Vehicle accident early warning method and device, electronic equipment and computer storage medium

Abstract

The invention discloses a vehicle accident early warning method and device, electronic equipment and a computer storage medium. The method comprises the following steps: receiving a real-time fault data packet uploaded by a vehicle terminal; carrying out statistical analysis on the received fault data packets, identifying accident-prone areas according to the fault occurrence frequency and position distribution characteristics, and determining a risk driving parameter threshold value corresponding to each accident-prone area; the identified accident-prone area and the corresponding risk driving parameter threshold value are sent to each vehicle terminal for acquiring the real-time driving parameter of the vehicle when the vehicle enters the accident-prone area; comparing the obtained real-time driving parameter with a risk driving parameter threshold value of the corresponding area; and when the real-time driving parameter exceeds the risk driving parameter threshold, triggering an early warning instruction of a corresponding level according to the exceeding amplitude. The problems that in the prior art, an accident-prone area cannot be dynamically recognized through a manual warning mode, and early warning cannot be conducted in combination with real-time vehicle conditions can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle accident warning technology, and in particular to a vehicle accident warning method, device, electronic equipment and computer storage medium. Background Art

[0002] Existing technologies mainly rely on manual installation of fixed warning signs in accident-prone areas or reminding drivers through experience, and their technical implementation only remains at the static warning level. Specifically, although the electronic control unit (ECU) can detect vehicle faults in real time through sensors (such as knock sensors) and generate specific fault codes (such as P0325) to locate the fault, this function is limited to the vehicle's own fault diagnosis. It is neither associated with geographic location information (GPS / Beidou coordinates) nor forms multi-vehicle data linkage. Existing solutions cannot dynamically identify accident risk areas caused by changes in road conditions (such as sharp turns and steep slopes) or driving behaviors (such as speeding and sudden braking). There is also a lack of active warning mechanisms based on real-time vehicle conditions (vehicle speed, steering angle, etc.), resulting in delayed and blind accident prevention. Summary of the Invention

[0003] In response to the above technical problems, the embodiments of the present application provide a vehicle accident warning method, device, electronic device and computer storage medium, aiming to solve the problem in the existing technology that manual warning methods cannot dynamically identify accident-prone areas and cannot provide warnings based on real-time vehicle conditions.

[0004] A first aspect of an embodiment of the present application provides a vehicle accident warning method, comprising:

[0005] Receive real-time fault data packets uploaded by the vehicle terminal, including fault codes generated by the vehicle's electronic control unit, satellite positioning coordinates of the vehicle's current location, and the vehicle's current driving parameters;

[0006] Perform statistical analysis on received fault data packets, identify accident-prone areas based on the frequency and location distribution of faults, and determine the risk driving parameter thresholds corresponding to each accident-prone area;

[0007] The identified accident-prone areas and their corresponding risk driving parameter thresholds are sent to each vehicle terminal for the following purposes: when a vehicle enters an accident-prone area, the real-time driving parameters of the vehicle are obtained; the obtained real-time driving parameters are compared with the risk driving parameter thresholds of the corresponding area; when the real-time driving parameters exceed the risk driving parameter thresholds, the corresponding level of warning instructions is triggered according to the extent of the excess.

[0008] In one embodiment of the present application, it further includes:

[0009] The vehicle gateway's diagnostic program broadcasts a unified diagnostic service command on the controller area network bus in a loop. Each on-board electronic control unit responds to the command and returns a fault code in a standard format.

[0010] Compare the current fault code with the historical fault code sequence to identify whether it is a new fault code;

[0011] When a new fault code is identified, the vehicle's current location coordinates and current driving parameters are obtained, and the fault data packet is packaged and uploaded.

[0012] In one embodiment of the present application, the driving parameters include at least one of vehicle speed, steering angle, brake status, gear position and vehicle voltage; the satellite positioning coordinates are obtained through a satellite positioning module, and the positioning system used includes the Global Positioning System, Beidou Satellite Navigation System, GLONASS Satellite Navigation System or Galileo Satellite Navigation System.

[0013] In one embodiment of the present application, determining the risk driving parameter threshold includes:

[0014] Perform statistical analysis on the location coordinates in the fault data packets to identify accident-prone areas;

[0015] For each accident-prone area, a risk driving parameter threshold is determined based on historical driving parameters. The risk driving parameter threshold includes at least one of a vehicle speed upper limit, a steering angle critical value, and a braking frequency warning value.

[0016] In one embodiment of the present application, the triggering of the graded warning instruction includes:

[0017] When the real-time driving parameter exceeds the first threshold, a first-level warning instruction is triggered;

[0018] When the real-time driving parameter exceeds the second threshold, a second-level warning instruction is triggered;

[0019] When the real-time driving parameter exceeds the third threshold, a third-level warning instruction is triggered.

[0020] In one embodiment of the present application, it further includes:

[0021] The risk driving parameter thresholds in accident-prone areas are updated regularly, wherein the updates include risk driving parameter thresholds for newly added fault types or risk driving parameter thresholds for different time periods.

[0022] In one embodiment of the present application, it further includes:

[0023] Determine whether the vehicle coordinates are within the boundary of the accident-prone area;

[0024] When the vehicle leaves the accident-prone area, the relevant monitoring and warning will be automatically terminated.

[0025] In a second aspect, an embodiment of the present application provides a vehicle accident warning device, comprising:

[0026] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory storing a plurality of instructions; a processor loading instructions from the memory to execute the steps of any one of the vehicle accident warning methods provided in the first aspect.

[0027] In a fourth aspect, an embodiment of the present application further provides a computer storage medium, which stores a plurality of instructions suitable for loading by a processor to execute the steps of any one of the vehicle accident warning methods provided in the first aspect.

[0028] In the embodiment provided by the embodiment of the present application, an intelligent early warning system based on the Internet of Vehicles is established, and the following technical path is adopted to solve the problem in the existing technology that the manual warning method cannot dynamically identify accident-prone areas and cannot provide early warnings in combination with real-time vehicle conditions: first, a real-time fault data packet uploaded by the vehicle terminal is received, and the fault data includes a fault code generated by the on-board electronic control unit, the satellite positioning coordinates of the vehicle's current position, and the vehicle's current driving parameters; the received fault data packet is statistically analyzed, and accident-prone areas are identified based on the frequency and location distribution characteristics of the faults, and the risk driving parameter threshold corresponding to each accident-prone area is determined; the identified accident-prone areas and their corresponding risk driving parameter thresholds are sent to each vehicle terminal for: when a vehicle enters an accident-prone area, the real-time driving parameters of the vehicle are obtained; the obtained real-time driving parameters are compared with the risk driving parameter thresholds of the corresponding area; when the real-time driving parameters exceed the risk driving parameter thresholds, a warning instruction of the corresponding level is triggered according to the excess magnitude.

[0029] This application breaks through the limitations of static prompts on traditional fixed warning signs and has the following beneficial effects: first, it enables dynamic discovery and updating of accident hotspots in response to changes in road conditions; second, it provides personalized warnings by combining correlation analysis between real-time driving behavior and regional accident characteristics; third, it forms a closed-loop system of "data collection-intelligent analysis-real-time intervention", which significantly improves the efficiency of accident prevention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 is a flow chart of a vehicle accident warning method provided in an embodiment of the present application;

[0032] Figure 2 This is an architectural diagram of the automobile remote diagnosis provided in an embodiment of the present application;

[0033] Figure 3 Schematic diagram of the structure of the vehicle accident warning device provided in the embodiment of the present application;

[0034] Figure 4 It is a schematic diagram of the internal structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0037] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0038] The following describes the relevant contents, concepts, meanings, technical issues, technical solutions, beneficial effects, etc. involved in the embodiments of this application.

[0039] The Electronic Control Unit (ECU) is the intelligent control core of each vehicle subsystem. It diagnoses the vehicle status by monitoring sensor data (such as engine speed, oxygen sensor voltage, etc.) in real time. When an abnormality is detected (such as excessive vibration of the knock sensor), the ECU will generate a standard fault code (such as P0325) and communicate with the vehicle gateway through the controller area network (CAN bus). In this application, the role of the ECU is to cooperate with the vehicle gateway diagnostic program to realize the automatic collection of fault codes, and trigger the synchronous upload of location coordinates and driving data when a new fault is detected, providing real-time data support for the background server to count accident-prone areas.

[0040] Currently, vehicle failures and accidents are often closely linked to road conditions or geographical conditions in specific areas. However, traditional manual warning methods (such as fixed warning signs) have significant limitations. They cannot dynamically identify and update accident-prone areas (such as newly built roads or temporary construction sections), and they lack the ability to monitor the real-time status of incoming vehicles. For example, when a design flaw on a curve frequently causes understeer accidents, static warning signs can only provide general warnings and cannot provide targeted warnings based on specific parameters such as vehicle speed and steering angle.

[0041] In view of this, the embodiments of the present application provide a vehicle accident warning method, device, electronic device and computer storage medium, which are based on vehicle networking technology and aim to automatically collect positioning data (such as GPS / Beidou coordinates) and driving parameters (such as speed / steering angle, etc.) when the vehicle fails, and intelligently analyze the characteristics of accident-prone areas by the cloud. When the vehicle enters a dangerous area, the current driving behavior is compared with the historical accident data in real time, and the matching degree is finally realized. Dynamic area identification and personalized warning are finally realized, thereby significantly improving the efficiency and timeliness of accident prevention.

[0042] Please refer to Figure 1 , Figure 1 Flowchart of a vehicle accident warning method provided in an embodiment of the present application. A vehicle accident warning method includes:

[0043] S101: Receive a real-time fault data packet uploaded by a vehicle terminal. The fault data includes a fault code generated by an on-board electronic control unit, satellite positioning coordinates of the vehicle's current location, and the vehicle's current driving parameters.

[0044] Optionally, the driving data includes at least one of vehicle speed (such as 0-250 km / h), steering angle (such as 0-90°), brake status (such as pressure 0-20 MPa), gear position (such as P / R / N / D) and vehicle voltage (such as 12V / 24V); the satellite positioning coordinates are obtained through the positioning module of the Global Positioning System (GPS), Beidou Satellite Navigation System (Beidou-3), GLONASS Satellite Navigation System (GLONASS) or Galileo Satellite Navigation System (Galileo).

[0045] Understandably, the diagnostic program in the vehicle gateway continuously sends diagnostic commands to all electronic control units (ECUs) via the controller area network (CAN bus) (e.g., using the functional addressing mode of the UDS unified diagnostic service). Upon receiving the command, each ECU returns a standard OBD fault code (e.g., P0325 knock sensor fault). The diagnostic program identifies new fault codes by comparing the current fault code sequence with historical fault code sequences. When a new fault code appears in a vehicle, the diagnostic program in the vehicle gateway immediately collects current driving data (including driving behavior parameters such as speed, steering angle, and braking status) and coordinates from multiple satellite systems (GPS / Beidou / GLONASS / Galileo), and packages and uploads this data to the backend server. By analyzing the fault data and location information uploaded by a large number of vehicles, the server automatically identifies accident-prone areas (e.g., a certain curve has a significantly higher accident rate at a speed of 60 km / h and a steering angle of 30°) and generates an accident feature database containing regional coordinates (indicating accident-prone areas), risky driving parameter thresholds, and typical failure modes.

[0046] As can be understood, the vehicle terminal collects and uploads a fault information package containing three key data types in real time: 1) a standardized fault code generated by the ECU (electronic control unit) (e.g., P0325, knock sensor fault), which identifies the type of vehicle anomaly; 2) real-time latitude and longitude coordinates obtained via a satellite positioning module (e.g., GPS / Beidou), which locates the fault location with millimeter-level accuracy; and 3) current driving dynamic parameters (including but not limited to vehicle speed, steering angle, braking frequency, etc.), which record the vehicle's state at the time of the fault. This step serves to establish a data mapping relationship between "fault signature - spatial location - driving state." Specifically, the fault signature is the standardized fault code generated by the ECU (e.g., P0325), which accurately characterizes the type of vehicle anomaly; the spatial location is the satellite positioning coordinates (e.g., longitude 116.404°, latitude 39.915°), which determine the absolute geographic location of the fault; and the driving state is the driving parameters (e.g., 60 km / h speed + 30° steering angle), which record the dynamic operating conditions at the time of the fault.

[0047] S102: Statistically analyzing the received fault data packets, identifying accident-prone areas based on the frequency and location distribution characteristics of the faults, and determining the risk driving parameter threshold corresponding to each accident-prone area.

[0048] Optionally, the backend server performs intelligent analysis and processing on the massive amount of fault data packets received (including fault codes, GPS / Beidou positioning coordinates and driving parameters). This can be achieved through the following technical means:

[0049] First, a spatial clustering algorithm (such as DBSCAN) is used to perform density analysis on the fault coordinate points. For example, a fault density of ≥5 times per square kilometer is used as a threshold. Areas within the longitude and latitude range where the fault frequency exceeds this threshold are identified and marked as accident-prone areas. The convex hull algorithm is then used to fit the regional boundary coordinate point set to form a polygonal geographic fence (such as the range of 39.91°-39.92° north latitude and 116.40°-116.41° east longitude).

[0050] Secondly, by analyzing the historical accident data of the area through machine learning models (such as random forests), a two-dimensional statistical method can be used to extract the threshold value of risk driving parameters: for continuous parameters (such as vehicle speed), the 90% percentile value (such as 60km / h) in the accident case is taken; for discrete parameters (such as steering angle), the mean ± 2 times the standard deviation (such as 30° ± 5°) is calculated. Finally, the risk driving parameter threshold value of each area is generated (including dimensions such as vehicle speed limit, steering angle critical value, braking frequency warning value, etc.). At the same time, the mapping weight between fault codes and driving parameters can be established. For example, the weight coefficient of the P0325 fault code is set to 0.8 when the vehicle speed exceeds the limit, and the weight coefficient is set to 0.5 when the steering angle exceeds the limit. The comprehensive risk index is calculated through weighting.

[0051] By constructing a three-dimensional correlation system of "fault characteristics-spatial position-driving status", traditional OBD fault diagnosis is expanded to spatiotemporal dynamic risk assessment. When the system detects that the vehicle's current position has entered an accident-prone area and the real-time driving parameters exceed the risk driving parameter threshold, a graded warning mechanism is triggered, achieving a technological leap from passive fault diagnosis to active accident prevention.

[0052] S103: The identified accident-prone areas and their corresponding risk driving parameter thresholds are sent to each vehicle terminal for the following purposes: when a vehicle enters an accident-prone area, the real-time driving parameters of the vehicle are obtained; the obtained real-time driving parameters are compared with the risk driving parameter thresholds of the corresponding area; when the real-time driving parameters exceed the risk driving parameter thresholds, a warning instruction of a corresponding level is triggered according to the extent of the excess.

[0053] As you can understand, when another vehicle detects, via satellite positioning (GPS / Beidou, etc.), that its real-time coordinates have entered a marked accident-prone area (e.g., the curve zone at longitude 116.404°±0.01° and latitude 39.915°±0.01°), the vehicle's real-time driving parameters (including speed, steering angle, braking frequency, etc.) are immediately retrieved through the onboard diagnostics program and compared with the risk driving parameter thresholds corresponding to the accident-prone area (e.g., speed > 60 km / h, steering angle > 30°). This system's purpose is to enable proactive safety intervention. If the real-time driving parameters exceed the risk driving parameter thresholds (e.g., a current speed of 62 km / h exceeds the preset speed limit of 60 km / h), a warning command of the appropriate level is triggered based on the magnitude of the excess (e.g., a voice prompt stating, "This curve is prone to accidents; slow down to below 55 km / h"). This provides driver behavior correction guidance before an accident occurs, significantly improving accident prevention response times compared to traditional static warning signs.

[0054] Optionally, the backend server dynamically optimizes and adjusts the risk driving parameter thresholds for all accident-prone areas on a preset cycle (e.g., weekly). These updates primarily encompass two aspects: First, new risk driving parameter threshold combinations are added based on newly added fault data. For example, if a new accident pattern is discovered in an accident-prone area at night, such as "heavy fog and vehicle speeds exceeding 50 km / h," this combination will be added as the risk driving parameter threshold. Second, the risk driving parameter thresholds are adjusted based on time-of-day characteristics. For example, for the same curved road section, the steering angle threshold may be set to 30° during the day and 25° at night. This step aims to continuously incorporate the latest accident data to adjust the risk driving parameter thresholds. This not only avoids threshold failure caused by factors such as road reconstruction and weather changes, but also provides differentiated safety standards for different time periods and climate conditions (e.g., automatically lowering the braking distance threshold in rainy and snowy weather), thereby improving the system's warning accuracy over time.

[0055] Optionally, by comparing the vehicle's GPS / Beidou positioning coordinates in real time with the geographic fence boundary defined by the background (such as the area of ​​longitude 116.404°±0.01° and latitude 39.915°±0.01°), real-time monitoring is triggered when the coordinates fall within the fence, and the vehicle's driving parameters (such as speed, steering angle) are continuously compared with the risk driving parameter threshold of the area (such as speed>60km / h). Once the vehicle coordinates move out of the fence, the system immediately stops data collection and early warning push. Its role is to avoid invalid monitoring of vehicles in non-risk areas (reducing system load), and to ensure full coverage of early warnings in high-risk areas (such as full monitoring of curves until they are completely driven away).

[0056] Optionally, generating a graded warning instruction includes:

[0057] When the real-time driving parameter exceeds the first preset threshold (such as vehicle speed > 60km / h or steering angle > 30°), a first-level warning instruction is generated;

[0058] When the real-time driving parameters exceed the second preset threshold (such as vehicle speed > 70 km / h and steering angle > 35 degrees), a second-level warning instruction is generated;

[0059] When the real-time driving parameters exceed the third preset threshold (such as vehicle speed > 80 km / h and steering angle > 40°), a third-level warning instruction is generated.

[0060] As can be understood, the vehicle's real-time driving parameters (e.g., current speed of 65 km / h, steering angle of 32°) are compared step by step with multiple risk driving parameter thresholds stored for the vehicle. For example, the first risk driving parameter threshold (e.g., speed > 55 km / h) triggers a voice prompt "Slow down!"; the second risk driving parameter threshold (e.g., speed > 70 km / h and steering angle > 35°) triggers a flashing icon warning; and the third risk driving parameter threshold (e.g., speed > 80 km / h and steering angle > 40°) triggers a strong vibration and buzzer alarm. Its purpose is to avoid the interference caused by frequent alarms through a progressive warning strategy (from prompts to forced intervention), while also implementing differentiated warnings for different levels of risk. Actual measurements show that this can reduce drivers' illegal operations in accident-prone areas.

[0061] Optionally, the system transmits the identified accident-prone areas and their corresponding risk driving parameter thresholds to each vehicle terminal in real time through vehicle-to-everything (V2X) communication technology (such as 5G C-V2X or DSRC). Specifically, the backend server will encrypt and push data packets containing regional geofence information, risk driving parameter thresholds (such as maximum speed limit, steering angle threshold, etc.) and valid time periods to the on-board T-BOX terminal via a cellular network base station or roadside unit (RSU). After receiving the data, the T-BOX stores the risk characteristic data of the area and activates the local risk monitoring function when the vehicle enters the area. Its functions are: 1) to achieve dynamic updates of risk area information (the update cycle is configurable); 2) to reduce the real-time computing pressure on the cloud and use the on-board terminal to perform localized risk judgments; 3) to ensure early warning capabilities in areas without network coverage (relying on pre-stored regional data).

[0062] The following describes this using an example.

[0063] Scenario setting: Assume that there is a sharp curve section in a city (coordinates: 39.91°-39.92°N, 116.40°-116.41°E). Due to the small curve radius and lack of warning signs, vehicle loss of control accidents frequently occur. The following is the actual operation process of this application:

[0064] (1) Data Collection Phase: When vehicle A passes the curve at 65 km / h, the electronic control unit (ECU) detects frequent activation of the anti-lock braking system (ABS) (generating standard fault code C0110), and the steering angle sensor records a peak of 35° (exceeding the safety threshold of 30° in this area). At this time, the gateway diagnostic program of vehicle A immediately collects and uploads three types of data:

[0065] Fault characteristics: ABS fault code C0110 (corresponding to "brake system circuit abnormality");

[0066] Spatial location: Beidou positioning coordinates (39.915°N, 116.405°E);

[0067] Real-time driving parameters: speed 65km / h, steering angle 35°, brake pressure 8MPa.

[0068] (2) Cloud analysis phase: The backend server uses the density clustering algorithm (DBSCAN) to analyze and find that there are 15 similar fault reports within a radius of 100 meters from the coordinate point (all accompanied by vehicle speed > 60 km / h and steering angle > 30°). It automatically marks it as an accident-prone area and generates risk driving parameter thresholds (such as vehicle speed ≤ 60 km / h, steering angle ≤ 30°).

[0069] (3) Data distribution phase: The server pushes the latest identified accident-prone areas and risky driving parameter thresholds (including geofence coordinates, risk parameter thresholds, and effective time periods) to all connected vehicle terminals in the area through the Internet of Vehicles, updating the local risk signature database. Data is updated using a differential update mechanism, transmitting only the modified content to reduce communication load.

[0070] (4) Local warning stage (taking vehicle B as an example): When vehicle B enters the accident-prone area, its on-board system automatically completes the following processes: (1) Positioning matching: matching the pre-stored geo-fence data through GNSS positioning; (2) Parameter monitoring: real-time collection of parameters such as the current vehicle speed (such as 63km / h, steering angle 31°); (3) Threshold comparison: calling the locally stored risk driving parameter threshold for comparison; (4) Graded warning: Level 1 warning, such as a voice prompt "500 meters ahead is a high-risk curve, it is recommended to control the speed below 60km / h"; Level 2 warning, triggering a voice warning "The current steering angle is too large, please drive carefully"; Level 3 warning, triggering a red alarm on the dashboard and automatically recording event data.

[0071] Traditional solutions rely on static warning signs (such as "accident-prone" signs), which have two major flaws: first, they cannot dynamically identify specific risk behaviors (such as speeding and sharp turns); second, they cannot be updated as road conditions change (such as the addition of new construction areas). This application is achieved through a three-in-one data linkage (fault code + positioning coordinates + driving parameters): first, dynamic risk driving parameter threshold optimization, automatically updating the risk driving parameters of each area every week (such as a 20% reduction in the speed threshold for rainy and snowy days); second, behavioral intervention, triggering progressive warnings based on the degree of deviation between real-time data and the risk driving parameter threshold.

[0072] Please refer to Figure 2 , Figure 2 This is an architectural diagram of the automobile remote diagnosis provided in the embodiments of the present application. Figure 2 It involves data interaction and processing between the vehicle terminal and the backend server, as follows:

[0073] 1. Data collection and upload process of vehicle 1.

[0074] The diagnostic software in the gateway in vehicle 1 continuously monitors various ECUs, including the engine electronic control unit (ECU), electronic steering ECU, and anti-lock braking system (ABS). When a new fault code appears, the diagnostic software reads the vehicle driving data in the corresponding ECU.

[0075] The positioning module can use the Global Positioning System (GPS), Beidou Satellite Navigation System, GLONASS Satellite Navigation System (GLONASS) or Galileo Satellite Navigation System to obtain the location information of the vehicle when a fault code occurs.

[0076] The diagnostic software transmits fault code information, driving data and location information to the vehicle communication terminal (T-BOX) through the gateway, and the T-BOX then uploads this data to the server backend.

[0077] 2. Data processing flow of the backend server.

[0078] The backend server receives vehicle fault codes, driving data, and location data through the vehicle data receiving and transmitting module. The vehicle data parsing module then analyzes these data. The vehicle fault statistics and analysis module analyzes the fault cause based on the fault code content and driving data. Based on the location information, the module calculates the fault area and determines the risk parameter threshold for that area. For example, using algorithms such as the DBSCAN algorithm, it can identify accident-prone areas based on fault frequency and location distribution.

[0079] The information packaging module packages accident-prone areas (indicating fault area information) and their corresponding risky driving parameter thresholds (indicating fault causes). These packaged accident-prone areas and their corresponding risky driving parameter thresholds are synchronized to other vehicles via the vehicle data receiving and transmitting module.

[0080] 3. Warning process for other vehicles.

[0081] The diagnostic software in other vehicles uses the positioning module to obtain current positioning information and determine whether the vehicle has entered the fault area (i.e., an accident-prone area). If the vehicle enters the fault area, the diagnostic software obtains the vehicle's driving data in real time. The real-time driving parameters are compared with the risk driving parameter threshold corresponding to the fault area. When the real-time driving parameters exceed the risk driving parameter threshold, a corresponding warning is triggered to alert the owner. For example, when driving parameters such as vehicle speed and steering angle exceed the risk driving parameter threshold, warning operations such as voice prompts, sound and light alarms are performed according to different levels such as level one, level two, and level three.

[0082] The vehicle accident warning device provided in the present application is described below. The vehicle accident warning device and the vehicle accident warning apparatus described below can be referenced to each other with the vehicle accident warning method described above.

[0083] Please refer to Figure 3 , Figure 3 FIG3 is a schematic diagram of the structure of a vehicle accident warning device provided in an embodiment of the present application. A vehicle accident warning device 300 includes a data receiving module 310 , a data analyzing module 320 and a warning execution module 330 .

[0084] Exemplarily, the data receiving module 310 is used for receiving real-time fault data packets uploaded by the vehicle terminal, wherein the fault data includes a fault code generated by the vehicle electronic control unit, the satellite positioning coordinates of the vehicle's current position, and the vehicle's current driving parameters.

[0085] Exemplarily, the data analysis module 320 is used to perform statistical analysis on the received fault data packets, identify accident-prone areas based on the frequency and location distribution characteristics of the faults, and determine the risk driving parameter threshold corresponding to each accident-prone area.

[0086] Exemplarily, the early warning execution module 330 is used to send the identified accident-prone areas and their corresponding risk driving parameter thresholds to each vehicle terminal, and is used to: obtain the real-time driving parameters of the vehicle when the vehicle enters the accident-prone area; compare the obtained real-time driving parameters with the risk driving parameter thresholds of the corresponding area; when the real-time driving parameters exceed the risk driving parameter thresholds, trigger a warning instruction of the corresponding level according to the degree of excess.

[0087] It can be understood that the embodiment of the present application effectively solves the technical bottleneck of the traditional manual warning method through an intelligent vehicle accident warning device: first, the data receiving module collects the vehicle's fault code, precise positioning coordinates and multi-dimensional driving parameters in real time to establish a dynamic data source; second, the data analysis module uses a machine learning algorithm to perform spatiotemporal clustering analysis on the fault data, automatically identifies accident-prone areas and generates accurate risk driving parameter thresholds; finally, the warning execution module pushes the analysis results to the vehicle terminal, realizing localized risk monitoring and graded warning based on geographic fences. Its advantages are: first, it is dynamic and can respond to changes in road conditions in real time and automatically update risk areas and thresholds; second, it can achieve accurate warnings and customize differentiated warning strategies for different areas through multi-dimensional data correlation analysis; third, it significantly improves response time and adopts an architecture that combines cloud analysis and edge execution to greatly shorten the warning response time. Compared with traditional static warning methods, this device can effectively reduce the accident rate and improve warning accuracy in actual applications.

[0088] It should be noted here that the above-mentioned vehicle accident warning device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0089] Figure 4 is a schematic diagram of the internal structure of the electronic device provided in the embodiment of the present application, such as Figure 4 As shown, the electronic device may include: a processor (Processor) 410, a communication interface (Communications Interface) 420, a memory (Memory) 430 and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 830 communicate with each other via the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the vehicle accident warning method.

[0090] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0091] On the other hand, the present application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the vehicle accident warning method provided by the above methods.

[0092] On the other hand, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented to execute the vehicle accident warning method provided above when the computer program is executed by a processor.

[0093] The embodiments of the present application provide an electronic device, a computer program product, and a processor-readable storage medium, on which the computer program stored enables the processor to implement all the method steps implemented in the above-mentioned method embodiment and to achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0094] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0095] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A vehicle accident early warning method, characterized in that: include: Receive real-time fault data packets uploaded by the vehicle terminal, including fault codes generated by the vehicle's electronic control unit, satellite positioning coordinates of the vehicle's current location, and the vehicle's current driving parameters; Perform statistical analysis on received fault data packets, identify accident-prone areas based on the frequency and location distribution of faults, and determine the risk driving parameter thresholds corresponding to each accident-prone area; The identified accident-prone areas and their corresponding risk driving parameter thresholds are sent to each vehicle terminal for the following purposes: when a vehicle enters an accident-prone area, the real-time driving parameters of the vehicle are obtained; the obtained real-time driving parameters are compared with the risk driving parameter thresholds of the corresponding area; when the real-time driving parameters exceed the risk driving parameter thresholds, the corresponding level of warning instructions is triggered according to the extent of the excess.

2. The vehicle accident warning method according to claim 1, characterized in that: Also includes: The vehicle gateway's diagnostic program broadcasts a unified diagnostic service command on the controller area network bus in a loop. Each on-board electronic control unit responds to the command and returns a fault code in a standard format. Compare the current fault code with the historical fault code sequence to identify whether it is a new fault code; When a new fault code is identified, the vehicle's current location coordinates and current driving parameters are obtained, and the fault data packet is packaged and uploaded.

3. The vehicle accident warning method according to claim 1, characterized in that: The driving parameters include at least one of vehicle speed, steering angle, brake status, gear position and vehicle voltage; the satellite positioning coordinates are obtained through a satellite positioning module, and the positioning system used includes the Global Positioning System, Beidou Satellite Navigation System, GLONASS Satellite Navigation System or Galileo Satellite Navigation System.

4. The vehicle accident warning method according to claim 1, characterized in that: Determining the risk driving parameter threshold includes: Perform statistical analysis on the location coordinates in the fault data packets to identify accident-prone areas; For each accident-prone area, a risk driving parameter threshold is determined based on historical driving parameters. The risk driving parameter threshold includes at least one of a vehicle speed upper limit, a steering angle critical value, and a braking frequency warning value.

5. The vehicle accident warning method according to claim 1, characterized in that: The triggering graded warning instruction includes: When the real-time driving parameter exceeds the first threshold, a first-level warning instruction is triggered; When the real-time driving parameter exceeds the second threshold, a second-level warning instruction is triggered; When the real-time driving parameter exceeds the third threshold, a third-level warning instruction is triggered.

6. The vehicle accident warning method according to claim 1, characterized in that: Also includes: The risk driving parameter thresholds in accident-prone areas are updated regularly, wherein the updates include risk driving parameter thresholds for newly added fault types or risk driving parameter thresholds for different time periods.

7. The vehicle accident warning method according to claim 1, characterized in that: Also includes: Determine whether the vehicle coordinates are within the boundary of the accident-prone area; When the vehicle leaves the accident-prone area, the relevant monitoring and warning will be automatically terminated.

8. A vehicle accident warning device, characterized in that: include: A data receiving module receives real-time fault data packets uploaded by the vehicle terminal. The fault data includes the fault code generated by the vehicle electronic control unit, the satellite positioning coordinates of the vehicle's current location, and the vehicle's current driving parameters; The data analysis module is used to perform statistical analysis on the received fault data packets, identify accident-prone areas based on the frequency and location distribution characteristics of the faults, and determine the risk driving parameter threshold corresponding to each accident-prone area; The early warning execution module is used to send the identified accident-prone areas and their corresponding risk driving parameter thresholds to each vehicle terminal, and is used to: obtain the real-time driving parameters of the vehicle when it enters the accident-prone area; compare the obtained real-time driving parameters with the risk driving parameter thresholds of the corresponding area; when the real-time driving parameters exceed the risk driving parameter thresholds, trigger the corresponding level of early warning instructions according to the degree of excess.

9. An electronic device, characterized in that: The system comprises a processor and a memory, wherein the memory stores a plurality of instructions; the processor loads instructions from the memory to execute the steps of the vehicle accident warning method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that The computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the steps of the vehicle accident warning method according to any one of claims 1 to 7.

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