Vehicle abnormal movement detection method, device and vehicle

Abstract

The present invention discloses a vehicle abnormal movement detection method, device and vehicle, which relate to the field of vehicle detection. The method includes: in response to the vehicle being in a dormant state, obtaining a first acceleration of the vehicle; in response to the first acceleration being greater than a first preset acceleration, obtaining vehicle data of the vehicle, wherein the vehicle data includes at least: a second acceleration of the vehicle, a target angular velocity of the vehicle, a target angular change of the vehicle's attitude, and a target position change of the vehicle, the first preset acceleration is determined based on a first number of times the vehicle has abnormal movement and the external conditions of the vehicle, the external conditions including at least: location data of the vehicle's location, environmental data of the location, seasonal data and weather data; determining whether the vehicle has abnormal movement based on the vehicle data. The present invention solves the technical problem of low accuracy of vehicle abnormal movement detection in related technologies.

Description

Technical Field

[0001] The present invention relates to the field of vehicle detection, and in particular to a vehicle abnormal movement detection method, device and vehicle. Background Art

[0002] Vehicle movement detection is an important technology to ensure passenger safety. Currently, the main implementation scheme for vehicle movement detection is to monitor the vehicle's acceleration value through an acceleration sensor and determine whether the vehicle has moved abnormally by comparing it with a fixed threshold. However, the accuracy of the above-mentioned vehicle movement detection is low. When the vehicle is in extreme weather or extreme environments such as strong winds and rain, the vehicle movement detection alarm is prone to missed reports or false alarms, resulting in the user being unable to understand the vehicle's condition in a timely manner, which may cause safety accidents.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] Embodiments of the present invention provide a vehicle abnormal movement detection method, device, and vehicle to at least solve the technical problem of low accuracy in vehicle abnormal movement detection in related technologies.

[0005] According to one aspect of an embodiment of the present invention, a vehicle movement detection method is provided, comprising: in response to the vehicle being in a dormant state, obtaining a first acceleration of the vehicle; in response to the first acceleration being greater than a first preset acceleration, obtaining vehicle data of the vehicle, wherein the vehicle data includes at least: a second acceleration of the vehicle, a target angular velocity of the vehicle, a target angular change of the vehicle posture, and a target position change of the vehicle, the first preset acceleration being determined based on a first number of vehicle movement abnormalities and external conditions of the vehicle, the external conditions including at least: position data of the vehicle's location, environmental data, seasonal data, and weather data of the location; determining whether the vehicle movement abnormalities occur based on the vehicle data.

[0006] Optionally, the above method also includes: determining a first number of times that the vehicle has undergone abnormal movements within a historical time period; determining a second number of times that the historical acceleration of the vehicle within the historical time period is greater than a second preset acceleration, where the second preset acceleration is the product of the first acceleration threshold and the first preset value; based on the comparison result of the first number and the second number, adjusting the acceleration threshold to obtain the first preset acceleration.

[0007] Optionally, based on the comparison result of the first number and the second number, the acceleration threshold is adjusted to obtain a first preset acceleration, including: obtaining the average value of historical acceleration to obtain a first average value; obtaining the difference between the second number and the first number; in response to the difference being greater than a preset difference, controlling the first acceleration threshold to decrease according to a preset ratio to obtain a second acceleration threshold; and adding the second acceleration threshold to the first average value to obtain the first preset acceleration.

[0008] Optionally, in response to the difference being greater than a preset difference, the method includes: controlling the first acceleration threshold to increase according to a preset ratio to obtain a third acceleration threshold; and adding the third acceleration threshold to the first average value to obtain the first preset acceleration.

[0009] Optionally, determining whether the vehicle has moved abnormally based on vehicle data includes: comparing the vehicle data with preset vehicle data, wherein the preset vehicle data is determined by the server based on external conditions of the vehicle; and determining that the vehicle has moved abnormally in response to the vehicle data being greater than the preset vehicle data.

[0010] Optionally, the above vehicle data greater than the preset vehicle data includes at least one of the following: the second acceleration is greater than the first preset acceleration, the target angular velocity is greater than the preset angular velocity, the target angle change is greater than the preset angle change, and the target position change is greater than the preset position change.

[0011] Optionally, obtaining vehicle data of the vehicle includes: obtaining multiple accelerations according to a first preset period, the number of the multiple accelerations being a first preset number of times, and filtering the multiple acceleration data to obtain a second acceleration of the vehicle; obtaining multiple angular velocities according to the first preset period, the number of the multiple angular velocities being a first preset number of times, and filtering the multiple angular velocities to obtain a target angular velocity of the vehicle; obtaining a target angle change based on an average value of the historical accelerations of the vehicle and an average value of the second acceleration within a historical time period; and comparing the historical position of the vehicle with the current position of the vehicle to obtain a target position change.

[0012] Optionally, in response to the vehicle being in a dormant state, the method further includes: obtaining location information and time information of the vehicle; sending the location information and time information to a cloud server, wherein the location information and time information are used by the cloud server to perform data analysis based on abnormality information sent by multiple vehicles within a historical time period to determine whether the vehicle has a risk of abnormality, wherein the abnormality information includes: the locations of multiple vehicles and the time when the abnormality occurred in multiple vehicles; receiving prompt information sent by the cloud server, wherein the prompt information is used to indicate that the vehicle has a risk of abnormality.

[0013] According to another aspect of an embodiment of the present invention, a vehicle is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the above methods.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided. The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned vehicle movement detection method.

[0015] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein the above-mentioned vehicle abnormal movement detection method is executed when the program is run.

[0016] In an embodiment of the present invention, a first acceleration of the vehicle is obtained in response to the vehicle being in a dormant state, and in response to the first acceleration being greater than a first preset acceleration, vehicle data of the vehicle is obtained, and then whether the vehicle has experienced unusual movement is determined based on the vehicle data. It is readily apparent that the first preset acceleration is determined based on the number of unusual vehicle movements and the vehicle's external conditions. That is, the first preset acceleration can be adjusted based on actual conditions and is no longer a fixed threshold. Furthermore, a comprehensive determination of whether the vehicle has experienced unusual movement is made based on a variety of different types of vehicle data. This achieves the technical effect of improving the accuracy of detecting unusual vehicle movement in extreme weather and environments, thereby resolving the technical issue of low accuracy in detecting unusual vehicle movement in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 is a flow chart of a vehicle abnormal movement detection method according to an embodiment of the present invention;

[0019] Figure 2 is a schematic diagram of an optional vehicle abnormal movement detection device according to an embodiment of the present invention;

[0020] Figure 3 2 is a schematic diagram of an optional vehicle abnormal movement detection device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] Example 1

[0024] According to an embodiment of the present invention, a method for detecting abnormal vehicle movement is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] Figure 1 FIG. 1 is a flow chart of a vehicle abnormal movement detection method according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0026] Step S102 : In response to the vehicle being in a dormant state, obtaining a first acceleration of the vehicle.

[0027] The dormant state may be a state in which the entire vehicle enters a low power consumption mode when the user is not using the vehicle, and the first acceleration may be the acceleration of the vehicle detected in real time when the vehicle movement detection is not started.

[0028] In an optional embodiment, it is possible to confirm that the vehicle has entered a dormant state 35 seconds after the vehicle doors are locked, or after the vehicle doors are unlocked but no operation is performed for 10 minutes, and then obtain the first acceleration of the vehicle through an acceleration sensor, wherein the acceleration sensor can be a three-axis acceleration sensor, but is not limited to a three-axis acceleration sensor, and can also be a two-axis acceleration sensor.

[0029] It should be noted that, since vehicle abnormal movement refers to vibration, displacement or other posture changes of the vehicle when the vehicle is not started, such as slipping, vehicle theft, etc., it is necessary to obtain the first acceleration of the vehicle when the vehicle is in a dormant state.

[0030] Step S104, in response to the first acceleration being greater than the first preset acceleration, obtaining vehicle data of the vehicle, wherein the vehicle data includes at least: the second acceleration of the vehicle, the target angular velocity of the vehicle, the target angular change of the vehicle posture, and the target position change of the vehicle, the first preset acceleration is determined based on the number of times the vehicle moves abnormally and the external conditions of the vehicle, and the external conditions include at least: position data of the vehicle's location, environmental data, seasonal data and weather data of the location.

[0031] The first preset acceleration may be an acceleration threshold adjusted based on the number of vehicle movements and external vehicle conditions, and is used to determine whether vehicle movement detection is necessary, i.e., to determine whether the vehicle may have moved, by comparison with the vehicle's first acceleration. The second acceleration may be the vehicle's acceleration detected in real time after vehicle movement detection is initiated. The target angular velocity may be the vehicle's angular velocity detected in real time after vehicle movement detection is initiated. The target angle change may be the angular change in the vehicle's posture relative to the vehicle's posture before the vehicle went into sleep mode, detected in real time after vehicle movement detection is initiated. The target position change may be the displacement change in the vehicle's position relative to the vehicle's position before the vehicle went into sleep mode, detected in real time after vehicle movement detection is initiated.

[0032] In an optional embodiment, the second acceleration of the vehicle can be obtained through an acceleration sensor, the target angular velocity of the vehicle can be obtained through an angular velocity sensor, and the target position change of the vehicle can be obtained through a vehicle-mounted positioning module. The position data of the vehicle can be the latitude and longitude of the vehicle. The vehicle's environmental data, seasonal data and weather data can be obtained through the Global Positioning System (GPS). The environmental data can be the city where the vehicle is located, the seasonal data can be whether the vehicle is in spring, summer, autumn or winter, and the weather data can be whether the weather at the vehicle's location is sunny, rainy, cloudy or other weather.

[0033] Optionally, the above method also includes: determining a first number of times that the vehicle has undergone abnormal movements within a historical time period; determining a second number of times that the historical acceleration of the vehicle within the historical time period is greater than a second preset acceleration, where the second preset acceleration is the product of the first acceleration threshold and the first preset value; based on the comparison result of the first number and the second number, adjusting the acceleration threshold to obtain the first preset acceleration.

[0034] Among them, the historical time period can be a period of time set arbitrarily before the vehicle is in a dormant state, the first number can be the number of times the vehicle actually moves during the historical time period, and the second preset acceleration can be the acceleration at which the vehicle may move during the historical time period, which can be determined based on the product of the first acceleration threshold and the first preset value. The first acceleration threshold here can be a minimum acceleration set in advance at which vehicle movement will definitely occur, and the first preset value can be a fixed value set in advance. For example, in an embodiment of the present invention, 0.7 is used as an example for illustration, but is not limited to this. When the historical acceleration of the vehicle reaches 70%-100% of the first acceleration threshold, it can be considered that the vehicle may move abnormally. When the historical acceleration of the vehicle does not reach 70% of the first acceleration threshold, it can be considered that the vehicle will not move abnormally.

[0035] Optionally, based on the comparison result of the first number and the second number, the acceleration threshold is adjusted to obtain a first preset acceleration, including: obtaining the average value of historical acceleration to obtain a first average value; obtaining the difference between the second number and the first number; in response to the difference being greater than the first preset difference, controlling the first acceleration threshold to decrease according to a preset ratio to obtain a second acceleration threshold; and adding the second acceleration threshold to the first average value to obtain the first preset acceleration.

[0036] Among them, the first average value can be the average value of the acceleration detected by the vehicle in a historical time period, the first preset difference can be a value set in advance for judging that the first number is much smaller than the second number, for example, the first preset difference can be 5, but is not limited to this, the preset ratio can be a ratio set in advance for adjusting the first acceleration threshold. In order to adjust the first acceleration threshold more accurately, the ratio can be a smaller number, such as 5%, but is not limited to this.

[0037] In an optional embodiment, to reduce the error in obtaining acceleration, the acceleration can be obtained multiple times, the maximum and minimum values ​​removed, and the remaining values ​​averaged to obtain a first average value. For example, the acceleration can be obtained 10 times, the maximum and minimum values ​​removed, and the remaining values ​​averaged to obtain the first average value. The difference between the second and first values ​​can then be determined by subtracting the second value from the first value. When the difference between the second and first values ​​is greater than a first preset difference, it indicates that the first value is much smaller than the second value. This indicates that the first acceleration threshold is too high, and therefore the first acceleration threshold needs to be lowered.

[0038] It should be noted that since the acceleration sensor will be affected by gravity, the second acceleration threshold needs to be adaptively adjusted according to the current posture of the vehicle to eliminate the influence of gravity on the second acceleration threshold. Therefore, the second acceleration threshold needs to be added to the first average value to obtain the acceleration threshold that is adapted to the vehicle posture, thereby achieving the effect of further improving the accuracy of vehicle movement detection.

[0039] Optionally, in response to the difference being less than or equal to a second preset difference, the method includes: controlling the first acceleration threshold to increase according to a preset ratio to obtain a third acceleration threshold; and adding the third acceleration threshold to the first average value to obtain the first preset acceleration.

[0040] The second preset difference may be a value set in advance to determine whether the first number is much larger than the second number. For example, the second preset difference may be -5, but is not limited thereto.

[0041] In an optional embodiment, after determining the difference between the second number and the first number, when the difference between the second number and the first number is less than a second preset difference, it means that the first number is much larger than the second number. It can be seen that the first acceleration threshold at this time is too small, and therefore the first acceleration threshold needs to be increased.

[0042] Similarly, since the acceleration sensor is affected by gravity, the third acceleration threshold needs to be adaptively adjusted according to the current posture of the vehicle to eliminate the influence of gravity on the third acceleration threshold. Therefore, the third acceleration threshold needs to be added to the first average value to obtain the acceleration threshold that is adapted to the vehicle posture, thereby further improving the accuracy of vehicle movement detection.

[0043] Step S106: Determine whether the vehicle has any abnormal movement based on the vehicle data.

[0044] Among them, vehicle data includes multiple types of vehicle data. If only one type of data is used to determine whether the vehicle has moved abnormally, the result will be too one-sided. Therefore, multiple vehicle data can be collected to comprehensively determine whether the vehicle has moved abnormally, thereby improving the accuracy of vehicle movement detection.

[0045] Optionally, determining whether the vehicle has moved abnormally based on vehicle data includes: comparing the vehicle data with preset vehicle data, wherein the preset vehicle data is determined by the server based on external conditions of the vehicle; and determining that the vehicle has moved abnormally in response to the vehicle data being greater than the preset vehicle data.

[0046] Among them, the preset vehicle data can be the vehicle data threshold at which the vehicle will move abnormally, which is preset in advance by the server based on the external conditions of the vehicle, and the user can modify the preset vehicle data according to actual conditions. When the vehicle data is greater than the preset vehicle data, it can be determined that the vehicle has moved abnormally; when the vehicle data is less than or equal to the preset vehicle data, it can be determined that the vehicle will not move abnormally.

[0047] In an optional embodiment, after acquiring multiple types of vehicle data, each piece of vehicle data can be compared with a corresponding vehicle data threshold to comprehensively determine whether the vehicle has moved. When any piece of vehicle data exceeds the corresponding vehicle data threshold, it is determined that the vehicle has moved. Furthermore, when a vehicle movement is determined to have occurred, the owner can be notified through an alarm, text message, or other means.

[0048] Optionally, the vehicle data greater than the preset vehicle data includes at least one of the following: the second acceleration is greater than the first preset acceleration, the target angular velocity is greater than the preset angular velocity, the target angle change is greater than the preset angle change, and the target position change is greater than the preset position change.

[0049] The preset angular velocity can be calculated from the first preset acceleration, and the specific formula is: a=rω 2 , where a represents the first preset acceleration, r represents the radius of the circular motion, and ω represents the preset angular velocity. The preset angle change can be a user-defined number, for example, 15 degrees. When the target angle change of the vehicle is greater than 15 degrees, it can be determined that the vehicle has moved abnormally. The preset position change can be a user-defined number, for example, 20 meters. When the target position change of the vehicle is greater than 20 meters, it can be determined that the vehicle has moved abnormally.

[0050] Optionally, obtaining vehicle data of the vehicle includes: obtaining multiple accelerations according to a first preset period, the number of the multiple accelerations being a first preset number of times, and filtering the multiple acceleration data to obtain a second acceleration of the vehicle; obtaining multiple angular velocities according to the first preset period, the number of the multiple angular velocities being a first preset number of times, and filtering the multiple angular velocities to obtain a target angular velocity of the vehicle; obtaining a target angle change based on an average value of the historical accelerations of the vehicle and an average value of the second acceleration within a historical time period; and comparing the historical position of the vehicle with the current position of the vehicle to obtain a target position change.

[0051] The first preset period may be a user-defined period, for example, 5 seconds. The first preset number of times may be a user-defined number, for example, 16 times. The historical position may be the position of the vehicle before entering the dormant state.

[0052] In an optional embodiment, multiple accelerations may be acquired by an acceleration sensor, multiple angular velocities may be acquired by an angular velocity sensor, and the historical angle and current angle of the vehicle may be obtained by the following formula: Among them, α represents the angle, Ax 2 Indicates the average acceleration on the x-axis of the three-axis sensor, Az 2 Indicates the average acceleration on the z-axis of the three-axis sensor, Ay 2 It represents the average acceleration on the y-axis of the three-axis sensor. After calculating the historical angle and the current angle using the above formula, the historical angle and the current angle are subtracted and the absolute value is taken to obtain the target angle change. The vehicle's historical position and current position can be obtained through the on-board positioning module. The target position change can be obtained by comparing the longitude and latitude of the vehicle's historical position with the longitude and latitude of the vehicle's current position.

[0053] In an optional embodiment, the filtering of multiple acceleration data to obtain the second acceleration of the vehicle can be performed by sorting the multiple accelerations obtained in an ascending order or a descending order, removing several largest and several smallest values ​​of the same number, and taking an average of the remaining values. For example, the 16 acceleration values ​​obtained can be sorted in an ascending order, removing the four largest values, removing the four smallest values, and taking an average of the remaining eight values ​​to obtain the second acceleration of the vehicle. The filtering of multiple angular velocities to obtain the target angular velocity of the vehicle also adopts the same filtering method.

[0054] It should be noted that in order to improve the accuracy of vehicle movement detection, the longitude and latitude of the historical position and the corresponding credibility output by the vehicle positioning module can be comprehensively compared with the longitude and latitude of the current position and the corresponding credibility to determine whether the vehicle has moved abnormally.

[0055] Optionally, in response to the vehicle being in a dormant state, the above method further includes: obtaining location information and time information of the vehicle; sending the location information and time information to a cloud server, wherein the location information and time information are used by the cloud server to perform data analysis based on abnormal movement information sent by multiple vehicles within a historical time period to determine whether the vehicle has a risk of abnormal movement, wherein the abnormal movement information includes: the locations of multiple vehicles, and the time when the multiple vehicles have abnormal movements; receiving prompt information sent by the cloud server, wherein the prompt information is used to indicate that the vehicle has a risk of abnormal movement.

[0056] Among them, the location information may include but is not limited to: the city and road section where the vehicle is currently located, the weather and temperature in the city where the vehicle is currently located, and other information; the time information may include but is not limited to: the specific time the vehicle is currently traveling, the season where the vehicle is currently located, and other information. The cloud server can be used to receive abnormal movement information of multiple vehicles, perform data analysis on the abnormal movement information, and convert the analysis results into prompt information and send it to the required vehicles.

[0057] In an optional embodiment, the location information of the vehicle can be obtained through the vehicle-mounted positioning module, the current Beijing time can be obtained according to the GPS signal through the vehicle-mounted navigation system, and then the time can be sent to the vehicle to obtain the vehicle's time information, the location information and time information can be sent to the cloud server through the vehicle-mounted communication terminal, and the prompt information sent by the cloud server can be received through the vehicle-mounted communication terminal, wherein the prompt information can be fed back in the form of text (such as at least one of words, numbers, letters, etc.) and / or images (such as at least one of pictures, patterns, graphics, etc.), and voice broadcast through the sound feedback device, which can be understood as any device that can realize voice broadcast, for example, it can be displayed in the form of text on the central control screen of the vehicle: Please note that the vehicle is currently at risk of abnormal movement, and the above text information is voice broadcast through the sound feedback device.

[0058] Figure 2 FIG. 1 is a schematic diagram of an optional vehicle abnormal movement detection device according to an embodiment of the present invention. Figure 2 As shown, the device includes: a vehicle posture detection device, an on-board communication terminal, and a cloud-based big data background. The vehicle posture detection device is used to detect the vehicle's posture and determine whether the vehicle has undergone posture changes such as vibration and displacement. The on-board communication terminal is used to realize data transmission and reception between the vehicle and the cloud-based big data background. The cloud-based big data background is used to receive data uploaded by the on-board communication terminal, perform data analysis, and send it to the vehicle.

[0059] The vehicle posture detection device includes: an acceleration sensor for acquiring acceleration data of the vehicle, and an angular velocity sensor for acquiring angular velocity data of the vehicle.

[0060] The vehicle-mounted communication terminal includes: a positioning module for outputting the vehicle's location information and its corresponding credibility.

[0061] Through the above steps, in an embodiment of the present invention, in response to the vehicle being in a dormant state, a first acceleration of the vehicle is obtained, and in response to the first acceleration being greater than a first preset acceleration, vehicle data of the vehicle is obtained, and whether the vehicle has experienced an unusual movement is determined based on the vehicle data. It is readily apparent that the first preset acceleration is determined based on the number of unusual movements of the vehicle and the vehicle's external conditions. That is, the first preset acceleration can be adjusted based on actual conditions and is no longer a fixed threshold. Furthermore, a comprehensive determination of whether the vehicle has experienced an unusual movement is made based on a variety of different types of vehicle data. This achieves the technical effect of improving the accuracy of detecting unusual vehicle movement in extreme weather and environments, thereby resolving the technical issue of low accuracy in detecting unusual vehicle movement in related technologies.

[0062] Example 2

[0063] According to another aspect of an embodiment of the present invention, a vehicle movement detection device is also provided, which can execute the vehicle movement detection method in the above-mentioned embodiment 1. The specific implementation scheme and application scenario in this embodiment are the same as those in the above-mentioned embodiment 1 and will not be repeated here.

[0064] Figure 3 FIG. 1 is a schematic diagram of an optional vehicle abnormal movement detection device according to an embodiment of the present invention. Figure 3 As shown, the device includes: an acceleration acquisition module 302, for acquiring a first acceleration of the vehicle in response to the vehicle being in a dormant state; a data acquisition module 304, for acquiring vehicle data of the vehicle in response to the first acceleration being greater than a first preset acceleration, wherein the vehicle data at least includes: a second acceleration of the vehicle, a target angular velocity of the vehicle, a target angular change of the vehicle posture, and a target position change of the vehicle, the first preset acceleration is determined based on the number of times the vehicle has moved abnormally and the external conditions of the vehicle, the external conditions at least include: position data of the vehicle's location, environmental data, seasonal data and weather data of the location; and an abnormality determination module 306, for determining whether the vehicle has moved abnormally based on the vehicle data.

[0065] The above-mentioned device also includes: a first determination module, used to determine a first number of times that the vehicle has undergone abnormal movement within a historical time period; a second determination module, used to determine a second number of times that the historical acceleration of the vehicle within the historical time period is greater than a second preset acceleration, where the second preset acceleration is the product of the first acceleration threshold and the first preset value; and a first adjustment module, used to adjust the acceleration threshold based on the comparison result of the first number and the second number to obtain the first preset acceleration.

[0066] The first adjustment module includes: an average value acquisition unit, used to obtain the average value of historical acceleration to obtain a first average value; a difference acquisition unit, used to obtain the difference between the second number and the first number; a threshold control unit, used to control the first acceleration threshold to decrease according to a preset ratio in response to the difference being greater than a preset difference to obtain a second acceleration threshold; and a first acquisition unit, used to add the second acceleration threshold to the first average value to obtain a first preset acceleration.

[0067] The threshold control unit is also used to control the first acceleration threshold to increase according to a preset ratio in response to the difference being less than or equal to the second preset difference, so as to obtain a third acceleration threshold; the first acquisition unit is also used to add the third acceleration threshold to the first average value to obtain the first preset acceleration.

[0068] The data acquisition module 304 includes: a second acquisition unit, used to acquire multiple accelerations according to a first preset period, the number of the multiple accelerations is a first preset number, and filter the multiple acceleration data to obtain the second acceleration of the vehicle; a third acquisition unit, used to acquire multiple angular velocities according to the first preset period, the number of the multiple angular velocities is a first preset number, and filter the multiple angular velocities to obtain the target angular velocity of the vehicle; a fourth acquisition unit, used to obtain the target angle change based on the average value of the historical acceleration of the vehicle and the average value of the second acceleration within a historical time period; a fifth acquisition unit, used to compare the historical position of the vehicle with the current position of the vehicle to obtain the target position change.

[0069] The abnormality determination module 306 includes: a data comparison unit, used to compare vehicle data with preset vehicle data, wherein the preset vehicle data is determined by the server based on the external conditions of the vehicle; and an abnormality determination unit, used to determine that a vehicle abnormality has occurred in response to the vehicle data being greater than the preset vehicle data.

[0070] The vehicle data being greater than the preset vehicle data includes at least one of the following: the second acceleration being greater than the first preset acceleration, the target angular velocity being greater than the preset angular velocity, the target angle change being greater than the preset angle change, and the target position change being greater than the preset position change.

[0071] The above-mentioned device also includes: an information acquisition module, which is used to obtain the location information and time information of the vehicle in response to the vehicle being in a dormant state; an information sending module, which is used to send the location information and time information to a cloud server, wherein the location information and time information are used by the cloud server to perform data analysis based on the abnormal movement information sent by multiple vehicles within a historical time period to determine whether the vehicle has a risk of abnormal movement, wherein the abnormal movement information includes: the locations of multiple vehicles and the time when the multiple vehicles have abnormal movements; an information receiving module, which is used to receive prompt information sent by the cloud server, wherein the prompt information is used to indicate that the vehicle has a risk of abnormal movement.

[0072] Example 3

[0073] According to another aspect of an embodiment of the present invention, a vehicle is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the above methods.

[0074] Example 4

[0075] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided. The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned vehicle movement detection method.

[0076] Example 5

[0077] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein the above-mentioned vehicle abnormal movement detection method is executed when the program is run.

[0078] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0079] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0081] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0082] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0083] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0084] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A vehicle movement detection method, characterized in that: The method comprises: In response to the vehicle being in a dormant state, obtaining a first acceleration of the vehicle; In response to the first acceleration being greater than a first preset acceleration, acquiring vehicle data of the vehicle, wherein the vehicle data includes at least: a second acceleration of the vehicle, a target angular velocity of the vehicle, a target angular change of the vehicle posture, and a target position change of the vehicle, the first preset acceleration being determined based on a first number of times the vehicle has moved abnormally and external conditions of the vehicle, the external conditions including at least: location data of the vehicle, environmental data, seasonal data, and weather data of the location; determining whether the vehicle has undergone an abnormal movement based on the vehicle data; The method also includes: determining a first number of times the vehicle has experienced abnormal movement within a historical time period; determining a second number of times within the historical time period that a historical acceleration of the vehicle is greater than a second preset acceleration, where the second preset acceleration is the product of a first acceleration threshold and a first preset value, wherein the first acceleration threshold is a pre-set minimum acceleration at which abnormal vehicle movement is certain to occur; and adjusting the first acceleration threshold based on a comparison result between the first number and the second number to obtain the first preset acceleration.

2. The vehicle abnormal movement detection method according to claim 1, characterized in that: Adjusting the first acceleration threshold based on a comparison result of the first number of times and the second number of times to obtain the first preset acceleration includes: Obtaining an average value of the historical acceleration to obtain a first average value; Obtaining a difference between the second number of times and the first number of times; In response to the difference being greater than a first preset difference, controlling the first acceleration threshold to decrease according to a preset ratio to obtain a second acceleration threshold; The second acceleration threshold is added to the first average value to obtain the first preset acceleration.

3. The vehicle abnormal movement detection method according to claim 2, characterized in that: In response to the difference being less than or equal to a second preset difference, the method further includes: controlling the first acceleration threshold to increase according to a preset ratio to obtain a third acceleration threshold; The first preset acceleration is obtained by adding the third acceleration threshold to the first average value.

4. The vehicle abnormal movement detection method according to claim 1, characterized in that: Determining whether the vehicle has an abnormal movement based on the vehicle data includes: comparing the vehicle data with preset vehicle data, wherein the preset vehicle data is determined by a server based on external conditions of the vehicle; In response to the vehicle data being greater than the preset vehicle data, it is determined that an abnormal movement occurs in the vehicle.

5. The vehicle abnormal movement detection method according to claim 4, characterized in that: The vehicle data being greater than the preset vehicle data includes at least one of the following: the second acceleration being greater than the first preset acceleration, the target angular velocity being greater than the preset angular velocity, the target angle change being greater than the preset angle change, and the target position change being greater than the preset position change.

6. The vehicle abnormal movement detection method according to claim 1, characterized in that: Acquiring vehicle data of the vehicle, including: Acquire a plurality of accelerations according to a first preset period, the number of the plurality of accelerations being a first preset number, and filter the plurality of acceleration data to obtain the second acceleration of the vehicle; acquiring a plurality of angular velocities according to the first preset period, the number of the plurality of angular velocities being the first preset number, and filtering the plurality of angular velocities to obtain the target angular velocity of the vehicle; Obtaining the target angle change based on an average value of historical acceleration of the vehicle within a historical time period and an average value of the second acceleration; The historical position of the vehicle is compared with the current position of the vehicle to obtain the target position change.

7. The vehicle abnormal movement detection method according to claim 1, characterized in that: In response to the vehicle being in a dormant state, the method further includes: Obtaining location information and time information of the vehicle; Sending the location information and the time information to a cloud server, wherein the location information and the time information are used by the cloud server to perform data analysis based on abnormal movement information sent by multiple vehicles within a historical time period to determine whether the vehicle has a risk of abnormal movement, wherein the abnormal movement information includes: the locations of the multiple vehicles and the times when the multiple vehicles have abnormal movements; Receive a prompt message sent by the cloud server, wherein the prompt message is used to indicate that the vehicle has a risk of abnormal movement.

8. A vehicle abnormal movement detection device, characterized in that: The device comprises: an acceleration acquisition module, configured to acquire a first acceleration of the vehicle in response to the vehicle being in a dormant state; a data acquisition module, configured to acquire vehicle data of the vehicle in response to the first acceleration being greater than a first preset acceleration, wherein the vehicle data includes at least: a second acceleration of the vehicle, a target angular velocity of the vehicle, a target angular change of the vehicle posture, and a target position change of the vehicle, wherein the first preset acceleration is determined based on a number of abnormal movements of the vehicle and external conditions of the vehicle, wherein the external conditions include at least: location data of the vehicle, environmental data, seasonal data, and weather data of the location; an abnormal movement determination module, configured to determine whether an abnormal movement occurs to the vehicle based on the vehicle data; The device also includes: a first determination module, used to determine a first number of times the vehicle has undergone abnormal movement within a historical time period; a second determination module, used to determine a second number of times the historical acceleration of the vehicle within the historical time period is greater than a second preset acceleration, where the second preset acceleration is the product of a first acceleration threshold and a first preset value, wherein the first acceleration threshold is a minimum acceleration set in advance at which abnormal vehicle movement will definitely occur; and a first adjustment module, used to adjust the first acceleration threshold based on a comparison result of the first number and the second number to obtain the first preset acceleration.

9. A vehicle, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

Images