Vehicle rapid acceleration risk detection method and platform and storage medium

Abstract

The invention provides a vehicle rapid acceleration risk detection method and platform and a storage medium, and relates to the technical field of data processing, vehicle rapid acceleration detection is carried out according to a real-time state data sequence, and frequent rapid acceleration early warning is output and has a risk time interval identifier; and according to the risk time interval, calling and obtaining the associated behavior data to carry out vehicle condition detection, and outputting vehicle condition detection information to carry out early warning grade updating of frequent and urgent acceleration early warning. The technical problem that in the prior art, due to the fact that the vehicle frequent rapid acceleration behavior detection precision is low, the driving behavior early warning timeliness and accuracy are poor is solved. The technical effects that frequent rapid acceleration behavior recognition and early warning in the driving process are rapidly carried out, and timely comprehensive early warning information of driving behaviors and vehicle conditions is provided for the driver, so that potential driving dangers are prevented are achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a vehicle sudden acceleration risk detection method, platform and storage medium. Background Art

[0002] With the continuous increase in traffic volume, especially in the context of accelerated urbanization, vehicle safety has become a focus of social attention. Frequent sudden acceleration not only increases vehicle fuel consumption and causes excessive wear of vehicle components, but may also cause traffic accidents in some cases, posing a serious threat to road safety.

[0003] Especially in complex road conditions such as highways and city streets, the sudden acceleration of vehicles is often accompanied by complex driving behaviors such as sudden braking and sharp turns, which makes the detection of dangerous driving behaviors more complicated and difficult.

[0004] In summary, the prior art has a technical problem that the detection accuracy of frequent sudden acceleration behavior of vehicles is low, resulting in poor timeliness and accuracy of driving behavior warnings. Summary of the invention

[0005] The present application provides a vehicle sudden acceleration risk detection method, platform and storage medium, which are used to solve the technical problem in the prior art that the detection accuracy of frequent sudden acceleration behavior of vehicles is low, resulting in poor timeliness and accuracy of driving behavior warning.

[0006] In view of the above problems, the present application provides a vehicle rapid acceleration risk detection method, platform and storage medium.

[0007] The first aspect of the present application provides a vehicle sudden acceleration risk detection method, the method comprising: presetting a state collection window; using the state collection window as a constraint, controlling a motion state unit to intermittently collect motion state data of a target vehicle to obtain a real-time state data sequence; performing vehicle sudden acceleration detection according to the real-time state data sequence, and outputting a frequent sudden acceleration warning, wherein the frequent sudden acceleration warning has a risk time interval identifier; performing a reverse call on the vehicle behavior data of the target vehicle according to the risk time interval to obtain associated behavior data; performing a vehicle condition detection according to the associated behavior data, and outputting vehicle condition detection information; and updating the warning level of the frequent sudden acceleration warning according to the vehicle condition detection information.

[0008] The second aspect of the present application provides a vehicle sudden acceleration risk detection platform, which includes: an acquisition function configuration unit, which is used to preset a state acquisition window; an acquisition function operation unit, which is used to control the motion state unit to intermittently acquire the motion state data of the target vehicle with the state acquisition window as a constraint to obtain a real-time state data sequence; a driving warning output unit, which is used to perform vehicle sudden acceleration detection according to the real-time state data sequence after multi-level data preprocessing of the real-time state data sequence, and output a frequent sudden acceleration warning, wherein the frequent sudden acceleration warning has a risk time interval identifier; a behavior data acquisition unit, which is used to reversely call the vehicle behavior data of the target vehicle according to the risk time interval to obtain associated behavior data; a vehicle condition detection execution unit, which is used to perform vehicle condition detection according to the associated behavior data and output vehicle condition detection information; and a warning level update unit, which is used to update the warning level of the frequent sudden acceleration warning according to the vehicle condition detection information.

[0009] According to a third aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the steps of the vehicle rapid acceleration risk detection method described in any one of the first aspects are implemented.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The method provided by the embodiment of the present application is to preset a state collection window; use the state collection window as a constraint to control the motion state unit to intermittently collect the motion state data of the target vehicle to obtain a real-time state data sequence; perform vehicle rapid acceleration detection according to the real-time state data sequence, and output a frequent rapid acceleration warning, wherein the frequent rapid acceleration warning has a risk time interval identifier; perform a reverse call on the vehicle behavior data of the target vehicle according to the risk time interval to obtain associated behavior data; perform vehicle condition detection according to the associated behavior data, and output vehicle condition detection information; and update the warning level of the frequent rapid acceleration warning according to the vehicle condition detection information. The technical effect of quickly identifying and warning of frequent rapid acceleration behavior during driving and providing the driver with timely comprehensive warning information on driving behavior and vehicle condition is achieved, thereby preventing potential driving hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram of the process flow of the vehicle rapid acceleration risk detection method provided in this application;

[0013] Figure 2 A schematic diagram of a process for outputting a frequent sudden acceleration warning in a vehicle sudden acceleration risk detection method provided in this application;

[0014] Figure 3This is a schematic diagram of the structure of the vehicle rapid acceleration risk detection platform provided in this application.

[0015] Explanation of the reference numerals: collection function configuration unit 11 , collection function operation unit 12 , driving warning output unit 13 , behavior data collection unit 14 , vehicle condition detection execution unit 15 , warning level update unit 16 . DETAILED DESCRIPTION

[0016] This application provides a vehicle sudden acceleration risk detection method, platform and storage medium, which are used to solve the technical problem that the detection accuracy of frequent sudden acceleration behavior of vehicles is low in the prior art, resulting in poor timeliness and accuracy of driving behavior warning. It achieves the technical effect of quickly identifying and warning of frequent sudden acceleration behavior during driving and providing the driver with timely comprehensive warning information on driving behavior and vehicle conditions, thereby preventing potential driving hazards.

[0017] Below, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. 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 the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0018] Embodiment 1

[0019] like Figure 1 As shown, the present application provides a vehicle rapid acceleration risk detection method, the method comprising:

[0020] A100: Preset status acquisition window.

[0021] Specifically, in this embodiment, the preset state acquisition window refers to a time period or a time interval for data acquisition defined when performing vehicle rapid acceleration detection. Specifically, the state acquisition window is used to control the timing of data acquisition. It specifies the time window for collecting data from vehicle motion state sensors (such as acceleration sensors and gyroscopes) (i.e., how often data is collected) and how much data is collected each time. The state acquisition window is usually in milliseconds (ms), for example, the data sampling frequency is set to 50Hz.

[0022] A200: Using the state collection window as a constraint, controlling the motion state unit to intermittently collect the motion state data of the target vehicle to obtain a real-time state data sequence.

[0023] In one embodiment, the state acquisition window is used as a constraint to control the motion state unit to intermittently acquire the motion state data of the target vehicle to obtain a real-time state data sequence. Previously, the method step A200 provided in the present application includes:

[0024] A201: Deploy the motion state unit on the target vehicle, wherein the motion state unit includes a three-axis acceleration sensor and a three-axis gyroscope.

[0025] A202: Perform deviation calibration and temperature compensation on the motion state unit to complete the localization of the data acquisition function of the motion state unit.

[0026] A203: Preset the data sampling frequency, and set the status acquisition window based on the data sampling frequency.

[0027] A204: Using the state collection window as a constraint, controlling the motion state unit to intermittently collect the motion state data of the target vehicle.

[0028] In one embodiment, the state acquisition window is used as a constraint to control the motion state unit to intermittently acquire the motion state data of the target vehicle to obtain a real-time state data sequence. Step A200 of the method provided by the present application further includes:

[0029] A210: Pre-construct the first doubly linked list and the second doubly linked list.

[0030] A220: After the target vehicle is started, the motion state unit is controlled to intermittently collect the motion state data of the target vehicle with the state collection window as a constraint to obtain first real-time state data, wherein the first real-time state data includes first acceleration data and first angular velocity data.

[0031] A230: Temporarily store the first acceleration data in the first bidirectional linked list, wherein a first cache node in the first bidirectional linked list for storing the first acceleration data includes pointers to previous and next nodes.

[0032] A240: Similarly, temporarily store the first angular velocity data in the second bidirectional linked list.

[0033] A250: Similarly, the motion state unit is controlled to intermittently collect the motion state data of the target vehicle, and the obtained real-time state data sequence is mapped and stored in the first bidirectional linked list and the second bidirectional linked list.

[0034] In one embodiment, the method steps provided by the present application further include:

[0035] A231: Predefined data buffer length.

[0036] A232: Use the data cache length to initialize a standard bidirectional linked list to obtain the first bidirectional linked list, wherein the first bidirectional linked list is provided with a ring buffer.

[0037] A233: When temporarily storing the first acceleration data in the first bidirectional linked list, the first bidirectional linked list synchronously performs dynamic removal of historical data of the ring buffer.

[0038] Specifically, before data collection, this embodiment deploys the motion state unit on the target vehicle, wherein the motion state unit includes a three-axis acceleration sensor and a three-axis gyroscope.

[0039] Among them, the three-axis acceleration sensor is used to collect the acceleration data of the vehicle in the three directions of X, Y, and Z (i.e., ACCx, ACCy, and ACCz) in real time, and the three-axis gyroscope is used to collect the angular velocity data of the vehicle in the three directions of X, Y, and Z (i.e., Gyrox, Gyroy, and Gyroz) in real time. Through the cooperation of these sensors, the motion state unit can accurately record the real-time motion state of the vehicle, providing raw data support for subsequent rapid acceleration detection, vehicle condition monitoring, etc.

[0040] In order to improve the accuracy and stability of data collection, the motion state unit needs to perform deviation calibration and temperature compensation before collecting data. Specifically, the initial deviation of the acceleration sensor and gyroscope is corrected to ensure that the data read by the sensor is close to the actual value. The measurement results of the sensor may be affected by changes in ambient temperature. Through temperature compensation, the impact of temperature fluctuations on sensor data is corrected to ensure the accuracy of data under different temperature conditions.

[0041] In this embodiment, in order to ensure the real-time and continuity of data collection, a data sampling frequency (e.g., 50 Hz) is preset, and the size of the state collection window is determined based on the sampling frequency. The sampling frequency determines the time interval of each data collection, which in turn affects the length of the state collection window.

[0042] In this embodiment, the state acquisition window is used to constrain the data acquisition period of the motion state unit. Each time the acquisition is performed, the acceleration data and angular velocity data of the target vehicle are intermittently acquired according to the size of the acquisition window to form a real-time state data sequence.

[0043] The storage management method of the real-time status data sequence is as follows:

[0044] A first bidirectional linked list and a second bidirectional linked list are pre-constructed to store acceleration data and angular velocity data respectively. Both the first bidirectional linked list and the second bidirectional linked list support efficient data storage and fast insertion / deletion operations.

[0045] After the target vehicle is started, the motion state unit is controlled to intermittently collect the motion state data of the target vehicle according to the set collection window to obtain first real-time state data, wherein the first real-time state data includes first acceleration data and first angular velocity data.

[0046] The first acceleration data is temporarily stored in the first bidirectional linked list, wherein the first cache node in the first bidirectional linked list for storing the first acceleration data includes pointers to the previous and next nodes, and the pointers and the data stored in the nodes ensure the continuity of the data and the traversal efficiency of the linked list.

[0047] In order to ensure that the amount of data stored in the doubly linked list is moderate, the cache length N (predefined data cache length) is set to ensure that the linked list memory is not over-occupied. The first doubly linked list is initialized with the predefined data cache length and a ring buffer is set for it. In this way, when the cache reaches the maximum length, the new data of the first doubly linked list will overwrite the oldest data.

[0048] When the first acceleration data is temporarily stored in the first bidirectional linked list, the first bidirectional linked list synchronously performs dynamic removal of historical data of the ring buffer, that is, when the data stored in the linked list reaches a set length N, the oldest data will be automatically removed, thereby maintaining the memory utilization and update efficiency of the linked list.

[0049] Similarly, the real-time angular velocity data is stored in the second bidirectional linked list. Each new data collected is inserted into the end of the linked list in chronological order. By storing the acceleration and angular velocity data in the bidirectional linked list, the continuity and stability of the data sequence are ensured, which is convenient for subsequent rapid acceleration detection and analysis.

[0050] This embodiment achieves the technical effect of optimizing memory management, ensuring the real-time nature of vehicle driving status data, and avoiding delays or storage overflows caused by data backlog by storing acceleration and angular velocity data in a bidirectional linked list and using a ring buffer to dynamically remove historical data.

[0051] A300: After performing multi-level data preprocessing on the real-time status data sequence, perform vehicle rapid acceleration detection according to the real-time status data sequence, and output a frequent rapid acceleration warning, wherein the frequent rapid acceleration warning has a risk time interval identifier.

[0052] In one embodiment, Figure 2 As shown, the vehicle rapid acceleration detection is performed according to the real-time status data sequence, and a frequent rapid acceleration warning is output, wherein the frequent rapid acceleration warning has a risk time interval identifier. The method step A300 provided in the present application also includes:

[0053] A310: Extracting and obtaining real-time acceleration data from the first bidirectional linked list based on a sliding time window.

[0054] A320: Calculate the average of the real-time acceleration data, and after obtaining the acceleration average, compare the preset acceleration threshold with the acceleration average.

[0055] A330: If the preset acceleration threshold is less than the acceleration mean, the sudden acceleration detection mechanism is triggered.

[0056] A340: After the sudden acceleration detection mechanism is triggered, sudden acceleration behaviors are accumulated to obtain real-time behavior accumulation records.

[0057] A350: When the real-time behavior accumulation record meets the preset acceleration behavior threshold, the frequent sudden acceleration warning is generated and sent.

[0058] In one embodiment, if the preset acceleration threshold is less than the acceleration mean, the sudden acceleration detection mechanism is triggered. Previously, the method step A300 provided in the present application also includes:

[0059] A331: Taking the acquisition time of the real-time acceleration data as a constraint, extract the real-time angular velocity data from the second bidirectional linked list based on a sliding time window.

[0060] A332: Predefine multi-dimensional angular velocity threshold and deviation frequency threshold.

[0061] A333: Use the multi-dimensional angular velocity threshold to traverse the real-time angular velocity data to record the angular velocity deviation and obtain the angular velocity deviation frequency.

[0062] A334: If the angular velocity deviation frequency does not meet the deviation frequency threshold, the rapid acceleration detection mechanism is triggered.

[0063] In this embodiment, the multi-level data preprocessing of the real-time status data sequence is essentially based on a low-pass filtering algorithm and a Kalman filtering algorithm to gradually filter out data noise and uncertainty at different levels, thereby improving the accuracy and stability of the data.

[0064] Specifically, the acceleration data (ACCx, ACCy, ACCz) and angular velocity data (Gyrox, Gyroy, Gyroz) collected in real time are used as input data, and the accuracy of the real-time data is optimized through conventional low-pass filtering and Kalman filtering to obtain optimized acceleration data and angular velocity data. Then, the optimized data are stored in a bidirectional linked list in real time. The stored data in the bidirectional linked list is used for subsequent judgment on whether the vehicle has frequent sudden acceleration.

[0065] The method for determining whether a vehicle has frequent sudden acceleration behavior based on the bidirectional linked list record data in this embodiment is as follows:

[0066] The real-time acceleration data (data sequence) is extracted from the first bidirectional linked list through a sliding time window, and the acceleration data sequence reflects the acceleration change of the vehicle in a specific time period. Through the sliding time window, the latest acceleration data can be continuously obtained and processed from the linked list to monitor the acceleration state of the vehicle in real time.

[0067] The data sequence of the extracted real-time acceleration data is averaged to smooth out short-term noise and sudden fluctuations, and obtain a more stable and accurate acceleration signal. Then, the calculated acceleration mean is compared with the preset acceleration threshold. If the acceleration mean is greater than the preset threshold, it means that the vehicle may be experiencing sudden acceleration behavior. At this time, step A330 will trigger the sudden acceleration detection mechanism to further analyze the vehicle's behavior.

[0068] In order to enhance the accuracy and reliability of sudden acceleration detection, the present invention also combines the analysis of angular velocity data. Specifically, based on the data sequence and timestamp of the collected real-time acceleration data, real-time angular velocity data (such as Gyrox, Gyroy, Gyroz) are extracted from the second bidirectional linked list. These angular velocity data are mainly used to analyze the dynamic changes of the vehicle, especially in the case of bumpy roads. The angular velocity data can help the system identify vibrations or shocks caused by uneven road surfaces.

[0069] The multi-dimensional angular velocity threshold and the deviation frequency threshold are preset to judge abnormal fluctuations in the angular velocity data. The multi-dimensional angular velocity threshold is specifically a set of angular velocity thresholds for different directions (or axes). These thresholds help judge whether the vehicle is experiencing vibrations caused by large turns, bumps or uneven roads, thereby avoiding misjudgment of sudden acceleration behavior.

[0070] By setting these thresholds, it is possible to monitor whether the vehicle's angular velocity deviation is caused by uneven road surfaces. If the angular velocity fluctuations in multiple directions exceed the set unified threshold, it can be determined that the vehicle is currently driving on an uneven road section, rather than the vehicle frequently accelerating suddenly.

[0071] Based on this, after obtaining the real-time angular velocity data, the real-time angular velocity data is traversed and the deviation frequency is recorded. When it is detected that the deviation of the angular velocity reaches the set frequency, it is considered that the current acceleration change deviates from the preset threshold value not due to frequent sudden acceleration, but due to uneven road surface, rather than sudden acceleration behavior. If the deviation frequency of the angular velocity does not meet the set deviation frequency threshold, the system will trigger the sudden acceleration detection mechanism to ensure that the warning will only be triggered when the characteristics of sudden acceleration and uneven road sections act together.

[0072] After the sudden acceleration detection mechanism is triggered, each time the acceleration is detected to exceed the preset threshold, the behavior will be recorded as a sudden acceleration event in the real-time behavior accumulation record. The record will be continuously updated to reflect the frequency of sudden acceleration behaviors that occur in the vehicle within a certain period of time.

[0073] When the real-time behavior accumulation record meets the preset acceleration behavior threshold, the frequent sudden acceleration warning is generated and sent.

[0074] Through high-frequency sampling and linked list cache management and analysis, this embodiment can process and analyze acceleration data in real time, achieve rapid response to frequent and sudden acceleration of the vehicle, and achieve the technical effect of being able to make judgments and provide feedback in a very short time when frequent and sudden acceleration behaviors suddenly occur during driving, thereby providing the driver with timely warning information and helping to prevent potential dangers.

[0075] A400: Reversely call the vehicle behavior data of the target vehicle according to the risk time interval to obtain associated behavior data.

[0076] Specifically, in this embodiment, during the sudden acceleration detection process, once a potential sudden acceleration risk is identified, the vehicle's behavior data (such as vehicle acceleration, steering, braking, etc.) will be reversely queried based on the risk time interval (i.e., the time period when the sudden acceleration event occurs), thereby providing the necessary information for subsequent vehicle condition detection.

[0077] A500: Perform vehicle condition detection according to the associated behavior data and output vehicle condition detection information.

[0078] In one embodiment, the vehicle condition detection is performed according to the associated behavior data, and the vehicle condition detection information is output. The method step A500 provided in the present application further includes:

[0079] A510: Interactively obtain multiple sample association data combinations of multiple sample vehicle failures.

[0080] A520: Decompose the associated behavior data based on the combination of the multiple sample associated data as constraints to obtain multiple associated behavior arrays.

[0081] A530: Perform vehicle condition fault directional analysis based on the multiple associated behavior arrays to obtain multiple real-time fault analysis results.

[0082] A540: Use the multiple real-time fault analysis results to traverse the fault risk assignment table to obtain multiple real-time fault risk coefficients.

[0083] A550: Add the multiple real-time fault risk coefficients to obtain the vehicle condition detection information, wherein the vehicle condition detection information is identified by the multiple real-time fault analysis results.

[0084] In one embodiment, based on the multiple associated behavior arrays, a vehicle condition fault directional analysis is performed to obtain multiple real-time fault analysis results. The method step A530 provided by the present application further includes:

[0085] A531: Using the plurality of sample associated data combinations as constraints, collect fault vehicle condition data to obtain a plurality of sample fault behavior arrays.

[0086] A532: Use the multiple sample vehicle faults and multiple sample fault behavior arrays for training to obtain multiple vehicle condition fault identification branches.

[0087] A533: Connect the multiple vehicle condition fault identification branches in parallel to complete the construction of the vehicle condition fault analysis model.

[0088] A534: According to the mapping relationship between the multiple associated behavior arrays and the multiple sample vehicle faults, the multiple associated behavior arrays are loaded into the multiple vehicle condition fault identification branches of the vehicle condition fault analysis model to simultaneously perform vehicle condition fault directional analysis to obtain the multiple real-time fault analysis results.

[0089] This embodiment uses the associated behavior data extracted from A400 to detect the vehicle condition to determine whether the vehicle has potential faults or abnormal working conditions under frequent and sudden acceleration.

[0090] Specifically, this embodiment constructs a vehicle condition fault analysis model for vehicle condition detection.

[0091] Samples are extracted from the fault data of different vehicles in an interactive manner and combined into multiple sample-related data combinations. Each sample-related data combination represents the vehicle condition data indicators that need to be applied to determine whether a certain fault has occurred.

[0092] With the combination of the multiple sample associated data as constraints, the fault vehicle condition data is collected to obtain multiple sample fault behavior arrays, which record in detail the dynamic behavior of the vehicle under different fault scenarios. Fault scenarios may include engine failure, brake failure, suspension system problems, etc. All collected fault behavior data will be used as input data for the analysis model.

[0093] Based on multiple sample fault behavior arrays for training, multiple vehicle condition fault identification branches are constructed using machine learning or pattern recognition algorithms. These branches represent independent identification logic for different fault types (such as engine failure, brake problems, etc.). During the training process, the algorithm will extract features from a large number of fault behavior arrays, learn the behavior patterns of different fault types, and build a dedicated identification model for each fault type. Each vehicle condition fault identification branch will determine whether the vehicle is in a specific fault state based on the input vehicle behavior data (such as acceleration, angular velocity, etc.).

[0094] The multiple vehicle condition fault identification branches are connected in parallel to complete the construction of the vehicle condition fault analysis model. The parallel structure means that all identification branches can run in parallel at the same time to analyze the behavior data of the same vehicle and identify multiple potential fault types. Each branch will independently output the judgment result of the fault type, and then these results will be integrated to finally generate a comprehensive vehicle condition analysis report.

[0095] The specific implementation method of performing vehicle condition fault analysis based on the vehicle condition fault analysis model is as follows:

[0096] The vehicle fault data is disassembled and classified by using the multiple sample association data combinations as constraint conditions to obtain multiple association behavior arrays. Each sample association data combination represents the behavior mode of the vehicle under different fault scenarios and includes multiple vehicle condition data indicators (such as acceleration, angular velocity, steering angle, etc.).

[0097] According to the mapping relationship between multiple associated behavior arrays and multiple sample vehicle faults, multiple associated behavior arrays are loaded into multiple vehicle condition fault identification branches of the vehicle condition fault analysis model for synchronous analysis. Each associated behavior array represents the vehicle behavior data under a specific fault type, and the mapping relationship between these data and different fault types has been established through training. According to these mapping relationships, the associated behavior arrays are respectively input into the corresponding vehicle condition fault identification branches for parallel fault-oriented analysis. Each identification branch independently analyzes the input data, identifies possible fault types, and outputs real-time fault analysis results to obtain the multiple real-time fault analysis results.

[0098] The fault risk assignment score table presets the risk assessment criteria for each fault type. The multiple real-time fault analysis results are used to traverse the fault risk assignment score table to obtain multiple real-time fault risk coefficients, and the multiple real-time fault risk coefficients are added to obtain the vehicle condition detection information, wherein the vehicle condition detection information is identified by the multiple real-time fault analysis results.

[0099] After detecting frequent sudden acceleration behaviors, this embodiment can identify potential vehicle failures or abnormal conditions behind sudden acceleration through further analysis of associated behavior data, thereby achieving the technical effect of providing real-time vehicle health status reminders on the basis of sudden acceleration risk reminders, thereby reducing potential risks and ensuring the safety and stable operation of the vehicle.

[0100] A600: Update the warning level of the frequent rapid acceleration warning according to the vehicle condition detection information.

[0101] Specifically, in this embodiment, after frequent sudden acceleration behavior is detected, the severity of the warning is adjusted in real time according to the health status of the vehicle and the fault detection results, ensuring that the driver can make appropriate safety decisions based on the actual risk situation of the vehicle.

[0102] For example, after frequent sudden acceleration behavior is detected, the vehicle condition detection result is scored as 20, and the original warning level is updated according to the corresponding relationship between the scoring and the level increase table.

[0103] This embodiment achieves the technical effect of being able to make judgments and provide feedback in a very short time when frequent rapid acceleration behaviors suddenly occur during driving, providing the driver with timely comprehensive warning information on driving behaviors and vehicle conditions, thereby preventing potential driving hazards.

[0104] Embodiment 2

[0105] Based on the same inventive concept as the vehicle rapid acceleration risk detection method in the aforementioned embodiment, Figure 3 As shown, the present application provides a vehicle rapid acceleration risk detection platform, wherein the platform includes:

[0106] The collection function configuration unit 11 is used to preset a state collection window.

[0107] The collection function operation unit 12 is used to control the motion state unit to perform intermittent collection of motion state data of the target vehicle based on the state collection window as a constraint, so as to obtain a real-time state data sequence.

[0108] The driving warning output unit 13 is used to perform vehicle rapid acceleration detection according to the real-time status data sequence after multi-level data preprocessing, and output a frequent rapid acceleration warning, wherein the frequent rapid acceleration warning has a risk time interval identifier.

[0109] The behavior data collection unit 14 is used to reversely call the vehicle behavior data of the target vehicle according to the risk time interval to obtain related behavior data.

[0110] The vehicle condition detection execution unit 15 is used to perform vehicle condition detection according to the associated behavior data and output vehicle condition detection information.

[0111] The warning level updating unit 16 is used to update the warning level of the frequent rapid acceleration warning according to the vehicle condition detection information.

[0112] In one embodiment, the acquisition function operation unit 12 further includes:

[0113] The motion state unit is deployed on the target vehicle, wherein the motion state unit includes a three-axis acceleration sensor and a three-axis gyroscope. The motion state unit is subjected to deviation calibration and temperature compensation to complete the localization of the data acquisition function of the motion state unit. A data sampling frequency is preset, and the state acquisition window is set based on the data sampling frequency. With the state acquisition window as a constraint, the motion state unit is controlled to intermittently acquire the motion state data of the target vehicle.

[0114] In one embodiment, the acquisition function operation unit 12 further includes:

[0115] Pre-construct a first bidirectional linked list and a second bidirectional linked list. After the target vehicle is started, the motion state unit is controlled to intermittently collect the motion state data of the target vehicle with the state collection window as a constraint to obtain first real-time state data, wherein the first real-time state data includes first acceleration data and first angular velocity data. The first acceleration data is temporarily stored in the first bidirectional linked list, wherein the first cache node in the first bidirectional linked list for storing the first acceleration data contains pointers to the front and rear nodes. Similarly, the first angular velocity data is temporarily stored in the second bidirectional linked list. Similarly, the motion state unit is controlled to intermittently collect the motion state data of the target vehicle, and the obtained real-time state data sequence is mapped and stored in the first bidirectional linked list and the second bidirectional linked list.

[0116] In one embodiment, the acquisition function operation unit 12 further includes:

[0117] A data cache length is predefined. A standard bidirectional linked list is initialized using the data cache length to obtain the first bidirectional linked list, wherein the first bidirectional linked list is provided with a ring buffer. When the first acceleration data is temporarily stored in the first bidirectional linked list, the first bidirectional linked list synchronously performs dynamic removal of historical data in the ring buffer.

[0118] In one embodiment, the driving warning output unit 13 further includes:

[0119] The real-time acceleration data is extracted from the first bidirectional linked list based on the sliding time window. The real-time acceleration data is averaged, and after the acceleration average is obtained, the preset acceleration threshold is compared with the acceleration average. If the preset acceleration threshold is less than the acceleration average, the sudden acceleration detection mechanism is triggered. After the sudden acceleration detection mechanism is triggered, the sudden acceleration behavior is accumulated to obtain the real-time behavior accumulation record. When the real-time behavior accumulation record meets the preset acceleration behavior threshold, the frequent sudden acceleration warning is generated and sent.

[0120] In one embodiment, the driving warning output unit 13 further includes:

[0121] Taking the acquisition time of the real-time acceleration data as a constraint, the real-time angular velocity data is extracted from the second bidirectional linked list based on a sliding time window. A multidimensional angular velocity threshold and a deviation frequency threshold are predefined. The real-time angular velocity data is traversed using the multidimensional angular velocity threshold to record the angular velocity deviation and obtain the angular velocity deviation frequency. If the angular velocity deviation frequency does not meet the deviation frequency threshold, the sudden acceleration detection mechanism is triggered.

[0122] In one embodiment, the vehicle condition detection execution unit 15 further includes:

[0123] Interactively obtain multiple sample association data combinations of multiple sample vehicle faults. Disassemble the association behavior data with the multiple sample association data combinations as constraints to obtain multiple association behavior arrays. Perform vehicle condition fault directional analysis based on the multiple association behavior arrays to obtain multiple real-time fault analysis results. Use the multiple real-time fault analysis results to traverse the fault risk assignment table to obtain multiple real-time fault risk coefficients. Add the multiple real-time fault risk coefficients to obtain the vehicle condition detection information, wherein the vehicle condition detection information is identified by the multiple real-time fault analysis results.

[0124] In one embodiment, the vehicle condition detection execution unit 15 further includes:

[0125] Taking the combination of the multiple sample associated data as a constraint, perform fault vehicle condition data collection to obtain multiple sample fault behavior arrays. Use the multiple sample vehicle faults and multiple sample fault behavior arrays to train to obtain multiple vehicle condition fault identification branches. Connect the multiple vehicle condition fault identification branches in parallel to complete the construction of the vehicle condition fault analysis model. According to the mapping relationship between the multiple associated behavior arrays and the multiple sample vehicle faults, load the multiple associated behavior arrays into the multiple vehicle condition fault identification branches of the vehicle condition fault analysis model to synchronously perform vehicle condition fault directional analysis to obtain the multiple real-time fault analysis results.

[0126] Embodiment 3

[0127] Based on the same inventive concept as the vehicle rapid acceleration risk detection method in the aforementioned embodiment one, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the steps of the vehicle rapid acceleration risk detection method described in any one of the aforementioned embodiments one are implemented.

[0128] Any of the methods or steps described above may be stored as computer instructions or programs in various types of computer memories, and the computer instructions or programs may be recognized by various types of computer processors to implement any of the methods or steps described above.

[0129] Based on the above specific embodiments of the present invention, any improvements and modifications made to the present invention by those skilled in the art without departing from the principles of the present invention shall fall within the patent protection scope of the present invention.

Claims

1. A vehicle rapid acceleration risk detection method, characterized in that: The method comprises: Preset status acquisition window; Using the state acquisition window as a constraint, controlling the motion state unit to intermittently acquire the motion state data of the target vehicle to obtain a real-time state data sequence; After multi-level data preprocessing is performed on the real-time status data sequence, vehicle rapid acceleration detection is performed according to the real-time status data sequence, and a frequent rapid acceleration warning is output, wherein the frequent rapid acceleration warning has a risk time interval identifier; Reversely call the vehicle behavior data of the target vehicle according to the risk time interval to obtain associated behavior data; Perform vehicle condition detection according to the associated behavior data and output vehicle condition detection information; The warning level of the frequent sudden acceleration warning is updated according to the vehicle condition detection information.

2. The vehicle rapid acceleration risk detection method according to claim 1, characterized in that: Taking the state acquisition window as a constraint, controlling the motion state unit to intermittently acquire the motion state data of the target vehicle to obtain a real-time state data sequence, before that, the method includes: Deploying the motion state unit on the target vehicle, wherein the motion state unit includes a three-axis acceleration sensor and a three-axis gyroscope; Performing deviation calibration and temperature compensation on the motion state unit to complete the localization of the data acquisition function of the motion state unit; Presetting a data sampling frequency, and setting the state acquisition window based on the data sampling frequency; The motion state unit is controlled to intermittently collect the motion state data of the target vehicle with the state collection window as a constraint.

3. The vehicle rapid acceleration risk detection method according to claim 2, characterized in that: Taking the state acquisition window as a constraint, controlling the motion state unit to intermittently acquire the motion state data of the target vehicle to obtain a real-time state data sequence, the method comprising: Pre-constructing a first doubly linked list and a second doubly linked list; After the target vehicle is started, the motion state unit is controlled to intermittently collect the motion state data of the target vehicle with the state collection window as a constraint to obtain first real-time state data, wherein the first real-time state data includes first acceleration data and first angular velocity data; Temporarily storing the first acceleration data in the first bidirectional linked list, wherein a first cache node in the first bidirectional linked list for storing the first acceleration data includes pointers to previous and next nodes; Similarly, the first angular velocity data is temporarily stored in the second bidirectional linked list; By analogy, the motion state unit is controlled to intermittently collect the motion state data of the target vehicle, and the obtained real-time state data sequence is mapped and stored in the first bidirectional linked list and the second bidirectional linked list.

4. The vehicle rapid acceleration risk detection method according to claim 3, characterized in that: The method comprises: Predefined data buffer length; Initializing a standard bidirectional linked list using the data cache length to obtain the first bidirectional linked list, wherein the first bidirectional linked list is provided with a ring buffer; When the first acceleration data is temporarily stored in the first bidirectional linked list, the first bidirectional linked list synchronously performs dynamic removal of historical data of the ring buffer.

5. The vehicle rapid acceleration risk detection method according to claim 4, characterized in that: Performing vehicle rapid acceleration detection according to the real-time status data sequence and outputting a frequent rapid acceleration warning, wherein the frequent rapid acceleration warning has a risk time interval identifier, the method comprising: Extracting and obtaining real-time acceleration data from the first bidirectional linked list based on a sliding time window; Calculating the mean of the real-time acceleration data, and after obtaining the acceleration mean, comparing a preset acceleration threshold with the acceleration mean; If the preset acceleration threshold is less than the acceleration mean, the rapid acceleration detection mechanism is triggered; After the sudden acceleration detection mechanism is triggered, the sudden acceleration behavior is accumulated to obtain real-time behavior accumulation records; When the real-time behavior accumulation record meets the preset acceleration behavior threshold, the frequent sudden acceleration warning is generated and sent.

6. The vehicle rapid acceleration risk detection method according to claim 5, characterized in that: If the preset acceleration threshold is less than the acceleration mean, the sudden acceleration detection mechanism is triggered. Previously, the method includes: Taking the acquisition time of the real-time acceleration data as a constraint, extracting the real-time angular velocity data from the second bidirectional linked list based on a sliding time window; Predefine multi-dimensional angular velocity thresholds and deviation frequency thresholds; The multi-dimensional angular velocity threshold is used to traverse the real-time angular velocity data to record angular velocity deviations, and obtain angular velocity deviation frequencies; If the angular velocity deviation frequency does not meet the deviation frequency threshold, the rapid acceleration detection mechanism is triggered.

7. The vehicle rapid acceleration risk detection method according to claim 1, characterized in that: Performing vehicle condition detection according to the associated behavior data and outputting vehicle condition detection information, the method comprises: Interactively obtain multiple sample association data combinations of multiple sample vehicle faults; Decomposing the associated behavior data with the combination of the plurality of sample associated data as constraints to obtain a plurality of associated behavior arrays; Performing vehicle condition fault directional analysis based on the multiple associated behavior arrays to obtain multiple real-time fault analysis results; Using the multiple real-time fault analysis results to traverse the fault risk assignment table to obtain multiple real-time fault risk coefficients; The multiple real-time fault risk coefficients are added to obtain the vehicle condition detection information, wherein the vehicle condition detection information is identified by the multiple real-time fault analysis results.

8. The vehicle rapid acceleration risk detection method according to claim 7, characterized in that: Based on the multiple associated behavior arrays, a vehicle condition fault directional analysis is performed to obtain multiple real-time fault analysis results, and the method includes: Taking the combination of the plurality of sample associated data as a constraint, collecting fault vehicle condition data to obtain a plurality of sample fault behavior arrays; Using the plurality of sample vehicle faults and the plurality of sample fault behavior arrays for training to obtain a plurality of vehicle condition fault identification branches; Connecting the plurality of vehicle condition fault identification branches in parallel to complete the construction of a vehicle condition fault analysis model; According to the mapping relationship between the multiple associated behavior arrays and multiple sample vehicle faults, the multiple associated behavior arrays are loaded into the multiple vehicle condition fault identification branches of the vehicle condition fault analysis model to simultaneously perform vehicle condition fault directional analysis to obtain the multiple real-time fault analysis results.

9. Vehicle rapid acceleration risk detection platform, characterized by: The vehicle rapid acceleration risk detection platform is used to implement the method for detecting the risk of rapid acceleration of a vehicle according to any one of claims 1 to 8, wherein the vehicle rapid acceleration risk detection platform comprises: A collection function configuration unit, used for presetting a state collection window; A collection function operation unit, used to control the motion state unit to intermittently collect the motion state data of the target vehicle with the state collection window as a constraint, so as to obtain a real-time state data sequence; A driving warning output unit, configured to perform vehicle rapid acceleration detection according to the real-time status data sequence after multi-level data preprocessing, and output a frequent rapid acceleration warning, wherein the frequent rapid acceleration warning has a risk time interval identifier; A behavior data collection unit, used to reversely call the vehicle behavior data of the target vehicle according to the risk time interval to obtain associated behavior data; A vehicle condition detection execution unit, used to perform vehicle condition detection according to the associated behavior data and output vehicle condition detection information; The warning level updating unit is used to update the warning level of the frequent rapid acceleration warning according to the vehicle condition detection information.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which implements the steps of the vehicle rapid acceleration risk detection method described in any one of claims 1 to 8 when executed.

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