Vehicle safety warning method, device, equipment and storage medium

Abstract

The application discloses a vehicle safety early warning method, device and equipment and a storage medium, and the method comprises the following steps: acquiring front vehicle flow information, wherein the front vehicle flow information comprises the number of front vehicles, the distance between the front vehicles and the speed of the front vehicles; performing prediction analysis on the front accident according to the current vehicle information and the front vehicle flow information, and generating the front accident situation; performing accident danger classification and accident danger grading on the current driving state according to the front accident situation, and generating safety early warning information; and performing vehicle safety early warning based on the safety early warning information. In the foregoing manner, the front accident is predicted and analyzed according to the vehicle information and the front vehicle flow information, the current driving state is classified and graded according to the accident danger of the front accident situation, and the vehicle safety early warning is performed, so that, in combination with the analysis of the current vehicle information and the front vehicle flow information from multiple aspects, more accurate and more comprehensive vehicle safety early warning can be realized, and the accident rate is reduced.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a vehicle safety warning method, device, equipment, and storage medium. Background Technology

[0002] In modern society, vehicle traffic has become an indispensable part of people's daily lives. However, due to various factors such as driver's personal factors, road conditions, and vehicle technology, vehicle traffic safety issues are becoming increasingly prominent. The accident rate is especially high on highways and other high-speed roads, making it an urgent problem to improve vehicle traffic safety.

[0003] Currently, some vehicle safety warning systems and methods exist, but most of these systems and methods only provide warnings based on a single factor and lack comprehensive analytical capabilities, resulting in less than ideal warning effects. Therefore, a more accurate and comprehensive vehicle safety warning method is urgently needed.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a vehicle safety early warning method, device, equipment, and storage medium, aiming to solve the technical problems of existing vehicle safety early warning technologies lacking comprehensive analysis capabilities and having unsatisfactory early warning effects.

[0006] To achieve the above objectives, the present invention provides a vehicle safety warning method, the method comprising the following steps:

[0007] Obtain information about the traffic flow ahead, including the number of vehicles ahead, the distance between the vehicles ahead, and the speed of the vehicles ahead.

[0008] Based on the current vehicle information and the traffic flow information ahead, predictive analysis of the accident ahead is performed to generate the accident situation ahead.

[0009] Based on the accident situation ahead, the current driving status is classified and graded according to the accident risk, and safety warning information is generated.

[0010] Vehicle safety warnings are issued based on the aforementioned safety warning information.

[0011] Optionally, the step of predicting and analyzing the accident ahead based on the current vehicle information and the traffic flow information ahead, and generating the accident situation ahead, includes:

[0012] Current vehicle information is obtained through vehicle sensors and positioning systems;

[0013] Obtain historical accident data ahead based on the current vehicle information;

[0014] Based on the current vehicle information, the historical accident data ahead, and the vehicle information ahead, a predictive analysis of the accident ahead is performed to generate the accident situation ahead.

[0015] Optionally, the step of predicting and analyzing the accident ahead based on the current vehicle information, the historical accident data ahead, and the vehicle information ahead, and generating the accident situation ahead, includes:

[0016] Based on the aforementioned accident data, obtain the accident incidence rate, accident type, and accident cause within a preset range ahead;

[0017] An accident risk level mapping table is generated based on the accident occurrence rate, accident type, and accident cause within the preset range.

[0018] Based on the current vehicle information, the vehicle information ahead, and the accident risk level mapping table, a predictive analysis of the accident ahead is performed to generate the accident situation ahead.

[0019] Optionally, the step of predicting and analyzing the upcoming accident based on the current vehicle information, the preceding vehicle information, and the accident risk level mapping table to generate the preceding accident situation includes:

[0020] The mapping relationship between the traffic flow ahead and the degree of accident risk is obtained through the accident risk level mapping table;

[0021] The degree of accident risk is determined based on the information of the vehicles ahead and the mapping relationship.

[0022] Based on the current vehicle information and the degree of accident risk, a predictive analysis of the accident ahead is performed to generate an accident situation ahead.

[0023] Optionally, the vehicle safety warning method further includes:

[0024] Anomaly analysis is performed on the current vehicle information to generate analysis results;

[0025] Determine if there are any anomalies in the analysis results;

[0026] Vehicle safety warnings are issued when anomalies are found in the analysis results.

[0027] Optionally, the step of performing anomaly analysis on the current vehicle information and generating analysis results includes:

[0028] Real-time acceleration monitoring and in-vehicle temperature monitoring are performed based on current vehicle information, and monitoring results are generated.

[0029] The monitoring results that exceed the preset acceleration threshold and preset temperature threshold are calibrated, and analysis results are generated.

[0030] Optionally, the vehicle safety warning method further includes:

[0031] Based on the traffic flow information ahead, determine whether the traffic flow ahead is greater than a preset traffic flow threshold;

[0032] If so, determine whether the current speed exceeds the safe range based on the distance between the current vehicle and the vehicle in front;

[0033] A vehicle safety warning is issued when the current vehicle speed exceeds the safe range.

[0034] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle safety warning device, the device comprising:

[0035] The information acquisition module is used to acquire information about the traffic flow ahead, including the number of vehicles ahead, the distance between the vehicle and the vehicle ahead, and the speed of the vehicle ahead.

[0036] The accident analysis module is used to predict and analyze the accidents ahead based on the current vehicle information and the traffic flow information ahead, and generate the accident situation ahead.

[0037] The information generation module is used to classify and grade the current driving status based on the accident situation ahead, and generate safety warning information.

[0038] The vehicle warning module is used to issue vehicle safety warnings based on the aforementioned safety warning information.

[0039] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle safety warning device, the device comprising: a memory, a processor, and a vehicle safety warning program stored in the memory and executable on the processor, the vehicle safety warning program being configured to implement the steps of the vehicle safety warning method as described above.

[0040] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing a vehicle safety warning program, which, when executed by a processor, implements the steps of the vehicle safety warning method as described above.

[0041] This invention discloses a vehicle safety warning method, device, equipment, and storage medium. The method includes: acquiring traffic flow information ahead, including the number of vehicles ahead, the distance between the current vehicle and the vehicles ahead, and the speed of the vehicles ahead; predicting and analyzing potential accidents ahead based on current vehicle information and traffic flow information to generate accident situation data; classifying and grading the current driving state based on the accident situation data to generate safety warning information; and issuing a vehicle safety warning based on the safety warning information. By using the above method to predict and analyze potential accidents ahead based on vehicle information and traffic flow information, and to classify and grade the current driving state based on the accident situation data to issue a vehicle safety warning, combined with multi-faceted analysis of current vehicle information and traffic flow information, more accurate and comprehensive vehicle safety warnings can be achieved, reducing the accident rate. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the structure of a vehicle safety warning device in the hardware operating environment involved in the embodiments of the present invention;

[0043] Figure 2 This is a flowchart illustrating the first embodiment of the vehicle safety warning method of the present invention;

[0044] Figure 3 This is a flowchart illustrating the second embodiment of the vehicle safety warning method of the present invention;

[0045] Figure 4 This is a flowchart illustrating the third embodiment of the vehicle safety warning method of the present invention;

[0046] Figure 5 This is a structural block diagram of the first embodiment of the vehicle safety warning device of the present invention.

[0047] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0048] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0049] Reference Figure 1 , Figure 1 This is a schematic diagram of the vehicle safety warning device structure in the hardware operating environment involved in the embodiments of the present invention.

[0050] like Figure 1As shown, the vehicle safety warning device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk storage device. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0051] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on vehicle safety warning devices and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0052] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a vehicle safety warning program.

[0053] exist Figure 1 In the vehicle safety warning device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the vehicle safety warning device of the present invention can be set in the vehicle safety warning device, and the vehicle safety warning device calls the vehicle safety warning program stored in the memory 1005 through the processor 1001 and executes the vehicle safety warning method provided in the embodiment of the present invention.

[0054] This invention provides a vehicle safety warning method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the vehicle safety warning method of the present invention.

[0055] In this embodiment, the vehicle safety warning method includes the following steps:

[0056] Step S10: Obtain traffic flow information ahead, which includes the number of vehicles ahead, the distance between the vehicles ahead, and the speed of the vehicles ahead.

[0057] It should be noted that the executing entity of the method in this embodiment can be a vehicle warning device with data processing, network communication, and program execution functions, such as a vehicle safety warning device; it can also be other electronic devices with the same or similar functions, or a vehicle safety warning system equipped with such electronic devices. This embodiment and the following embodiments will use a vehicle safety warning device as the executing entity to illustrate the vehicle safety warning method in this embodiment and the following embodiments.

[0058] It is understandable that the traffic flow information ahead can be the traffic flow information within a preset range ahead of the current vehicle as it travels to its destination. This information can be obtained through vehicle-mounted cameras, vehicle-mounted radar (when there are significant obstructions), or real-time traffic flow information via satellite navigation systems.

[0059] Step S20: Based on the current vehicle information and the traffic flow information ahead, predict and analyze the accident ahead to generate the accident situation ahead.

[0060] It should be noted that the current vehicle information can be the current status information of the vehicle, which may include the current vehicle speed, current location coordinates, current interior temperature, and current acceleration.

[0061] Understandably, when analyzing potential accidents ahead, vehicle sensors and GPS positioning technologies can be used to obtain the current vehicle's location information, which, combined with historical accident data, can be used to analyze the situation. Vehicle safety warning equipment uses the current vehicle information and the information about the vehicles ahead to predict and analyze potential accidents, generating a scenario for the upcoming accident.

[0062] Step S30: Based on the accident situation ahead, classify and grade the current driving status according to the accident risk, and generate safety warning information.

[0063] Understandably, classifying and grading the risk of accidents based on the current driving situation can be done using a neural network model. Predicting the risk level of an accident ahead using a neural network model: Assuming the input features are the number of vehicles ahead, vehicle speed, and historical accident occurrence rate, after processing through a hidden layer, the output is the risk level of the accident ahead, which can be expressed using the following formula:

[0064] h1 = ReLU(W1X + b1)

[0065] Where X is the input feature vector, W1 is the weight matrix from the input layer to the hidden layer, b1 is the bias vector of the hidden layer, ReLU is the activation function, and h1 is the output vector of the hidden layer.

[0066] h2=σ(W2h1+b2)

[0067] Where W2 is the weight matrix from the hidden layer to the output layer, b2 is the bias vector of the output layer, and sigma(σ) is the activation function of the output layer, used to map the output value to the range of 0-1. h2 is the output vector, representing the danger level of the accident ahead. When training the neural network, a certain amount of accident data needs to be prepared as samples, including features and labels such as the number of vehicles ahead, vehicle speed, historical accident occurrence rate, and actual danger level. Through the backpropagation algorithm, the weight matrix and bias vector can be adjusted to minimize the error between the predicted output and the actual label.

[0068] In practical applications, by collecting real-time data such as the number of vehicles ahead, their speed, and the historical accident rate, and inputting this data into a neural network for prediction, the danger level of the accident ahead can be determined, thereby enabling early warning and safety measures.

[0069] Step S40: Issue a vehicle safety warning based on the aforementioned safety warning information.

[0070] It should be noted that different levels of warnings can be issued based on the degree of danger, such as audible and visual alarms, vibration alerts, etc. Vehicle safety warnings can also be optimized and improved, for example, by adding voice prompts and map displays during warnings to enhance the user experience.

[0071] Furthermore, the vehicle safety warning method also includes: determining whether the traffic flow ahead is greater than a preset traffic flow threshold based on the traffic flow information ahead; if so, determining whether the current vehicle speed exceeds a safe range based on the distance between the current vehicle and the vehicle ahead; and issuing a vehicle safety warning when the current vehicle speed exceeds a safe range.

[0072] Understandably, the Roadside Unit (RSU) sensor detects the distance and number of vehicles ahead, obtaining traffic flow information. If the traffic flow ahead is large, exceeding the normal range, it can be determined as a situation of heavy traffic ahead. If the current vehicle speed is high: the vehicle speed sensor collects the current speed information and compares it with the distance and speed of vehicles ahead to calculate whether the current speed is too high. For example, if the current speed is high and there is a significant difference between it and the speed of vehicles ahead, it can be determined as a situation of high current speed. Combining these examples, by analyzing the traffic flow ahead in real time, potential traffic congestion can be detected, thus enabling vehicle safety warnings.

[0073] In this embodiment, information on the traffic flow ahead is acquired, including the number of vehicles ahead, the distance between the current vehicle and the vehicles ahead, and the speed of the vehicles ahead. Based on the current vehicle information and the traffic flow information ahead, a predictive analysis of potential accidents ahead is performed to generate a scenario of the potential accident. Based on the scenario of the potential accident, the current driving state is classified and graded for accident risk, generating safety warning information. Finally, a vehicle safety warning is issued based on the safety warning information. By using the above method to predict and analyze potential accidents ahead based on vehicle information and traffic flow information, and to classify and grade the current driving state for accident risk based on the scenario of the potential accident and issue vehicle safety warnings, combined with multi-faceted analysis of current vehicle information and traffic flow information, more accurate and comprehensive vehicle safety warnings can be achieved, reducing the accident rate.

[0074] refer to Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the vehicle safety warning method of the present invention.

[0075] Furthermore, based on the first embodiment described above, in this embodiment, step S20 further includes:

[0076] Step S201: Obtain current vehicle information through vehicle sensors and positioning system.

[0077] Understandably, vehicle sensors can be common sensors found in current vehicles, including inertial measurement units (IMUs), radar sensors, cameras, barometers, and thermometers. GPS receivers provide precise vehicle location information, including longitude, latitude, and altitude. Inertial measurement units measure acceleration, angular velocity, and direction, helping to determine the vehicle's orientation and motion. Radar sensors can detect the distance and speed of surrounding objects. Cameras can identify lane lines, traffic standards, and other vehicles. Barometers and thermometers help determine the vehicle's altitude and temperature.

[0078] Step S202: Obtain historical accident data ahead based on the current vehicle information.

[0079] Understandably, historical accident data ahead can be characteristic data of accidents in the vicinity of the previous location. By analyzing historical accident data, we can analyze the accident incidence rate, accident type, accident cause and other characteristic information of the location ahead, and judge the degree of accident risk at the current location.

[0080] Step S203: Based on the current vehicle information, the historical accident data ahead, and the vehicle information ahead, perform predictive analysis on the accident ahead to generate the accident situation ahead.

[0081] Furthermore, step S203 further includes: obtaining the accident incidence rate, accident type, and accident cause within a preset range ahead based on the accident data ahead; generating an accident risk level mapping table based on the accident incidence rate, accident type, and accident cause within the preset range ahead; and performing predictive analysis on the accidents ahead based on the current vehicle information, the vehicle information ahead, and the accident risk level mapping table to generate the accident situation ahead.

[0082] It should be understood that the mapping relationship between the traffic flow ahead and the accident risk level is obtained through the accident risk level mapping table; the accident risk level is determined based on the vehicle information ahead and the mapping relationship; and the accident situation ahead is generated by predicting and analyzing the accident ahead based on the current vehicle information and the accident risk level.

[0083] It should be noted that the vehicle safety warning device obtains the accident incidence rate, accident type, and accident cause within a preset range ahead based on the aforementioned accident data; the vehicle safety warning device generates an accident risk level mapping table based on the accident incidence rate, accident type, and accident cause within the preset range ahead; the vehicle safety warning device performs predictive analysis on the accidents ahead based on the current vehicle information, the vehicle information ahead, and the accident risk level mapping table, and generates the accident situation ahead.

[0084] In this embodiment, current vehicle information is acquired through vehicle sensors and a positioning system; historical accident data ahead is obtained based on the current vehicle information; and predictive analysis of ahead accidents is performed based on the current vehicle information, the historical accident data ahead, and the information of vehicles ahead, generating a forecast of ahead accidents. By acquiring current vehicle information, obtaining historical accident data ahead based on the current vehicle information, and performing predictive analysis based on the current vehicle information, historical accident data ahead, and the information of vehicles ahead, more accurate and comprehensive vehicle safety warnings can be achieved, reducing the accident rate.

[0085] refer to Figure 4 , Figure 4 This is a flowchart illustrating the third embodiment of the vehicle safety warning method of the present invention.

[0086] Furthermore, based on the first embodiment described above, this embodiment further includes:

[0087] Step S01: Perform anomaly analysis on the current vehicle information and generate analysis results.

[0088] Understandably, the process of anomaly analysis can involve identifying abnormal information in the current vehicle information and generating analysis results.

[0089] Furthermore, step S01 also includes: real-time acceleration monitoring and real-time in-vehicle temperature monitoring based on current vehicle information, generating monitoring results; calibrating the monitoring content that exceeds preset acceleration thresholds and preset temperature thresholds in the monitoring results, and generating analysis results.

[0090] It is understandable that the calibration process can be a process of calibrating abnormal information, which can distinguish abnormal information in the current vehicle information.

[0091] It should be understood that the vehicle safety warning equipment performs real-time acceleration detection and real-time monitoring of the vehicle interior temperature based on the current vehicle information, and generates monitoring results; the vehicle safety warning equipment calibrates the monitoring content that exceeds the preset acceleration threshold and preset temperature threshold in the monitoring results, and generates analysis results.

[0092] Step S02: Determine whether there are any abnormalities in the analysis results.

[0093] It should be noted that the presence of calibrated information in the current vehicle information is used to determine whether there are any anomalies in the analysis results.

[0094] Step S03: Issue a vehicle safety warning when anomalies are found in the analysis results.

[0095] It should be noted that when the vehicle is in motion, real-time information such as speed, acceleration, engine speed, coolant temperature, oil pressure, and fuel consumption can be collected through vehicle sensors. Based on these parameters, it is possible to determine whether there are any abnormal conditions in the vehicle, such as abnormal acceleration, sudden braking, or engine overheating.

[0096] To illustrate, consider these examples: Abnormal acceleration: By comparing the rate of change of the current vehicle speed with the previous moment, it can be determined whether the vehicle is accelerating abnormally. For instance, if the vehicle accelerates too quickly in a short period, exceeding the normal acceleration range, it can be considered abnormal acceleration. Abnormal interior temperature: Interior temperature information can be collected by in-vehicle temperature sensors to determine if the interior temperature is normal. For instance, in hot summer weather, if the interior temperature exceeds a certain range, it can be considered abnormal. In summary, by analyzing vehicle information in real time, abnormal situations can be detected, allowing for vehicle safety warnings.

[0097] It should be noted that the vehicle safety warning device performs anomaly analysis on the current vehicle information, generates analysis results, determines whether there are any abnormalities in the analysis results, and issues a vehicle safety warning when there are abnormalities in the analysis results.

[0098] In this embodiment, anomaly analysis is performed on the current vehicle information to generate analysis results; it is determined whether there are any anomalies in the analysis results; and a vehicle safety warning is issued when anomalies are found in the analysis results. By performing anomaly analysis on the current vehicle information, generating analysis results, determining whether there are any anomalies in the analysis results, and issuing a vehicle safety warning when anomalies are found in the analysis results, more accurate and comprehensive vehicle safety warnings can be achieved, reducing the accident rate.

[0099] Furthermore, this embodiment of the invention also proposes a storage medium storing a vehicle safety warning program, which, when executed by a processor, implements the steps of the vehicle safety warning method described above.

[0100] Reference Figure 5 , Figure 5 This is a structural block diagram of the first embodiment of the vehicle safety warning device of the present invention.

[0101] like Figure 5 As shown, the vehicle safety warning device proposed in this embodiment of the invention includes: an information acquisition module 501, an accident analysis module 502, an information generation module 503, and a vehicle warning module 504.

[0102] The information acquisition module 501 is used to acquire traffic flow information ahead, which includes the number of vehicles ahead, the distance between the vehicle ahead and the vehicle ahead, and the speed of the vehicle ahead.

[0103] It is understandable that the traffic flow information ahead can be the traffic flow information within a preset range ahead of the current vehicle as it travels to its destination. This information can be obtained through vehicle-mounted cameras, vehicle-mounted radar (when there are significant obstructions), or real-time traffic flow information via satellite navigation systems.

[0104] The accident analysis module 502 is used to predict and analyze the accident ahead based on the current vehicle information and the traffic flow information ahead, and generate the accident situation ahead.

[0105] It should be noted that the current vehicle information can be the current status information of the vehicle, which may include the current vehicle speed, current location coordinates, current interior temperature, and current acceleration.

[0106] Understandably, when analyzing potential accidents ahead, vehicle sensors and GPS positioning technologies can be used to obtain the current vehicle's location information, which, combined with historical accident data, can be used to analyze the situation. Vehicle safety warning equipment uses the current vehicle information and the information about the vehicles ahead to predict and analyze potential accidents, generating a scenario for the upcoming accident.

[0107] The information generation module 503 is used to classify and grade the current driving status based on the accident situation ahead, and generate safety warning information.

[0108] Understandably, classifying and grading the risk of accidents based on the current driving situation can be done using a neural network model. Predicting the risk level of an accident ahead using a neural network model: Assuming the input features are the number of vehicles ahead, vehicle speed, and historical accident occurrence rate, after processing through a hidden layer, the output is the risk level of the accident ahead, which can be expressed using the following formula:

[0109] h1 = ReLU(W1X + b1)

[0110] Where X is the input feature vector, W1 is the weight matrix from the input layer to the hidden layer, b1 is the bias vector of the hidden layer, ReLU is the activation function, and h1 is the output vector of the hidden layer.

[0111] h2=σ(W2h1+b2)

[0112] Where W2 is the weight matrix from the hidden layer to the output layer, b2 is the bias vector of the output layer, and sigma(σ) is the activation function of the output layer, used to map the output value to the range of 0-1. h2 is the output vector, representing the danger level of the accident ahead. When training the neural network, a certain amount of accident data needs to be prepared as samples, including features and labels such as the number of vehicles ahead, vehicle speed, historical accident occurrence rate, and actual danger level. Through the backpropagation algorithm, the weight matrix and bias vector can be adjusted to minimize the error between the predicted output and the actual label.

[0113] In practical applications, by collecting real-time data such as the number of vehicles ahead, their speed, and the historical accident rate, and inputting this data into a neural network for prediction, the danger level of the accident ahead can be determined, thereby enabling early warning and safety measures.

[0114] The vehicle warning module 504 is used to provide vehicle safety warnings based on the safety warning information.

[0115] It should be noted that different levels of warnings can be issued based on the degree of danger, such as audible and visual alarms, vibration alerts, etc. Vehicle safety warnings can also be optimized and improved, for example, by adding voice prompts and map displays during warnings to enhance the user experience.

[0116] Furthermore, the vehicle safety warning method also includes: determining whether the traffic flow ahead is greater than a preset traffic flow threshold based on the traffic flow information ahead; if so, determining whether the current vehicle speed exceeds a safe range based on the distance between the current vehicle and the vehicle ahead; and issuing a vehicle safety warning when the current vehicle speed exceeds a safe range.

[0117] Understandably, the Roadside Unit (RSU) sensor detects the distance and number of vehicles ahead, obtaining traffic flow information. If the traffic flow ahead is large, exceeding the normal range, it can be determined as a situation of heavy traffic ahead. If the current vehicle speed is high: the vehicle speed sensor collects the current speed information and compares it with the distance and speed of vehicles ahead to calculate whether the current speed is too high. For example, if the current speed is high and there is a significant difference between it and the speed of vehicles ahead, it can be determined as a situation of high current speed. Combining these examples, by analyzing the traffic flow ahead in real time, potential traffic congestion can be detected, thus enabling vehicle safety warnings.

[0118] In this embodiment, information on the traffic flow ahead is acquired, including the number of vehicles ahead, the distance between the current vehicle and the vehicles ahead, and the speed of the vehicles ahead. Based on the current vehicle information and the traffic flow information ahead, a predictive analysis of potential accidents ahead is performed to generate a scenario of the potential accident. Based on the scenario of the potential accident, the current driving state is classified and graded for accident risk, generating safety warning information. Finally, a vehicle safety warning is issued based on the safety warning information. By using the above method to predict and analyze potential accidents ahead based on vehicle information and traffic flow information, and to classify and grade the current driving state for accident risk based on the scenario of the potential accident and issue vehicle safety warnings, combined with multi-faceted analysis of current vehicle information and traffic flow information, more accurate and comprehensive vehicle safety warnings can be achieved, reducing the accident rate.

[0119] Other embodiments or specific implementations of the vehicle safety warning device of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0120] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0121] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0122] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0123] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A vehicle safety warning method, characterized by, The vehicle safety warning method includes the following steps: Obtain information about the traffic flow ahead, including the number of vehicles ahead, the distance between the vehicles ahead, and the speed of the vehicles ahead. Based on the current vehicle information and the traffic flow information ahead, predictive analysis of the accident ahead is performed to generate the accident situation ahead. Based on the accident situation ahead, the current driving status is classified and graded according to the accident risk, and safety warning information is generated. Vehicle safety warnings are issued based on the aforementioned safety warning information; The step of predicting and analyzing the accident ahead based on the current vehicle information and the traffic flow information ahead, and generating the accident situation ahead, includes: Current vehicle information is obtained through vehicle sensors and positioning systems; Obtain historical accident data ahead based on the current vehicle information; Based on the current vehicle information, the historical accident data ahead, and the vehicle information ahead, a predictive analysis of the accident ahead is performed to generate the accident situation ahead; The step of predicting and analyzing the accident ahead based on the current vehicle information, the historical accident data ahead, and the vehicle information ahead, and generating the accident situation ahead, includes: Based on the historical accident data, obtain the accident incidence rate, accident type, and accident cause within the preset range ahead; An accident risk level mapping table is generated based on the accident occurrence rate, accident type, and accident cause within the preset range. Based on the current vehicle information, the vehicle information ahead, and the accident risk level mapping table, a predictive analysis of the accident ahead is performed to generate the accident situation ahead.

2. The vehicle safety warning method of claim 1, wherein, The step of predicting and analyzing the upcoming accident based on the current vehicle information, the preceding vehicle information, and the accident risk level mapping table to generate the preceding accident situation includes: The mapping relationship between the traffic flow ahead and the degree of accident risk is obtained through the accident risk level mapping table; The degree of accident risk is determined based on the information of the vehicles ahead and the mapping relationship. Based on the current vehicle information and the degree of accident risk, a predictive analysis of the accident ahead is performed to generate an accident situation ahead.

3. The vehicle safety early warning method as described in claim 1, characterized in that, The vehicle safety early warning method also includes: Anomaly analysis is performed on the current vehicle information to generate analysis results; Determine if there are any anomalies in the analysis results; Vehicle safety warnings are issued when anomalies are found in the analysis results.

4. The vehicle safety early warning method as described in claim 3, characterized in that, The steps for performing anomaly analysis on the current vehicle information and generating analysis results include: Real-time acceleration monitoring and in-vehicle temperature monitoring are performed based on current vehicle information, and monitoring results are generated. The monitoring results that exceed the preset acceleration threshold and preset temperature threshold are calibrated, and analysis results are generated.

5. The vehicle safety warning method as described in any one of claims 1-4, characterized in that, The vehicle safety warning method further includes: Based on the traffic flow information ahead, determine whether the traffic flow ahead is greater than a preset traffic flow threshold; If so, determine whether the current speed exceeds the safe range based on the distance between the current vehicle and the vehicle in front; A vehicle safety warning is issued when the current vehicle speed exceeds the safe range.

6. A vehicle safety warning device, characterized in that, The device includes: The information acquisition module is used to acquire information about the traffic flow ahead, including the number of vehicles ahead, the distance between the vehicle and the vehicle ahead, and the speed of the vehicle ahead. The accident analysis module is used to predict and analyze the accidents ahead based on the current vehicle information and the traffic flow information ahead, and generate the accident situation ahead. The information generation module is used to classify and grade the current driving status based on the accident situation ahead, and generate safety warning information. The vehicle warning module is used to provide vehicle safety warnings based on the aforementioned safety warning information. The accident analysis module is also used to acquire current vehicle information through vehicle sensors and a positioning system; acquire historical accident data ahead based on the current vehicle information; and perform predictive analysis on the accident ahead based on the current vehicle information, the historical accident data ahead, and the vehicle information ahead, to generate the accident situation ahead. The accident analysis module is further configured to obtain the accident incidence rate, accident type, and accident cause within a preset range ahead based on the accident data ahead; generate an accident risk level mapping table based on the accident incidence rate, accident type, and accident cause within the preset range ahead; and perform predictive analysis on the accidents ahead based on the current vehicle information, the vehicle information ahead, and the accident risk level mapping table to generate the accident situation ahead.

7. A vehicle safety warning device, characterized in that, The device includes: a memory, a processor, and a vehicle safety warning program stored in the memory and executable on the processor, the vehicle safety warning program being configured to implement the steps of the vehicle safety warning method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores a vehicle safety warning program, which, when executed by a processor, implements the steps of the vehicle safety warning method as described in any one of claims 1 to 5.

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