CN117014849A - Disaster early warning method and device for vehicle, terminal equipment and storage medium - Google Patents
Disaster early warning method and device for vehicle, terminal equipment and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of auxiliary driving, and provides a disaster early warning method and device for a vehicle, terminal equipment and a storage medium. The method comprises the following steps: acquiring disaster prediction data of a target area; determining first disaster early warning information according to disaster prediction data and vehicle state data of a first vehicle aiming at the first vehicle in the target area; and sending the first disaster early warning information to a first vehicle. Through the arrangement, the disaster early warning service with pertinence can be provided for each vehicle according to the disaster prediction data and the vehicle state of each vehicle in the area, so that the accuracy of disaster early warning for the vehicles is improved.
Description
Technical Field
The present application relates to the field of assisted driving technologies, and in particular, to a disaster warning method and apparatus for a vehicle, a terminal device, and a storage medium.
Background
The natural disasters such as heavy rain, heavy snow, accumulated water/ice on the road surface, heavy fog, mountain torrents, debris flows, earthquakes and the like can bring great threat to the safe running of vehicles. Aiming at the problem, the existing map navigation platform can provide a certain degree of disaster early warning service for all vehicles in a disaster occurrence area, so that the driving safety of the vehicles is improved. However, the above-mentioned method belongs to a broad-range disaster early warning information broadcast, and has the problem of low early warning accuracy. For example, if a region is in heavy rain, the map navigation platform may send heavy rain early warning information to all vehicles in the region, but some road segments in the region may not have rainfall or have less rainfall, and vehicles running in the road segments may also receive heavy rain early warning information, thereby generating false alarms.
Disclosure of Invention
In view of the above, the embodiments of the present application provide a disaster early warning method, apparatus, terminal device and storage medium for a vehicle, which can improve the accuracy of disaster early warning for the vehicle.
A first aspect of an embodiment of the present application provides a disaster early warning method for a vehicle, including:
acquiring disaster prediction data of a target area;
determining first disaster early warning information according to disaster prediction data and vehicle state data of a first vehicle aiming at the first vehicle in the target area;
and sending the first disaster early warning information to a first vehicle.
In the embodiment of the application, disaster prediction data of a target area are firstly obtained, corresponding disaster early warning information is determined according to the disaster prediction data and vehicle state data of any vehicle in the target area, and finally the disaster early warning information is sent to the vehicle, so that disaster early warning service is provided for the vehicle. Through the arrangement, the disaster early warning service with pertinence can be provided for each vehicle according to the disaster prediction data and the vehicle state of each vehicle in the area, so that the accuracy of disaster early warning for the vehicles is improved. For example, when it is determined that a heavy rain weather occurs in a certain area according to disaster prediction data, for a certain vehicle in the area, the actual influence of the heavy rain weather on the vehicle can be estimated according to vehicle state data such as the position and the vehicle speed of the vehicle, and then disaster early warning information conforming to the actual influence is sent to the vehicle, so that the accuracy of disaster early warning can be effectively improved.
In an implementation manner of the embodiment of the present application, the determining the first disaster early-warning information according to the disaster prediction data and the vehicle state data of the first vehicle may include:
determining the disaster type of the target area according to the disaster prediction data;
determining a severity of impact of the disaster type on the first vehicle based on the vehicle status data of the first vehicle;
and determining the severity level of the first disaster early warning information according to the influence severity level.
Further, the vehicle state data of the first vehicle includes a vehicle speed, a position, and a vehicle size of the first vehicle, and the disaster type includes road surface water accumulation and road surface wet skid; the determining the severity of the impact of the disaster type on the first vehicle according to the vehicle state data of the first vehicle may include:
if the disaster type is road surface ponding, determining a ponding place according to disaster prediction data; if the position of the first vehicle is closer to the water accumulation place and the vehicle size of the first vehicle is smaller, determining that the influence severity of the disaster type on the first vehicle is higher;
if the disaster type is that the road surface is slippery, acquiring the speed of the first vehicle; the higher the speed of the first vehicle, the higher the severity of the impact of the disaster type on the first vehicle is determined.
In one implementation manner of the embodiment of the application, the disaster types of the target area include a plurality of disaster early-warning information, and the first disaster early-warning information includes disaster early-warning information corresponding to each disaster type; the sending the first disaster early warning information to the first vehicle may include:
according to the vehicle state data of the first vehicle, respectively determining the pre-warning priority corresponding to each disaster type;
and sequentially sending disaster early warning information corresponding to each disaster type to the first vehicle according to the early warning priority corresponding to each disaster type.
In one implementation manner of the embodiment of the application, the vehicle state data of the first vehicle comprises the position, the vehicle speed and the navigation information of the first vehicle, and the first disaster early warning information comprises disaster avoidance information, vehicle speed control information and updated navigation paths; the determining the first disaster early warning information according to the disaster prediction data and the vehicle state data of the first vehicle may include:
generating the disaster avoidance information according to the disaster prediction data and the position of the first vehicle;
generating the vehicle speed control information according to the disaster prediction data and the vehicle speed of the first vehicle;
and generating the planned navigation path according to the disaster prediction data and the navigation information of the first vehicle.
In one implementation of the embodiment of the present application, the vehicle state data of the first vehicle includes a position of the first vehicle, the disaster prediction data includes earthquake prediction data, and the first disaster early warning information includes earthquake early warning information; the determining the first disaster early warning information according to the disaster prediction data and the vehicle state data of the first vehicle may include:
determining the earthquake middle position and intensity of the earthquake according to the earthquake prediction data;
calculating the time of the seismic wave of the earthquake reaching the position of the first vehicle according to the position of the first vehicle and the position of the earthquake center;
and generating earthquake early warning information according to the intensity and the time.
In an implementation manner of the embodiment of the present application, the method may further include:
judging whether the second vehicle can drive into the target area or not according to navigation information of the second vehicle aiming at the second vehicle outside the target area;
if the second vehicle can drive into the target area, determining second disaster early warning information according to disaster prediction data and vehicle state data of the second vehicle;
and sending second disaster early warning information to a second vehicle.
A second aspect of an embodiment of the present application provides a disaster early-warning device for a vehicle, including:
The disaster prediction data acquisition module is used for acquiring disaster prediction data of the target area;
the first disaster early warning information determining module is used for determining first disaster early warning information according to disaster prediction data and vehicle state data of the first vehicle aiming at the first vehicle in the target area;
the first disaster early warning information sending module is used for sending first disaster early warning information to a first vehicle.
A third aspect of the embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the disaster early warning method for a vehicle according to the first aspect of the embodiment of the present application when the processor executes the computer program.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the disaster warning method for a vehicle as provided in the first aspect of the embodiments of the present application.
A fifth aspect of the embodiments of the present application provides a computer program product, which when run on a terminal device, causes the terminal device to perform the disaster warning method for a vehicle as provided in the first aspect of the embodiments of the present application.
It will be appreciated that the advantages of the second to fifth aspects may be found in the relevant description of the first aspect, and are not described here again.
Drawings
Fig. 1 is a flowchart of a disaster early warning method for a vehicle according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a display interface of earthquake early warning information according to an embodiment of the present application;
fig. 3 is a schematic diagram of an operating principle of a vehicle cloud platform according to an embodiment of the present application;
fig. 4 is a schematic diagram of an architecture of a vehicle cloud platform according to an embodiment of the present application;
fig. 5 is a structural frame diagram of a disaster early warning device for a vehicle according to an embodiment of the present application;
fig. 6 is a schematic diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail. Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Various natural disasters such as storm, snow storm, road surface ponding/ice accumulation, heavy fog, mountain torrent, debris flow, earthquake and the like can bring great threat to the safe running of vehicles, and weather broadcasting service for all vehicles is provided for the threat at present through a map navigation platform or other third party service providers so as to carry out disaster early warning on all vehicles. However, the large-scale unified disaster early warning mode has the problem of low early warning accuracy. Aiming at the problem, the embodiment of the application provides a disaster early warning method, a disaster early warning device, terminal equipment and a storage medium for vehicles, which can improve the accuracy of disaster early warning for the vehicles. For more specific technical implementation details of the embodiments of the present application, please refer to the method embodiments described below.
It should be understood that the implementation subject of the method embodiments of the present application is various types of terminal devices or servers, such as mobile phones, tablet computers, desktop computers, wearable devices, in-vehicle controllers, in-vehicle terminals, augmented reality (augmented reality, AR)/Virtual Reality (VR) devices, notebook computers, ultra-mobile personal computer (UMPC), personal digital assistants (personal digital assistant, PDA), and so on, and the specific type of the terminal devices or servers is not limited in this embodiment of the present application.
Referring to fig. 1, a disaster early warning method for a vehicle provided by an embodiment of the present application includes:
101. acquiring disaster prediction data of a target area;
it should be understood that the execution body of the embodiment of the present application may be a certain terminal device or a server at the cloud platform end, which may be connected to a vehicle-mounted terminal of each vehicle in the specified range area and perform data interaction. For convenience of description, the following represents an execution subject of an embodiment of the present application by using a vehicle cloud platform.
First, the vehicle cloud platform may acquire disaster prediction data of a target area. The target area may be a geographical area with a certain range of sizes, for example, a country, a province, a city, a administrative district, a highway section, or the like, and the shape and the size of the target area are not limited in the embodiment of the present application. The cloud platform may acquire corresponding disaster prediction data through interfacing with various third party service platforms (for example, a weather broadcasting center, a seismic disaster reduction research institute, etc.) providing weather service or disaster prediction service, and may specifically include rainfall prediction data, snowfall prediction data, hail prediction data, storm prediction data, visibility prediction data, road surface state prediction data, seismic prediction data, and falling stone prediction data for a target area, etc. In actual operation, disaster prediction data may be acquired and refreshed from time to time, for example, 2 hours high frequency refresh data (30 minutes update), fusion of 48 hours prediction data (first 2 hours update 30 minutes update 2 hours later) and 7 days 6 hours prediction data (12 hours update), etc., and part of the third party service platform (e.g., kuWeather, etc.) may provide such prediction data.
102. Determining first disaster early warning information according to disaster prediction data and vehicle state data of a first vehicle aiming at the first vehicle in the target area;
the vehicle cloud platform can be connected with the vehicle-mounted terminals of all vehicles in the specified range area (including the target area) in a network mode and the like, and data interaction is performed, and the vehicle-mounted terminal of each vehicle can upload the vehicle state data of each vehicle to the vehicle cloud platform. Thus, the vehicle cloud platform may obtain vehicle status data for each vehicle within the target area. The vehicle state data may include, but is not limited to: vehicle speed, vehicle position, vehicle size, vehicle performance, vehicle type and navigation information, and the like.
Assuming that the first vehicle is any vehicle in the target area, after the disaster prediction data and the vehicle state data of the first vehicle are acquired, the vehicle cloud platform can determine and generate disaster early warning information adapted to the first vehicle according to the disaster prediction data and the vehicle state data of the first vehicle, and the disaster early warning information is represented by the first disaster early warning information. Specifically, according to the disaster prediction data, disaster source information such as which disasters may occur in the target area, severity of the disasters, driving notes and the like can be determined, then the actual influence of the disasters on the first vehicle can be estimated by combining the vehicle state data of the first vehicle, the most safe and effective disaster avoidance suggestions for the first vehicle are analyzed, and finally disaster early warning information which is most suitable for the first vehicle can be generated based on the disaster source information, the actual influence of the disasters on the first vehicle and the disaster avoidance suggestions. For example, if the disaster prediction data is snowfall prediction data, and the first vehicle is at the position a at a relatively high speed, disaster warning information such as "snowfall weather, vehicle speed is reduced, and the first vehicle is traveling to the evacuation site B … closest to the position a" can be generated.
In an implementation manner of the embodiment of the present application, the determining the first disaster early-warning information according to the disaster prediction data and the vehicle state data of the first vehicle may include:
(1) Determining the disaster type of the target area according to the disaster prediction data;
(2) Determining a severity of impact of the disaster type on the first vehicle based on the vehicle status data of the first vehicle;
(3) And determining the severity level of the first disaster early warning information according to the influence severity level.
From the disaster prediction data, specific disaster types of the target area, such as heavy rain, heavy snow, accumulated water/ice on the road surface, heavy fog, torrential flood, debris flow, earthquake, etc., can be determined. Then, in combination with the vehicle state data of the first vehicle, the severity of the impact of the disaster type occurring in the target area on the first vehicle is evaluated. For example, based on the location of the first vehicle, the specific location where the disaster occurred, and the environment in the vicinity of the location where the first vehicle is located, the actual impact of the disaster on the first vehicle may be evaluated to determine the severity of the impact on the first vehicle. In practice, a number of different levels of impact severity may be set, e.g. 1-10 total levels may be set, where level 1 indicates the lowest impact severity and level 10 indicates the highest impact severity. And finally, determining the severity level of the generated disaster early warning information according to the influence severity level. Specifically, the severity level of disaster early warning information can be divided into different levels such as mild early warning, general early warning, severe early warning and severe early warning in advance, if the severity level is lower, the severity level of the generated disaster early warning information can be determined to be mild early warning, and general warning information (serving as first disaster early warning information) is generated at the moment to remind a driver to pay attention to slowing down; if the influence degree is higher, the severity level of the generated disaster early warning information can be determined to be serious early warning, and serious warning information (serving as first disaster early warning information) can be generated at the moment, so that a driver is required to immediately go to a safe refuge place, and the like.
Further, the vehicle state data of the first vehicle includes a vehicle speed, a position, and a vehicle size of the first vehicle, and the disaster type includes road surface water accumulation and road surface wet skid; the determining the severity of the impact of the disaster type on the first vehicle according to the vehicle state data of the first vehicle may include:
(1) If the disaster type is road surface ponding, determining a ponding place according to disaster prediction data; if the position of the first vehicle is closer to the water accumulation place and the vehicle size of the first vehicle is smaller, determining that the influence severity of the disaster type on the first vehicle is higher;
(2) If the disaster type is that the road surface is slippery, acquiring the speed of the first vehicle; the higher the speed of the first vehicle, the higher the severity of the impact of the disaster type on the first vehicle is determined.
For a specific disaster type, if the disaster type occurring in the target area is road surface water accumulation, determining water accumulation places according to disaster prediction data, for example, determining possible water accumulation places according to rainfall and low-lying positions such as culverts, tunnels, underground garages and the like in the target area; the vehicle size and the position of the first vehicle can be determined through the vehicle state data of the first vehicle, if the first vehicle is smaller in size (such as a car) and the vehicle position is closer to the position of a culvert, a tunnel and other water-logging places, the possibility of flooding the vehicle is higher, the threat to the life and property safety of personnel in the vehicle is higher, and therefore the influence severity of the disaster type on the first vehicle can be determined; conversely, if the first vehicle is larger in size (e.g., a bus) and the vehicle is located farther from the culvert and tunnel, the less likely the vehicle is flooded, the less threatening the safety of personnel and lives in the vehicle, and thus the lower severity of the impact of the disaster type on the first vehicle can be determined.
If the disaster type of the target area is road surface wet skid, determining the speed of the first vehicle according to the vehicle state data of the first vehicle; if the speed of the first vehicle is higher, the possibility that the first vehicle has tire slipping to cause accidents is higher, the threat to the life and property safety of personnel in the vehicle is higher, and therefore the influence severity of the disaster type on the first vehicle can be determined to be higher; conversely, if the speed of the first vehicle is lower, the lower the possibility that the first vehicle has tire slip to cause accident is indicated, the less the threat to the life and property safety of personnel in the vehicle is, and thus the lower the severity of the impact of the disaster type on the first vehicle can be determined.
In addition, if the disaster type occurring in the target area is a mountain flood or a debris flow, the position thereof may be determined by the vehicle state data of the first vehicle first, and if the vehicle position is closer to the mountain, the higher the severity of the impact of the disaster type on the first vehicle is determined, otherwise, the lower the severity of the impact of the disaster type on the first vehicle is determined. If the disaster type occurring in the target area is hail or heavy rain, the vehicle type of the disaster type can be determined through the vehicle state data of the first vehicle, if the vehicle type is a convertible vehicle, the influence severity of the disaster type on the first vehicle is determined to be high, otherwise, the influence severity of the disaster type on the first vehicle is determined to be low. If the type of disaster occurring in the target area is an earthquake, it can be determined that the severity of the impact on all vehicles in the target area is highest, and so on.
Further, the disaster type of the target area comprises a plurality of disaster types; the determining the severity of the impact of the disaster type on the first vehicle according to the vehicle state data of the first vehicle may include:
the severity of the impact of each disaster type on the first vehicle is determined separately from the vehicle status data of the first vehicle.
In some cases, multiple different types of natural disasters may occur simultaneously in the target area, where the severity of the impact of each disaster type on the first vehicle may be determined separately based on the vehicle status data of the first vehicle. For example, assuming that two natural disasters of earthquake and storm occur in the target area at the same time, the severity of the impact of the earthquake on the first vehicle (for example, the highest level 10) and the severity of the impact of the storm on the first vehicle (for example, the level 3) can be determined respectively in combination with the characteristics of the earthquake and the storm and the vehicle state data of the first vehicle, and so on.
The determining the severity level of the first disaster warning information according to the influence severity degree may include:
and determining the severity level of disaster early warning information corresponding to each disaster type according to the influence severity degree of each disaster type on the first vehicle.
If a plurality of different types of natural disasters occur simultaneously, after the influence severity of each disaster type on the first vehicle is determined, the severity level of disaster early warning information corresponding to each disaster type can be determined according to the influence severity level. For example, for the above example of two natural disasters of earthquake and heavy rain occurring simultaneously, since the severity of the earthquake on the first vehicle is highest, the severity of the disaster warning information corresponding to the earthquake can be determined to be serious warning, and since the severity of the heavy rain on the first vehicle is general, the severity of the disaster warning information corresponding to the heavy rain can be determined to be general warning, and so on.
In one implementation manner of the embodiment of the application, the vehicle state data of the first vehicle comprises the position, the vehicle speed and the navigation information of the first vehicle, and the first disaster early warning information comprises disaster avoidance information, vehicle speed control information and planned navigation paths; the determining the first disaster early warning information according to the disaster prediction data and the vehicle state data of the first vehicle may include:
(1) Generating disaster avoidance information according to the disaster prediction data and the position of the first vehicle;
(2) Generating vehicle speed control information according to disaster prediction data and the vehicle speed of the first vehicle;
(3) And generating a planned navigation path according to the disaster prediction data and the navigation information of the first vehicle.
Disaster early warning services provided by the vehicle cloud platform for the vehicle can comprise a plurality of aspects such as disaster avoidance suggestions, speed control reminding, navigation path planning and the like. According to the first aspect, disaster avoidance information can be generated according to disaster prediction data and the position of the vehicle, and the disaster avoidance information is mainly used for guiding a driver of the vehicle to avoid danger. For example, the disaster prediction data can be used to determine the type of disaster that is currently occurring, and then find the refuge site related to the type of disaster in the vicinity of the vehicle position, so as to generate disaster avoidance information such as "please go to a nearby refuge site to refuge. In the second aspect, vehicle speed control information can be generated according to disaster prediction data and the vehicle speed of the vehicle, and the vehicle speed control information is mainly used for reminding a driver to keep safe vehicle speed running and cannot overspeed. For example, conditions such as road surface humidity can be judged through disaster prediction data, so that road surface friction coefficient is determined, and theoretical braking distance can be calculated by combining parameters such as vehicle speed and performance of a vehicle, so that a driver is reminded of keeping a safe distance with a preceding vehicle and the vehicle speed is controlled. In the third aspect, planning can be performed according to disaster prediction data and navigation information of the vehicle to obtain a planned navigation path of the vehicle. The vehicle cloud platform can determine the destination and the initial navigation path of the vehicle through the navigation information of the vehicle, and can judge whether the initial navigation path can normally and safely run or not through disaster prediction data, if not, the vehicle cloud platform can recalculate other routes of the vehicle to the destination through various path planning algorithms, and the planned navigation path is obtained. The disaster avoidance information, the vehicle speed control information, and the planned navigation path may be part of the first disaster warning information.
In one implementation of the embodiment of the present application, the vehicle state data of the first vehicle includes a position of the first vehicle, the disaster prediction data includes earthquake prediction data, and the first disaster early warning information includes earthquake early warning information; the determining the first disaster early warning information according to the disaster prediction data and the vehicle state data of the first vehicle may include:
(1) Determining the earthquake middle position and intensity of the earthquake according to the earthquake prediction data;
(2) Calculating the time of the seismic wave of the earthquake reaching the position of the first vehicle according to the position of the first vehicle and the position of the earthquake center;
(3) And generating earthquake early warning information according to the intensity and the time.
The vehicle cloud platform can also provide earthquake early warning function for the vehicle. Specifically, the acquired disaster prediction data may include earthquake prediction data (the data may be provided by an earthquake bureau or a disaster reduction institute, etc.), and the vehicle cloud platform may determine the position and intensity of the earthquake in the earthquake according to the acquired earthquake prediction data; then, by combining the position of the first vehicle and the position of the earthquake center (the position can be expressed by longitude and latitude), the time for the earthquake wave to reach the position of the first vehicle can be estimated by utilizing the propagation speed of the earthquake wave; then, according to the earthquake intensity and the time, corresponding earthquake early warning information can be generated, and the earthquake early warning information is used as first disaster early warning information and is sent to the first vehicle. For example, dense earthquake early warning sensors (the distance is about 15 km) can be distributed in a earthquake area, when an earthquake occurs in the earthquake, the characteristic that electromagnetic wave propagation is faster than that of earthquake waves is utilized, early warning time which is different from a few seconds to a few tens of seconds can be provided for a vehicle before the destructive earthquake arrives, the intensity of the earthquake is informed, and a driver can utilize the early warning time to carry out emergency danger avoidance after receiving the earthquake early warning, for example, the vehicle is abandoned immediately and the vehicle goes to an open area, so that the occurrence of casualties and secondary disasters is reduced.
Fig. 2 is a schematic diagram of a display interface of earthquake early warning information according to an embodiment of the present application. After the vehicle cloud platform sends the earthquake early warning information to the vehicle, the interface shown in the figure 2 can be seen through the vehicle-mounted terminal screen of the vehicle so as to remind a vehicle driver of carrying out emergency danger avoidance. In fig. 2, information such as the position of the middle of the earthquake, the intensity of the earthquake, and the arrival time of the earthquake wave is shown.
103. And sending the first disaster early warning information to a first vehicle.
After the first disaster early warning information is determined and generated, the vehicle cloud platform can send the first disaster early warning information to the first vehicle, so that disaster early warning of the first vehicle is realized. It should be noted that, the vehicle cloud platform may adopt the same method as the first vehicle, and provide disaster early warning service with pertinence (related to vehicle state data) for each vehicle, so as to improve accuracy of disaster early warning for the vehicle.
In one implementation manner of the embodiment of the application, the disaster types of the target area include a plurality of disaster early-warning information, and the first disaster early-warning information includes disaster early-warning information corresponding to each disaster type; the sending the first disaster early warning information to the first vehicle may include:
(1) According to the vehicle state data of the first vehicle, respectively determining the pre-warning priority corresponding to each disaster type;
(2) And sequentially sending disaster early warning information corresponding to each disaster type to the first vehicle according to the early warning priority corresponding to each disaster type.
For the situation that a plurality of different types of natural disasters occur in the target area in the previous description, when first disaster early warning information occurs to a first vehicle, early warning priorities corresponding to each disaster type can be respectively determined according to vehicle state data of the first vehicle. Specifically, the vehicle state data of the first vehicle may be used to evaluate the severity of the impact of each disaster type on the first vehicle in the manner described above, and then the pre-warning priority corresponding to each disaster type is determined according to the severity of the impact of each disaster type on the first vehicle. And finally, sequentially sending disaster early warning information corresponding to each disaster type to the first vehicle according to the early warning priority corresponding to each disaster type. For example, for the above-mentioned two natural disasters of earthquake and storm, since the severity of the impact of the earthquake on the first vehicle is higher than the severity of the impact of the storm on the first vehicle, it can be determined that the pre-warning priority corresponding to the earthquake is higher than the pre-warning priority corresponding to the storm, so that the disaster pre-warning information corresponding to the earthquake is preferentially sent to the first vehicle, and then the disaster pre-warning information corresponding to the storm is sent to the first vehicle. By the arrangement, the actual influence of different types of natural disasters on the vehicle can be distinguished, and a driver can firstly receive the early warning information of the current most serious natural disasters, so that the most accurate and timely risk avoidance measures are made.
In an implementation manner of the embodiment of the present application, the method may further include:
(1) Judging whether the second vehicle can drive into the target area or not according to navigation information of the second vehicle aiming at the second vehicle outside the target area;
(2) If the second vehicle can drive into the target area, determining second disaster early warning information according to disaster prediction data and vehicle state data of the second vehicle;
(3) And sending second disaster early warning information to a second vehicle.
The foregoing describes a disaster early warning method for vehicles in the target area, however, in actual operation, the vehicle cloud platform may also perform corresponding disaster early warning for vehicles outside the target area. Assuming that the second vehicle is any vehicle outside the target area, the cloud platform may first acquire navigation information of the second vehicle, and determine whether the second vehicle will drive into the target area according to the navigation information, for example, determine a destination and a driving path of the second vehicle according to the navigation information, and if the destination is inside the target area or the driving path passes through the target area, determine that the second vehicle will drive into the target area. If it is determined that the second vehicle will drive into the target area, then second disaster warning information may be determined based on the disaster prediction data of the target area and the vehicle state data of the second vehicle, and the second disaster warning information may be transmitted to the second vehicle. That is, the same method as the first vehicle can be adopted to provide the disaster early warning service with pertinence for the second vehicle.
In the embodiment of the application, disaster prediction data of a target area are firstly obtained, corresponding disaster early warning information is determined according to the disaster prediction data and vehicle state data of any vehicle in the target area, and finally the disaster early warning information is sent to the vehicle, so that disaster early warning service is provided for the vehicle. Through the arrangement, the disaster early warning service with pertinence can be provided for each vehicle according to the disaster prediction data and the vehicle state of each vehicle in the area, so that the accuracy of disaster early warning for the vehicles is improved. For example, when it is determined that a heavy rain weather occurs in a certain area according to disaster prediction data, for a certain vehicle in the area, the actual influence of the heavy rain weather on the vehicle can be estimated according to vehicle state data such as the position and the vehicle speed of the vehicle, and then disaster early warning information conforming to the actual influence is sent to the vehicle, so that the accuracy of disaster early warning can be effectively improved.
Fig. 3 is a schematic diagram of a working principle of a vehicle cloud platform according to an embodiment of the present application, where the vehicle cloud platform may be deployed at a certain terminal device or server in the cloud. In fig. 3, various disaster prediction data obtained through a third party service platform, such as minute precipitation and hail prediction, road surface condition prediction, visibility hazard level prediction, road surface hazard level prediction, and earthquake wave arrival time prediction, etc., may be stored by using a cloud crowd-sourced database. Vehicle state data such as the speed, GPS position and destination of each vehicle uploaded to the vehicle cloud platform are utilized, the vehicle position and navigation route are identified by combining with an AI analysis algorithm, weather, road conditions and corresponding disaster early warning information are pushed for each vehicle, and specifically, the method can comprise the steps of rescheduling the navigation route, prompting vehicle speed control, suggesting disaster avoidance and the like.
Fig. 4 is a schematic diagram of an architecture of a vehicle cloud platform according to an embodiment of the present application. The framework mainly comprises three parts of information screening, material integration and classified output, wherein the information screening means that the vehicle cloud platform can analyze and sort collected disaster prediction data (such as disaster weather, disaster events and the like) and extract effective disaster information. The integrated material is that the vehicle cloud platform can further process the extracted disaster information, fuses four types of disastrous weather (rainfall, haze, hail and snowfall) and four types of disaster events (waterlogging, flood, landslide and collapse), and judges the actual influence of various disasters by combining the actual state of each vehicle, so that the priority level of various disaster early warning and specific early warning information (position of disaster influence, road, area and the like) are determined. The classified output means that the vehicle cloud platform can sequentially issue the generated early warning information of various disasters to vehicles in a range and vehicles passing through navigation according to the priority order of early warning, so that the disaster early warning of the vehicles is realized.
In summary, according to the embodiment of the application, by acquiring the disaster prediction data and the vehicle state data of each vehicle, different disaster early warning information can be generated according to different states of each vehicle, so that a targeted disaster early warning service is provided for each vehicle, and the accuracy of disaster early warning on the vehicle is improved.
It should be understood that the sequence numbers of the steps in the foregoing embodiments do not mean the order of execution, and the execution order of the processes should be determined by the functions and the internal logic, and should not be construed as limiting the implementation process of the embodiments of the present application.
The disaster early-warning method of a vehicle is mainly described above, and a disaster early-warning device of a vehicle will be described below.
Referring to fig. 5, an embodiment of a disaster early warning device for a vehicle according to an embodiment of the present application includes:
a disaster prediction data obtaining module 501, configured to obtain disaster prediction data of a target area;
a first disaster early-warning information determining module 502, configured to determine, for a first vehicle located in the target area, first disaster early-warning information according to the disaster prediction data and vehicle state data of the first vehicle;
and a first disaster early warning information sending module 503, configured to send the first disaster early warning information to the first vehicle.
In an implementation manner of the embodiment of the present application, the first disaster early warning information determining module may include:
a disaster type determining unit, configured to determine a disaster type of the target area according to the disaster prediction data;
An influence severity determination unit configured to determine an influence severity of the disaster type on the first vehicle based on vehicle state data of the first vehicle;
and the first disaster early warning information determining unit is used for determining the severity level of the first disaster early warning information according to the influence severity level.
Further, the vehicle status data of the first vehicle includes a vehicle speed, a position, and a vehicle size of the first vehicle, and the disaster type includes road surface water and road surface wet skid; the influence severity determination unit may include:
a first influence severity determination subunit, configured to determine a ponding location according to the disaster prediction data if the disaster type is a road surface ponding; if the position of the first vehicle is closer to the water accumulation place and the vehicle size of the first vehicle is smaller, determining that the influence severity of the disaster type on the first vehicle is higher;
a second influence severity determination subunit, configured to obtain a vehicle speed of the first vehicle if the disaster type is slippery road surface; and if the speed of the first vehicle is higher, determining that the influence severity of the disaster type on the first vehicle is higher.
Further, the disaster types of the target area comprise a plurality of disaster early warning information, and the first disaster early warning information comprises disaster early warning information corresponding to each disaster type; the first disaster early warning information transmission module may include:
the early warning priority determining unit is used for respectively determining the early warning priority corresponding to each disaster type according to the vehicle state data of the first vehicle;
and the disaster early warning information sending unit is used for sequentially sending disaster early warning information corresponding to each disaster type to the first vehicle according to the early warning priority corresponding to each disaster type.
In one implementation manner of the embodiment of the present application, the vehicle state data of the first vehicle includes a position, a vehicle speed and navigation information of the first vehicle, and the first disaster early warning information includes disaster avoidance information, vehicle speed control information and a planned navigation path; the first disaster warning information determining module may include:
a disaster avoidance information generating unit configured to generate the disaster avoidance information according to the disaster prediction data and the position of the first vehicle;
a vehicle speed control information generating unit configured to generate the vehicle speed control information according to the disaster prediction data and a vehicle speed of the first vehicle;
And the navigation path planning unit is used for generating the planned navigation path according to the disaster prediction data and the navigation information of the first vehicle.
In one implementation manner of the embodiment of the present application, the vehicle state data of the first vehicle includes a position of the first vehicle, the disaster prediction data includes earthquake prediction data, and the first disaster early-warning information includes earthquake early-warning information; the first disaster warning information determining module may include:
the earthquake parameter determining unit is used for determining the earthquake middle position and intensity of the earthquake according to the earthquake prediction data;
a seismic wave arrival time calculation unit for calculating a time when the seismic wave of the earthquake arrives at the position of the first vehicle according to the position of the first vehicle and the earthquake center position;
and the earthquake early warning information generating unit is used for generating the earthquake early warning information according to the intensity and the time.
In an implementation manner of the embodiment of the present application, the disaster early warning device for a vehicle may further include:
the vehicle entering judging module is used for judging whether a second vehicle is driven into the target area according to navigation information of the second vehicle aiming at the second vehicle which is positioned outside the target area;
The second disaster early warning information determining module is used for determining second disaster early warning information according to the disaster prediction data and the vehicle state data of the second vehicle if the second vehicle can drive into the target area;
and the second disaster early warning information sending module is used for sending the second disaster early warning information to the second vehicle.
The embodiment of the application also provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the disaster warning method for a vehicle as described in any one of the above embodiments.
The embodiment of the application also provides a computer program product, which when run on a terminal device, causes the terminal device to execute the disaster early warning method for realizing the vehicle described in any one of the embodiments.
Fig. 6 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in fig. 6, the terminal device 6 of this embodiment includes: a processor 60, a memory 61 and a computer program 62 stored in said memory 61 and executable on said processor 60. The processor 60, when executing the computer program 62, implements the steps in the embodiments of the disaster warning method for each vehicle described above, such as steps 101 to 103 shown in fig. 1. Alternatively, the processor 60, when executing the computer program 62, performs the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules 501 to 503 shown in fig. 5.
The computer program 62 may be partitioned into one or more modules/units, one of which
Or a plurality of modules/units are stored in the memory 61 and executed by the processor 60 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program 62 in the terminal device 6.
The processor 60 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may be an internal storage unit of the terminal device 6, such as a hard disk or a memory of the terminal device 6. The memory 61 may be an external storage device of the terminal device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the terminal device 6. The memory 61 is used for storing the computer program and other programs and data required by the terminal device. The memory 61 may also be used for temporarily storing data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present application.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. A disaster warning method for a vehicle, comprising:
acquiring disaster prediction data of a target area;
determining first disaster early warning information according to the disaster prediction data and the vehicle state data of the first vehicle aiming at the first vehicle in the target area;
and sending the first disaster early warning information to the first vehicle.
2. The method of claim 1, wherein the determining first disaster warning information based on the disaster prediction data and the vehicle state data of the first vehicle comprises:
determining the disaster type of the target area according to the disaster prediction data;
Determining a severity of impact of the disaster type on the first vehicle according to the vehicle state data of the first vehicle;
and determining the severity level of the first disaster early warning information according to the influence severity level.
3. The method of claim 2, wherein the vehicle status data of the first vehicle includes a vehicle speed, a position, and a vehicle size of the first vehicle, and the hazard type includes road surface water and road surface wet skid; the determining, according to the vehicle state data of the first vehicle, the severity of the impact of the disaster type on the first vehicle includes:
if the disaster type is road surface ponding, determining a ponding place according to the disaster prediction data; if the position of the first vehicle is closer to the water accumulation place and the vehicle size of the first vehicle is smaller, determining that the influence severity of the disaster type on the first vehicle is higher;
if the disaster type is that the road surface is slippery, acquiring the speed of the first vehicle; and if the speed of the first vehicle is higher, determining that the influence severity of the disaster type on the first vehicle is higher.
4. The method of claim 1, wherein the disaster type of the target area includes a plurality of disaster early-warning information, and the first disaster early-warning information includes disaster early-warning information corresponding to each of the disaster types; the sending the first disaster warning information to the first vehicle includes:
according to the vehicle state data of the first vehicle, respectively determining the pre-warning priority corresponding to each disaster type;
and sequentially sending disaster early warning information corresponding to each disaster type to the first vehicle according to the early warning priority corresponding to each disaster type.
5. The method of claim 1, wherein the vehicle status data of the first vehicle includes a position, a vehicle speed, and navigation information of the first vehicle, and the first disaster early warning information includes disaster avoidance information, vehicle speed control information, and a planned navigation path; the determining the first disaster early warning information according to the disaster prediction data and the vehicle state data of the first vehicle includes:
generating the disaster avoidance information according to the disaster prediction data and the position of the first vehicle;
generating the vehicle speed control information according to the disaster prediction data and the vehicle speed of the first vehicle;
And generating the planned navigation path according to the disaster prediction data and the navigation information of the first vehicle.
6. The method of claim 1, wherein the vehicle status data of the first vehicle comprises a location of the first vehicle, the disaster prediction data comprises seismic prediction data, and the first disaster warning information comprises seismic warning information; the determining the first disaster early warning information according to the disaster prediction data and the vehicle state data of the first vehicle includes:
determining the earthquake middle position and intensity of the earthquake according to the earthquake prediction data;
calculating the time of arrival of the seismic wave of the earthquake at the position of the first vehicle according to the position of the first vehicle and the earthquake center position;
and generating the earthquake early warning information according to the intensity and the time.
7. The method of any one of claims 1 to 6, further comprising:
judging whether a second vehicle is driven into the target area according to navigation information of the second vehicle aiming at the second vehicle which is positioned outside the target area;
if the second vehicle can drive into the target area, determining second disaster early warning information according to the disaster prediction data and the vehicle state data of the second vehicle;
And sending the second disaster early warning information to the second vehicle.
8. A disaster warning device for a vehicle, comprising:
the disaster prediction data acquisition module is used for acquiring disaster prediction data of the target area;
the first disaster early warning information determining module is used for determining first disaster early warning information according to the disaster prediction data and the vehicle state data of the first vehicle aiming at the first vehicle in the target area;
and the first disaster early warning information sending module is used for sending the first disaster early warning information to the first vehicle.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the disaster warning method of the vehicle according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the disaster warning method of the vehicle according to any one of claims 1 to 7.
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| CN202310974436.XA CN117014849A (en) | 2023-08-03 | 2023-08-03 | Disaster early warning method and device for vehicle, terminal equipment and storage medium |
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| CN202310974436.XA CN117014849A (en) | 2023-08-03 | 2023-08-03 | Disaster early warning method and device for vehicle, terminal equipment and storage medium |
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