Drunk driving judgment method, device and equipment, storage medium and product

Abstract

The invention discloses a drunk driving judgment method, device and equipment, a storage medium and a product, and relates to the technical field of traffic safety monitoring, and the method comprises the steps: obtaining a parking position and a parking time period of a vehicle from a vehicle-mounted terminal; determining a current regional drunk driving risk of the parking position based on a preset corresponding relationship among the position, the time and the regional drunk driving risk; obtaining a parking time length and a dining place staying time length of the user in a parking period, and determining a current dinner party risk of the user in combination with the parking time period; after the user returns to the parking position, the current state of the user is obtained; determining a dangerous driving risk according to a comparison result of the current state and the state of the user before parking; the drunk driving behavior judgment result of the user is determined according to the current area drunk driving risk, the current dinner party risk and the dangerous driving risk, a large number of unmanned aerial vehicles do not need to be additionally configured, the risk of safety accidents caused in the drunk driving judgment process is reduced, and higher practicability is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic safety monitoring, and in particular to a method, device, equipment, storage medium and product for distinguishing drunk driving Background Art

[0002] In order to expand the detection scope of drunk driving and improve the detection efficiency, so as to eliminate the traffic safety hazards caused by it as much as possible, the current technology uses drones to shoot the driving trajectory of vehicles and analyze the vehicle's driving trajectory to determine whether there is drunk driving. However, this drunk driving identification method requires a large number of drones, and there is a risk of drones falling when flying on the road, which is easy to cause safety accidents. Summary of the invention

[0003] The purpose of the embodiments of the present invention is to provide a method, device, equipment, storage medium and product for identifying drunk driving, which can comprehensively analyze whether the user has drunk driving behavior by identifying the parking location, the length of time the user stays in the catering place, and the state of the user when leaving the car and returning. There is no need to configure a large number of additional drones, which reduces the risk of safety accidents caused by the drunk driving identification process and has stronger practicality.

[0004] To achieve the above purpose, an embodiment of the present invention provides a method for distinguishing drunk driving, comprising:

[0005] Obtain the parking location and parking period of the vehicle from the vehicle terminal;

[0006] Determine the current regional drunk driving risk of the parking location based on the preset correspondence between the location, time and regional drunk driving risk;

[0007] Obtain the parking time and the length of time the user stays at the dining venue during the parking period, and determine the user's current dining risk based on the parking period;

[0008] After the user returns to the parking location, obtaining the current state of the user;

[0009] Determining a risk of dangerous driving based on a comparison result of the current state and the state of the user before parking;

[0010] The drunk driving behavior identification result of the user is determined according to the current area drunk driving risk, the current dinner party risk and the dangerous driving risk.

[0011] As an improvement of the above solution, the current regional drunk driving risk of the parking location is determined based on the correspondence between the preset location, time and regional drunk driving risk, including:

[0012] The drunk driving risk coefficient of the current area is calculated based on the electronic fence where the parking position is located and the weight coefficient of the electronic fence.

[0013] As an improvement of the above solution, the electronic fence includes at least one of a first electronic fence, a second electronic fence and a third electronic fence, wherein the electronic fence is divided as follows:

[0014] Draw the first electronic fence in the catering enterprise cluster area and the business district area;

[0015] During the preset dining peak period, the second electronic fence is drawn in the vehicle dynamic gathering area; wherein the vehicle dynamic gathering area has the following characteristics during the preset dining peak period: the number of vehicles gathering is greater than a first preset threshold, the vehicle repetition rate between different time nodes during the preset dining peak period is less than a second preset threshold, and the time interval between adjacent time nodes is greater than a third preset threshold;

[0016] In the village residential gathering area, the personnel gathering area is identified based on the mobile terminal and the vehicle-mounted terminal, so as to draw the third electronic fence in the personnel gathering area.

[0017] As an improvement to the above solution, the method of obtaining the parking time and the user's stay time at the restaurant during the parking period, and calculating the user's current dining risk in combination with the parking period, includes:

[0018] Based on the correspondence between the time period and the gathering weight coefficient, the current gathering weight coefficient is determined according to the parking time period;

[0019] The current dinner gathering risk coefficient is calculated based on the current dinner gathering weight coefficient, the length of stay at the dining venue and the parking time; wherein the current dinner gathering risk coefficient is positively correlated with the ratio of the length of stay at the dining venue to the parking time and the current dinner gathering weight coefficient, respectively.

[0020] As an improvement of the above solution, the step of determining the risk of dangerous driving based on the comparison result between the current state and the state of the user before parking includes:

[0021] Comparing the physiological states before and after parking to obtain a first comparison result for determining the risk of dangerous driving; wherein the physiological state includes at least one of heart rate, facial features, walking behavior and body posture;

[0022] and / or,

[0023] The driving conditions before and after parking are compared to obtain a second comparison result for determining the risk of dangerous driving; wherein the driving conditions include at least one of traffic light braking behavior, acceleration behavior, degree of swaying of the driving route, and driving behavior when encountering obstacles.

[0024] As an improvement of the above solution, the current regional drunk driving risk of the parking location is determined based on the correspondence between the preset location, time and regional drunk driving risk, including:

[0025] Based on the correspondence between the preset location, time and regional drunk driving risk, determining whether the parking location falls into a gathering risk area;

[0026] The obtaining of the parking time and the length of time the user stays at the dining venue during the parking period, and determining the current dining risk of the user in combination with the parking period, includes:

[0027] When the parking location falls into the gathering risk area, the parking duration and the length of time the user stays at the dining venue during the parking period are obtained, and based on the parking duration, the length of time the user stays at the dining venue and the parking period, it is determined whether the user has a gathering behavior;

[0028] After the user returns to the parking position, obtaining the current state of the user, and determining the risk of dangerous driving according to a comparison result between the current state and the state of the user before parking, includes:

[0029] When the user has the dining behavior, if the user returns to the parking location, the current state of the user is obtained, and based on the comparison result of the current state and the state of the user before parking, it is determined whether the user has a drinking behavior;

[0030] The determining of the drunk driving behavior judgment result of the user according to the current area drunk driving risk, the current dinner party risk and the dangerous driving risk includes:

[0031] When the parking location falls into the dinner party risk area, the user has the dinner party behavior, and the user has the drinking behavior, it is determined that the user has drunk driving behavior.

[0032] To achieve the above purpose, an embodiment of the present invention further provides a drunk driving identification device, comprising:

[0033] The parking information acquisition module is used to obtain the parking location and parking period of the vehicle from the vehicle terminal;

[0034] A regional drunk driving risk determination module, used to determine the current regional drunk driving risk of the parking location based on a preset correspondence between location, time and regional drunk driving risk;

[0035] A dining party risk determination module, used to obtain the parking time and the length of time the user stays at the dining venue during the parking period, and determine the user's current dining party risk in combination with the parking period;

[0036] A user status acquisition module, used to acquire the current status of the user after the user returns to the parking position;

[0037] A dangerous driving determination module, used to determine the risk of dangerous driving based on a comparison result between the current state and the state of the user before parking;

[0038] The drunk driving behavior determination module is used to determine the drunk driving behavior determination result of the user according to the drunk driving risk in the current area, the current gathering risk and the dangerous driving risk.

[0039] To achieve the above objectives, an embodiment of the present invention further provides a drunk driving identification device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the drunk driving identification method as described in any of the above embodiments is implemented.

[0040] To achieve the above objectives, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the drunk driving identification method as described in any of the above embodiments.

[0041] To achieve the above objectives, an embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the drunk driving identification method as described in any of the above embodiments.

[0042] Compared with the prior art, the drunk driving identification method, device, equipment, storage medium and product provided by the embodiment of the present invention obtain the parking position and parking period of the vehicle from the vehicle terminal; determine the current regional drunk driving risk of the parking position based on the correspondence between the preset position, time and regional drunk driving risk; obtain the parking time and the user's stay time in the restaurant during the parking period, and determine the user's current gathering risk in combination with the parking period; obtain the user's current state after the user returns to the parking position; determine the risk of dangerous driving based on the comparison result of the current state and the user's state before parking; determine the drunk driving behavior identification result of the user based on the current regional drunk driving risk, the current gathering risk and the dangerous driving risk. The embodiment of the present invention can comprehensively analyze whether the user has drunk driving behavior by identifying the parking position, the user's stay time in the restaurant, and the user's state when returning after leaving the car, without the need to configure a large number of additional drones, reducing the risk of safety accidents caused by the drunk driving identification process, and having stronger practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1This is a flow chart of a drunk driving identification method provided by one embodiment of the present invention;

[0044] Figure 2 is a schematic diagram of a drunk driving prediction and warning system provided by an embodiment of the present invention;

[0045] Figure 3 It is a schematic diagram of a vehicle-mounted terminal obtaining in-vehicle electronic equipment information provided by an embodiment of the present invention;

[0046] Figure 4 It is a schematic diagram of a merchant enterprise gateway and a shared charging device acquiring device information provided by an embodiment of the present invention;

[0047] Figure 5 is a schematic diagram of a first electronic fence provided by an embodiment of the present invention;

[0048] Figure 6 is a schematic diagram of a third electronic fence provided by an embodiment of the present invention;

[0049] Figure 7 is a flow chart of a method for distinguishing drunk driving provided by another embodiment of the present invention;

[0050] Figure 8 is a structural diagram of a drunk driving identification device provided by an embodiment of the present invention;

[0051] Fig. 9 It is a structural diagram of a drunk driving identification device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0054] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0055] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0056] See also Figure 1 , is a drunk driving identification method provided by an embodiment of the present invention, comprising steps S1 to S6:

[0057] S1. Obtain the parking location and parking period of the vehicle from the vehicle terminal;

[0058] S2. Determine the current regional drunk driving risk of the parking location based on the preset correspondence between the location, time and regional drunk driving risk;

[0059] S3, obtaining the parking time and the length of time the user stays at the dining venue during the parking period, and determining the current dining risk of the user in combination with the parking period;

[0060] S4, after the user returns to the parking location, obtaining the current state of the user;

[0061] S5. Determine the risk of dangerous driving based on a comparison result between the current state and the state of the user before parking;

[0062] S6. Determine a drunk driving behavior identification result of the user according to the current area drunk driving risk, the current gathering risk, and the dangerous driving risk.

[0063] It is worth noting that the drunk driving identification method described in the embodiment of the present invention can be implemented by a pre-built V2X (vehicle to X, vehicle wireless communication technology) vehicle-road collaborative operation management platform, and the V2X vehicle-road collaborative operation management platform, vehicle-mounted terminals, merchant enterprise gateways, and video surveillance together constitute a drunk driving prediction and warning system. Figure 2 , is a schematic diagram of a drunk driving prediction and warning system provided by an embodiment of the present invention, comprising Figure 2It can be seen that the vehicle terminal has built-in V2X-SDK (vehicle to X-Software Development Kit), data communication module, high-precision positioning module, smart device sniffing module and alarm module, and the vehicle terminal communicates with the V2X vehicle-road collaborative operation management platform through the cellular network; the merchant enterprise gateway has built-in V2X-SDK and data communication module; video surveillance includes parking monitoring, business district monitoring and V2X-SDK; the V2X vehicle-road collaborative operation management platform provides vehicle management, road information management, equipment management, merchant enterprise gateway AP (Access Point, wireless access point) management, electronic fences in drunk driving areas and other information management services, and provides vehicles with real-time drunk driving detection and alarm services.

[0064] In step S1, the V2X vehicle-road collaborative operation management platform obtains the parking location and parking period of the vehicle through the vehicle terminal. Specifically, the vehicle terminal sends the high-precision location information, vehicle information and owner information of the vehicle to the V2X vehicle-road collaborative operation management platform at a fixed frequency, where the vehicle information can be the license plate number and the owner information can be the owner's mobile phone number. Figure 3 , is a schematic diagram of an on-board terminal obtaining in-vehicle electronic equipment information provided by an embodiment of the present invention. Exemplarily, the on-board terminal sniffs in-vehicle electronic equipment (mobile phones, smart bracelets and smart watches, etc.) information in real time through the network (WIFI, Bluetooth and Star Flash, etc.), such as device mac address (Media Access Control Address), IMEI (International Mobile Equipment Identity) and ssid (Service Set Identifier), etc., and generates a unique identification mark, the time the device stays in the car and other information after the sniffed device information is hashed and encrypted, and transmitted to the V2X vehicle-road collaborative operation management platform. Then, the V2X vehicle-road collaborative operation management platform further analyzes the parking position and parking period of the vehicle based on the high-precision location information of the vehicle at each moment, the time the device stays in the car and other information.

[0065] It is worth noting that drunk driving usually occurs after a dinner party, and the dinner party is usually held at a restaurant. Therefore, in step S2, based on the parking position and parking period of the vehicle, it is determined whether the vehicle is parked in a pre-defined dinner party risk area during the dinner party period, so as to determine whether the vehicle has a regional drunk driving risk.

[0066] In step S3, it is necessary to determine the user's current dining risk based on the parking duration, the length of stay in the dining venue, and the parking period. Exemplarily, in some embodiments, the merchant installs a merchant enterprise gateway, the merchant enterprise gateway is connected to the V2X vehicle-road collaborative operation management platform, and the merchant nature is registered on the V2X vehicle-road collaborative operation management platform. For example, the merchant nature can be a dining venue. Furthermore, the merchant store covers the network. When people consume in practical stores, mobile phones or smart wearable devices are connected to the store network, such as when connecting to WIFI. Device information, such as device mac address, IME and ssid, etc., is left behind. The merchant enterprise gateway will generate a unique identification mark, the device's stay time in the store, the nature of the merchant, and other information after hashing and encrypting the sniffed device information, and transmit it to the V2X vehicle-road collaborative operation management platform.

[0067] Furthermore, when a shared charging device is provided in a store or shopping mall, the shared charging device can desensitize the location and rental period, and generate a unique identification tag, rental time, charging device deployment store location and other information after hash encryption to transmit to the V2X vehicle-road collaborative operation management platform. Figure 4 , is a schematic diagram of the merchant enterprise gateway and the shared charging device obtaining device information provided by an embodiment of the present invention. The V2X vehicle-road cooperative transportation management platform analyzes the user's stay time at the restaurant and the current risk of dining together in combination with the above information transmitted back by the merchant enterprise gateway and / or the shared charging device.

[0068] It is worth noting that even if the user leaves the vehicle for a period of time, the user does not necessarily have a meal, and even if the user has a meal, the user does not necessarily have a drinking behavior. Therefore, in steps S4 and S5, it is necessary to obtain the user's current state, and determine whether there is a risk of dangerous driving based on the user's current state and the user's state before parking, that is, determine whether the user has a drinking behavior. Exemplarily, the user's state can be a physiological state, such as heart rate, facial features, walking behavior and body shape; the user's state can also be a driving condition, such as traffic light braking behavior, acceleration behavior, the degree of swaying of the driving route, and driving behavior when encountering obstacles.

[0069] In step S6, it is necessary to comprehensively consider the current area's drunk driving risk, current gathering risk, and dangerous driving risk to determine whether the user has drunk driving behavior.

[0070] Compared with the prior art, the embodiments of the present invention can comprehensively analyze whether a user has drunk driving behavior by identifying the parking location, the length of time the user stays in the dining place, and the state of the user when leaving the car and returning. There is no need to configure a large number of additional drones, which reduces the risk of safety accidents caused by the drunk driving identification process and has stronger practicality.

[0071] As one optional implementation, the determining of the current regional drunk driving risk of the parking location based on the correspondence between the preset location, time and regional drunk driving risk includes:

[0072] The drunk driving risk coefficient of the current area is calculated based on the electronic fence where the parking position is located and the weight coefficient of the electronic fence.

[0073] It is worth noting that, in the embodiment of the present invention, electronic fences are drawn in advance in the dinner gathering risk area, and a corresponding weight coefficient is set for each electronic fence. The larger the weight coefficient, the higher the dinner gathering risk in the corresponding area.

[0074] For example, the current regional drunk driving risk factor is calculated by formula (1):

[0075]

[0076] Among them, R d Indicates the risk factor of drunk driving in the current area; p i represents the weight coefficient of the i-th electronic fence; F i Indicates whether the parking position of the vehicle is within the i-th electronic fence, F i =0 means the parking position of the vehicle is not within the i-th electronic fence, F i =1 indicates that the parking position of the vehicle is within the i-th electronic fence; N indicates the total number of electronic fences.

[0077] It is worth noting that the areas divided by different electronic fences may overlap. Therefore, the same vehicle may stay in multiple electronic fences at the same time. Therefore, in formula (1), it is necessary to determine whether the parking position of the vehicle is within each electronic fence.

[0078] As an optional implementation manner, the electronic fence includes at least one of a first electronic fence, a second electronic fence and a third electronic fence, wherein the electronic fence is divided as follows:

[0079] Draw the first electronic fence in the catering enterprise cluster area and the business district area;

[0080] During the preset dining peak period, the second electronic fence is drawn in the vehicle dynamic gathering area; wherein the vehicle dynamic gathering area has the following characteristics during the preset dining peak period: the number of vehicles gathering is greater than a first preset threshold, the vehicle repetition rate between different time nodes during the preset dining peak period is less than a second preset threshold, and the time interval between adjacent time nodes is greater than a third preset threshold;

[0081] In the village residential gathering area, the personnel gathering area is identified based on the mobile terminal and the vehicle-mounted terminal, so as to draw the third electronic fence in the personnel gathering area.

[0082] For example, the first electronic fence can be divided based on the analysis of population geographic location big data and the open API (Application Program Interface) of business district distribution. For example, the source of population geographic location big data can be the urban population geographic big data platform of Baidu Map Huiyan, and the open API of business district distribution can be the open catering service API of Meituan. Figure 5 , is a schematic diagram of a first electronic fence provided by an embodiment of the present invention, comprising Figure 5 It can be seen that the area demarcated by the first electronic fence is a business district, and parking lots in business districts are generally equipped with parking monitoring. The status comparison results before and after the user's parking can be obtained by checking the parking monitoring.

[0083] It is worth noting that in addition to catering enterprises gathering areas such as business districts, there are also some catering enterprises with more dispersed distribution. In this case, a second electronic fence can be divided based on information such as the historical positioning, historical trajectory and historical parking period of the vehicle to divide the above catering enterprises into the electronic fence. Specifically, during peak dining hours, such as 11:00-14:00 noon and 6:00-9:00 pm, the vehicle dynamic gathering area is divided into the second electronic fence, where vehicles are concentrated in the vehicle dynamic gathering area during the peak dining hours, and the vehicles are not fixed and change frequently. The second electronic fence can be delineated in advance and updated regularly.

[0084] Furthermore, in areas where residential buildings are concentrated in villages, large-scale events such as weddings and funerals are sometimes held. People tend to drink alcohol at these events, and the risk of drunk driving is high. Therefore, it is necessary to divide these areas into third electronic fences. However, such events are usually not held in catering venues and at no fixed time, and have a certain degree of randomness. Therefore, when dividing the third electronic fence, it can be divided based on the areas where people gather that are identified in real time. For example, based on mobile phone 5G signals, Beidou high-precision positioning, and vehicle positioning information, multi-dimensional perception of areas where people gather can be used to divide the third electronic fence. See Figure 6, is a schematic diagram of the third electronic fence provided by an embodiment of the present invention. It is worth noting that the rural population is relatively sparse compared to the urban population. Therefore, the use of mobile phone signals in rural areas can better perceive crowd gathering scenes. The information of the gathered electronic devices, such as IP address (Internet Protocol Address), network and MAC address, etc., can be encrypted by hash through the 5G-a (5G Evolution, 5G-Advanced) base station and synchronized to the V2X vehicle-road collaborative operation management platform. It is worth noting that people are also gathered in residential areas, but the flow of people in residential areas is regular. Based on this, residential areas and the above-mentioned areas where people gather can be distinguished to avoid mistakenly dividing residential areas into areas where people gather.

[0085] As one optional implementation, the acquiring of the parking duration and the duration of the user's stay at the dining venue during the parking period, and the calculation of the user's current dining risk in combination with the parking period, includes:

[0086] Based on the correspondence between the time period and the gathering weight coefficient, the current gathering weight coefficient is determined according to the parking time period;

[0087] The current dinner gathering risk coefficient is calculated based on the current dinner gathering weight coefficient, the length of stay at the dining venue and the parking time; wherein the current dinner gathering risk coefficient is positively correlated with the ratio of the length of stay at the dining venue to the parking time and the current dinner gathering weight coefficient, respectively.

[0088] It is worth noting that since dining time is relatively concentrated, the probability of dining behavior occurring in different time periods is different for the same dining risk area. Therefore, different dining weight coefficients are set for different time periods in advance. Before calculating the current dining risk coefficient, the current dining weight coefficient is obtained according to the parking period. Furthermore, the longer the user stays in the dining place, the greater the dining risk. Therefore, the current dining risk coefficient is also calculated in combination with the "ratio of the length of stay in the dining place to the length of parking". For example, the current dining risk coefficient can be calculated by formula (2):

[0089]

[0090] Among them, R dinner represents the current risk factor of the gathering; p Δt Indicates the current dinner weight coefficient; ΔT 餐饮场所停留时长 Indicates the length of stay at a restaurant; ΔT 停车时长 Indicates the parking duration.

[0091] As an optional implementation manner, determining the risk of dangerous driving according to a comparison result between the current state and the state of the user before parking includes:

[0092] Comparing the physiological states before and after parking to obtain a first comparison result for determining the risk of dangerous driving; wherein the physiological state includes at least one of heart rate, facial features, walking behavior and body posture;

[0093] and / or,

[0094] The driving conditions before and after parking are compared to obtain a second comparison result for determining the risk of dangerous driving; wherein the driving conditions include at least one of traffic light braking behavior, acceleration behavior, degree of swaying of the driving route, and driving behavior when encountering obstacles.

[0095] Exemplarily, the V2X vehicle-road collaborative operation management platform matches the user location based on the terminal information, and based on the parking location and the nature of the merchant, retrieves the business district monitoring, electronic road eye cameras and parking lot video monitoring of the relevant location, and compares the user's current and before parking facial features, walking behavior and posture, etc., to obtain a first comparison result.

[0096] Furthermore, when the user wears a smart wearable device (such as a smart bracelet and a smart watch), the biometric information recorded by the smart wearable device, such as heart rate and other indicators, can be obtained, and the historical statistical data of the relevant indicators can be combined to make a judgment. When the change in the indicator is greater than the preset threshold, that is, P s >ΔP, the user's physiological state is judged to be abnormal. When the change in the indicator is less than or equal to the preset threshold, that is, P s ≤ΔP, the user's physiological state is judged to be normal to obtain the first result, where P s It indicates the change of the indicator, and ΔP indicates the preset threshold.

[0097] For example, the second comparison result can be obtained by comparing the traffic light braking behavior, acceleration behavior, swaying degree of the driving route and driving behavior when encountering obstacles before and after parking. For example, in rural areas where basic monitoring facilities are not perfect, it may be difficult to obtain the physiological state of the user when picking up the car. At this time, the second comparison result can be determined based on the comparison results of the driving conditions before and after parking to serve as the user's current dangerous driving risk coefficient R pt , R pt The larger the value, the greater the probability that the user has drinking behavior. pt The smaller the value, the lower the probability that the user has drinking behavior.

[0098] Furthermore, according to the user's current area drunk driving risk coefficient R d 、Current dinner risk factor R dinner and the current risk factor of dangerous driving R pt , calculate the user's real-time drunk driving risk coefficient R vt, real-time drunk driving risk factor R vt It can be calculated by formula (3):

[0099] R vt =R d ×R dinner ×R pt (3)

[0100] Furthermore, when the real-time drunk driving risk factor R vt When the value is greater than the set threshold, the user can be reminded of dangerous driving and security or traffic police can be notified to handle the violation. Furthermore, it can also help traffic law enforcement departments trace the evidence of drunk driving. The V2X vehicle-road collaborative transportation management platform can trace the evidence of drunk driving based on the comparison results of driving behaviors before and after the vehicle stops, the vehicle's driving trajectory, the information of the merchants where the vehicle stops, the length of time it stays at a catering venue, the vehicle's parking location and parking monitoring.

[0101] Furthermore, when the real-time drunk driving risk factor R vt When it is greater than the set threshold, early warning information can also be sent to surrounding vehicles and pedestrians to remind them to avoid suspected drunk driving vehicles and avoid loss of personal and property safety.

[0102] As one optional implementation, the determining of the current regional drunk driving risk of the parking location based on the correspondence between the preset location, time and regional drunk driving risk includes:

[0103] Based on the correspondence between the preset location, time and regional drunk driving risk, determining whether the parking location falls into a gathering risk area;

[0104] The obtaining of the parking time and the length of time the user stays at the dining venue during the parking period, and determining the current dining risk of the user in combination with the parking period, includes:

[0105] When the parking location falls into the gathering risk area, the parking duration and the length of time the user stays at the dining venue during the parking period are obtained, and based on the parking duration, the length of time the user stays at the dining venue and the parking period, it is determined whether the user has a gathering behavior;

[0106] After the user returns to the parking position, obtaining the current state of the user, and determining the risk of dangerous driving according to a comparison result between the current state and the state of the user before parking, includes:

[0107] When the user has the dining behavior, if the user returns to the parking location, the current state of the user is obtained, and based on the comparison result of the current state and the state of the user before parking, it is determined whether the user has a drinking behavior;

[0108] The determining of the drunk driving behavior judgment result of the user according to the current area drunk driving risk, the current dinner party risk and the dangerous driving risk includes:

[0109] When the parking location falls into the dinner party risk area, the user has the dinner party behavior, and the user has the drinking behavior, it is determined that the user has drunk driving behavior.

[0110] It is worth noting that since drunk driving occurs after drinking, which usually occurs after dining, and dining usually occurs in dining risk areas such as catering venues, the embodiments of the present invention further determine whether the user has a dining experience after determining that the parking location falls into the dining risk area (that is, when the parking location does not fall into the dining risk area, no dining risk judgment and dangerous driving judgment are made); after determining that the user has a dining experience, further determine whether the user has a drinking experience (that is, when the user has not had a dining experience, no dangerous driving judgment is made); finally, when the parking location falls into the dining risk area and the user has a dining experience and drinks, the user is determined to be driving under the influence. Compared with the prior art, the embodiments of the present invention set the judgment order and judgment logic of the three risks of regional drunk driving risk, dining risk and dangerous driving risk, thereby eliminating the need to simultaneously judge the regional drunk driving risk, dining risk and dangerous driving risk for all vehicles. Instead, it is only necessary to judge the dining risk when the vehicle has a regional drunk driving risk, and to judge the dangerous driving when the vehicle has a dining risk. This can reduce the workload of drunk driving judgment and improve the efficiency of drunk driving judgment while ensuring accurate identification of drunk driving behavior.

[0111] In order to understand the method of judging drunk driving more clearly, the following is an example to illustrate. Figure 7 , is a flow chart of a method for distinguishing drunk driving provided by another embodiment of the present invention:

[0112] First, it is necessary to divide the electronic fences and determine the weight coefficients for each electronic fence; then, determine whether the parking location falls into the risk area for dining together; if the parking location falls into the risk area for dining together, before parking, obtain the electronic equipment information and the user's biometric information in the car, and after parking, analyze the user's length of stay in the dining place and the nature of the merchant based on the merchant enterprise gateway and shared charging device, and continue to monitor the user's biometric information; then, determine whether the user has dining together; if the user has dining together, analyze the user's physiological state through electronic road eyes and parking monitoring, compare the relationship between the user's current biometric information and historical statistical data through smart wearable devices, and compare the driving conditions before and after parking; based on the analysis results of the physiological state, biometric information and driving conditions, determine whether the user has drunk alcohol; if the user has drunk alcohol, determine that the user has drunk driving.

[0113] Compared with the prior art, the drunk driving identification method provided by the embodiment of the present invention obtains the parking position and parking period of the vehicle from the vehicle terminal; determines the current regional drunk driving risk of the parking position based on the correspondence between the preset position, time and regional drunk driving risk; obtains the parking time and the user's stay time in the restaurant during the parking period, and determines the user's current gathering risk in combination with the parking period; obtains the user's current state after the user returns to the parking position; determines the risk of dangerous driving based on the comparison result of the current state and the user's state before parking; determines the drunk driving behavior identification result of the user based on the current regional drunk driving risk, the current gathering risk and the dangerous driving risk. The embodiment of the present invention can comprehensively analyze whether the user has drunk driving behavior by identifying the parking position, the user's stay time in the restaurant, and the user's state when returning after leaving the car, without the need to configure a large number of additional drones, reducing the risk of safety accidents caused by the drunk driving identification process, and has stronger practicality.

[0114] See also Figure 8 The embodiment of the present invention further provides a drunk driving identification device 10, which is characterized by comprising:

[0115] The parking information acquisition module 11 is used to obtain the parking location and parking period of the vehicle from the vehicle terminal;

[0116] The regional drunk driving risk determination module 12 is used to determine the current regional drunk driving risk of the parking location based on the corresponding relationship between the preset location, time and regional drunk driving risk;

[0117] A dining party risk determination module 13 is used to obtain the parking time and the length of time the user stays at the dining venue during the parking period, and determine the current dining party risk of the user in combination with the parking period;

[0118] A user status acquisition module 14, used to acquire the current status of the user after the user returns to the parking position;

[0119] A dangerous driving determination module 15, configured to determine the risk of dangerous driving according to a comparison result between the current state and the state of the user before parking;

[0120] The drunk driving behavior determination module 16 is used to determine the drunk driving behavior determination result of the user according to the drunk driving risk in the current area, the current gathering risk and the dangerous driving risk.

[0121] In one implementation, the regional drunk driving risk determination module 12 is specifically used to:

[0122] The drunk driving risk coefficient of the current area is calculated based on the electronic fence where the parking position is located and the weight coefficient of the electronic fence.

[0123] In one embodiment, the device further includes an electronic fence division module for dividing the electronic fence, wherein the electronic fence includes at least one of a first electronic fence, a second electronic fence, and a third electronic fence, wherein the electronic fence is divided as follows:

[0124] Draw the first electronic fence in the catering enterprise cluster area and the business district area;

[0125] During the preset dining peak period, the second electronic fence is drawn in the vehicle dynamic gathering area; wherein the vehicle dynamic gathering area has the following characteristics during the preset dining peak period: the number of vehicles gathering is greater than a first preset threshold, the vehicle repetition rate between different time nodes during the preset dining peak period is less than a second preset threshold, and the time interval between adjacent time nodes is greater than a third preset threshold;

[0126] In the village residential gathering area, the personnel gathering area is identified based on the mobile terminal and the vehicle-mounted terminal, so as to draw the third electronic fence in the personnel gathering area.

[0127] In one implementation, the dinner party risk determination module 13 is specifically used to:

[0128] Based on the correspondence between the time period and the gathering weight coefficient, the current gathering weight coefficient is determined according to the parking time period;

[0129] The current dinner gathering risk coefficient is calculated based on the current dinner gathering weight coefficient, the length of stay at the dining venue and the parking time; wherein the current dinner gathering risk coefficient is positively correlated with the ratio of the length of stay at the dining venue to the parking time and the current dinner gathering weight coefficient, respectively.

[0130] In one implementation, the dangerous driving determination module 15 is specifically configured to:

[0131] Comparing the physiological state before and after parking to obtain a first comparison result for determining the risk of dangerous driving; wherein the physiological state includes at least one of heart rate, facial features, walking behavior and body posture;

[0132] and / or,

[0133] The driving conditions before and after parking are compared to obtain a second comparison result for determining the risk of dangerous driving; wherein the driving conditions include at least one of traffic light braking behavior, acceleration behavior, degree of swaying of the driving route, and driving behavior when encountering obstacles.

[0134] In one implementation, the regional drunk driving risk determination module 12 is specifically used to:

[0135] Based on the correspondence between the preset location, time and regional drunk driving risk, determining whether the parking location falls into a gathering risk area;

[0136] The dining risk determination module 13 is specifically used for:

[0137] When the parking location falls into the gathering risk area, the parking duration and the length of time the user stays at the dining venue during the parking period are obtained, and based on the parking duration, the length of time the user stays at the dining venue and the parking period, it is determined whether the user has a gathering behavior;

[0138] The user status acquisition module 14 and the dangerous driving determination module 15 are specifically used for:

[0139] When the user has the dining behavior, if the user returns to the parking location, the current state of the user is obtained, and based on the comparison result of the current state and the state of the user before parking, it is determined whether the user has a drinking behavior;

[0140] The drunk driving behavior determination module 16 is specifically used for:

[0141] When the parking location falls into the dinner party risk area, the user has the dinner party behavior, and the user has the drinking behavior, it is determined that the user has drunk driving behavior.

[0142] The drunk driving identification device provided in the embodiment of the present invention can implement all the process steps of the drunk driving identification method described in the above embodiment. The functions of each module and unit in the device and the technical effects achieved are respectively the same as the functions and technical effects achieved by the drunk driving identification method described in the above embodiment, and the specific implementation method is not repeated here.

[0143] See also Fig. 9 The embodiment of the present invention further provides a drunk driving identification device 20, comprising a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, the steps in the above-mentioned drunk driving identification method embodiment are implemented, for example Figure 1 S1 to S6 in the above description; or, the processor 21 implements the functions of each module in the above-mentioned device embodiments when executing the computer program.

[0144] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the drunk driving identification device 20. For example, the computer program may be divided into multiple modules, and the specific functions of each module are as follows:

[0145] The parking information acquisition module 11 is used to obtain the parking location and parking period of the vehicle from the vehicle terminal;

[0146] The regional drunk driving risk determination module 12 is used to determine the current regional drunk driving risk of the parking location based on the corresponding relationship between the preset location, time and regional drunk driving risk;

[0147] A dining party risk determination module 13 is used to obtain the parking time and the length of time the user stays at the dining venue during the parking period, and determine the current dining party risk of the user in combination with the parking period;

[0148] A user status acquisition module 14, used to acquire the current status of the user after the user returns to the parking position;

[0149] A dangerous driving determination module 15, configured to determine the risk of dangerous driving according to a comparison result between the current state and the state of the user before parking;

[0150] The drunk driving behavior determination module 16 is used to determine the drunk driving behavior determination result of the user according to the drunk driving risk in the current area, the current gathering risk and the dangerous driving risk.

[0151] The specific working process of each module can refer to the working process of the drunk driving identification device described in the above embodiment, which will not be repeated here.

[0152] The drunk driving identification device 20 can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The drunk driving identification device 20 can include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art can understand that the drunk driving identification device 20 can also include input and output devices, network access devices, buses, etc.

[0153] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 21 is the control center of the drunk driving identification device 20, and various interfaces and lines are used to connect various parts of the entire drunk driving identification device 20.

[0154] The memory 22 can be used to store the computer program and / or module, and the processor 21 realizes various functions of the drunk driving identification device 20 by running or executing the computer program and / or module stored in the memory 22, and calling the data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory 22 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0155] Wherein, if the module integrated in the drunk driving discrimination device 20 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 such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor 21. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0156] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the drunk driving identification method as described in any of the above embodiments.

[0157] Compared with the prior art, the drunk driving identification device, equipment, storage medium and product provided by the embodiment of the present invention obtain the parking position and parking period of the vehicle from the vehicle terminal; determine the current regional drunk driving risk of the parking position based on the corresponding relationship between the preset position, time and regional drunk driving risk; obtain the parking time and the user's stay time in the catering place during the parking period, and determine the current gathering risk of the user in combination with the parking period; obtain the current state of the user after the user returns to the parking position; determine the risk of dangerous driving based on the comparison result of the current state and the state of the user before parking; determine the drunk driving behavior identification result of the user based on the current regional drunk driving risk, the current gathering risk and the dangerous driving risk. The embodiment of the present invention can comprehensively analyze whether the user has drunk driving behavior by identifying the parking position, the length of time the user stays in the catering place, and the state of the user when returning after leaving the car, without the need to configure a large number of additional drones, reducing the risk of safety accidents caused by the drunk driving identification process, and having stronger practicality.

[0158] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for distinguishing drunk driving, characterized in that: include: Obtain the parking location and parking period of the vehicle from the vehicle terminal; Determine the current regional drunk driving risk of the parking location based on the preset correspondence between the location, time and regional drunk driving risk; Obtain the parking time and the length of time the user stays at the dining venue during the parking period, and determine the user's current dining risk based on the parking period; After the user returns to the parking location, obtaining the current state of the user; Determining a risk of dangerous driving based on a comparison result of the current state and the state of the user before parking; The drunk driving behavior identification result of the user is determined according to the current area drunk driving risk, the current dinner party risk and the dangerous driving risk.

2. The drunk driving identification method according to claim 1, characterized in that: The determining of the current regional drunk driving risk of the parking location based on the correspondence between the preset location, time and regional drunk driving risk includes: The drunk driving risk coefficient of the current area is calculated based on the electronic fence where the parking position is located and the weight coefficient of the electronic fence.

3. The drunk driving identification method according to claim 2, characterized in that: The electronic fence includes at least one of a first electronic fence, a second electronic fence and a third electronic fence, wherein the electronic fence is divided as follows: Draw the first electronic fence in the catering enterprise cluster area and the business district area; During the preset dining peak period, the second electronic fence is drawn in the vehicle dynamic gathering area; wherein the vehicle dynamic gathering area has the following characteristics during the preset dining peak period: the number of vehicles gathering is greater than a first preset threshold, the vehicle repetition rate between different time nodes during the preset dining peak period is less than a second preset threshold, and the time interval between adjacent time nodes is greater than a third preset threshold; In the village residential gathering area, the personnel gathering area is identified based on the mobile terminal and the vehicle-mounted terminal, so as to draw the third electronic fence in the personnel gathering area.

4. The drunk driving identification method according to claim 1, characterized in that: The obtaining of the parking time and the length of time the user stays at the dining venue during the parking period, and calculating the user's current dining risk in combination with the parking period, includes: Based on the correspondence between the time period and the gathering weight coefficient, the current gathering weight coefficient is determined according to the parking time period; The current dinner gathering risk coefficient is calculated based on the current dinner gathering weight coefficient, the length of stay at the dining venue and the parking time; wherein the current dinner gathering risk coefficient is positively correlated with the ratio of the length of stay at the dining venue to the parking time and the current dinner gathering weight coefficient, respectively.

5. The drunk driving identification method according to claim 1, characterized in that: The determining the risk of dangerous driving according to the comparison result of the current state and the state of the user before parking includes: Comparing the physiological states before and after parking to obtain a first comparison result for determining the risk of dangerous driving; wherein the physiological state includes at least one of heart rate, facial features, walking behavior and body posture; and / or, The driving conditions before and after parking are compared to obtain a second comparison result for determining the risk of dangerous driving; wherein the driving conditions include at least one of traffic light braking behavior, acceleration behavior, degree of swaying of the driving route, and driving behavior when encountering obstacles.

6. The drunk driving identification method according to claim 1, characterized in that: The determining of the current regional drunk driving risk of the parking location based on the correspondence between the preset location, time and regional drunk driving risk includes: Based on the correspondence between the preset location, time and regional drunk driving risk, determining whether the parking location falls into a gathering risk area; The obtaining of the parking time and the length of time the user stays at the dining venue during the parking period, and determining the current dining risk of the user in combination with the parking period, includes: When the parking location falls into the gathering risk area, the parking duration and the length of time the user stays at the dining venue during the parking period are obtained, and based on the parking duration, the length of time the user stays at the dining venue and the parking period, it is determined whether the user has a gathering behavior; After the user returns to the parking position, obtaining the current state of the user, and determining the risk of dangerous driving according to a comparison result between the current state and the state of the user before parking, includes: When the user has the dining behavior, if the user returns to the parking location, the current state of the user is obtained, and according to the comparison result of the current state and the state of the user before parking, it is determined whether the user has a drinking behavior; The determining of the drunk driving behavior judgment result of the user according to the current area drunk driving risk, the current dinner party risk and the dangerous driving risk includes: When the parking location falls into the dinner party risk area, the user has the dinner party behavior, and the user has the drinking behavior, it is determined that the user has drunk driving behavior.

7. A drunk driving identification device, characterized in that: include: The parking information acquisition module is used to obtain the parking location and parking period of the vehicle from the vehicle terminal; A regional drunk driving risk determination module, used to determine the current regional drunk driving risk of the parking location based on a preset correspondence between location, time and regional drunk driving risk; A dining party risk determination module, used to obtain the parking time and the length of time the user stays at the dining venue during the parking period, and determine the user's current dining party risk in combination with the parking period; A user status acquisition module, used to acquire the current status of the user after the user returns to the parking position; A dangerous driving determination module, used to determine the risk of dangerous driving based on a comparison result between the current state and the state of the user before parking; The drunk driving behavior determination module is used to determine the drunk driving behavior determination result of the user according to the drunk driving risk in the current area, the current gathering risk and the dangerous driving risk.

8. A drunk driving identification device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the drunk driving identification method as claimed in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the drunk driving identification method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the drunk driving identification method as described in any one of claims 1 to 6 is implemented.

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