support for traffic safety management. Moreover, it has a wide range of applicable scenarios, including urban suburbs, rural areas, and other scenarios. It is applicable to high-grade highways such as national and provincial highways, as well as other low-grade highways and substandard highways, so as to improve the recognition accuracy of roads passing through villages and towns. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0055] Figure 1 Scenario illustration of the road segment identification processing method provided in this application Figure 1 ;

[0056] Figure 2 Schematic diagram of the process of road segment identification processing method provided in this application Figure 2 ;

[0057] Figure 3A schematic diagram of the overall process of the road segment identification processing method provided in this application;

[0058] Figure 4 A schematic diagram of the structure of the road section identification and processing device provided in this application;

[0059] Figure 5 A schematic diagram of the structure of the road section identification and processing device provided in this application;

[0060] Figure 6 A schematic diagram of the structure of the electronic device provided in this application.

[0061] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0062] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0063] At present, roads through villages and towns are roads that pass through villages, towns, suburbs and other areas outside the urban built-up area. Along the roads, there are a large number of residential and commercial functional areas, and there may also be schools, hospitals, transportation stations and other essential facilities. It is the area with the highest social and economic activities in villages, towns and suburbs. There are a large number of vehicles, pedestrians, and non-motor vehicles mixed and crossing each other, and the risk of traffic accidents is also increased accordingly. Therefore, effective management of traffic safety on roads through villages and towns can not only protect the lives of drivers and pedestrians, but also promote the stable development of social economy and the smooth flow of transportation.

[0064] In one example, based on the vehicle-mounted forward-looking camera to collect driving video, AI and other algorithms are used to identify the structures on both sides of the road in the video to determine whether they are houses. Based on the distance between consecutive houses, it is determined whether it is a road section through villages and towns. However, in the prior art, since there is basically no difference in shape between different types of houses, the video recognition method cannot accurately identify the type of house when identifying roads through villages and towns, and it is impossible to identify the type of house according to the characteristics of the village and town sections. In addition, the video recognition range is limited, and it is difficult to accurately identify houses that are far away, resulting in low accuracy in identifying roads through villages and towns. In addition, the above approach has the disadvantages of a long collection process, high data processing and solution costs, and difficulty in operating the collection equipment.

[0065] Combining the existing technology, it can be seen that in the existing technology, there is a technical problem of low recognition accuracy when using video to identify road sections passing through villages and towns.

[0066] The road section identification and processing method provided in this application is based on multi-source data for identification, and is deeply combined with the characteristics of roads passing through villages and towns. It can effectively identify road sections passing through villages and towns with a high level of road traffic safety risk. It has high identification accuracy, low operating difficulty, high identification efficiency, is independent of hardware equipment, and has low computing costs. It provides basic data support for traffic safety management. It is also applicable to a variety of scenarios, including urban suburbs, rural areas, and other high-grade highways such as national and provincial highways, as well as other low-grade highways and substandard highways, to achieve the effect of improving the identification accuracy of roads passing through villages and towns.

[0067] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0068] Figure 1 Schematic diagram of the process of road segment identification processing method provided in this application Figure 1 ,like Figure 1 As shown, the method includes:

[0069] S201. Obtain a road segment identification request, and obtain target data of a vehicle terminal according to the road segment identification request; wherein the target data represents at least one type of preset data within a preset range area in front of a real-time position of the vehicle terminal.

[0070] For example, the execution subject of this embodiment may be an electronic device, or a terminal device, a vehicle terminal, or a road segment identification processing device or device, or other devices or equipment that can execute this embodiment, and there is no limitation to this. In this embodiment, the execution subject is introduced as an electronic device.

[0071] In this step, the preset navigation map includes multiple routes, places on the routes, etc. Click on the vehicle terminal, then obtain the road segment identification request, and navigate the route according to the road segment identification request, determine the real-time position of the vehicle terminal in the preset navigation map, and determine the target data of the preset range area in front of the real-time position.

[0072] Among them, the preset range area can be an area divided according to a preset range value, or it can be the range occupied by the first town to be passed in front of the real-time position identified in the navigation map, and the range occupied by the first town to be passed is determined as the preset range area, or it can be that the ranges occupied by multiple towns to be passed on the navigation route are all preset range areas, and there is no limitation on this.

[0073] The target data includes at least one preset type of data, and the target data includes navigation map data and signaling data. The navigation map data includes building attribute information and road attribute information of the preset range area, the building attribute information is the data of the point of interest, and the road attribute information is the road grade of the road where the vehicle terminal is located. The signaling data is the data of the preset range area obtained from the preset operator, and the signaling data represents the communication data generated by the terminal device in the preset range area. The terminal device represents the device that generates mobile communication in the range area. The device can be a user terminal device such as a mobile phone, a computer, and a tablet. The signaling data can represent the number of users in the preset range area.

[0074] Specifically, the navigation map data includes a point of interest (POI) layer, and the POI layer includes points of interest (POI). In addition to recording the location of the POI, the POI data layer also records the relevant attributes of the POI. The building attribute information includes the attribute information of the buildings in the preset range area. For example, the building attribute information includes POI data. The POI data includes multiple preset types, and each preset type of point of interest data includes multiple points of interest data. Combined with the distribution characteristics of the main functional areas of villages and towns, m types of POI data such as accommodation, catering, wholesale and retail, automobile sales and services, finance and insurance, education and culture, health and social security, sports and leisure, announcement facilities, and resident services are extracted and recorded as POI i (i=1,2,...,m). The road attribute information includes the urban built-up area domain data and the road grade of the road where the vehicle terminal is located. The urban built-up area domain data is the area data within the preset edge range within the map range corresponding to the city where the vehicle terminal is located. The preset edge range can be the edge area surrounding the city center, that is, the area away from the prosperous area. The road attribute information is the road grade of the road where the vehicle terminal is located, for example, the road grade is national highway, provincial highway, etc.

[0075] S202: Determine a core area in a preset range area according to the target data.

[0076] For example, according to a plurality of preset types of data, a core area in a preset range area may be determined, and the core area is an area where POI data is relatively dense.

[0077] S203. Determine, based on the core area, a road section passing through villages and towns in a preset range.

[0078] Exemplarily, the core area can be determined in the preset range area based on the building attribute information, road attribute information, and signaling data in the core area. Specifically, in the urban built-up area area data of the road attribute information, multiple target roads corresponding to the road grade in the road attribute information are extracted, and the core area and the multiple target roads are spatially superimposed to determine that the overlapping area is the road section passing through the village and town in the preset range area. Furthermore, the target road corresponding to the real-time position of the vehicle terminal can also be determined from the multiple target roads, and the target road corresponding to the real-time position of the vehicle terminal and the core area are directly spatially superimposed to determine that the overlapping area is the road section passing through the village and town in the preset range area.

[0079] For example, the road grade in the road attribute information is the national highway grade. In the urban built-up area data, multiple target national highways corresponding to the national highway grade in the road attribute information are extracted, and the core area is spatially superimposed with the multiple target national highways to determine that the overlapping area is the road section passing through villages and towns in the preset range area. Furthermore, the target national highway corresponding to the real-time position of the vehicle terminal can be determined from the multiple target national highways, and the target national highway corresponding to the real-time position of the vehicle terminal and the core area are directly spatially superimposed to determine that the overlapping area is the road section passing through villages and towns in the preset range area.

[0080] It should be noted that the data sources in this application are highly accessible, which facilitates the analysis of road sections based on multi-source data; the road traffic engineering characteristics of sections passing through villages and towns are deeply explored, and the core areas and sections passing through villages and towns are determined, and the analysis process is designed accordingly, which has a high engineering rationality; the data processing method used is relatively mature and highly reusable. In addition, the solution of this application can be migrated to several other scenarios, such as identifying sections passing through urban residential areas, sections with frequent accidents, sections with frequent violations of laws and regulations, etc., without limitation.

[0081] The road section identification processing method provided in the embodiment of the present application obtains a road section identification request, and obtains the target data of the vehicle terminal according to the road section identification request; wherein the target data represents at least one preset type of data in a preset range area in front of the real-time position of the vehicle terminal. According to the target data, the core area in the preset range area is determined. According to the core area, the road section passing through the village and town in the preset range area is determined. In this scheme, in the process of identifying the road passing through the village and town, the road section passing through the village and town can be obtained by analyzing and calculating the obtained reliable static multiple multi-source preset type data. Therefore, the present application is based on multi-source data for identification, and deeply combines the characteristics of the road passing through the village and town, which can effectively identify the road section passing through the village and town with a high level of road traffic safety risk, with high recognition accuracy, low operation difficulty, high recognition efficiency, no dependence on hardware equipment, low calculation cost, and provides basic data support for traffic safety management. And the applicable scenarios are comprehensive and rich, applicable to various scenarios such as urban suburbs and rural areas, applicable to high-grade highways such as national and provincial highways, and also applicable to other low-grade highways and off-grade highways, so as to achieve the effect of improving the recognition accuracy of the road passing through the village and town.

[0082] Figure 2 Schematic diagram of the process of road segment identification processing method provided in this application Figure 2 ,like Figure 2 As shown, in this embodiment Figure 1 Based on the embodiment, a road segment identification processing method is described in detail, and the method includes:

[0083] S301. Obtain a road segment identification request, and according to the road segment identification request, obtain target data of a vehicle terminal; wherein the target data represents at least one type of preset data within a preset range area in front of a real-time position of the vehicle terminal.

[0084] In one example, the target data includes navigation map data and signaling data; wherein the navigation map data includes building attribute information and road attribute information of a preset range area; the signaling data represents the communication data generated by the terminal device within the preset range area, and the terminal device represents the device that generates mobile communication within the range area.

[0085] For example, this step can be referred to Figure 1 Step 201 in the above is not described in detail.

[0086] S302: Determine the density distribution of static interest points according to the interest point data of each preset type in the building attribute information.

[0087] In one example, S302 includes: for each preset type of point of interest data, determining a preliminary point of interest density distribution of the point of interest data according to a preset static density formula; determining a static point of interest density distribution of the point of interest data according to the preliminary point of interest density distribution, a preset superposition formula, and a first preset weight of each preset type of point of interest data.

[0088] For example, kernel density analysis is used to calculate the distribution density of a certain indicator of a spatial point in its neighborhood, and to form a continuous distribution simulation in the study area, thereby obtaining the distribution characteristics of elements at any position in the space. For each preset type of point of interest data, the preset type of POI is subjected to kernel density analysis using the following static density formula:

[0089]

[0090] Among them, ρ i (x) is the density distribution of any point x to be estimated in the interest point data; n is the total number of interest point data; r is the preset search radius; π is the preset value; j is the sample interest point within the search radius r of the point x to be estimated; d j is the distance between the sample interest point j and the point to be estimated x; φ j is the second preset weight at point j, and the second preset weight is the scale weight for interest points of the same preset type but different scales, that is, the scale weights of interest points of the same preset type but different scales are different.

[0091] After determining the preliminary interest point density distribution ρ of each preset type of interest point data i (x), according to the preliminary interest point density distribution, the preset superposition formula, and the first preset weight of each preset type of interest point data, the static interest point density distribution ρ of the interest point data is determined by using the superposition analysis method. POI (x), the superposition formula is as follows:

[0092]

[0093] Among them, ρ i =ρ i (x), β i is a first preset weight, which is a weight for interest point data of different preset types, that is, the weights of interest point data of different preset types are different.

[0094] S303. Determine the dynamic activity density distribution according to the signaling data in the target data.

[0095] Exemplarily, the signaling data is cleaned to obtain the cleaned signaling data, and the dynamic activity density distribution is determined based on the cleaned signaling data. Specifically, first, the night data in the signaling data is eliminated. The dynamic activity density distribution is calculated based on the signaling data in the target data. Since the intensity of various activities at night is low and the impact on traffic is small, the mobile phone signaling data within the preset time period should be eliminated when performing dynamic activity density analysis. For example, the signaling data between 0:00-6:00 is eliminated. The elimination time period is only an example and is not limited to this. The second step is to clean the noise data. The noise data in the signaling data mainly includes three categories: repetition, drift, and ping-pong switching. Sort the mobile phone signaling data by ID and time to obtain the complete signaling data set data of traveler i. i :{p i,1 ,p i,2 ,…,p i,n}, where p i,j :(time i,j ,lng i,j ,lat i,j ).

[0096] For duplicate data, remove data with completely identical fields such as ID, timei,j, lngi,j, lati,j, etc.

[0097] For drift data, calculate according to the following formula: calculate the adjacent three points p in the drift data i,j-1 、p i,j 、p i,j+1 The distance between i,j-1 , ΔD i,j , and p i,j-1 、p i,j 、p i,j+1 The angle α formed by the three points i,j , if ΔD i,j-1 , ΔD i,j Both exceed the preset distance threshold D th And form an angle α i,j <30°, then mark point p i,j The drift points are removed.

[0098]

[0099] For ping-pong switching data, if lng i,j-1 =lng i,j+1 ≠lng i,j ,lat i,j-1 =lat i,j+1 ≠lat i,j And Δt i,j <t th , t i,j+1<t th , t th Determine the time threshold of ping-pong switching data, then p i,j Ping-pong switching data is eliminated.

[0100] According to the preset dynamic density formula, the dynamic activity density distribution ρ of the cleaned signaling data can be calculated i (s), the dynamic density formula is as follows:

[0101]

[0102] Among them, ρ i (s) is the density distribution of any point x to be estimated in the signaling data; n is the total number of signaling data; r is the preset search radius; π is the preset value; j is the signaling data within the search radius r of the point x to be estimated; d j is the distance between the signaling data j and the point x to be estimated.

[0103] S304: Determine a core area in a preset range area according to the static interest point density distribution and the dynamic activity density distribution.

[0104] In one example, S304 includes: determining the comprehensive thermal density of villages and towns in a preset range area according to a third preset weight corresponding to the static interest point density distribution and a fourth preset weight corresponding to the dynamic vitality density distribution; dividing the data represented by the comprehensive thermal density into multiple groups according to a preset natural breakpoint method; determining the thermal value of each group, and determining the group with the highest thermal value as the core area of ​​the village and town.

[0105] Exemplarily, the static point of interest (POI) density distribution ρ is obtained POI (x) and the dynamic activity density distribution ρ i (s), according to the third preset weight λ corresponding to the static interest point density distribution POI , the fourth preset weight λ corresponding to the dynamic activity density distribution S , the preset thermal density formula is used to calculate the comprehensive thermal density of villages and towns in the preset range. The third preset weight and the fourth preset weight need to be manually adjusted and determined according to the data aggregation results. For example, both weights are 1, and there is no limitation on this. The thermal density formula is as follows:

[0106] ρ(x)=λ POI ρ POI (x)+λ S ρ S (x)

[0107] Then, since the natural breakpoint method can identify the inherent breakpoints in the data sample, the data is divided into a specified number of groups, so that the differences between the groups are the largest, and the average deviation of each sample within the group is the smallest. According to the preset natural breakpoint method, the data represented by the comprehensive thermal density is divided into multiple groups, and the thermal value of each group is calculated. The thermal value is the thermal density ρ(x) of each group. The thermal values ​​are sorted, and the group with the highest thermal value is determined as the core area of ​​the village or town.

[0108] S305. Extract the target road corresponding to the road grade in the road attribute information from the urban built-up area data.

[0109] In one example, the road attribute information includes urban built-up area domain data and the road grade of the road where the vehicle terminal is located; wherein the urban built-up area domain data is the area data within a preset edge range of the city where the vehicle terminal is located on the corresponding map; the road grade includes multiple grades.

[0110] For example, although there are also a large number of functional areas and frequent life activities around the roads in the urban built-up areas, the planning, design and construction of roads in the built-up areas are relatively standardized, and the safety facilities and equipment such as guardrails, traffic lights, signs and markings are relatively complete. In addition, the driving speed of motor vehicles can be maintained at a low level under the influence of various control measures, and the road safety level can be guaranteed to a certain extent. Therefore, this application focuses on the road network outside the built-up areas, that is, the more prosperous areas that have been built must be excluded. The area data of the urban built-up areas is the road network outside the built-up areas within the map range corresponding to the city where the vehicle terminal is located, that is, the area data within the preset edge range.

[0111] In this step, the target road corresponding to the road grade in the road attribute information is extracted from the urban built-up area data, which is recorded as R k , the extracted target roads are the roads in the road network outside the built-up area.

[0112] S306: spatially superimpose the core area and the target road, and determine that the overlapping area is a road section passing through villages and towns in a preset range.

[0113] For example, the core area Z town With the target road R k Perform spatial superposition to determine the overlapping area, which is the road section R passing through the village and town in the preset range area. (k,town) .

[0114] The road section identification processing method provided in the embodiment of the present application obtains a road section identification request, and obtains the target data of the vehicle terminal according to the road section identification request; wherein the target data represents at least one preset type of data located in a preset range area in front of the real-time position of the vehicle terminal. According to the interest point data of each preset type in the building attribute information, the static interest point density distribution is determined. According to the signaling data in the target data, the dynamic vitality density distribution is determined. According to the static interest point density distribution and the dynamic vitality density distribution, the core area is determined in the preset range area. In the urban built-up area area data, the target road corresponding to the road grade in the road attribute information is extracted. The core area and the target road are spatially superimposed, and the overlapping area is determined as the village and town section in the preset range area. Therefore, the present application is based on multi-source data for identification, and is deeply combined with the characteristics of the village and town roads, so as to effectively identify the village and town sections with a high level of road traffic safety risk, with high identification accuracy, low operation difficulty, high identification efficiency, no dependence on hardware equipment, low computing cost, and provides basic data support for traffic safety management. Moreover, it has a wide range of applicable scenarios, including urban suburbs, rural areas, and other scenarios. It is applicable to high-grade highways such as national and provincial highways, as well as other low-grade highways and substandard highways, so as to improve the recognition accuracy of roads passing through villages and towns.

[0115] In one example, the key to identifying the road sections through villages and towns is to accurately map out the hot spots in villages and towns. There are two main characteristics in analyzing the hot spots in villages and towns: ① Compared with other areas in villages and towns, hot spots have more concentrated residential, commercial, school, hospital, station and other functional areas, which is reflected in the significant concentration of POI data distribution on the data side, and the POI density is significantly higher than that in the surrounding areas; ② Compared with other areas in villages and towns, the population activity heat in hot spots is higher, and the fundamental reason is that there are more people gathered. Thanks to the large-scale popularization and application of mobile phones, a large amount of signaling data generated by mobile terminal users in the operator network when making calls, text messages, surfing the Internet and changing paging areas can reflect the characteristics of population aggregation after specific processing and analysis. Therefore, the signaling data distribution heat in hot spots in villages and towns is significantly higher than that in surrounding areas.

[0116] Based on the above principles, Figure 3 The overall flow chart of the road segment identification processing method provided in this application is as follows: Figure 3As shown, it includes: POI data, signaling data, urban built-up area, and road network data; the urban built-up area represents the prosperous area in the area data of the urban built-up area. According to the urban built-up area, the POI data outside the urban built-up area can be determined. The POI data includes multiple preset types: Class A POI, Class B POI, Class C POI, ...; the kernel density analysis is performed on the POI data of each preset type to obtain the preliminary POI density distribution of each type of POI; the static POI density distribution is obtained by weighted superposition. The signaling data outside the urban built-up area is obtained, and the kernel density analysis is performed on the signaling data to obtain the dynamic vitality density distribution. The static POI density distribution and the dynamic vitality density distribution are weighted superimposed and processed by the natural breakpoint method to obtain the core area of ​​the village and town. The road network data refers to multiple road data in the navigation map data. Multiple roads corresponding to the road level n of the vehicle terminal are selected in the road network data to obtain the n-level road network; the n-level road network is superimposed with the urban built-up area to extract the n-level roads outside the urban built-up area; the n-level roads are spatially superimposed with the core area of ​​the village and town to obtain the road section through the village and town.

[0117] Therefore, for national and provincial highways and other levels of highways in urban suburbs, villages and towns, etc., to identify whether they are sections that pass through villages and towns, this application is based on multi-source data such as electronic navigation map data, POI data, signaling data, urban built-up area data, etc. First, the corresponding level of roads, such as national highways, are identified from the navigation map, and then a certain level of roads outside the urban built-up area are extracted by spatial overlay analysis with the urban built-up area. According to the POI data, the static POI density distribution related to villages and towns is calculated by classification, and the dynamic vitality density distribution of villages and towns is calculated according to the signaling data. Based on the dynamic and static density distribution, the core area of ​​villages and towns is identified using the natural breakpoint method. The extracted roads of a certain level outside the built-up area are spatially overlaid with the core area of ​​villages and towns to obtain sections that pass through villages and towns.

[0118] Figure 4 A schematic diagram of the structure of the road segment identification processing device provided in this application, such as Figure 4 As shown, the road segment identification processing device 40 provided in this embodiment includes:

[0119] The first acquisition module 41 is used to obtain a road segment identification request, and obtain target data of the vehicle terminal according to the road segment identification request; wherein the target data represents at least one type of preset data within a preset range area in front of the real-time position of the vehicle terminal;

[0120] A first determination module 42 is used to determine a core area in a preset range area according to target data;

[0121] The second determining module 43 is used to determine the road section passing through villages and towns in the preset range area according to the core area.

[0122] Figure 5 A schematic diagram of the structure of another road segment identification processing device provided in an embodiment of the present application, Figure 4 Based on the embodiment shown, Figure 5 As shown, the target data includes navigation map data and signaling data; wherein the navigation map data includes building attribute information and road attribute information of a preset range area; the signaling data represents the communication data generated by the terminal device within the preset range area, and the terminal device represents the device that generates mobile communication within the range area.

[0123] In a possible implementation, the building attribute information includes a plurality of preset types of interest point data; the first determination module 42 includes:

[0124] The first determining unit 421 is used to determine the density distribution of static interest points according to the interest point data of each preset type in the building attribute information;

[0125] A second determining unit 422, configured to determine a dynamic activity density distribution according to the signaling data in the target data;

[0126] The third determining unit 423 is used to determine the core area in the preset range area according to the static interest point density distribution and the dynamic activity density distribution.

[0127] In a possible implementation manner, the first determining unit 421 is specifically configured to:

[0128] For each preset type of point of interest data, determine the preliminary point of interest density distribution of the point of interest data according to a preset static density formula;

[0129] The static interest point density distribution of the interest point data is determined according to the preliminary interest point density distribution, the preset superposition formula, and the first preset weight of each preset type of interest point data.

[0130] In a possible implementation manner, the second determining unit 422 is specifically configured to:

[0131] Determine the comprehensive thermal density of villages and towns in the preset range area according to the third preset weight corresponding to the static interest point density distribution and the fourth preset weight corresponding to the dynamic vitality density distribution;

[0132] According to the preset natural breakpoint method, the data represented by the comprehensive thermal density are divided into multiple groups;

[0133] Determine the thermal value of each group, and identify the group with the highest thermal value as the core area of ​​the village or town.

[0134] In a possible implementation, the road attribute information includes urban built-up area data and the road grade of the road where the vehicle terminal is located;

[0135] Among them, the urban built-up area data is the regional data within the preset edge range of the city where the vehicle terminal is located on the corresponding map; the road grade includes multiple grades.

[0136] In a possible implementation, the second determining module 43 is specifically configured to:

[0137] In the urban built-up area data, the target road corresponding to the road grade in the road attribute information is extracted;

[0138] The core area and the target road are spatially superimposed to determine the overlapping area as the road section passing through villages and towns in the preset range.

[0139] The road segment identification processing provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be described in detail here.

[0140] Figure 6 This is a schematic diagram of the structure of the vehicle terminal provided by this application. Figure 6 As shown, the vehicle terminal 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0141] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0142] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0143] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0144] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0145] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0146] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0147] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0148] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0149] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0150] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0151] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0153] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0154] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0155] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A road section identification and processing method, characterized in that: The method comprises: Obtaining a road segment identification request, and obtaining target data of the vehicle terminal according to the road segment identification request; wherein the target data represents at least one type of preset data within a preset range area in front of the real-time position of the vehicle terminal; Determine a core area in the preset range according to the target data; According to the core area, the road sections passing through villages and towns in the preset range are determined.

2. The method according to claim 1, characterized in that: The target data includes navigation map data and signaling data; wherein, the navigation map data includes building attribute information and road attribute information of the preset range area; the signaling data represents the communication data generated by the terminal device within the preset range area, and the terminal device represents the device that generates mobile communication within the range area.

3. The method according to claim 2, characterized in that The building attribute information includes a plurality of preset types of point of interest data; and determining the core area in the preset range area according to the target data includes: Determining the density distribution of static interest points according to the interest point data of each preset type in the building attribute information; Determining a dynamic activity density distribution according to the signaling data in the target data; A core area is determined in the preset range area according to the static interest point density distribution and the dynamic activity density distribution.

4. The method according to claim 3, characterized in that: The step of determining the density distribution of static points of interest according to the points of interest data of each preset type in the building attribute information includes: For each preset type of interest point data, determining a preliminary interest point density distribution of the interest point data according to a preset static density formula; The static interest point density distribution of the interest point data is determined according to the preliminary interest point density distribution, a preset superposition formula, and a first preset weight of each preset type of interest point data.

5. The method according to claim 3, characterized in that: Determining a core area in the preset range area according to the static interest point density distribution and the dynamic activity density distribution includes: Determining the comprehensive thermal density of villages and towns in the preset range area according to the third preset weight corresponding to the static interest point density distribution and the fourth preset weight corresponding to the dynamic vitality density distribution; According to a preset natural breakpoint method, the data represented by the comprehensive thermal density is divided into a plurality of groups; The thermal value of each group is determined, and the group with the highest thermal value is determined as the core area of ​​the village or town.

6. The method according to any one of claims 2 to 5, characterized in that: The road attribute information includes urban built-up area data and the road grade of the road where the vehicle terminal is located; Among them, the urban built-up area data is the area data within a preset edge range of the city where the vehicle terminal is located on the corresponding map; and the road grade includes multiple grades.

7. The method according to claim 6, characterized in that Determining the road section passing through villages and towns in the preset range area according to the core area includes: Extracting a target road corresponding to the road grade in the road attribute information from the urban built-up area data; The core area and the target road are spatially superimposed to determine that the overlapping area is a road section passing through villages and towns in the preset range area.

8. A road section identification and processing device, characterized in that: include: A first acquisition module is used to acquire a road segment identification request, and acquire target data of the vehicle terminal according to the road segment identification request; wherein the target data represents at least one type of preset data within a preset range area in front of the real-time position of the vehicle terminal; A first determining module, configured to determine a core area in the preset range area according to the target data; The second determination module is used to determine the road section passing through villages and towns in the preset range area according to the core area.

9. A vehicle terminal, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium / computer program product, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor; and / or, The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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