Risk prompting method, device and equipment, readable storage medium and program product

Abstract

The invention relates to a risk prompting method, device and equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring state information of historical vehicles in a target driving road section, wherein the state information comprises a driving position and driving state information corresponding to the driving position; under the condition of determining that target driving state information in the driving state information is risk information, determining a driving position corresponding to the target driving state information as a risk position of the historical vehicle in the target driving road section; risk prompt information is output to a target vehicle, the risk prompt information comprises the risk position, and the driving distance between the target vehicle and the target driving road section is smaller than a preset distance. By adopting the method, the real-time performance of alarming can be improved.

Description

Technical Field

[0001] This application relates to the technical field of vehicle networking, and particularly to a risk prompting method, device, equipment, readable storage medium, and program product. Background Art

[0002] Vehicle networking technology refers to the information exchange and interconnection between vehicles and other traffic participants (such as other vehicles, pedestrians, infrastructure, cloud platforms, etc.) through wireless communication technology.

[0003] In the prior art, vehicles or user devices can only receive warning information from the cloud server under specific conditions, and these information are usually triggered by external events or conditions.

[0004] The limitation of this method is that users and vehicles can only obtain warnings within the preset alarm range and cannot take proactive prevention or intervention according to real-time situations or other unknown potential dangers.

[0005] Therefore, the alarm method of the traditional system has the problem of poor real-time performance. Summary of the Invention

[0006] Based on this, it is necessary to provide a risk prompting method, device, equipment, readable storage medium, and program product that can improve the real-time performance of alarms for the above technical problems.

[0007] In a first aspect, this application provides a risk prompting method, including:

[0008] Obtain the status information of historical vehicles in the target driving section, where the status information includes: driving position, and driving status information corresponding to the driving position;

[0009] When it is determined that the target driving status information in the driving status information is risk information, determine the driving position corresponding to the target driving status information as the risk position of the historical vehicle in the target driving section;

[0010] Output a risk prompting message to the target vehicle, where the risk prompting message includes the risk position, and the driving distance between the target vehicle and the target driving section is less than a preset distance.

[0011] In one of the embodiments, the above-mentioned determining the driving position corresponding to the target driving status information as the risk position of the historical vehicle in the target driving section includes:

[0012] Obtain the map information in the target driving section;

[0013] Match the driving position corresponding to the target driving status information with the map information to determine the risk position.

[0014] In one embodiment, after determining the risk location of the historical vehicle within the target driving section, the method further includes:

[0015] Correspondingly save the risk location and the time information when the target driving state information is collected;

[0016] Output a risk prompt message to the target vehicle, including:

[0017] If it is determined that the risk location is valid according to the time information when the target driving state information is collected and the first preset duration, output a risk prompt message to the target vehicle.

[0018] In one embodiment, the target driving state information includes a pothole image. Determining that the target driving state information in the state information is risk information includes:

[0019] Based on the pothole image, determine the pothole area and the distance between any two points on the edge of the pothole image;

[0020] Perform a comparison process on the distance between any two points on the edge of the pothole image to determine the maximum distance;

[0021] In the case where the pothole area is greater than a preset area threshold and / or the maximum distance is greater than a preset distance threshold, determine that the pothole image is risk information.

[0022] In one embodiment, the above-mentioned determining the pothole area based on the pothole image includes:

[0023] Map the pothole image to a geographic grid, and determine the number of grids covered by the shadow area projected on the geographic grid, where the geographic area of each grid in the geographic grid is the same;

[0024] Based on the number of grids, determine the pothole area.

[0025] In one embodiment, the target driving state information includes speed information, and the speed information includes wheel rotation speed and vehicle speed. Determining that the target driving state information in the state information is risk information includes:

[0026] In the case where the speed information of the historical vehicle exceeds a preset condition, determine that the speed information is risk information; where the preset condition includes at least one of the following:

[0027] The vehicle speed of the historical vehicle exceeds the first preset speed threshold;

[0028] The wheel rotation speeds of any two wheels exceed the preset rotation speed threshold;

[0029] The rotation speed of any one wheel exceeds the vehicle speed.

[0030] In one embodiment, the target driving state information includes headlight information. The target driving state information in the above-determined state information is a risk information, including:

[0031] When the headlight information indicates that the speed of the historical vehicle exceeds the second preset speed threshold and the time for which the hazard lights of the historical vehicle are lit exceeds the preset time threshold, determine the headlight information as risk information.

[0032] In one embodiment, the target driving state information includes tire pressure information. The target driving state information in the above-determined state information is a risk information, including:

[0033] When the number of historical vehicles with tire pressure failures within the second preset duration is greater than the preset number threshold, determine the tire pressure information as risk information.

[0034] In a second aspect, the present application further provides a risk prompting device, including:

[0035] An information acquisition module, configured to acquire the state information of historical vehicles within a target driving section, where the state information includes: driving position, and driving state information corresponding to the driving position;

[0036] A position determination module, configured to, when determining that the target driving state information in the driving state information is risk information, determine the driving position corresponding to the target driving state information as the risk position of the historical vehicle within the target driving section;

[0037] An information output module, configured to output a risk prompting information to the target vehicle, where the risk prompting information includes the risk position, and the driving distance between the target vehicle and the target driving section is less than a preset distance.

[0038] In a third aspect, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0039] Acquire the state information of historical vehicles within a target driving section, where the state information includes: driving position, and driving state information corresponding to the driving position;

[0040] When determining that the target driving state information in the driving state information is risk information, determine the driving position corresponding to the target driving state information as the risk position of the historical vehicle within the target driving section;

[0041] Output a risk prompting information to the target vehicle, where the risk prompting information includes the risk position, and the driving distance between the target vehicle and the target driving section is less than a preset distance.

[0042] Fourthly, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0043] Obtain the status information of historical vehicles in the target driving section, where the status information includes: driving position, and driving status information corresponding to the driving position;

[0044] When it is determined that the target driving status information in the driving status information is risk information, determine the driving position corresponding to the target driving status information as the risk position of the historical vehicle in the target driving section;

[0045] Output a risk prompt message to the target vehicle, where the risk prompt message includes the risk position, and the driving distance between the target vehicle and the target driving section is less than a preset distance.

[0046] Fifthly, the present application also provides a computer program product, including a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0047] Obtain the status information of historical vehicles in the target driving section, where the status information includes: driving position, and driving status information corresponding to the driving position;

[0048] When it is determined that the target driving status information in the driving status information is risk information, determine the driving position corresponding to the target driving status information as the risk position of the historical vehicle in the target driving section;

[0049] Output a risk prompt message to the target vehicle, where the risk prompt message includes the risk position, and the driving distance between the target vehicle and the target driving section is less than a preset distance.

[0050] For the above risk prompting method, device, equipment, readable storage medium and program product, the status information of historical vehicles in the target driving section is obtained through a cloud server. The status information includes the driving position and the driving status information corresponding to this position. When it is determined that a certain driving status information is risk information, the server marks the corresponding driving position as the risk position of the historical vehicle in the target driving section. Then, the server outputs a risk prompt message including the risk position to the target vehicle. When the driving distance between the target vehicle and the target driving section is less than the preset distance, the risk prompt is triggered. Compared with the traditional method, this method can judge whether the target vehicle is approaching the target section based on the real-time driving distance between the target vehicle and the target driving section. And when the target vehicle is approaching the risk position, the cloud server can timely output a risk prompt message to the target vehicle. This dynamic early warning mechanism based on the actual driving status, the status information of historical vehicles and the instant risk information can notify the driver in advance near the dangerous position, thereby effectively reducing the risk of accidents. Brief Description of the Drawings

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0052] Figure 1 It is an application environment diagram of the risk prompt method in an embodiment;

[0053] Figure 2 It is a schematic flowchart of the risk prompt method in an embodiment;

[0054] Figure 3 It is a schematic flowchart of the risk prompt method in another embodiment;

[0055] Figure 4 It is a schematic diagram of the risk position marking points of the target driving section in an embodiment;

[0056] Figure 5 It is a schematic flowchart of the risk prompt method in another embodiment;

[0057] Figure 6 It is a schematic diagram of mapping a pothole image in another embodiment;

[0058] Figure 7 It is a schematic flowchart of the risk prompt method in another embodiment;

[0059] Figure 8 It is a structural block diagram of the risk prompt device in an embodiment;

[0060] Figure 9 It is an internal structure diagram of a computer device in an embodiment. Detailed Description of the Embodiments

[0061] In order to make the purpose, technical solutions and advantages of the present application more clear and understandable, the following further details the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0062] The risk prompt method provided by the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the vehicle system 01 communicates through the Telematics Service Platform (TSP) 02 and the cloud server 03. Among them, the vehicle system 01 may include an in-vehicle host 011, a body controller 012, a camera control module 013, a headlight control module 014, a vehicle stability system controller 015, and a Telematics Box (T-BOX) 016. The in-vehicle host 011, the body controller 012, the camera control module 013, the headlight control module 014, and the vehicle stability system controller 015 in the vehicle system 01 respectively send the collected status information to the T-BOX 016, and the T-BOX 016 then sends the collected status information to the TSP 02. Finally, the TSP 02 uploads the collected status information to the cloud server 03. The cloud server 03 obtains the status information of historical vehicles in the target driving section. The status information includes: the driving position and the driving status information corresponding to the driving position; when it is determined that the target driving status information in the driving status information is risk information, the driving position corresponding to the target driving status information is determined as the risk position of the historical vehicle in the target driving section; and a risk prompt information is output to the target vehicle. The risk prompt information includes the risk position, and the driving distance between the target vehicle and the target driving section is less than a preset distance.

[0063] In an exemplary embodiment, as Figure 2 shown, a risk prompt method is provided. Taking the cloud server in Figure 1 as an example, the method includes the following steps 201 to step 203.

[0064] Among them:

[0065] Step 201, obtain the status information of historical vehicles in the target driving section. The status information includes: the driving position and the driving status information corresponding to the driving position.

[0066] Among them, the target driving section refers to the section that the vehicle is about to drive or has already driven.

[0067] Historical vehicles refer to the vehicles that have driven on the target driving section before. Example: All vehicles passing through a specific section within a certain period of time, such as the vehicles driving on the target driving section the previous day.

[0068] The status information refers to various types of data related to the driving of historical vehicles, which may include the current position of the vehicle, the driving status, speed, acceleration, etc.

[0069] The driving position refers to the geographical coordinates of the vehicle at a certain moment, indicating the specific position of the vehicle on the road.

[0070] In the embodiment of the present application, it is first necessary to determine the target driving section. In this process, the historical vehicle identifies the target driving section where the historical vehicle is located through the in-vehicle host inside the vehicle body, and sends the information related to the target driving section to the T-BOX. The T-BOX then sends this information to the TSP, and finally uploads it to the cloud server through the TSP.

[0071] Next, the cloud server can extract the historical vehicle driving records related to the target section from the historical data sources stored in the traffic monitoring system, cloud database or vehicle networking platform. Then, the cloud server filters out the status information of the vehicles that have ever driven on this section according to the geographical boundary or section identifier of the target driving section, ensuring that the retrieved data is related to the target driving section.

[0072] After the status information selection is completed, the cloud server can extract the specific driving positions of each historical vehicle on the target section. Among them, the driving position is usually represented in the form of Global Positioning System (GPS) coordinates, combined with a timestamp, to form the driving trajectory of the vehicle. Each position point is associated with the corresponding time information to accurately track the driving conditions of the vehicle at different time periods.

[0073] Finally, the cloud server extracts the driving status information corresponding to each driving position. The driving status information may include the vehicle's speed, acceleration, braking situation, steering state, and whether it stops, etc. In addition, the cloud server can also mark some key events, such as hard braking or traffic accidents, and associate these events with specific driving positions, providing a basis for subsequent risk assessment and early warning.

[0074] Step 202, when it is determined that the target driving status information in the driving status information is risk information, the driving position corresponding to the target driving status information is determined as the risk position of the historical vehicle within the target driving section.

[0075] Among them, the risk information refers to the data that may indicate a dangerous or unsafe driving state. Examples: the location where hard braking occurs, the section where a traffic accident occurs, the traffic conditions caused by bad weather, etc.

[0076] The risk position refers to the geographical location where potential risks are identified during the driving process of the historical vehicle. These positions are usually the gathering places where risk events occur to multiple historical vehicles at the same location. Examples: a certain section often has traffic accidents, or a certain road surface is icy and causes skidding.

[0077] In the embodiment of the present application, after it is determined that the target driving status information is risk information, the cloud server needs to mark the driving position corresponding to the target driving status information as the risk position of the historical vehicle within the target driving section.

[0078] First, the cloud server extracts the driving status information corresponding to each driving position from the status information of historical vehicles. Then, through a preset risk determination criterion, such as sudden braking, abnormal vehicle speed, or parking, etc., it identifies which driving status information belongs to potential risk information.

[0079] In the case where the target driving status information in the driving status information is identified as risk information, the cloud server associates these target driving status information with their corresponding driving positions. Each target driving status information that meets the risk standard will be marked and bound to a specific geographical location (such as GPS coordinates or road section position). In this way, the cloud server can determine the risk positions of historical vehicles on the target driving section.

[0080] In some embodiments, the cloud server records and stores all the identified risk positions. These risk position data will be aggregated into a database for further query, analysis, and risk warning in the future. In addition, the cloud server can also aggregate the risk positions of multiple historical vehicles to generate a risk heat map, so as to visually display the distribution of high-risk areas on the target section and further improve the accuracy of risk assessment.

[0081] Step 203, output a risk prompt message to the target vehicle. The risk prompt message includes the risk position, and the driving distance between the target vehicle and the target driving section is less than a preset distance.

[0082] Among them, the target vehicle refers to a vehicle that is currently driving or about to drive on the target section. Example: The owner using the navigation system or a certain vehicle about to drive into a risk section.

[0083] The driving distance refers to the spatial distance between the current position of the target vehicle and the target section or a specific position. Example: The target vehicle is still 500 meters away from the accident section ahead.

[0084] In the embodiment of the present application, first, the cloud server can obtain the current position of the target vehicle in real time through the GPS of the target vehicle. Next, the cloud server calculates the distance between the target vehicle and the target driving section. Through a geographic information system (GIS) or other positioning algorithms, the cloud server can calculate the geographical location relationship between the current position of the vehicle and the target driving section and determine whether the distance between the two is less than a preset safety threshold.

[0085] After confirming that the target vehicle is approaching the target driving section, the cloud server will query and identify the risk positions on the target section from the historical vehicle data, compare the current position of the target vehicle with these risk positions, and check whether it is approaching the risk area. If the target vehicle approaches these risk positions, the cloud server will generate corresponding risk warning messages.

[0086] Among them, the generated risk warning messages can include the risk positions, the distances between the target vehicle and the risk positions, and the possible risk types. In addition, the cloud server will further improve the warning messages by combining the real-time driving state of the target vehicle (such as vehicle speed and acceleration), so as to help the target vehicle better judge potential risks. Subsequently, the cloud server sends the risk warning messages to the T-BOX through TSP. The in-vehicle host of the target vehicle receives the risk warning messages sent by the T-BOX and notifies the driver in the current vehicle through a pop-up window.

[0087] In some embodiments, the cloud server will continuously monitor the driving state of the target vehicle to ensure the timely update of the warning messages. If the target vehicle continues to approach the risk positions, the cloud server will send risk warning messages again according to the real-time data, or take corresponding safety measures, such as suggesting deceleration or adjusting the driving route, to improve driving safety.

[0088] In the above risk warning method, the state information of historical vehicles within the target driving section is obtained through the cloud server. These state information include the driving positions and the driving state information corresponding to these positions. When determining that a certain driving state information is risk information, the server marks the corresponding driving position as the risk position of the historical vehicle within the target driving section. Then, the server outputs a risk warning message containing the risk position to the target vehicle. When the driving distance between the target vehicle and the target driving section is less than the preset distance, the risk warning is triggered. Compared with the traditional method, this method can judge whether the target vehicle is approaching the target section based on the real-time driving distance between the target vehicle and the target driving section. And when the target vehicle approaches the risk positions, the cloud server can timely output risk warning messages to the target vehicle. This dynamic warning mechanism based on the actual driving state, the state information of historical vehicles and the instant risk information can notify the driver in advance near the dangerous positions, thus effectively reducing the risk of accidents.

[0089] In an exemplary embodiment, as Figure 3 shown, the above embodiment of "determining the driving position corresponding to the target driving state information as the risk position of the historical vehicle within the target driving section" includes steps 301 to 302. Among them:

[0090] Step 301, obtain the map information within the target driving section.

[0091] In the embodiments of the present application, the historical vehicle obtains the map information of the target driving section through the in-vehicle host and transmits this map data to the vehicle's T-BOX in real time. After receiving the map information, the T-BOX sorts, encodes, and sends this data to the TSP. The TSP uploads the map information to the cloud server.

[0092] Step 302: Match the driving position corresponding to the target driving state information with the map information to determine the risk position.

[0093] In the embodiments of the present application, after receiving the map information, the cloud server first parses the map information. Then, based on the driving state information of the target vehicle, the server extracts the specific driving position of the target vehicle. Next, the server matches the driving position of the target vehicle with the road coordinates on the map to determine the specific coordinates of this driving position on the map and marks this coordinate as the risk position.

[0094] In some embodiments, as Figure 4 shown, in Figure 4 , the X-axis represents the length of the target driving section, the Y-axis represents the width of the target driving section, and t represents the time of vehicle driving.

[0095] First, based on the obtained map information, the cloud server subdivides the target driving section into multiple small areas. These small areas can be a certain length of each section of the journey, intersections, section turning points, or specific geographical features, etc. Each small area can have an independent identifier. In this way, the cloud server can accurately divide the driving trajectory of the vehicle to ensure that there is a clear route record on the map every time the vehicle drives.

[0096] Next, the vehicle system will monitor and detect possible risks in real time (such as speeding, sudden changes in road conditions, vehicle abnormalities, etc.). Whenever a potential risk occurs, the vehicle controller will report this risk information and its specific occurrence time (such as Figure 4 the time points Q1 to Q9 in

[0097] to the cloud server.

[0098] After receiving the reported risk time points, the cloud server matches these risk time points with the driving positions stored in the map information. By comparing the driving time, driving path, and specific position of the vehicle at that time, the cloud server can locate the corresponding risk points on the map information. The risk information corresponding to each time point will be marked on the map to form a risk line.

[0098] After time matching, the server marks each risk information (such as uneven road surface, traffic jam, unclear vision, etc.) at the corresponding position on the map information. In this way, the driving path of the vehicle includes not only the normal section information but also these potential risk points.

[0099] Exemplarily, assume a vehicle is driving on an urban street and the map information of a certain section of the journey has been divided into several small areas. For example, a certain turning point is marked as a small area. When the vehicle passes through this area and reports a risk (such as exceeding the speed limit or having an obstacle), the server will record the time when the risk occurs (such as Q2), and then match this time point with the path data on the map information to determine the exact location where the risk occurs. Subsequently, the cloud server will mark this risk point within this area and provide subsequent risk alerts and warnings to the vehicle.

[0100] In the above embodiments, by obtaining the map information of the target driving section and matching it with the target driving state information, it is possible to achieve precise tracking and real-time monitoring of the historical vehicle driving path. Map information usually includes content such as geographical location, road section details, and road features. After matching, the specific driving position of the vehicle can be clearly identified, and based on this, potential risk positions can be accurately located.

[0101] In an exemplary embodiment, after the above embodiment of "determining the risk position of the historical vehicle within the target driving section", the method further includes:

[0102] Step A, correspondingly save the risk position and the time information of collecting the target driving state information.

[0103] In the embodiments of the present application, after determining the risk position of the historical vehicle within the target driving section, the cloud server needs to associate and save this risk position with the time when the target driving state information is collected. The cloud server records the exact time when the target driving state information is collected and stores this time together with the corresponding risk position data to ensure that the specific time and position when the risk occurs can be traced. This process provides a time reference for subsequent judgment of the effectiveness of the risk.

[0104] On this basis, the above embodiment of "outputting risk prompt information to the target vehicle" includes:

[0105] Step B, if it is determined that the risk position is valid according to the time information of collecting the target driving state information and the first preset duration, then output risk prompt information to the target vehicle.

[0106] Wherein, the first preset duration is a preset time window used to determine whether the risk position is still valid.

[0107] In an embodiment of the present application, the cloud server compares the time difference between the current time of the target vehicle and the time information of the recorded collected target driving state information to determine whether the risk location still has a risk within the specified time window. For example, if the target vehicle is approaching or has reached the risk location and the location is within the valid time range, it is considered that the risk location is still valid.

[0108] If the cloud server determines through calculation that the risk location is still valid, the cloud server sends a risk prompt message to the target vehicle. Among them, the risk prompt message may include the specific location of the risk location, the potential risk type of the location (such as traffic accidents, road construction, etc.), and a reminder to the target vehicle to slow down, adjust the driving route or take other safety measures.

[0109] After the risk prompt message is confirmed to be valid, the cloud server transmits the information to the T-BOX of the target vehicle in real time through TSP, and then sends the risk prompt message to the in-vehicle host through the T-BOX. At the same time, the cloud server can also provide additional guidance, such as the remaining distance to the risk location, the estimated arrival time, etc., to further enhance the effectiveness of the prompt message.

[0110] In the above embodiment, the cloud server can not only accurately identify and record the risk location, but also output timely and effective risk prompt messages to the target vehicle at an appropriate time, so that the driver can take necessary safety measures, thereby reducing the occurrence of potential accidents.

[0111] In an exemplary embodiment, the above target driving state information includes pothole images. On this basis, as Figure 5 shown, the above embodiment of "determining that the target driving state information in the state information is risk information" includes steps 401 to 403. Among them:

[0112] Step 401, based on the pothole image, determine the pothole area and the distance between any two points on the edge of the pothole image.

[0113] Among them, the pothole image is collected by the camera control module in the vehicle system to determine the visual image of potholes, cracks or other damages existing on the surface of the target driving section. The pothole image is usually captured by a high-resolution camera and other sensing devices and used to analyze the road damage situation.

[0114] The pothole area refers to the total area of the road damage area shown in the pothole image. This area can be calculated through image processing algorithms and is usually expressed in planar area units (for example, square meters). The pothole area is one of the important indicators to measure the degree of road damage. If the area of the pothole is too large, it means that there are serious safety hazards in the road area.

[0115] The distance between any two points on the edge of the pothole image refers to the straight-line distance between any two points on the edge of the pothole image.

[0116] In the embodiments of the present application, the cloud server obtains the pothole image from the target driving state information. The pothole image is collected in real time by in-vehicle sensors (such as cameras or lidar) of the camera control module, aiming to reflect possible potholes, cracks or other damage problems on the road surface.

[0117] After obtaining the pothole image, the cloud server performs image processing and analysis to extract key features in the pothole image. For example, the cloud server identifies the area of the pothole through image processing algorithms and calculates the area of the area. At the same time, the cloud server also extracts the distance between any two points on the edge of the pothole image, so as to determine the distance between any two points on the edge of the pothole image.

[0118] In some embodiments, the in-vehicle camera in the camera control module captures information about the road surface (such as potholes) during driving, identifies the shape of the pothole through the camera, generates a 2D image, and uploads the 2D image to the cloud server. The cloud server maps the generated 2D image onto a geographical grid with coordinate axes according to the shape of the 2D image, and marks the intersection points of the image and the coordinate axes, such as (x1, y1), (x2, y2), (x3, y3), (x4, y4),..., (xn, yn). Calculate the distance between any two points as shown in formula (1): Figure 6 As shown, map the generated 2D image onto a geographical grid with coordinate axes, mark the intersection points of the image and the coordinate axes, such as (x1, y1), (x2, y2), (x3, y3), (x4, y4),..., (xn, yn). Calculate the distance between any two points as shown in formula (1):

[0119]

[0120] In formula (1), Ln represents the distance between any two points, (xn, yn) represents the coordinates of the nth point, and (xi, yi) represents the coordinates of the ith point.

[0121] In some embodiments, when there are multiple potholes in a certain area, the distance between any two points on the edge of the pothole can be determined.

[0122] Step 402: Compare and process the distance between any two points on the edge of the pothole image to determine the maximum distance.

[0123] Among them, the maximum distance refers to the maximum value among all the distances calculated between any two points on the edge of the pothole image. By comparing the distances between different points, the cloud server can identify the widest part of the pothole, and then judge the size and potential danger of the pothole. The maximum distance usually reflects the influence range and degree of the pothole on vehicle driving.

[0124] In the embodiments of the present application, the cloud server compares all the calculated distances between the two edge points and finds the maximum distance. This maximum distance reflects the widest part of the pothole in the image.

[0125] Step 403, when the pothole area is greater than a preset area threshold and / or the maximum distance is greater than a preset distance threshold, determine that the pothole image is risk information.

[0126] Among them, the preset area threshold is a preset value used to determine whether the pothole area reaches a certain level. If the area of the pothole is greater than this preset value, the pothole is considered a risk factor that poses a danger to vehicle driving. This area threshold is usually set according to road safety standards, traffic management regulations, or historical data.

[0127] The preset distance threshold refers to a set value used to determine whether the maximum distance in the pothole image exceeds a safe range. If the maximum distance is greater than this threshold, it means that the width or size of the pothole will pose a threat to vehicle driving safety and is thus regarded as risk information.

[0128] In the embodiments of the present application, the cloud server compares the calculated pothole area and the maximum distance with the preset area threshold and distance threshold. If the pothole area is greater than the preset area threshold or the maximum distance is greater than the preset distance threshold, the cloud server will determine that the pothole is in a potentially dangerous state.

[0129] Finally, when these conditions are met, the cloud server marks the pothole image as risk information. This indicates that the pothole poses a threat to driving safety and needs to be processed through risk warnings or other emergency measures.

[0130] In the above embodiments, by analyzing based on the area and maximum distance of the pothole image, the size and shape of the pothole on the road surface can be accurately quantified, which can help accurately evaluate the severity of the pothole, thereby identifying areas that pose a potential threat to vehicle safety. If the area of the pothole is too large or the maximum distance of the pothole exceeds the preset threshold, it can be determined that the pothole is a high-risk area, thus effectively screening out potholes that may cause vehicle damage or driving hazards.

[0131] In an exemplary embodiment, as Figure 7 shown, the above embodiment "determine the pothole area based on the pothole image" includes steps 501 to 502. Among them:

[0132] Step 501, map the pothole image onto a geographic grid and determine the number of grids covered by the shadow area projected onto the geographic grid, where each grid in the geographic grid has the same geographic area.

[0133] Among them, a geographic grid refers to dividing a target driving section into multiple regular cells (i.e., grids) with the same geographic area according to a geographic coordinate system. The size of each grid is preset and is usually related to the scale and resolution of the map.

[0134] Projection refers to the process of mapping an object or image in three-dimensional space onto a two-dimensional plane. In the embodiments of the present application, the pothole image is "projected" onto the geographic grid, indicating that the image is transformed into the form of a geographic grid for subsequent analysis. This projection process ensures that the pothole image is aligned with the geographic coordinates so that it can be matched with the cells in the geographic grid.

[0135] The shadow area refers to the shadow region generated after the pothole image is projected onto the geographic grid. This area corresponds to the geographical location of the pothole in the geographic grid and represents the area covered by the pothole image on the geographic grid. The shadow area refers to the area where the image is projected onto the grid and can reflect the geographical distribution of the potholes.

[0136] The number of grids refers to the number of grids covered by the shadow area after the pothole image is projected in the geographic grid. Each grid has the same geographic area, so the number of grids covered reflects the size and scope of the pothole image in the geographical area. The more grids there are, the larger the scope of the pothole.

[0137] In the embodiments of the present application, first, the cloud server transforms the pothole image into an area that can be precisely corresponded in the geographic coordinate system to ensure that the image is precisely aligned with the geographical location.

[0138] Next, the cloud server determines the shadow area covered by the pothole image according to the projection of the pothole image on the geographic grid and further calculates the number of grids involved. In this process, the cloud server determines the number of grids according to whether multiple grids are covered by the pothole image. For example, assuming that the area of each geographic grid is 1 square kilometer, the cloud server will calculate the shadow area of the pothole image to see how many grids it covers. If the image only partially covers a grid, then according to the preset rules, the cloud server may count this grid as a covered grid. The calculation rules may include:

[0139] (1) Full coverage: If the pothole image completely covers a grid, then this grid will be included in the covered area and counted as one grid.

[0140] (2) Partial coverage: Even if the pothole image only covers a part of a grid, as long as it covers any area of the grid, this grid will also be counted as a covered grid. For example, if the pothole image covers more than 50% of the grid, this grid will be regarded as covered.

[0141] (3) Coverage exceeding a certain ratio: The cloud server sets a threshold for the coverage ratio. For example, if the covered grid area exceeds 50%, the grid is considered covered. If the shadow area of the pothole image covers half or more of a certain grid, the grid will be counted as a valid grid.

[0142] Step 502, determine the pothole area based on the number of grids.

[0143] In the embodiment of the present application, when calculating the total area of the pothole, the cloud server first needs to determine two key pieces of information: the area of each geographical grid and the number of grids covered by the pothole image. Among them, the area of each geographical grid is fixed, usually a preset value, such as 1 square meter or 1 square kilometer, etc. The pothole image is docked with the geographical grid through projection, and the cloud server counts the number of grids covered by the image. According to the preset rules, even if the pothole image only covers part of the grids, as long as there is overlap, the cloud server will count it as a covered grid.

[0144] After determining the number of grids covered by the pothole image, the cloud server multiplies the number of covered grids by the geographical area of each grid to obtain the pothole area.

[0145] In the above embodiment, by analyzing the area and the maximum edge distance of the pothole, the size and shape of the pothole can be accurately measured. This kind of data analysis based on quantification can more accurately evaluate the possible impact of the pothole on vehicle safety. For example, a large-area and deep pothole or a pothole with a large width may cause greater damage to the vehicle, while a small-area and shallow pothole is less harmful. Therefore, the determination based on these criteria can more accurately identify potholes with high risks, thereby effectively reducing potential safety hazards.

[0146] In an exemplary embodiment, the above target driving state information includes speed information, and the speed information includes wheel rotation speed and vehicle speed. On this basis, the above embodiment "determine that the target driving state information in the state information is risk information" includes:

[0147] Step C, when the speed information of the historical vehicle exceeds a preset condition, determine that the speed information is risk information.

[0148] Among them, the preset conditions include at least one of the following:

[0149] The vehicle speed of the historical vehicle exceeds the first preset speed threshold;

[0150] The wheel rotation speeds of any two wheels exceed the preset rotation speed threshold;

[0151] The rotation speed of any one wheel exceeds the vehicle speed.

[0152] Vehicle speed refers to the speed at which the entire vehicle travels, that is, the forward speed of the vehicle. Wheel speed refers to the rotational speed of each wheel of the vehicle. The rotational speed of each wheel is usually measured by a sensor on the wheel. Wheel speed can be used to estimate vehicle speed and can also reflect problems such as whether the wheel has a fault or slips.

[0153] The rotational speed threshold refers to the boundary value indicating a potential risk when the wheel speed exceeds a certain standard. The rotational speed threshold can be set according to factors such as the vehicle design, environmental conditions, and traffic rules.

[0154] The vehicle speed threshold refers to the speed that is considered dangerous when the vehicle speed exceeds a certain preset value. The vehicle speed threshold is usually set according to road type, traffic conditions, and safety regulations. Overspeed driving may pose a significant traffic safety risk. The vehicle speed threshold can be set to 5 Km / h.

[0155] In the embodiments of the present application, after receiving the speed and wheel speed information of historical vehicles, the cloud server will check one by one whether this information meets the preset conditions. If the speed or wheel speed of the historical vehicle exceeds the preset threshold, the cloud server will identify this information as risk information. At this time, the cloud server will confirm that this risk information indicates that the vehicle has an abnormal driving condition, such as overspeed driving or wheel slipping, which may cause potential safety hazards.

[0156] In some embodiments, assuming that the rotational speeds of the four wheels are N1, N2, N3, and N4 respectively, by comparing the different rotational speeds of the four wheels, if the rotational speeds of any two wheels exceed a certain value (for example, N1 - N2 > 20 r / min, this value can be calibrated) and the deviation between the rotational speed of any current wheel and the current vehicle speed is large (for example: the vehicle speed is 5 km / h and the rotational speed is 10 r / min, this value can be calibrated), it is determined that the vehicle is on a slippery road section at this time; that is, this speed information is risk information.

[0157] In the above embodiments, when the speed or wheel speed of the historical vehicle exceeds the preset conditions, the cloud server can identify potential dangers in real time and send out warning information in a timely manner. Through accurate monitoring of the preset conditions, feedback can be provided at the early stage of the problem occurrence, reducing accidents caused by excessive speed or uneven wheel speeds.

[0158] In an exemplary embodiment, the above target driving state information includes headlight information. On this basis, the above embodiment "determining that the target driving state information in the state information is risk information" includes:

[0159] Step D, when the headlight information indicates that the vehicle speed of the historical vehicle exceeds the second preset speed threshold and the flashing lights of the historical vehicle have been lit for more than the preset time threshold, determining that the headlight information is risk information.

[0160] Among them, the vehicle light information refers to the working status of the vehicle lights, usually including the switch status and lighting duration of the headlights, taillights, turn signals, hazard lights, etc. The vehicle light information can reflect the visibility of the vehicle during driving and whether there is an emergency. For example, the hazard lights are often used to indicate that an emergency has occurred in the vehicle, and special attention from other vehicles may be required.

[0161] The second preset speed threshold is a set upper speed limit. When the speed of the historical vehicle exceeds this threshold, it indicates that the vehicle may have a relatively high driving risk. For example, if a historical vehicle is speeding, it may increase the risk of traffic accidents.

[0162] The hazard lights refer to the emergency warning lights of the vehicle (also known as hazard lights), which play a warning role in traffic and are usually used to indicate that the vehicle may be in a stationary, faulty or other abnormal state.

[0163] The lighting time refers to the duration from when the hazard lights are turned on to when they are turned off. This time period can reflect the severity of the emergency state or problem experienced by the vehicle. If the hazard lights are lit for too long, it may indicate that the vehicle has been in a dangerous state for a long time or may be dealing with an emergency.

[0164] In the embodiments of the present application, the cloud server obtains the vehicle light information from the vehicle light control module of the historical vehicle. Among them, the vehicle light information includes the status of the hazard lights and the lighting time. The vehicle speed information reflects the driving speed of the vehicle, and the status of the hazard lights shows whether the vehicle is passively in a situation of abnormal vehicle speed. The cloud server then detects whether the speed of the historical vehicle exceeds the preset second preset speed threshold. If the speed exceeds this threshold, it indicates that the vehicle may have a risk of driving too fast.

[0165] At the same time, the cloud server monitors the status of the hazard lights and records the turn-on time of the hazard lights. If the turn-on time of the hazard lights exceeds the preset time threshold, it means that the historical vehicle has exceeded the second preset speed threshold within the preset time threshold. Next, the cloud server checks both conditions at the same time: that is, whether the speed of the historical vehicle exceeds the second preset speed threshold, and whether the hazard lights have been lit for more than the preset time threshold. When both of these conditions are met, the cloud server determines the vehicle light information as risk information.

[0166] In some embodiments, the cloud server can also only detect the speed of the historical vehicle. During the monitoring process, if the speed of the historical vehicle still continuously exceeds the second preset speed threshold when it exceeds the preset time threshold, the cloud server regards this speed as an abnormal situation and marks it as risk information.

[0167] In the above embodiments, by combining the information of vehicle speed and the status of hazard lights, it is possible to evaluate the driving status of historical vehicles from multiple dimensions. For example, relying solely on vehicle speed may not fully reflect the actual degree of danger of the vehicle, and relying solely on the lighting time of the hazard lights may not fully consider the vehicle speed and driving environment. By combining these two pieces of information, it is possible to more accurately judge the abnormal speeding behavior of historical vehicles and make an effective response to the surrounding environment. This multi-dimensional monitoring method improves the accuracy of risk judgment, thereby effectively reducing the probability of accidents.

[0168] In an exemplary embodiment, the above target driving status information includes tire pressure information. On this basis, the above embodiment of "determining that the target driving status information in the status information is risk information" includes:

[0169] Step E, within a second preset duration, if the number of historical vehicles with tire pressure failures is greater than a preset number threshold, the tire pressure information is determined as risk information.

[0170] Among them, a tire pressure failure refers to an abnormal tire pressure of the vehicle, such as too low or too high tire pressure. A tire pressure failure may cause the vehicle to drive unstably and even increase the risk of accidents.

[0171] The preset number threshold specifies the maximum allowable value of the number of historical vehicles with tire pressure failures. If the number of historical vehicles with tire pressure failures exceeds this threshold within the set time period, it is considered that this phenomenon poses a greater risk, and the tire pressure information is regarded as risk information.

[0172] Tire pressure information refers to the relevant data on the tire pressure of the vehicle, which can be used to judge whether there is a tire pressure failure and help predict and prevent possible traffic accidents.

[0173] In the embodiments of the present application, at the beginning of this process, it is first necessary to set a second preset duration to monitor and count the tire pressure failure situations of vehicles during this period. This time period can be flexibly adjusted according to requirements, such as a few minutes or a few hours, with the aim of ensuring that tire pressure abnormalities within a reasonable period of time can be captured.

[0174] Next, the cloud server checks each piece of tire pressure information of all historical vehicles collected within the second preset duration one by one to determine whether there is a tire pressure failure, that is, whether the tire pressure is lower or higher than the safe range. When it is found that the tire pressure information of a certain vehicle is abnormal, the cloud server will mark it as having a tire pressure failure.

[0175] As data collection progresses, the cloud server will count the historical number of vehicles with tire pressure faults. Within the second preset time period, if multiple vehicles have tire pressure problems in a certain area or section, the cloud server will accumulate these fault events to obtain a total number of faulty vehicles. Subsequently, the cloud server will compare the number of faulty vehicles with a preset quantity threshold.

[0176] When the detected number of vehicles with tire pressure faults exceeds the preset quantity threshold, it indicates that this phenomenon is not accidental but represents a potential safety risk. In this case, the cloud server will calibrate the tire pressure information during this period as risk information, indicating that the tire pressure problem in this area may pose a threat to other vehicles.

[0177] In the above embodiment, by monitoring and analyzing the tire pressure faults of historical vehicles in a short period of time, the cloud server can identify some potential dangerous areas. If multiple vehicles have tire pressure faults in the same area and at the same time period, it may indicate that there are some environmental factors affecting vehicle tire pressure in this area, such as extreme weather, heavily polluted roads, poor road quality, etc. The cloud server can focus on monitoring this area based on this information and issue a risk warning to remind other passing vehicles to take preventive measures to reduce the likelihood of similar faults occurring.

[0178] In one embodiment, a risk prompt method is provided, including:

[0179] Step 1, obtain the status information of historical vehicles in the target driving section, where the status information includes: driving position and driving status information corresponding to the driving position.

[0180] Step 2, map the pothole image onto a geographical grid, and determine the number of grids covered by the shadow area projected on the geographical grid, where the geographical area of each grid in the geographical grid is the same.

[0181] Step 3, determine the pothole area based on the number of grids.

[0182] Step 4, determine the distance between any two points on the edge of the pothole image based on the pothole image.

[0183] Step 5, perform a comparison process on the distance between any two points on the edge of the pothole image to determine the maximum distance.

[0184] Step 6, in the case where the pothole area is greater than the preset area threshold and / or the maximum distance is greater than the preset distance threshold, determine that the pothole image is risk information.

[0185] Step 7, in the case where the pothole image in the driving status information is determined to be risk information, obtain the map information of the target driving section.

[0186] Step 8: Match the driving position corresponding to the pothole image with the map information to determine the risk position.

[0187] Step 9: Corresponding save the risk position and the time information of collecting the pothole image.

[0188] Step 10: If it is determined that the risk position is valid according to the time information of collecting the pothole image and the first preset duration, output a risk prompt message to the target vehicle. The risk prompt message includes the risk position, and the driving distance between the target vehicle and the target driving section is less than the preset distance.

[0189] In one embodiment, a risk prompt method is provided, including:

[0190] Step 1: Obtain the status information of historical vehicles in the target driving section. The status information includes: driving position, and driving status information corresponding to the driving position.

[0191] Step 2: When the speed information of the historical vehicle exceeds the preset condition, determine the speed information as risk information; wherein, the preset condition includes at least one of the following:

[0192] The vehicle speed of the historical vehicle exceeds the first preset speed threshold;

[0193] The rotational speeds of any two wheels exceed the preset rotational speed threshold;

[0194] The rotational speed of any one wheel exceeds the vehicle speed.

[0195] Step 3: When it is determined that the speed information in the driving status information is risk information, obtain the map information in the target driving section.

[0196] Step 4: Match the driving position corresponding to the speed information with the map information to determine the risk position.

[0197] Step 5: Corresponding save the risk position and the time information of collecting the speed information.

[0198] Step 6: If it is determined that the risk position is valid according to the time information of collecting the speed information and the first preset duration, output a risk prompt message to the target vehicle. The risk prompt message includes the risk position, and the driving distance between the target vehicle and the target driving section is less than the preset distance.

[0199] In one embodiment, a risk prompt method is provided, including:

[0200] Step 1: Obtain the status information of historical vehicles in the target driving section. The status information includes: driving position, and driving status information corresponding to the driving position.

[0201] Step 2, when the vehicle speed indicated by the headlight information of the historical vehicle exceeds the second preset speed threshold and the time when the hazard lights of the historical vehicle are lit exceeds the preset time threshold, determine the headlight information risk information.

[0202] Step 3, when it is determined that the headlight information in the driving state information is risk information, obtain the map information within the target driving section.

[0203] Step 4, match the driving position corresponding to the headlight information with the map information to determine the risk position.

[0204] Step 5, correspondingly save the risk position and the time information of collecting the headlight information.

[0205] Step 6, if it is determined that the risk position is valid according to the time information of collecting the headlight information and the first preset duration, output a risk prompt message to the target vehicle. The risk prompt message includes the risk position, and the driving distance between the target vehicle and the target driving section is less than the preset distance.

[0206] In one embodiment, a risk prompt method is provided, including:

[0207] Step 1, obtain the state information of the historical vehicle within the target driving section. The state information includes: the driving position and the driving state information corresponding to the driving position.

[0208] Step 2, when the number of historical vehicles with tire pressure failures within the second preset duration is greater than the preset number threshold, determine the tire pressure information as risk information.

[0209] Step 3, when it is determined that the tire pressure information in the driving state information is risk information, obtain the map information within the target driving section.

[0210] Step 4, match the driving position corresponding to the tire pressure information with the map information to determine the risk position.

[0211] Step 5, correspondingly save the risk position and the time information of collecting the tire pressure information.

[0212] Step 6, if it is determined that the risk position is valid according to the time information of collecting the tire pressure information and the first preset duration, output a risk prompt message to the target vehicle. The risk prompt message includes the risk position, and the driving distance between the target vehicle and the target driving section is less than the preset distance.

[0213] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0214] Based on the same inventive concept, an embodiment of the present application further provides a risk prompt device for implementing the risk prompt method involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the risk prompt device provided below can refer to the limitations on the risk prompt method in the above text, and will not be repeated here.

[0215] In an exemplary embodiment, as Figure 8 shown, a risk prompt device is provided, including: an information acquisition module 601, a position determination module 602, and an information output module 603, where:

[0216] The information acquisition module 601 is used to acquire the status information of historical vehicles in the target driving section, and the status information includes: driving position and driving status information corresponding to the driving position;

[0217] The position determination module 602 is used to determine the driving position corresponding to the target driving status information as the risk position of the historical vehicle in the target driving section when it is determined that the target driving status information in the driving status information is risk information;

[0218] The information output module 603 is used to output a risk prompt message to the target vehicle, and the risk prompt message includes the risk position, and the driving distance between the target vehicle and the target driving section is less than a preset distance.

[0219] In an exemplary embodiment, the above position determination module 602 is specifically used to acquire the map information in the target driving section; match the driving position corresponding to the target driving status information with the map information to determine the risk position.

[0220] In an exemplary embodiment, the above information output module 603 is specifically used to output a risk prompt message to the target vehicle if it is determined that the risk position is valid according to the time information for collecting the target driving status information and the first preset duration.

[0221] In an exemplary embodiment, the above-mentioned position determination module 602 is specifically configured to determine the pothole area and the distance between any two points on the edge of the pothole image based on the pothole image;

[0222] Perform a comparison process on the distance between any two points on the edge of the pothole image to determine the maximum distance;

[0223] In the case where the pothole area is greater than a preset area threshold and / or the maximum distance is greater than a preset distance threshold, determine that the pothole image is risk information.

[0224] In an exemplary embodiment, the above-mentioned position determination module 602 is specifically configured to map the pothole image onto a geographic grid and determine the number of grids covered by the shadow area projected on the geographic grid, where the geographic area of each grid in the geographic grid is the same;

[0225] Determine the pothole area based on the number of grids.

[0226] In an exemplary embodiment, the above-mentioned position determination module 602 is specifically configured to determine that the speed information is risk information when the speed information of the historical vehicle exceeds a preset condition; where the preset condition includes at least one of the following:

[0227] The vehicle speed of the historical vehicle exceeds a first preset speed threshold;

[0228] The rotational speeds of any two wheels exceed a preset rotational speed threshold;

[0229] The rotational speed of any one wheel exceeds the vehicle speed.

[0230] In an exemplary embodiment, the above-mentioned position determination module 602 is specifically configured to determine that the headlight information is risk information when the headlight information indicates that the vehicle speed of the historical vehicle exceeds a second preset speed threshold and the hazard warning lights of the historical vehicle have been lit for more than a preset time threshold.

[0231] In an exemplary embodiment, the above-mentioned position determination module 602 is specifically configured to determine the tire pressure information as risk information when the number of historical vehicles with tire pressure failures within a second preset duration is greater than a preset number threshold.

[0232] Each module in the above-mentioned risk warning device can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned each module.

[0233] In an exemplary embodiment, a computer device is provided. The computer device may be a cloud server, and its internal structure diagram may be as shown in Figure 9 . The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data during the risk warning process. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a risk warning method.

[0234] Those skilled in the art can understand that Figure 9 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0235] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0236] Obtain the status information of historical vehicles in the target driving section. The status information includes: driving position, and driving status information corresponding to the driving position;

[0237] In the case where it is determined that the target driving status information in the driving status information is risk information, determine the driving position corresponding to the target driving status information as the risk position of the historical vehicle in the target driving section;

[0238] Output a risk warning message to the target vehicle. The risk warning message includes the risk position, and the driving distance between the target vehicle and the target driving section is less than a preset distance.

[0239] In an embodiment, when the processor executes the computer program, the following steps are further implemented:

[0240] Obtain the map information in the target driving section;

[0241] Match the driving position corresponding to the target driving state information with the map information to determine the risk position.

[0242] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0243] Correspondingly save the risk position and the time information of collecting the target driving state information;

[0244] If it is determined that the risk position is valid according to the time information of collecting the target driving state information and the first preset duration, output a risk prompt information to the target vehicle.

[0245] In one embodiment, the target driving state information includes a pothole image. When the processor executes the computer program, the following steps are further implemented:

[0246] Based on the pothole image, determine the pothole area and the distance between any two points on the edge of the pothole image;

[0247] Perform a comparison process on the distance between any two points on the edge of the pothole image to determine the maximum distance;

[0248] In the case where the pothole area is greater than the preset area threshold and / or the maximum distance is greater than the preset distance threshold, determine that the pothole image is risk information.

[0249] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0250] Map the pothole image onto a geographic grid, and determine the number of grids covered by the shadow area projected on the geographic grid, where the geographic area of each grid in the geographic grid is the same;

[0251] Based on the number of grids, determine the pothole area.

[0252] In one embodiment, the target driving state information includes speed information, and the speed information includes wheel rotation speed and vehicle speed. When the processor executes the computer program, the following steps are further implemented:

[0253] In the case where the speed information of the historical vehicle exceeds the preset conditions, determine that the speed information is risk information; where the preset conditions include at least one of the following:

[0254] The vehicle speed of the historical vehicle exceeds the first preset speed threshold;

[0255] The wheel rotation speeds of any two wheels exceed the preset rotation speed threshold;

[0256] The rotation speed of any one wheel exceeds the vehicle speed.

[0257] In one embodiment, the target driving state information includes headlight information. When the processor executes the computer program, the following steps are further implemented:

[0258] When the vehicle lamp information indicates that the speed of the historical vehicle exceeds the second preset speed threshold and the time for which the hazard warning lights of the historical vehicle are lit exceeds the preset time threshold, determine the risk information of the vehicle lamp information.

[0259] In one embodiment, the target driving state information includes tire pressure information, and when the processor executes the computer program, the following steps are further implemented:

[0260] When the number of historical vehicles with tire pressure failures within the second preset duration is greater than the preset number threshold, determine the tire pressure information as risk information.

[0261] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0262] Obtain the state information of historical vehicles within the target driving section, where the state information includes: driving position, and driving state information corresponding to the driving position;

[0263] When it is determined that the target driving state information in the driving state information is risk information, determine the driving position corresponding to the target driving state information as the risk position of the historical vehicle within the target driving section;

[0264] Output a risk prompt message to the target vehicle, where the risk prompt message includes the risk position, and the driving distance between the target vehicle and the target driving section is less than the preset distance.

[0265] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0266] Obtain the map information within the target driving section;

[0267] Match the driving position corresponding to the target driving state information with the map information to determine the risk position.

[0268] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0269] Correspondingly save the risk position and the time information of the moment when the target driving state information is collected;

[0270] If it is determined that the risk position is valid according to the time information of the moment when the target driving state information is collected and the first preset duration, output a risk prompt message to the target vehicle.

[0271] In one embodiment, the target driving state information includes a pothole image, and when the computer program is executed by the processor, the following steps are further implemented:

[0272] Based on the pothole image, determine the pothole area and the distance between any two points on the edge of the pothole image;

[0273] Perform a comparison process on the distance between any two points on the edge of the pothole image to determine the maximum distance;

[0274] In the case where the pothole area is greater than a preset area threshold and / or the maximum distance is greater than a preset distance threshold, determine that the pothole image is risk information.

[0275] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0276] Map the pothole image onto a geographic grid, and determine the number of grids covered by the shadow area projected onto the geographic grid, where the geographic area of each grid in the geographic grid is the same;

[0277] Based on the number of grids, determine the pothole area.

[0278] In one embodiment, the target driving state information includes speed information, and the speed information includes wheel rotation speed and vehicle speed. When the computer program is executed by a processor, the following steps are further implemented:

[0279] In the case where the speed information of a historical vehicle exceeds a preset condition, determine that the speed information is risk information; where the preset condition includes at least one of the following:

[0280] The vehicle speed of the historical vehicle exceeds a first preset speed threshold;

[0281] The wheel rotation speeds of any two wheels exceed a preset rotation speed threshold;

[0282] The rotation speed of any one wheel exceeds the vehicle speed.

[0283] In one embodiment, the target driving state information includes headlight information. When the computer program is executed by a processor, the following steps are further implemented:

[0284] In the case where the headlight information indicates that the vehicle speed of the historical vehicle exceeds a second preset speed threshold and the flashing hazard lights of the historical vehicle are lit for a time exceeding a preset time threshold, determine that the headlight information is risk information.

[0285] In one embodiment, the target driving state information includes tire pressure information. When the computer program is executed by a processor, the following steps are further implemented:

[0286] In the case where the number of historical vehicles with tire pressure failures within a second preset duration is greater than a preset number threshold, determine the tire pressure information as risk information.

[0287] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0288] Obtain the status information of historical vehicles within the target driving section, where the status information includes: driving position and the corresponding driving status information of the driving position;

[0289] When it is determined that the target driving status information in the driving status information is risk information, determine the driving position corresponding to the target driving status information as the risk position of the historical vehicle within the target driving section;

[0290] Output a risk prompt message to the target vehicle, where the risk prompt message includes the risk position, and the driving distance between the target vehicle and the target driving section is less than a preset distance.

[0291] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0292] Obtain the map information within the target driving section;

[0293] Match the driving position corresponding to the target driving status information with the map information to determine the risk position.

[0294] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0295] Correspondingly save the risk position and the time information of collecting the target driving status information;

[0296] If it is determined that the risk position is valid according to the time information of collecting the target driving status information and the first preset duration, output a risk prompt message to the target vehicle.

[0297] In one embodiment, the target driving status information includes a pothole image, and when the computer program is executed by a processor, the following steps are further implemented:

[0298] Based on the pothole image, determine the pothole area and the distance between any two points on the edge of the pothole image;

[0299] Perform a comparison process on the distance between any two points on the edge of the pothole image to determine the maximum distance;

[0300] When the pothole area is greater than the preset area threshold and / or the maximum distance is greater than the preset distance threshold, determine that the pothole image is risk information.

[0301] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0302] Map the pothole image onto a geographic grid, and determine the number of grids covered by the shadow area projected onto the geographic grid, where the geographic area of each grid in the geographic grid is the same;

[0303] Determine the pothole area based on the number of grids.

[0304] In one embodiment, the target driving state information includes speed information, and the speed information includes wheel speed and vehicle speed. When the computer program is executed by the processor, the following steps are further implemented:

[0305] When the speed information of the historical vehicle exceeds a preset condition, determine the speed information as risk information; wherein, the preset condition includes at least one of the following:

[0306] The vehicle speed of the historical vehicle exceeds the first preset speed threshold;

[0307] The wheel speeds of any two wheels exceed the preset speed threshold;

[0308] The wheel speed of any one wheel exceeds the vehicle speed.

[0309] In one embodiment, the target driving state information includes headlight information. When the computer program is executed by the processor, the following steps are further implemented:

[0310] When the headlight information indicates that the vehicle speed of the historical vehicle exceeds the second preset speed threshold and the flashing lights of the historical vehicle are lit for more than the preset time threshold, determine the headlight information as risk information.

[0311] In one embodiment, the target driving state information includes tire pressure information. When the computer program is executed by the processor, the following steps are further implemented:

[0312] When the number of historical vehicles with tire pressure failures within the second preset duration is greater than the preset number threshold, determine the tire pressure information as risk information.

[0313] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0314] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0315] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in the present application.

[0316] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A risk warning method, characterized in that: The method comprises: Acquire historical vehicle status information in the target driving section, the status information including: driving position, and driving status information corresponding to the driving position; In the case where it is determined that the target driving state information in the driving state information is risk information, determining the driving position corresponding to the target driving state information as the risk position of the historical vehicle in the target driving section; Outputting risk warning information to the target vehicle, the risk warning information including the risk position, and the driving distance between the target vehicle and the target driving section is less than a preset distance.

2. The method according to claim 1, characterized in that The step of determining the driving position corresponding to the target driving state information as the risk position of the historical vehicle in the target driving section includes: Obtain map information within the target driving section; The driving position corresponding to the target driving state information is matched with the map information to determine the risk position.

3. The method according to claim 1, characterized in that After determining the risk position of the historical vehicle in the target driving section, the method further includes: Correspondingly storing the risk location and the time information of collecting the target driving status information; The outputting risk warning information to the target vehicle includes: If the risk position is determined to be valid based on the time information of collecting the target driving state information and the first preset time length, risk warning information is output to the target vehicle.

4. The method according to any one of claims 1 to 3, characterized in that: The target driving state information includes a pothole image, and determining that the target driving state information in the state information is risk information includes: Based on the pit image, determining the pit area and the distance between any two points on the edge of the pit image; Comparing the distances between any two points on the edge of the pit image to determine the maximum distance; When the pit area is greater than a preset area threshold, and / or the maximum distance is greater than a preset distance threshold, the pit image is determined to be the risk information.

5. The method according to claim 4, characterized in that The determining the pit area based on the pit image includes: Mapping the pit image onto a geographic grid, and determining the number of grids covered by a shadow area projected onto the geographic grid, wherein the geographic area of ​​each grid in the geographic grid is the same; Based on the grid number, the pit area is determined.

6. The method according to any one of claims 1 to 3, characterized in that: The target driving state information includes speed information, and the speed information includes wheel speed and vehicle speed. The determining that the target driving state information in the state information is risk information includes: When the speed information of the historical vehicle exceeds a preset condition, the speed information is determined to be the risk information; wherein the preset condition includes at least one of the following: The speed of the historical vehicle exceeds a first preset speed threshold; The wheel speeds of any two wheels exceed a preset speed threshold; The rotation speed of any one of the wheels exceeds the vehicle speed.

7. The method according to any one of claims 1 to 3, characterized in that: The target driving state information includes vehicle light information, and determining that the target driving state information in the state information is risk information includes: When the vehicle light information indicates that the speed of the historical vehicle exceeds a second preset speed threshold, and the hazard lights of the historical vehicle are turned on for a time that exceeds a preset time threshold, the risk information of the vehicle light information is determined.

8. The method according to any one of claims 1 to 3, characterized in that: The target driving state information includes tire pressure information, and determining that the target driving state information in the state information is risk information includes: If the number of historical vehicles that have experienced tire pressure failures within a second preset time period is greater than a preset number threshold, the tire pressure information is determined as the risk information.

9. A risk warning device, characterized in that: The device comprises: An information acquisition module is used to acquire the status information of historical vehicles in the target driving section, wherein the status information includes: a driving position and driving status information corresponding to the driving position; a position determination module, for determining, when determining that target driving state information in the driving state information is risk information, a driving position corresponding to the target driving state information as a risk position of the historical vehicle within the target driving section; The information output module is used to output risk warning information to the target vehicle, wherein the risk warning information includes the risk position, and the driving distance between the target vehicle and the target driving section is less than a preset distance.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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