Data processing method, device, electronic device and storage medium

Abstract

The present disclosure provides a data processing method, apparatus, electronic device, and storage medium. The method comprises: obtaining a probability distribution for the number of jumps in a vehicle driving risk level, the jump number being used to characterize the degree of jumps in the vehicle driving risk level; obtaining an expected probability of occurrence for the number of jumps in the vehicle driving risk level; obtaining an expected number of jumps in the vehicle driving risk level based on the probability distribution and the expected probability of occurrence; and determining whether the target number of jumps in the vehicle driving risk level is abnormal based on a comparison between the target number of jumps in the vehicle driving risk level and the expected number of jumps during vehicle driving. Embodiments of the present disclosure can improve the accuracy of identifying jumps in the vehicle driving risk level.

Description

Technical Field

[0001] The present disclosure relates to the field of cloud-based Internet of Things, and in particular to a data processing method, device, electronic device, and storage medium. Background Art

[0002] With the in-depth development of internet technology in the transportation sector, the application of cloud technology to provide early warnings to vehicles on the road based on their driving risk level has significant application value. During this early warning process, the vehicle's driving risk level may sometimes fluctuate. These fluctuations can be normal or abnormal. Existing technologies either assume that the vehicle's driving risk level will never fluctuate, and thus treat all fluctuations as abnormal, thereby increasing the false alarm rate of early warnings; or they assume that all fluctuations in the vehicle's driving risk level are normal, thereby increasing the false alarm rate of early warnings. Summary of the Invention

[0003] One purpose of the present disclosure is to provide a data processing method, device, electronic device and storage medium that can improve the accuracy of identifying jumps in vehicle driving risk levels.

[0004] According to one aspect of an embodiment of the present disclosure, a data processing method is disclosed, the method comprising:

[0005] Obtaining a probability distribution obeyed by a jump level of the vehicle driving risk level, wherein the jump level is used to characterize a degree of jump of the vehicle driving risk level;

[0006] Obtaining an expected probability of occurrence of a jump level of the vehicle driving risk level;

[0007] Based on the probability distribution and the expected occurrence probability, obtaining an expected jump level of the vehicle driving risk level;

[0008] Based on a comparison between the target jump level of the vehicle driving risk level and the expected jump level during vehicle driving, it is determined whether the target jump level is abnormal.

[0009] According to one aspect of an embodiment of the present disclosure, a data processing device is disclosed, the device comprising:

[0010] A first acquisition module is configured to obtain a probability distribution obeyed by a jump level of the vehicle driving risk level, wherein the jump level is used to represent a degree of jump of the vehicle driving risk level;

[0011] A second acquisition module is configured to obtain an expected probability of occurrence of a jump level of the vehicle driving risk level;

[0012] a third acquisition module configured to acquire an expected jump level of the vehicle driving risk level based on the probability distribution and the expected occurrence probability;

[0013] The determination module is configured to determine whether the target jump level is abnormal based on a comparison between the target jump level of the vehicle driving risk level and the expected jump level during vehicle driving.

[0014] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0015] Obtaining an average jump level of the vehicle driving risk level;

[0016] The average jump series in unit time is used as a parameter to generate a Poisson distribution obeyed by the jump series.

[0017] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0018] Obtaining a distance between the first vehicle and the second vehicle;

[0019] Acquiring first motion information of the first vehicle and second motion information of the second vehicle;

[0020] determining a time required for the first vehicle to meet the second vehicle based on the distance, the first motion information, and the second motion information;

[0021] The unit time is determined based on the time required for the encounter.

[0022] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0023] Obtaining an average number of external events that trigger a jump in the vehicle driving risk level;

[0024] The Poisson distribution obeyed by the jump series is generated by taking the average quantity scale in unit time as a parameter.

[0025] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0026] Obtaining a response time corresponding to a response to the external event, wherein the response to the external event is used to trigger a jump in the vehicle driving risk level;

[0027] The unit time is determined based on the response time.

[0028] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0029] Obtain the accident rate of the road;

[0030] The expected occurrence probability is obtained based on the accident occurrence rate.

[0031] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0032] Obtaining a target direction for representing a direction of vehicle driving risk;

[0033] A probability distribution obeyed by the jump level of the vehicle driving risk level in the target direction is obtained.

[0034] According to one aspect of an embodiment of the present disclosure, a data processing electronic device is disclosed, comprising: a memory storing computer-readable instructions; and a processor reading the computer-readable instructions stored in the memory to execute any one of the methods described in the above claims.

[0035] According to one aspect of an embodiment of the present disclosure, a computer program medium is disclosed, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the method according to any one of the above claims.

[0036] In the disclosed embodiment, the expected number of vehicle driving risk level transitions is determined based on the probability distribution of the transitions. This is then used to determine whether the target number of transitions during driving is abnormal. This method ensures the recognition rate of abnormal transitions while minimizing the misjudgment rate of normal transitions, thereby improving the accuracy of identifying vehicle driving risk level transitions.

[0037] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0038] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail example embodiments thereof with reference to the attached drawings.

[0040] Figure 1 The figure shows a system architecture diagram applied to the intelligent transportation field related to the cloud Internet of Things field according to an embodiment of the present disclosure.

[0041] Figure 2 A diagram showing a terminal interface in the field of intelligent transportation related to the field of cloud Internet of Things according to an embodiment of the present disclosure is shown.

[0042] Figure 3A flow chart of a data processing method according to an embodiment of the present disclosure is shown.

[0043] Figure 4 A block diagram of a data processing device according to an embodiment of the present disclosure is shown.

[0044] Figure 5 A hardware diagram of a data processing electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0045] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be more comprehensive and complete, and will fully convey the concepts of the example embodiments to those skilled in the art. The accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures indicate identical or similar parts, and thus repeated descriptions thereof will be omitted.

[0046] In addition, the described features, structures or characteristics can be combined in any suitable manner in one or more example embodiments. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure can be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. can be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0047] Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0048] The disclosed embodiments provide a data processing method related to the field of cloud IoT, and more specifically, to the field of intelligent transportation related to the cloud IoT. The disclosed embodiments are applicable to applications that provide early warnings for vehicle driving risks, and are used to improve the accuracy of identifying jumps in vehicle driving risk levels.

[0049] The Internet of Things (IoT) refers to the ubiquitous connection between objects and people, enabling the intelligent perception, identification, and management of objects and processes through various devices and technologies, including information sensors, radio frequency identification (RFID), global positioning systems (GPS), infrared sensors, and laser scanners. The IoT is an information carrier based on the internet and traditional telecommunications networks, enabling all independently addressable, ordinary physical objects to form an interconnected network.

[0050] Cloud IOT aims to connect the information sensed and instructions received by sensor devices in traditional IoT to the Internet, truly realize networking, and achieve massive data storage and computing through cloud computing technology. Since the characteristic of IoT is that things are connected to each other and the current operating status of each "object" is perceived in real time, a large amount of data information will be generated in this process. How to aggregate this information and how to filter useful information from the massive information to support decision-making for subsequent development have become key issues affecting the development of IoT. Therefore, physical cloud based on cloud computing and cloud storage technology has become a strong support for IoT technology and applications.

[0051] Figure 1 The present invention illustrates an embodiment of the present invention applied to the intelligent transportation field related to the cloud Internet of Things field.

[0052] In this embodiment, the vehicle 10 collaborates with the server 20 (for example, the vehicle control system in the vehicle 10 collaborates with the server 20) to jointly implement a vehicle warning system for warning of vehicle driving risks. Information is transmitted between the vehicle 10 and the server 20 via a transmission network. The server 20 can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. On the user side, the vehicle warning system can be integrated into the terminal of the driver of the vehicle 10, such as a mini-program or a map application on the terminal. The terminal can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and the server 20 can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. The transmission network can be a 4G network or a 5G network established by a 4G (the fourth generation mobile communication technology) base station or a 5G (the fifth generation mobile communication technology) base station.

[0053] Specifically, when each vehicle 10 is driving on the road, the vehicle warning system will issue a warning to the corresponding vehicle 10 based on the vehicle driving risk level. During this process, the vehicle driving risk level may jump. There are two possible reasons for this phenomenon: in the first case, the jump in the vehicle driving risk level is abnormal (for example, due to information processing issues, the vehicle driving risk level jumps when it should not have jumped); in the second case, the jump in the vehicle driving risk level is normal (for example, due to a sudden external event on the road, the vehicle driving risk level does jump).

[0054] It should be noted that this embodiment is merely an example of the basic system architecture used in one embodiment of the present disclosure and should not limit the functions and scope of use of the present disclosure. In specific applications, the composition and structure of the system architecture can be adjusted as needed. For example, the composition of the system architecture may also include road test equipment (e.g., ultrasonic speed measurement equipment installed on the roadside) for collecting vehicle driving information as part of the vehicle warning system.

[0055] Figure 2 A diagram showing a terminal interface of an embodiment of the present disclosure applied to the field of intelligent transportation related to the field of cloud Internet of Things is shown.

[0056] In this embodiment, a vehicle warning system for providing driving risk warnings is integrated into the user's terminal's map application. The user clicks on the map application to enter the application. During driving, the vehicle warning system uses acquired information to issue warnings about driving risks. The warning system responds accordingly based on the vehicle's driving risk level.

[0057] As shown in the figure, when the vehicle in front is a certain distance away from this vehicle, the vehicle warning system will issue a warning that the vehicle driving risk level needs to be indicated by "yellow" based on the vehicle driving risk level in this situation, and display the warning information indicated by "yellow" on the terminal interface.

[0058] As the distance between the vehicle in front and this vehicle narrows to a certain extent, the vehicle warning system will issue a warning that the vehicle driving risk level needs to be indicated in "light red" based on the vehicle driving risk level in this situation, and display the warning information in "light red" on the terminal interface.

[0059] As the distance between the vehicle in front and this vehicle further decreases, the vehicle warning system will issue a warning that the vehicle driving risk level needs to be indicated in "red" based on the vehicle driving risk level in this situation, and display the warning information in "red" on the terminal interface.

[0060] Specifically, in this embodiment, the following settings can be used to achieve Figure 2 The specific performance shown.

[0061] Set three thresholds, e1, e2, and e3, where e1, e2, and e3 are all greater than 0 and less than 1, and e1 is less than e2, and e2 is less than e3. These three thresholds divide the vehicle driving risk level into four intervals: [0, e1), [e1, e2), [e2, e3), and [e3, 1].

[0062] If the vehicle driving risk level is detected to be in the range of [0,e1), it is deemed that there is no driving risk and no warning is issued; if the vehicle driving risk level is detected to be in the range of [e1,e2), it is deemed that there is a low driving risk and a warning indicated by "yellow" is issued; if the vehicle driving risk level is detected to be in the range of [e2,e3), it is deemed that there is a medium driving risk and a warning indicated by "light red" is issued; if the vehicle driving risk level is detected to be in the range of [e3,1], it is deemed that there is a high driving risk and a warning indicated by "red" is issued.

[0063] It is understood that in this embodiment, under normal circumstances, a vehicle driving risk level transition from "yellow" to "light red," or from "light red" to "red," or from "red" to "light red" is generally normal. However, if the vehicle driving risk level jumps directly from "yellow" to "red," or from "red" to "yellow," this jump may be normal or abnormal.

[0064] It should be noted that this embodiment only exemplarily shows the terminal interface diagram on the user side of an embodiment of the present disclosure and should not limit the functions and scope of use of the present disclosure.

[0065] In order to ensure the recognition rate of abnormal jumps while minimizing the misjudgment rate of normal jumps, the embodiment of the present disclosure proposes a data processing method. It should be noted that the jump of the vehicle driving risk level in the embodiment of the present disclosure mainly refers to jumping to non-adjacent vehicle driving risk levels. Correspondingly, the jump level is the number of vehicle driving risk levels skipped in the middle. For example: the vehicle driving risk level is divided into "level 0", "level 1", "level 2" and "level 3" in order from low to high. If the vehicle driving risk level changes from "Level 0" to "Level 1", it is not a jump; if the vehicle driving risk level changes from "Level 0" to "Level 2", it is a jump with a jump level of 1; if the vehicle driving risk level changes from "Level 0" to "Level 3", it is a jump with a jump level of 2; if the vehicle driving risk level changes from "Level 3" to "Level 2", it is not a jump; if the vehicle driving risk level changes from "Level 3" to "Level 1", it is a jump with a jump level of 1; if the vehicle driving risk level changes from "Level 3" to "Level 0", it is a jump with a jump level of 2.

[0066] Figure 3 A data processing method according to an embodiment of the present disclosure is shown. The method is exemplarily performed by a vehicle warning system implemented through collaboration between a vehicle and a server in the field of intelligent transportation related to the cloud Internet of Things. The method includes:

[0067] Step S310: Obtain a probability distribution obeyed by a jump level of the vehicle driving risk level, wherein the jump level is used to characterize a jump degree of the vehicle driving risk level;

[0068] Step S320: obtaining the expected probability of occurrence of the jump level of the vehicle driving risk level;

[0069] Step S330: obtaining an expected jump level of the vehicle driving risk level based on the probability distribution and the expected occurrence probability;

[0070] Step S340: Based on a comparison between the target jump level of the vehicle driving risk level and the expected jump level during the vehicle driving process, determine whether the target jump level is abnormal.

[0071] In the disclosed embodiment, the expected number of vehicle driving risk level transitions is determined based on the probability distribution of the transitions. This is then used to determine whether the target number of transitions during driving is abnormal. This method ensures the recognition rate of abnormal transitions while minimizing the misjudgment rate of normal transitions, thereby improving the accuracy of identifying vehicle driving risk level transitions.

[0072] In the disclosed embodiment, the jump level of the vehicle driving risk level mainly obeys the Poisson distribution. Depending on the specific application scenario, the jump level of the vehicle driving risk level obeys other distributions besides the Poisson distribution, such as the normal distribution.

[0073] The following describes the specific implementation process of the embodiment of the present disclosure when the jump series of the vehicle driving risk level obeys the Poisson distribution.

[0074] In one embodiment, obtaining the probability distribution obeyed by the jump level of the vehicle driving risk level includes:

[0075] Obtain the average jump level of the vehicle's driving risk level;

[0076] The average jump series in unit time is used as a parameter to generate a Poisson distribution obeyed by the jump series.

[0077] In this embodiment, the vehicle warning system uses the average jump level in unit time as a parameter to generate a Poisson distribution obeyed by the jump level of the vehicle driving risk level.

[0078] Specifically, the Poisson distribution obeyed by the jump series of vehicle driving risk level can be expressed as:

[0079]

[0080] Among them, k represents the jump level of the vehicle driving risk level, N(t)=k represents the event of "the vehicle driving risk level jumps to level k", p[N(t)=k] represents the probability of the event "the vehicle driving risk level jumps to level k", e represents a natural constant, t represents unit time, and λ1 represents the average jump level of the vehicle driving risk level.

[0081] The advantage of this embodiment is that the average jump level of the vehicle driving risk level is used as a parameter, which ensures the applicability of the generated Poisson distribution in macro-statistics.

[0082] In one embodiment, obtaining the average jump level of the vehicle driving risk level includes:

[0083] Obtain the historical jump levels of vehicle driving risk levels in a preset area within a preset time period;

[0084] The average transition level is obtained based on the historical transition levels.

[0085] In this embodiment, the vehicle warning system determines the average jump level based on the historical jump levels within a certain time period and within a certain area.

[0086] Specifically, the vehicle warning system obtains the historical jump levels of vehicle driving risk levels within a preset area (e.g., Road A) within a preset time period (e.g., one week past the current time point), and then obtains the average jump level of the vehicle driving risk level based on the historical jump levels. The vehicle warning system can average the historical jump levels to obtain the average jump level used as a Poisson distribution parameter; it can also average the historical jump levels and then modify them according to application requirements to obtain the average jump level used as a Poisson distribution parameter.

[0087] It should be noted that in this embodiment, the area can be a physical area or a logical area. For example, based on the physical location, Road A is pre-set as a physical area for obtaining historical jump levels, so that the vehicle warning system obtains the average jump level based on the historical jump levels on Road A during a preset time period. For another example, based on business needs, each vehicle used for logistics transportation is pre-set as a logical area for obtaining historical jump levels, so that the vehicle warning system obtains the average jump level based on the historical jump levels of each vehicle used for logistics transportation during a preset time period.

[0088] The advantage of this embodiment is that the accuracy of the obtained average jump level is guaranteed by presetting the time period and the area.

[0089] It should be noted that this embodiment is only an illustrative description and should not limit the function and scope of use of the present disclosure.

[0090] In one embodiment, obtaining the average jump level of the vehicle driving risk level includes:

[0091] Obtaining a historical jump level of a vehicle driving risk level of the first vehicle during a current driving process;

[0092] An average jump level of the first vehicle is obtained based on the historical jump levels.

[0093] In this embodiment, the vehicle warning system obtains an average jump level of the first vehicle's vehicle driving risk level based on the historical jump levels of the first vehicle's vehicle driving risk level during the current driving process. Preferably, the first vehicle may be the vehicle corresponding to the target jump level to be determined as abnormal.

[0094] For example, vehicle A starts driving at 10:00:00, and this driving process continues until the current time point. The vehicle warning system obtains the historical jump levels of vehicle A's driving risk level from 10:00:00 to the current time point, and then obtains the average jump level based on this; then obtains the Poisson distribution obeyed by the jump level of vehicle A's driving risk level, the expected probability of occurrence of the jump level of vehicle A's driving risk level, and the expected jump level of vehicle A's driving risk level; then, based on the comparison of the target jump level of vehicle A's driving risk level with the expected jump level during the driving process of vehicle A, determines whether the target jump level is abnormal.

[0095] The advantage of this embodiment is that the average jump level of a specific vehicle in the current driving process is obtained, which ensures the timeliness of the average jump level.

[0096] In one embodiment, the unit time is determined by the following method:

[0097] Obtaining a distance between the first vehicle and the second vehicle;

[0098] Acquiring first motion information of the first vehicle and second motion information of the second vehicle;

[0099] Determining a time required for the first vehicle to meet the second vehicle based on the distance, the first motion information, and the second motion information;

[0100] The unit time is determined based on the time required for the encounter.

[0101] In this embodiment, the vehicle warning system determines the time required for the first vehicle and the second vehicle to meet as the unit time for the parameters used to generate the Poisson distribution. The motion information includes the vehicle speed and acceleration of the corresponding vehicle. Preferably, the first vehicle may be the vehicle corresponding to the target jump level to be determined as abnormal.

[0102] Specifically, let the distance between the first vehicle and the second vehicle be L, the speed of the first vehicle be v1, the acceleration of the first vehicle be a1, the speed of the second vehicle be v2, the acceleration of the second vehicle be a2, and the time required for the two to meet be t. Then, according to the motion formula:

[0103] (v1t+0.5a1t 2 )-(v2t+0.5a2t 2 )=L

[0104] The time t required for the encounter is calculated, and then, based on the calculated t, the unit time of the parameters used to generate the Poisson distribution is determined. For example, the calculated t is determined as the unit time; or, the calculated t is modified according to application requirements, and the modified t is then determined as the unit time.

[0105] It should be noted that if the required time t for encounter cannot be calculated based on the motion formula, that is, based on the motion states of the first and second vehicles at the current time, the first and second vehicles will not encounter each other, in this case, any jump in the vehicle driving risk level is considered normal.

[0106] In one embodiment, obtaining the probability distribution obeyed by the jump level of the vehicle driving risk level includes:

[0107] Obtain the average number of external events that trigger a jump in the driving risk level of the vehicle;

[0108] The Poisson distribution obeyed by the jump series is generated by taking the average quantity scale in unit time as a parameter.

[0109] In this embodiment, the vehicle warning system uses the average number of external events per unit time as a parameter to generate a Poisson distribution for the number of jumps in the vehicle's driving risk level. External events primarily refer to events from the vehicle's external environment that trigger a jump in the vehicle's driving risk level (for example, during driving, a first vehicle gradually approaches a second vehicle in front. A third vehicle suddenly intervenes between the first and second vehicles, triggering a jump in the first vehicle's driving risk level).

[0110] Specifically, changes in vehicle driving risk levels are primarily triggered by external events. The greater the number of external events per unit time, the greater the level of the resulting change (for example, if a third vehicle suddenly intervenes between a first and second vehicle, the first vehicle's driving risk level will jump by level 1; if two third vehicles suddenly intervene between the first and second vehicles, the first vehicle's driving risk level will jump by level 2).

[0111] The Poisson distribution obeyed by the jump series of vehicle driving risk level can be expressed as:

[0112]

[0113] Among them, k represents the jump level of the vehicle driving risk level, N(t)=k represents the event of "the vehicle driving risk level jumps to level k", p[N(t)=k] represents the probability of occurrence of the event "the vehicle driving risk level jumps to level k", e represents a natural constant, t represents unit time, and λ2 represents the average number scale of external events.

[0114] The advantage of this embodiment is that the granularity of applicable scenarios of the generated Poisson distribution is reduced by taking the average number of external events that trigger the transition as a parameter.

[0115] In one embodiment, obtaining the average number of external events that trigger a jump in the vehicle driving risk level includes:

[0116] Get the historical number and scale of external events in a preset area within a preset time period;

[0117] The average quantity scale is obtained based on the historical quantity scale.

[0118] In this embodiment, the vehicle warning system determines the average number of external events based on the historical number of external events within a certain period of time and within a certain area.

[0119] It can be understood that the specific implementation process of this embodiment is the same as the specific implementation process of the above-mentioned embodiment of "the vehicle warning system determines the average jump level based on the historical jump level within a certain time and a certain area", so it will not be repeated here.

[0120] In one embodiment, obtaining the average number of external events that trigger a jump in the vehicle driving risk level includes:

[0121] Obtaining a historical quantity scale of external events during a current driving process of the first vehicle;

[0122] The average quantity scale is obtained based on the historical quantity scale.

[0123] In this embodiment, the vehicle warning system obtains the average number scale of external events based on the historical number scale of external events during the current driving process of the first vehicle.

[0124] It can be understood that the specific implementation process of this embodiment is the same as the specific implementation process of the above-mentioned embodiment of "the vehicle warning system obtains the average jump level of the vehicle driving risk level of the first vehicle based on the historical jump level of the vehicle driving risk level of the first vehicle during the current driving process", so it will not be repeated here.

[0125] In one embodiment, the unit time is determined by the following method:

[0126] Obtaining a response time corresponding to a response to the external event, wherein the response to the external event is used to trigger a jump in the vehicle driving risk level;

[0127] The unit time is determined based on the response time.

[0128] In this embodiment, the vehicle warning system determines the response time corresponding to the response to the external event as the unit time of the parameter of the user-generated Poisson distribution. The response to the external event is used to trigger a jump in the vehicle driving risk level.

[0129] For example, if a number of vehicles suddenly cut in between a first and second vehicle, the vehicle warning system typically requires 0.2 seconds to respond, triggering a jump level that matches the number of vehicles. In this case, the vehicle warning system determines the unit time for the parameters of the Poisson distribution based on the 0.2-second response time.

[0130] The response time may be obtained by reading the working attribute information of the vehicle warning system; or it may be determined based on the historical response time of the vehicle warning system to external events.

[0131] It should be noted that this embodiment is only an illustrative description and should not limit the function and scope of use of the present disclosure.

[0132] In one embodiment, obtaining the expected probability of occurrence of a jump level of the vehicle driving risk level includes:

[0133] Obtain the accident rate of the road;

[0134] The expected occurrence probability is obtained based on the accident occurrence rate.

[0135] In this embodiment, the vehicle warning system obtains the expected probability of a jump level of the vehicle driving risk level based on the accident rate of the road. The expected probability of occurrence is usually a probability interval.

[0136] Specifically, let the accident rate on a road be i. The vehicle warning system can determine the expected probability of occurrence within the probability interval [0, i). Alternatively, the accident rate i can be modified based on specific application requirements to obtain a modified accident rate i', and the expected probability of occurrence can be further determined within the probability interval [0, i').

[0137] The advantage of this embodiment is that the jump in the vehicle driving risk level will lead to the occurrence of accidents on the road to a certain extent. By obtaining the expected probability of occurrence through this method, the processing based on the expected probability of occurrence can reduce the accident rate on the road.

[0138] It should be noted that this embodiment is merely an exemplary description. It is understandable that the expected probability of occurrence can also be pre-set according to specific application requirements, and this embodiment should not limit the function and scope of use of the present disclosure.

[0139] In the disclosed embodiment, after the vehicle warning system obtains the probability distribution obeyed by the jump level of the vehicle driving risk level and the corresponding expected occurrence probability, it obtains the expected jump level of the vehicle driving risk level based on the two.

[0140] In one embodiment, the probability distribution of the jump level of the vehicle driving risk level obtained by the vehicle warning system is:

[0141]

[0142] Wherein, k represents the jump level of the vehicle driving risk level, e represents the natural constant, t represents the acquired unit time, and λ1 represents the average jump level of the acquired vehicle driving risk level.

[0143] The expected probability of the jump level of the vehicle driving risk level obtained by the vehicle warning system is the probability interval [0, i). The vehicle warning system then calculates the expected jump level of the vehicle driving risk level using the following formula:

[0144]

[0145] The range of k values ​​obtained from this formula represents the range of expected jumps in the vehicle driving risk level. If k does not exist according to this formula, any jump in the vehicle driving risk level is considered normal.

[0146] It should be noted that this embodiment is merely an exemplary description. It is understandable that the expected occurrence probability can be modified according to specific application requirements and then combined with the probability distribution to obtain the corresponding expected jump level. This embodiment should not limit the function and scope of use of the present disclosure.

[0147] In the embodiment of the present disclosure, for the target jump level of the vehicle driving risk level during vehicle driving, the vehicle warning system compares the target jump level with the expected jump level, and determines whether the target jump level is abnormal based on the comparison result.

[0148] In one embodiment, the expected jump level of the vehicle driving risk level obtained by the vehicle warning system is recorded as k0, and the target jump level of the vehicle driving risk level during the vehicle driving process is recorded as k real If k0 contains k real , then it is determined that the target jump level is not abnormal; if k0 does not contain k real , it is determined that the target jump level is abnormal.

[0149] It should be noted that this embodiment is merely an example. The method for comparing the target jump level with the expected jump level can be adjusted according to specific application requirements; the correspondence between the comparison result of the target jump level with the expected jump level and whether an abnormality occurs can also be adjusted according to specific application requirements. This embodiment should not limit the functionality and scope of use of the present disclosure.

[0150] In one embodiment, the method further includes: obtaining a target direction for representing a direction where the vehicle driving risk is located;

[0151] Obtaining the probability distribution obeyed by the jump level of the vehicle driving risk level includes: obtaining the probability distribution obeyed by the jump level of the vehicle driving risk level in the target direction.

[0152] In this embodiment, the vehicle warning system issues a warning based on the vehicle driving risk level in a target direction and then handles the jump in the vehicle driving risk level in the target direction accordingly. The target directions include: the front, left, right, and rear of the vehicle. The target directions can be pre-set based on specific application requirements, or each direction can be determined as a target direction in actual application.

[0153] Specifically, the vehicle warning system obtains a target direction where the vehicle driving risk is located; obtains a probability distribution obeyed by the jump level of the vehicle driving risk level in the target direction; obtains an expected probability of occurrence of the jump level of the vehicle driving risk level in the target direction; based on the probability distribution and the expected probability of occurrence, obtains an expected jump level of the vehicle driving risk level in the target direction; based on the expected jump level, determines whether the target jump level of the vehicle driving risk level in the target direction during vehicle driving is abnormal.

[0154] For example, the vehicle warning system can respectively issue warnings for the driving risks of the first vehicle in front of the vehicle, on the left side of the vehicle, on the right side of the vehicle, and behind the vehicle.

[0155] The vehicle warning system can select the front of the vehicle as the target direction to determine whether the target jump level of the vehicle driving risk level in front of the first vehicle during the driving process is abnormal; it can also select the right of the vehicle as the target direction to determine whether the target jump level of the vehicle driving risk level to the right of the first vehicle during the driving process is abnormal; and so on, which will not be repeated here.

[0156] The advantage of this embodiment is that by setting the target direction, the processing of the vehicle driving risk level is more targeted, and accordingly, the warning made on this basis is also more targeted.

[0157] It should be noted that this embodiment is only an illustrative description and should not limit the function and scope of use of the present disclosure.

[0158] Table 1 below shows a comparison of the effects of an embodiment of the present disclosure and the prior art in terms of missed alarm rate and false alarm rate for warning vehicles during driving in the field of intelligent transportation.

[0159] Among them, missed alarm refers to the situation where the vehicle driving risk level should have jumped during the vehicle driving process, but no warning corresponding to the jump was issued; false alarm refers to the situation where the vehicle driving risk level should not have jumped during the vehicle driving process, but a warning corresponding to the jump was issued.

[0160]

[0161]

[0162] Table 1

[0163] It can be seen from the data shown in this embodiment that, compared with the prior art, the method provided by the embodiment of the present disclosure can significantly reduce the omission rate and false alarm rate of jumps in vehicle driving risk levels, and improve the recognition accuracy of jumps in vehicle driving risk levels.

[0164] It should be noted that this embodiment is only an illustrative description and should not limit the function and scope of use of the present disclosure.

[0165] Figure 4 A data processing device according to an embodiment of the present disclosure is shown, the device comprising:

[0166] A first acquisition module 410 is configured to obtain a probability distribution obeyed by a jump level of the vehicle driving risk level, wherein the jump level is used to represent a degree of jump of the vehicle driving risk level;

[0167] The second acquisition module 420 is configured to obtain the expected probability of occurrence of the jump level of the vehicle driving risk level;

[0168] A third acquisition module 430 is configured to acquire an expected jump level of the vehicle driving risk level based on the probability distribution and the expected occurrence probability;

[0169] The determination module 440 is configured to determine whether the target jump level is abnormal based on a comparison between the target jump level of the vehicle driving risk level and the expected jump level during vehicle driving.

[0170] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0171] Obtaining an average jump level of the vehicle driving risk level;

[0172] The average jump series in unit time is used as a parameter to generate a Poisson distribution obeyed by the jump series.

[0173] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0174] Obtaining a distance between the first vehicle and the second vehicle;

[0175] Acquiring first motion information of the first vehicle and second motion information of the second vehicle;

[0176] determining a time required for the first vehicle to meet the second vehicle based on the distance, the first motion information, and the second motion information;

[0177] The unit time is determined based on the time required for the encounter.

[0178] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0179] Obtaining an average number of external events that trigger a jump in the vehicle driving risk level;

[0180] The Poisson distribution obeyed by the jump series is generated by taking the average quantity scale in unit time as a parameter.

[0181] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0182] Obtaining a response time corresponding to a response to the external event, wherein the response to the external event is used to trigger a jump in the vehicle driving risk level;

[0183] The unit time is determined based on the response time.

[0184] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0185] Obtain the accident rate of the road;

[0186] The expected occurrence probability is obtained based on the accident occurrence rate.

[0187] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0188] Obtaining a target direction for representing a direction of vehicle driving risk;

[0189] A probability distribution obeyed by the jump level of the vehicle driving risk level in the target direction is obtained.

[0190] Reference below Figure 5 The data processing electronic device 50 according to an embodiment of the present disclosure will be described. Figure 5 The data processing electronic device 50 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0191] like Figure 5 As shown, the data processing electronic device 50 is implemented as a general-purpose computing device. Components of the data processing electronic device 50 may include, but are not limited to, the at least one processing unit 510 described above, the at least one storage unit 520 described above, and a bus 530 connecting various system components (including the storage unit 520 and the processing unit 510).

[0192] The storage unit stores program codes that can be executed by the processing unit 510, so that the processing unit 510 performs the steps according to various exemplary embodiments of the present invention described in the description of the exemplary method above. For example, the processing unit 510 can perform the following steps: Figure 3 The steps shown in .

[0193] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 5201 and / or a cache memory unit 5202 , and may further include a read-only memory unit (ROM) 5203 .

[0194] The storage unit 520 may also include a program / utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0195] Bus 530 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0196] The data processing electronic device 50 can also communicate with one or more external devices 600 (e.g., a keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with the data processing electronic device 50, and / or any device that enables the data processing electronic device 50 to communicate with one or more other computing devices (e.g., a router, modem, etc.). This communication can occur via an input / output (I / O) interface 550. The I / O interface 550 is connected to a display unit 540. Furthermore, the data processing electronic device 50 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 560. As shown, the network adapter 560 communicates with other modules of the data processing electronic device 50 via a bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the data processing electronic device 50, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0197] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0198] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is further provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the method described in the above method embodiment.

[0199] According to one embodiment of the present disclosure, a program product for implementing the method in the above method embodiment is also provided. The program product may be a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0200] The program product may be implemented in any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0201] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0202] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0203] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as JAVA, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0204] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0205] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0206] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0207] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

Claims

1. A data processing method, characterized in that: The method comprises: Obtaining a probability distribution obeyed by a jump level of the vehicle driving risk level, wherein the jump level is used to characterize the degree of jump of the vehicle driving risk level; the probability distribution includes occurrence probabilities corresponding to different jump levels; Obtaining an expected probability of occurrence of a jump level of the vehicle driving risk level; the expected probability of occurrence is obtained based on the accident rate of the road; Based on the probability distribution and the expected occurrence probability, obtaining an expected jump level of the vehicle driving risk level; the expected jump level includes a jump level in the probability distribution whose corresponding occurrence probability is less than or equal to the maximum value of the expected occurrence probability, and the expected jump level is a normal jump level; Based on a comparison between the target jump level of the vehicle driving risk level and the expected jump level during vehicle driving, it is determined whether the target jump level is abnormal.

2. The method according to claim 1, characterized in that Obtain the probability distribution of the jump level of the vehicle driving risk level, including: Obtaining an average jump level of the vehicle driving risk level; The average jump series in unit time is used as a parameter to generate a Poisson distribution obeyed by the jump series.

3. The method according to claim 2, characterized in that The unit time is determined by the following method: Obtaining a distance between the first vehicle and the second vehicle; Acquiring first motion information of the first vehicle and second motion information of the second vehicle; determining a time required for the first vehicle to meet the second vehicle based on the distance, the first motion information, and the second motion information; The unit time is determined based on the time required for the encounter.

4. The method according to claim 1, wherein Obtain the probability distribution of the jump level of the vehicle driving risk level, including: Obtaining an average number of external events that trigger a jump in the vehicle driving risk level; The Poisson distribution obeyed by the jump series is generated by taking the average quantity scale in unit time as a parameter.

5. The method according to claim 4, characterized in that The unit time is determined by the following method: Obtaining a response time corresponding to a response to the external event, wherein the response to the external event is used to trigger a jump in the vehicle driving risk level; The unit time is determined based on the response time.

6. The method according to claim 1, characterized in that Obtaining the expected probability of occurrence of a jump level of the vehicle driving risk level includes: Obtain the accident rate of the road; The expected occurrence probability is obtained based on the accident occurrence rate.

7. The method according to claim 1, characterized in that The method further includes: obtaining a target direction for representing a direction where a vehicle driving risk is located; Obtaining the probability distribution obeyed by the jump level of the vehicle driving risk level includes: obtaining the probability distribution obeyed by the jump level of the vehicle driving risk level in the target direction.

8. A data processing device, characterized in that: The device comprises: A first acquisition module is configured to obtain a probability distribution obeyed by a jump level of the vehicle driving risk level, wherein the jump level is used to represent the degree of jump of the vehicle driving risk level; the probability distribution includes occurrence probabilities corresponding to different jump levels; A second acquisition module is configured to obtain an expected probability of occurrence of a jump level of the vehicle driving risk level; the expected probability of occurrence is obtained based on the accident rate of the road; a third acquisition module configured to acquire an expected jump level of the vehicle driving risk level based on the probability distribution and the expected occurrence probability; the expected jump level includes a jump level in the probability distribution whose corresponding occurrence probability is less than a maximum value in the expected occurrence probability, and the expected jump level is a normal jump level; The determination module is configured to determine whether the target jump level is abnormal based on a comparison between the target jump level of the vehicle driving risk level and the expected jump level during vehicle driving.

9. A data processing electronic device, characterized in that: include: a memory storing computer-readable instructions; A processor reads the computer-readable instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

11. A program product, characterized in that The invention comprises a program code, which, when executed by a processor of a computer, causes the computer to perform the method according to any one of claims 1 to 7.

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