CN111829538A - Traffic safety navigation method, storage medium and electronic equipment - Google Patents
Traffic safety navigation method, storage medium and electronic equipment Download PDFInfo
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Abstract
The present application relates to a traffic safety navigation method, a storage medium, and an electronic device, the traffic safety navigation method being for a mobile body, including: acquiring a plurality of selectable paths between an initial place and a destination; acquiring road condition data of each optional path, wherein the road condition data comprises at least one of geographic environment condition data and weather condition data; scoring the corresponding selectable path according to each piece of road condition data; and screening one path from the plurality of selectable paths as a navigation path according to the scores so as to enable the driver and the passenger to be far away from danger. The method and the system combine the navigation requirements of the user, calculate the path in real time, accurately predict the weather condition of the road along the road by combining the Internet of vehicles, send early warning or road avoidance options to the vehicle owner in advance, and avoid accidents.
Description
Technical Field
The present application relates to the field of navigation technologies, and in particular, to a traffic safety navigation method, a storage medium, and an electronic device.
Background
With the development of navigation technology, navigation is more and more intelligent, and for common users, traveling is relatively simple through the navigation technology.
However, in the driving process, the road condition is often influenced by external factors such as weather, and sometimes when extremely severe conditions are met, the life and property safety of drivers and passengers can be greatly influenced.
Therefore, it is desirable to provide a traffic safety navigation method.
Disclosure of Invention
An object of the present application is to provide a traffic safety navigation method, a storage medium and an electronic device, so as to solve a real-time road condition early warning.
The traffic safety navigation method provided by the application comprises the following steps: acquiring a plurality of selectable paths between an initial place and a destination; acquiring road condition data of each optional path, wherein the road condition data comprises at least one of geographic environment condition data and weather condition data; scoring the corresponding selectable path according to each piece of road condition data; and screening one path from the plurality of selectable paths as a navigation path according to the scores so as to enable the driver and the passenger to be far away from danger.
In an embodiment, when the road condition data includes geographic environment condition data and weather condition data, the step of scoring the corresponding selectable path according to each piece of road condition data includes: respectively endowing geographic environment condition data and weather condition data in all the road condition data with a first set weight and a second set weight; and scoring the corresponding selectable path according to the first set weight and the second set weight of each road.
In one embodiment, the road condition data further comprises congestion condition data and economic condition data, and the economic condition data comprises fuel consumption condition data and high-speed charging condition data; the step of scoring the corresponding selectable path according to each piece of road condition data includes: determining all user demand types, wherein the user demand types comprise the shortest time, the safest and the most economical; respectively endowing a first set weight and a second set weight to geographic environment condition data and weather condition data in all road condition data corresponding to different user demand types; respectively endowing a third set weight and a fourth set weight to the congestion condition data and the economic condition data in all the road condition data corresponding to the demand types of different users; and grading the corresponding selectable paths according to the first set weight, the second set weight, the third set weight and the fourth set weight of each road corresponding to the demand types of different users.
In an embodiment, when the road condition data of each selectable path includes real-time road condition data and historical road condition data within a preset time, the traffic safety navigation method further includes: collecting GPS data of vehicles on each optional path, analyzing road condition data of each optional path in an off-line manner, and analyzing the average running speed of the moving body along the navigation path from the initial position to the destination within a preset time period; acquiring the predicted time for reaching the path according to the distance between the initial position and the path position in the navigation path and the average driving speed; and predicting the road condition of the approach place when the predicted time is reached according to real-time road condition data, wherein the road condition data comprises at least one of geographic environment condition data and weather condition data, so as to give early warning to the driver and passengers and enable the driver and passengers to be far away from danger.
In one embodiment, the step of scoring the corresponding selectable path according to each piece of road condition data includes: dividing each selectable path into a plurality of road sections; respectively endowing each road section with a weight according to the road condition; for each optional path, adding the weights of all road sections to obtain a weighted sum value; and using the weighted sum value as a dividend and the number of the road sections as a divisor, calling a divider, and calculating to obtain a quotient value as a score of the road section.
In one embodiment, the traffic safety navigation method further includes the steps of: after one path is screened out to be used as a navigation path, reminding a driver and passengers of the road safety condition existing on the path in advance so as to enable the driver and passengers to be far away from danger; the road safety condition refers to factors influencing road traffic safety, and comprises a road surface water accumulation condition, a road surface excavation condition and a mountain landslide condition.
In one embodiment, the acquiring the road condition data of each selectable path includes: and acquiring the road condition data of each optional path from the real-time data of the national disaster reduction center.
The present application further provides a storage medium storing a computer program, wherein the computer program is executed by a processor to implement any one of the above-mentioned traffic safety navigation methods.
The application also provides an electronic device, which comprises a processor and a memory, wherein the processor is used for executing the computer program stored in the memory so as to realize any one of the traffic safety navigation methods.
According to the method and the system, the path is calculated in real time in combination with the navigation requirements of the user, the weather conditions of the road on the way are accurately predicted in combination with the Internet of vehicles, for example, the weather conditions when the road passes through the passing point can be accurately predicted through congestion prediction, and meanwhile, according to historical data such as whether the road is a culvert or not, whether a slope exists around the culvert or not, whether the mountain body has the experience of landslide or not and the like, the vehicle owner is sent out early warning or road avoidance in advance, and accidents are avoided.
The foregoing description is only an overview of the technical solutions of the present application, and in order to make the technical means of the present application more clearly understood, the present application may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present application more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic flow chart of a traffic safety navigation method according to an embodiment of the present application.
Fig. 2 is a schematic application environment diagram of the traffic safety navigation method shown in fig. 1.
Fig. 3 is a flowchart illustrating a traffic safety navigation method according to another embodiment of the present application.
Fig. 4 is a flowchart illustrating a traffic safety navigation method according to still another embodiment of the present application.
Fig. 5 is a flowchart illustrating a traffic safety navigation method according to another embodiment of the present application.
Fig. 6 is a schematic structural diagram of an electronic device capable of executing a traffic safety navigation method according to an embodiment of the present application.
Detailed Description
To further explain the technical means and effects of the present application for achieving the intended application, the following detailed description of embodiments, methods, steps, structures, features and effects of the traffic safety navigation method, the storage medium and the electronic device according to the present application will be made with reference to the accompanying drawings and preferred embodiments.
The foregoing and other technical matters, features and effects of the present application will be apparent from the following detailed description of preferred embodiments, which is to be read in connection with the accompanying drawings. While the present application is susceptible to embodiment and specific details, specific reference will now be made in detail to the present disclosure for the purpose of illustrating the general principles of the invention.
Fig. 1 is a schematic flow chart of a traffic safety navigation method according to a first embodiment of the present application. Fig. 2 is a schematic application environment diagram of the traffic safety navigation method shown in fig. 1. Referring to fig. 1 and fig. 2, the navigation terminal may be, but not limited to, a smart phone, a car machine, a PDA, a notebook computer, or even a wheelchair of a special user. The traffic safety navigation method of the embodiment comprises the following steps:
step S21: acquiring a plurality of selectable paths between an initial place and a destination;
in this embodiment, initially is the coordinate information provided by the satellite positioning system. That is, the initial point in the present application includes not only the position where the mobile body is located before navigation but also a route point between the position and the destination, and the setting of the initial point dynamically changes as the mobile body travels. At present, the technology of a satellite positioning system is mature, the precision is high, and the number of optional positioning systems is large, such as GPS, Galileo, Beidou and the like. In practical design, the positioning system can be developed by itself, which does not affect the application of the invention.
In the embodiment, after the initial location and the destination are obtained, a high-precision map is used to provide accurate road information and vehicle position information to calculate a plurality of preset paths, and in the existing navigation system, generally 3 preset paths are provided, but the number of the preset paths is not limited to 3, and may be 2, 4 or other numbers; the preset route may or may not include information that can represent a congestion condition, a safety condition, and an economic condition.
In this embodiment, the initial information includes location data and collection time information of the location data, and the collection time information is reported to the server by the vehicle-mounted terminal. The in-vehicle terminal is configured to periodically report the location information to the server. The position information generally includes position data and acquisition time information of the position data, and the position data corresponds to the acquisition time information, that is, each time the position data is acquired, the vehicle-mounted terminal records the acquisition time, and reports the acquisition time and the position data to the server.
In order to ensure the safety of the user information, user permission needs to be obtained before the vehicle position information is obtained from the server. In other words, the vehicle position information can be acquired only after the user approval, and the information cannot be acquired otherwise. In this embodiment, the authentication is performed between the user and the server through an information acquisition protocol, VIP account login information, a user password, a temporary oral consent of the user, and the like.
In the present embodiment, the position data includes position coordinates of the in-vehicle terminal and a position attribute corresponding to the position coordinates, or one of the above data. With the increasing demand, in another embodiment, the position data further includes information such as a position coordinate correction value. The location attribute includes a name of a geographic location where the vehicle is located, a type of the geographic location, or a point of interest name. For example, the vehicle is located at a geographical position with the name of a new source road and a Changji road intersection, the vehicle is located at a geographical position with the name of a Xujiahui businessman or a Jiating huiessman, and the vehicle is located at a geographical position with the name of a Shanghai automobile park or an Anhui old street.
The location attribute is mostly added by the server. The vehicle-mounted terminal can leave related service information after sending a service request to the call center every time, and the call center can feed the service information back to the server, so that the information of the vehicle-mounted terminal can be stored in the server. If the vehicle-mounted terminal sends a similar service request next time, the server can provide corresponding information in a short time, so that the speed of replying the request is increased, and the service quality is improved.
Step S23: and acquiring road condition data of each optional path, wherein the road condition data comprises at least one of geographic environment condition data and weather condition data.
Specifically, in the present embodiment, the geographical environment condition refers to a factor affecting road traffic safety, and includes a surface water accumulation condition, a surface excavation condition, a landslide condition, and the like. Similarly, weather conditions also refer to factors that affect road traffic safety, including, but not limited to, hail, typhoon, rainstorm, and the like.
Further, in the present embodiment, acquiring the road condition data of each of the selectable paths includes: and acquiring the road condition data of each optional path from the real-time data of the national disaster reduction center to improve the real-time performance, and the weather condition can be accurate to the minute level, so that the prediction accuracy is improved.
Further, the step of acquiring the road condition data of each of the selectable paths further includes: acquiring historical road condition data in preset time on each optional path; and acquiring real-time road condition data on each optional path. In the present embodiment, the historical road condition data is defined as road condition data within a preset time before the current time. For example, data within 24 hours to 2400 hours from the current time is defined as history data. Thus, when the historical data acquisition command is received, the road condition data within the preset time is acquired by default. In the present embodiment, the real-time road condition data is defined as road condition data within a preset time period before the current time point. For example, if the current time is 11/29/8 in 2018, the system defaults to real-time data obtained when 30 is divided into 8 in 11/29/7 in 2018.
The road conditions of the same time period and the same time period in different periods on the same road have historical similarity. In another embodiment, the historical road condition data is defined only as road condition data for the same period of time as calendar days of calendar months. In other words, the historical road condition data refers to the historical road condition information of a certain road calendar, a month calendar, a week calendar and the same time period recorded in the server. For the geographic environment condition, the historical data of the geographic environment condition acquired by the user refers to the road geographic environment information in the same time period as the calendar days of the calendar months of the calendar years, and the user can select the historical date of the calendar months of the calendar days of the calendar years, for example, the user selects the vehicle running speed information in the time period to be inquired through a historical data inquiry module of a navigation system. It may also be default set by the system in advance, such as default the same time of the previous day, for example, when the current time is 5 pm on 11/20/2018, the default history data of the system is road condition data on 5 pm on 11/2018, 19/pm. In this embodiment, the step of acquiring the road condition data of each of the alternative paths includes: acquiring historical road condition data of the calendar, the calendar month, the calendar day and the week calendar in the same time period on each optional path; and acquiring real-time road condition data on each optional path. By narrowing the range of the historical data and based on the narrowed historical data, the historical road condition can be analyzed more accurately.
As described above, not only the historical road condition can be analyzed but also the future road condition can be predicted based on the historical data. Referring to fig. 3, in yet another embodiment, historical data is used to predict road conditions at a location that the user will arrive at in the future. The method comprises the following specific steps: s231, collecting GPS data of vehicles on each optional path, analyzing road condition data of each optional path in an off-line manner, and analyzing the average running speed of the moving body along the navigation path from the initial position to the destination within a preset time period; s233, obtaining the predicted time of reaching the path according to the distance between the initial position and the path position in the navigation path and the average running speed; s235, predicting road conditions of the approach place when the predicted time is reached according to real-time road condition data, wherein the road condition data comprises at least one of geographic environment condition data and weather condition data, and pre-warning drivers and passengers to enable the drivers and passengers to be far away from danger. For example, if the user wants to go from shanghai to suzhou, and go to several places in the kun mountain selectively, the user may screen out the navigation path, and after the navigation starts, obtain the average traveling speed of the navigation path, for example, obtain the average traveling speed of the navigation path as 50km/h through the vehicle position collecting information disclosed in step S21. Then, the kunshan thousand lamps are selected as a way, the predicted time of the way is obtained according to the initial distance from the way and the average driving speed, for example, the initial distance from the kunshan thousand lamps is 100km, and the predicted time of reaching the way can be obtained as 2 hours according to the obtained average driving speed of the navigation path of 50 km/h. Then, the user selects road condition data of the thousand lights 2 hours later in a day before the current week of the current month of the current year, and according to historical similarity, the user can preliminarily predict the road condition of the thousand lights two hours later. In combination with the change of the current real-time road condition data relative to the historical road data, for example, the speed is reduced by 10% basically, or the speed is reduced by 10 kilometers per hour, the user can predict the road condition of thousands of lights after two hours more accurately.
In yet another embodiment, the user does not navigate, and the prediction of future road conditions is made based only on data stored in the historical database.
In still another embodiment, the step of acquiring the road condition data of each of the alternative paths further includes: and obtaining predicted road condition data according to the historical road condition data and the real-time road condition data. In this embodiment, the specific process of obtaining the predicted road condition from the historical road condition data and the real-time road condition data includes: firstly, a server carries out algorithm simulation according to road condition data stored in a memory, such as the condition of surface water accumulation on each optional path, so as to obtain road condition information on each optional path; secondly, the server collects vehicle information which is on the selectable path and adopts a navigation system, and makes a preliminary prediction of the road condition on the general driving path information of the vehicles which are possibly on the selectable path and obtained by monitoring at ordinary times; reversely predicting the real-time traffic state on the basis of the preliminary prediction to obtain a reverse traffic normalized innovation square; step four, making a ratio lambda of the traffic normalized innovation square of the traffic condition of the same route and the reverse traffic normalized innovation square of the traffic condition of the same route at the same time in history, setting a threshold value L, directly outputting the preliminary traffic prediction of the time period when the proportion of the time number meeting the condition lambda < L in the predicted time period to all the time numbers is more than 50%, and selecting the optimal route after calculation and comparison; step five, if the predicted traffic flow and the average speed of the vehicles are larger than or smaller than the real-time traffic flow and the average speed of the vehicles and exceed the numerical value of more than 6 in the predicted time period of the traffic condition, returning to the step three; step six: if the proportion of the time number meeting the condition that the lambda is less than L in the first predicted time period to all the time numbers is less than 50%, calculating to obtain the traffic state prediction in a future time period by adopting a calibrated traffic state preset value and a standard Kalman filtering algorithm according to the real-time traffic state, and returning to the fifth step; if the proportion of the time number meeting the condition that the lambda is less than L in the predicted time period obtained from the second time to all the time numbers is less than 50%, predicting the traffic state in the set time in the future by adjusting the innovation covariance of the k +1 time, and returning to the fifth step; when the set time is up, the steps are carried out again: and when the prediction result within the set time is not accurate, returning to the step of triple prediction.
Step S25: scoring the corresponding selectable path according to each piece of road condition data;
refer to fig. 4. In this embodiment, when the road condition data includes geographic environment condition data and weather condition data, the step of scoring the corresponding selectable route according to each piece of road condition data includes:
step S252, respectively endowing geographic environment condition data and weather condition data in all the road condition data with a first set weight and a second set weight;
specifically, each geographical environment condition existing on each optional path is respectively given a first set weight according to the degree of danger and in combination with the weather condition. For example, a first set weight 1 is given to the same surface water environment condition when the weather is clear; after the weather is changed from clear weather to middle and small rainy weather for one day, a first set weight value is given to be 0.7; and in rainstorm weather, the depth of the accumulated water rapidly becomes deep, and when the vehicle traveling is possibly seriously influenced, the first set weight is given to 0.3. In other words, the server is configured to assign a value to each road condition according to a preset weight assignment algorithm.
Similarly, the weather condition is also combined with the road condition, and the algorithm is given according to the preset weight value to carry out assignment. For example, if the weather is clear and the wind power is small, a second set weight 1 is given; if the weather is clear and the wind power is large, giving a second set weight value of 0.9; if the weather is clear and typhoon yellow warning exists, giving a second set weight value of 0.6; and if the weather is rainstorm, giving a second set weight value of 0.1.
And step S254, scoring the corresponding selectable path according to the first set weight and the second set weight of each road.
In the present embodiment, the score of each alternative route is the sum of the weighted sums of the routes divided by the number of routes. That is, the weighted sum of each link of each alternative path is used as a dividend, the number of links is used as a divisor, and the calculated quotient is used as the score of the link.
Bearing the above, for the above road section, when the weather is clear and the wind power is small, the weighted sum is 1+ 1-2; when the weather is clear and the wind power is large, the weighted sum is 1+0.9 to 1.9; in heavy rain weather, the weighted sum is 0.3+0.1 ═ 0.4.
The user has two alternative paths from the A place to the B place. The first optional path is divided into three sections according to the geographic environmental conditions, the weighted sum of the first section is 0.9, the weighted sum of the second section is 1.5, the weighted sum of the third section is 1.6, the sum of the weighted sums of the sections of the path is equal to 0.9+1.5+1.6 ═ 4, and the score of the optional path is equal to 4 ÷ 3 ═ 1.33. The second optional path is divided into two ends according to the geographic environmental conditions, the first segment weighted sum is 1.8, the second segment weighted sum is 0.8, the sum of the segment weighted sums of the path is equal to 1.8+ 0.8-2.6, and the score of the optional path is equal to 2.6-3-0.87. And by analogy, obtaining the score of each path. And default screening out the path with the highest score as a navigation path.
Under the condition of navigating driving travel, each path is generally set to be segmented only according to the geographic environment condition, and when the path is long, segmentation is carried out according to the weather condition. In one embodiment, the linear distance is set to exceed 20 km while the segmentation is performed according to weather conditions. In the case of bus travel, the passenger transfer point is taken as an end point.
Further, in another embodiment, the road condition data includes congestion conditions and economic conditions including fuel consumption conditions and high-speed toll conditions in addition to geographic environment condition data and weather condition data. Refer to fig. 5. In this embodiment, the step of scoring the corresponding selectable path according to each piece of road condition data includes: step S251, determining all user requirement types, wherein the user requirement types comprise the shortest time, the safest and the most economical; step S253, a first set weight and a second set weight are respectively given to the geographic environment condition data and the weather condition data in all the road condition data corresponding to different user requirement types; step S255, respectively endowing a third set weight and a fourth set weight to the congestion condition data and the economic condition data in all the road condition data corresponding to the demand types of different users; step S257, corresponding to the demand types of different users, scoring the corresponding selectable paths according to the first set weight, the second set weight, the third set weight, and the fourth set weight of each road.
Specifically, in the present embodiment, the user requirement types are different, and the assigned first setting weight, second setting weight, third setting weight, and fourth setting weight are also different.
Again taking the above initially a, destination B as an example:
in the first case, the type of demand of the user is the safest, and in the present embodiment, the scoring thereof is referred to the above explanation of the users from a to B. The first set weight and the second set weight are unchanged.
In the second case, the demand type of the user is the fastest, and in the present embodiment, the scoring algorithm is set in advance such that the score is congestion score + safety score + economic score. In this case, the first setting weight and the second setting weight in the safety condition are 50% of the first setting weight, respectively. In other words, the user will divide the first optional route from the a place to the B place into three segments according to the geographical environment conditions, in this embodiment, the first segment weighted sum is 0.9 × 0.5-0.45, the second segment weighted sum is 1.5 × 0.5-0.75, the third segment weighted sum is 1.6 × 0.5-0.8, the sum of the segment weighted sums of the route is equal to 0.45+0.75+ 0.8-2, and the safety condition score of the optional route is equal to 2 ÷ 3 ═ 0.67. The second optional path is divided into two ends according to the geographic environment condition, the first segment weighted sum is 1.8 multiplied by 0.5-0.9, the second segment weighted sum is 0.8 multiplied by 0.5-0.4, the sum of the segment weighted sums of the path is equal to 0.9+ 0.4-1.3, and the safety condition score of the optional path is equal to 1.3-3-0.43.
Or when the demand type is the fastest, the score is congestion condition score +0.5 × safety condition score +0.1 × economic condition score. In the present embodiment, if the congestion status score or the third set weight is 0.7 and the economic status score or the fourth set weight is 0.9, the first alternative route is scored as 1.125, i.e., 0.7+0.5 × 0.67+0.1 × 0.9. For the second alternative route, if the congestion status score or the third set weight is 0.85 and the economic status score or the fourth set weight is 0.8, the score is 0.85+0.43 × 0.5+0.1 × 0.8 — 1.145.
In the third case, the demand type of the user is the most economical, and the predetermined scoring algorithm is that the score is equal to the economic condition score +0.5 × the safety condition score +0.1 × the congestion condition score. And taking the score of each road condition in the second case as a standard, and aiming at the first path: since the economic score or the fourth set weight is 0.9, the safety score is 0.5 × 0.67, and the congestion score or the third set weight is 0.1 × 0.7, the first route score is 1.305, i.e., 0.9+0.5 × 0.67+0.1 × 0.7. For the second path: since the economic score or the fourth set weight is 0.8 and the safety score is 0.5 × 0.43, the congestion score or the third set weight is 0.1 × 0.85, the second route score is 1.1 — 0.8+0.5 × 0.43+0.1 × 0.85.
That is, the step of scoring the corresponding selectable path according to each piece of road condition data includes: dividing each selectable path into a plurality of road sections; respectively endowing each road section with a weight according to the road condition; for each optional path, adding the weights of all road sections to obtain a weighted sum value; and using the weighted sum value as a dividend and the number of the road sections as a divisor, calling a divider, and calculating to obtain a quotient value as a score of the road section.
Step S27: and screening one path from the multiple selectable paths according to the scores to serve as a navigation path.
In one embodiment, the path with the highest score is selected as the navigation path by default.
When the user requirement type is the safest, screening out the first path as the navigation path, wherein the first path is 1.33 (the first path) >0.87 (the second path); when the user demand type is the fastest speed, screening out a second path as a navigation path, wherein the first path is 1.125 (1.145); and when the user demand type is the most economical, screening out the first path as the navigation path, wherein the first path is 1.305 (first path) >1.1 (second path).
In another embodiment, after one route is screened out as a navigation route, the driver and the passenger are reminded of the road safety condition existing on the route in advance so as to enable the driver and the passenger to be far away from danger; the road safety condition refers to factors influencing road traffic safety, and comprises a road surface water accumulation condition, a road surface excavation condition and a mountain landslide condition. For example, the user is reminded of what road section and where there are dangerous factors at the beginning of the navigation, and the driver is reminded of the dangerous location by sound warning when the vehicle is about to arrive at the dangerous location.
The present application further provides a storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any one of the above-mentioned traffic safety navigation methods.
The present application further provides an electronic device, referring to fig. 6, the electronic device includes a processor 402 and a memory 404, the processor 402 is configured to execute a computer program stored in the memory 404 to implement any one of the above-mentioned traffic safety navigation methods.
The current vehicle machine can only display weather singly, and firstly, the real-time performance is not high, secondly, the purpose is not strong, and thirdly, early warning can not be realized. The existing navigation algorithm only considers the shortest path or further considers congestion avoidance generally, but in the practical application process, the existing navigation algorithm still brings great trouble to drivers, for example, serious ponding of partial road sections caused by heavy rain in the front section time, even a culvert is flooded or a mountain landslide beside a road. According to the method, the early warning is given to the driver in advance according to the past historical data collection and the real-time weather forecast, so that the driver is far away from danger.
The traffic safety navigation method, the storage medium and the electronic device provide a weather road environment early warning scheme, and are realized on the premise of acquiring real-time road conditions, wherein a general mode for acquiring real-time road condition data is to firstly utilize a mobile phone with a position service opened and other mobile devices to collect position information or moving speed information, and then obtain the traffic condition of a certain road section through weighted estimation of various data. The larger the collection amount of the data is, the more reliable the early warning result is. Specifically, the vehicle GPS data are collected to the background server at first, the server side is provided with a platform for analyzing road conditions offline, the GPS data can be collected, and the average speed of each road can be analyzed. Meanwhile, the server side also comprises a server for acquiring the weather condition of each road and historical data related to road safety in real time, so that the intervention early warning is started under the condition of severe weather, and the vehicle owner is helped to plan a navigation line which is far away from danger and has a better road condition based on the previous road condition information.
The method and the system combine historical big data and butt joint real-time data of the national disaster reduction center, provide service with high real-time performance, enable weather conditions to be accurate to the level of minutes, and achieve high prediction accuracy. The method and the device are completely the prediction made aiming at the navigation passing points of the client, and have strong pertinence.
The traffic safety navigation method, the storage medium and the electronic device combine navigation requirements of users to calculate paths in real time, accurately predict weather conditions of roads along the way, for example, the weather conditions when passing through passing points can be accurately predicted through congestion prediction, and early warning or road avoidance selection can be sent to vehicle owners in advance according to historical data of actual conditions of the roads, such as whether culverts exist, whether hills exist around the roads, whether landslide experiences exist in mountains, and the like, so that accidents are avoided.
Although the present application has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the application, and all changes, substitutions and alterations that fall within the spirit and scope of the application are to be understood as being covered by the following claims.
Claims (9)
1. A traffic safety navigation method is characterized by comprising the following steps:
acquiring a plurality of selectable paths between an initial place and a destination;
acquiring road condition data of each optional path, wherein the road condition data comprises at least one of geographic environment condition data and weather condition data;
scoring the corresponding selectable path according to each piece of road condition data; and
and screening one path from the plurality of selectable paths as a navigation path according to the scores so as to enable the driver and the passengers to be far away from danger.
2. The traffic safety navigation method according to claim 1, wherein when the road condition data includes geographic environment condition data and weather condition data, the step of scoring the corresponding selectable path according to each piece of road condition data includes:
respectively endowing geographic environment condition data and weather condition data in all the road condition data with a first set weight and a second set weight;
and scoring the corresponding selectable path according to the first set weight and the second set weight of each road.
3. The traffic safety navigation method according to claim 2, wherein the road condition data further comprises: congestion condition data and economic condition data, wherein the economic condition data comprises oil consumption condition data and high-speed charging condition data; the step of scoring the corresponding selectable path according to each piece of road condition data includes:
determining all user demand types, wherein the user demand types comprise the shortest time, the safest and the most economical;
respectively endowing a first set weight and a second set weight to geographic environment condition data and weather condition data in all road condition data corresponding to different user demand types;
respectively endowing a third set weight and a fourth set weight to the congestion condition data and the economic condition data in all the road condition data corresponding to the demand types of different users;
and grading the corresponding selectable paths according to the first set weight, the second set weight, the third set weight and the fourth set weight of each road corresponding to the demand types of different users.
4. The traffic safety navigation method according to claim 1, wherein when the road condition data of each of the selectable paths includes real-time road condition data and historical road condition data within a preset time, the traffic safety navigation method further includes:
collecting GPS data of vehicles on each optional path, analyzing road condition data of each optional path in an off-line manner, and analyzing the average running speed of the moving body along the navigation path from the initial position to the destination within a preset time period;
acquiring the predicted time for reaching the path according to the distance between the initial position and the path position in the navigation path and the average driving speed; and
and predicting the predicted road conditions of the approach place when the predicted time is reached according to real-time road condition data, wherein the road condition data comprises at least one of geographic environment condition data and weather condition data, so as to give early warning to the driver and passengers and enable the driver and passengers to be far away from danger.
5. The traffic safety navigation method according to claim 1, wherein the step of scoring the corresponding selectable path according to each piece of road condition data includes:
dividing each selectable path into a plurality of road sections;
respectively endowing each road section with a weight according to the road condition;
for each optional path, adding the weights of all road sections to obtain a weighted sum value; and
and taking the weighted sum as a dividend and the number of the road sections as a divisor, calling a divider, and calculating to obtain a quotient value as the score of the road section.
6. The traffic safety navigation method according to claim 1, further comprising the steps of: after one path is screened out to be used as a navigation path, reminding a driver and passengers of the road safety condition existing on the path in advance so as to enable the driver and passengers to be far away from danger; the road safety condition refers to factors influencing road traffic safety, and comprises a road surface water accumulation condition, a road surface excavation condition and a mountain landslide condition.
7. The traffic safety navigation method according to claim 1, wherein the acquiring road condition data of each selectable path includes: and acquiring the road condition data of each optional path from the real-time data of the national disaster reduction center.
8. A storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the traffic safety navigation method according to any one of claims 1 to 7.
9. An electronic device, characterized in that the electronic device comprises a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the traffic safety navigation method according to any one of claims 1 to 7.
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