Drunk driving judgment method, device and equipment for new energy vehicle

Abstract

The invention relates to a drunk driving judgment method, device and equipment for a new energy vehicle, and relates to the field of intelligent traffic. According to the most frequent travel trajectory sequence of each new energy vehicle, the similarity between the current travel trajectory sequence and the most frequent travel trajectory sequence is obtained by combining the longest common subsequence algorithm, and whether the current trajectory is an abnormal trajectory is judged; and determining drunk driving of the new energy vehicle based on a pre-defined electronic fence in combination with a judgment result of judging whether the current track is an abnormal track or not. Under the condition, through combination of vehicle state information uploaded by a new energy vehicle in real time, a reasonable drunk driving study and judgment model is constructed, big data and artificial intelligence technologies are utilized, driving behavior characteristics are analyzed, an abnormal driving mode is identified, a traffic control department is helped to actively find a vehicle suspected of drunk driving, the accuracy and timeliness of monitoring are improved, and the driving safety is improved. And moreover, the waste of police resources can be effectively reduced, active prevention and control of drunk driving behaviors are realized, and the road traffic safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a method, device and equipment for determining drunk driving of new energy vehicles. Background Art

[0002] Drunk driving can easily lead to serious traffic accidents and pose a serious threat to people's lives and safety. At present, the main means for traffic control departments to combat drunk driving is to set up checkpoints and use alcohol detectors for indiscriminate testing. This method not only requires a large amount of police resources, but is also inefficient and difficult to fully cover all potential drunk driving behaviors. Because drunk driving behaviors are highly concealed and random, traditional checkpoint detection methods can often only detect illegal behaviors after the fact, and cannot achieve effective active prevention and control, posing a major hidden danger to road traffic safety.

[0003] According to the national standard "Technical Specifications for Electric Vehicle Remote Service and Management System" (GB / T 32960), new energy vehicles upload vehicle status information at least once every 30 seconds, including vehicle timestamp, latitude and longitude location, vehicle speed, vehicle operating status, battery status, motor status, mileage, environmental parameters, driving behavior and other information. These data provide relatively fresh and accurate traffic trajectories, providing a solid foundation for real-time monitoring and analysis of driving behavior. By combining the vehicle status information uploaded in real time by new energy vehicles, a reasonable drunk driving research and judgment model can be constructed, and big data and artificial intelligence technology can be used to analyze driving behavior characteristics, identify abnormal driving patterns, and help traffic control departments proactively discover vehicles suspected of drunk driving. This method not only improves the accuracy and timeliness of monitoring, but also effectively reduces the waste of police resources, realizes active prevention and control of drunk driving behavior, and ensures road traffic safety. Summary of the invention

[0004] The purpose of the present invention is to provide a method, device and equipment for determining drunk driving of new energy vehicles to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] In a first aspect, the present invention provides a method for determining drunk driving of a new energy vehicle, the method comprising:

[0007] Monitor vehicle status information uploaded by new energy vehicles in real time;

[0008] Based on the vehicle status information uploaded in real time by the new energy vehicles, and in combination with the fast bundle algorithm, the most frequent travel trajectory sequence of each new energy vehicle is determined;

[0009] According to the most frequent travel trajectory sequence of each new energy vehicle, the similarity between the current travel trajectory sequence and the most frequent travel trajectory sequence is obtained in combination with the longest common subsequence algorithm, and whether the current trajectory is an abnormal trajectory is determined;

[0010] Based on the pre-delineated electronic fence and in combination with the determination result of whether the current trajectory is an abnormal trajectory, drunk driving of a new energy vehicle is determined.

[0011] In one possible implementation, the vehicle status information includes at least:

[0012] Timestamp, which records the specific time point when the vehicle status data is generated or collected, and is used to ensure the time sequence and synchronization of the data;

[0013] Longitude and latitude location points, which provide the vehicle's current geographic location information and are used to track the vehicle's location and driving trajectory;

[0014] Vehicle speed, which records the current speed of the vehicle and is used to monitor the vehicle's driving status and speed changes;

[0015] Vehicle operating status, including vehicle startup status information, shutdown status information, driving status information, and fault information;

[0016] Battery status, including battery remaining capacity information, battery temperature information, and charging status information;

[0017] Motor status, including motor operating status information, speed information, and temperature information;

[0018] Mileage, which records the vehicle's cumulative mileage;

[0019] Environmental parameters, which provide information about the temperature inside the vehicle and the temperature of the external environment;

[0020] Driving behavior, which provides the vehicle's sudden acceleration status information and sudden braking status information.

[0021] In a possible implementation, the method of determining the most frequent travel trajectory sequence of each new energy vehicle based on the vehicle status information uploaded in real time by the new energy vehicle and in combination with the fast bundle algorithm includes:

[0022] Obtain vehicle status information corresponding to each new energy vehicle;

[0023] Arrange in ascending order according to the timestamps, starting from the first longitude and latitude position point, and divide once in response to the time stamp interval between two consecutive longitude and latitude position points being greater than 180 minutes;

[0024] If the vehicle status information of the new energy vehicle is divided n-1 times to form n travel trajectory sequences, each of the travel trajectory sequences is interpolated to ensure that the number of latitude and longitude position points of each travel trajectory is the same;

[0025] The travel trajectories are numbered from 1 to N, travel trajectory 1 is defined as cluster center 1, and the Euclidean distances between all points in travel trajectory 2 and all points in travel trajectory 1 are calculated. If all distances are less than or equal to a set threshold, travel trajectory 2 and travel trajectory 1 are classified into one category;

[0026] At the same time, the average value of each latitude and longitude position point of travel trajectory 1 and travel trajectory 2 is taken to replace the original cluster center 1. If the distance is greater than the set threshold, travel trajectory 2 is used as cluster center 2, and all travel trajectories are traversed in turn to complete the cluster analysis of all travel trajectories;

[0027] The cluster center with the largest number of corresponding trajectory sequences after being solved by the fast bundle algorithm is the most frequent travel trajectory sequence of each new energy vehicle.

[0028] In a possible implementation, the most frequent travel trajectory sequence of each new energy vehicle is used in combination with the longest common subsequence algorithm to obtain the similarity between the current driving trajectory sequence and the most frequent travel trajectory sequence, and determining whether the current trajectory is an abnormal trajectory includes:

[0029] Convert the latitude and longitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence into a one-dimensional data sequence;

[0030] Solving the one-dimensional data sequence converted from the longitude and latitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence based on the longest common subsequence algorithm to obtain the longest common subsequence of the current driving trajectory sequence and the most frequent travel trajectory sequence;

[0031] Determining the number of common trajectory points according to the longest common subsequence of the current driving trajectory sequence and the most frequent travel trajectory sequence;

[0032] The number of common trajectory points is divided by the number of longitude and latitude position points in the current driving trajectory sequence to obtain the trajectory similarity α, and a threshold θ is set. When α is less than θ, the current trajectory is determined to be an abnormal trajectory.

[0033] In a possible implementation, converting the latitude and longitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence into a one-dimensional data sequence includes:

[0034] Based on the Geohash algorithm, the longitude and latitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence are converted into a one-dimensional Base32 encoded data sequence.

[0035] In a possible implementation, the determination of drunk driving of a new energy vehicle based on a pre-defined electronic fence and in combination with the determination result of whether the current trajectory is an abnormal trajectory includes:

[0036] According to the actual situation of the drinking business district in the jurisdiction, a number of non-repeating electronic fences are delineated on the electronic map, and each electronic fence is composed of at least three non-repeating latitude and longitude location points;

[0037] The vehicle status information uploaded in real time by new energy vehicles before the current time t is defined as historical data, and the vehicle status information uploaded in real time by new energy vehicles after the current time t is defined as real-time data;

[0038] The most frequent travel trajectory sequence of each new energy vehicle determined based on the historical data is defined as sequence A, and the current travel trajectory sequence determined based on the real-time data is defined as sequence B;

[0039] Dynamically update the sequence B, add each trajectory data after the current time t to the sequence B, and if the time stamp interval between the newly collected trajectory data and the previous trajectory data exceeds 180 minutes, delete all trajectory data before the newly collected trajectory data in the sequence B;

[0040] Set the calculation period T. If the current trajectory is judged to be normal, the calculation ends and the drunk driving judgment is performed again after waiting for T time period.

[0041] If the current trajectory is determined to be abnormal, the vehicle status information uploaded by the new energy vehicle in real time will be continuously monitored from the moment the current trajectory is determined to be abnormal, to determine whether the new energy vehicle has entered the designated electronic fence, and if the detention time exceeds 120 minutes, the new energy vehicle will be determined to be suspected of drunk driving, and the drunk driving determination will be made again after the T period of time;

[0042] In response to the continuous monitoring time exceeding 180 minutes, during which the new energy vehicle has not entered the designated electronic fence or the new energy vehicle has stayed in the electronic fence for less than 120 minutes, the current calculation is terminated and another drunk driving determination is made after the T time period.

[0043] In a possible implementation, the calculation period T is a time period, which means that a determination is made on whether the current trajectory of the sequence B is an abnormal trajectory every T time period.

[0044] In a second aspect, the present invention provides a new energy vehicle drunk driving determination device, characterized in that the device is applicable to the new energy vehicle drunk driving determination method as described above, and the device comprises:

[0045] Monitoring module, used to monitor vehicle status information uploaded in real time by new energy vehicles;

[0046] A determination module, used to determine the most frequent travel trajectory sequence of each new energy vehicle based on the vehicle status information uploaded in real time by the new energy vehicle and in combination with a fast bundle algorithm;

[0047] A determination module, used to determine whether the current trajectory is an abnormal trajectory based on the most frequent travel trajectory sequence of each new energy vehicle and the similarity between the current travel trajectory sequence and the most frequent travel trajectory sequence in combination with the longest common subsequence algorithm;

[0048] The determination module is also used to determine whether the new energy vehicle is driven under the influence of alcohol based on a pre-defined electronic fence and in combination with the determination result of whether the current trajectory is an abnormal trajectory.

[0049] In a third aspect, the present invention provides a computer device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the processor can load and execute at least one instruction, at least one program, a code set or an instruction set to implement the method for determining drunk driving of new energy vehicles provided above.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores at least one instruction, at least one program, code set or instruction set. The processor can load and execute at least one instruction, at least one program, code set or instruction set to implement the method for determining drunk driving of new energy vehicles provided above.

[0051] In a fifth aspect, the present invention provides a computer program product or a computer program, which includes computer program instructions stored in a computer-readable storage medium. The processor reads the computer instructions from the computer-readable storage medium and executes the computer instructions, so that the computer device executes the new energy vehicle drunk driving determination method provided above.

[0052] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0053] The present invention determines whether the current trajectory is an abnormal trajectory based on the most frequent travel trajectory sequence of each new energy vehicle and the similarity between the current travel trajectory sequence and the most frequent travel trajectory sequence in combination with the longest common subsequence algorithm; determines whether the new energy vehicle is driving under the influence of alcohol based on the pre-defined electronic fence and the determination result of whether the current trajectory is an abnormal trajectory. In this case, by combining the vehicle status information uploaded by new energy vehicles in real time, a reasonable drunk driving research and judgment model is constructed, and big data and artificial intelligence technology are used to analyze driving behavior characteristics and identify abnormal driving patterns, helping traffic control departments to actively discover vehicles suspected of drunk driving, which not only improves the accuracy and timeliness of monitoring, but also effectively reduces the waste of police resources, realizes active prevention and control of drunk driving behavior, and ensures road traffic safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0055] Figure 1 A flow chart of a method for determining drunk driving in a new energy vehicle provided by an exemplary embodiment of the present invention is shown.

[0056] Figure 2 A logic block diagram of a method for determining drunk driving of a new energy vehicle provided by an exemplary embodiment of the present invention is shown.

[0057] Figure 3 A structural block diagram of a device for determining drunk driving for new energy vehicles provided by an exemplary embodiment of the present invention is shown.

[0058] Figure 4 A schematic diagram of the structure of a computer device for executing a method for determining drunk driving of a new energy vehicle provided by an exemplary embodiment of the present invention is shown. DETAILED DESCRIPTION

[0059] 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.

[0060] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0061] Figure 1 A schematic diagram of a flow chart of a method for determining drunk driving of a new energy vehicle provided by an exemplary embodiment of the present invention is shown. Figure 2A logic block diagram of a method for determining drunk driving of a new energy vehicle provided by an exemplary embodiment of the present invention is shown. The method for determining drunk driving of a new energy vehicle specifically comprises the following steps:

[0062] Step 101, monitoring vehicle status information uploaded by new energy vehicles in real time.

[0063] In one example, monitoring of vehicle status information uploaded by new energy vehicles in real time is achieved by acquiring data uploaded by a smart vehicle terminal.

[0064] In an embodiment of the present application, the vehicle status information includes at least the vehicle's: timestamp, which records the specific time point when the vehicle status data is generated or collected, for ensuring the time sequence and synchronization of the data; longitude and latitude location points, which provide the vehicle's current geographic location information, for tracking the vehicle's location and driving trajectory; vehicle speed, which records the vehicle's current speed, for monitoring the vehicle's driving status and speed changes; vehicle operating status, which includes the vehicle's startup status information, ignition off status information, driving status information, and fault information; battery status, which includes the battery's remaining power information, battery temperature information, and charging status information; motor status, which includes the motor's working status information, speed information, and temperature information; mileage, which records the vehicle's cumulative mileage; environmental parameters, which provide in-vehicle temperature information and external ambient temperature information; driving behavior, which provides the vehicle's sudden acceleration status information and sudden braking status information.

[0065] Step 102, based on the vehicle status information uploaded by the new energy vehicles in real time and in combination with the fast bundle algorithm, the most frequent travel trajectory sequence of each new energy vehicle is determined.

[0066] Among them, this step is based on the timestamp and longitude and latitude position points in the vehicle status information uploaded by the new energy vehicle in real time.

[0067] In detail, the above-mentioned vehicle status information uploaded in real time by new energy vehicles is combined with the fast bundle algorithm to determine the most frequent travel trajectory sequence of each new energy vehicle, including:

[0068] Obtain vehicle status information corresponding to each new energy vehicle;

[0069] Arrange in ascending order according to the timestamps, starting from the first longitude and latitude position point, and divide once in response to the time stamp interval between two consecutive longitude and latitude position points being greater than 180 minutes;

[0070] If the vehicle status information of the new energy vehicle is divided n-1 times to form n travel trajectory sequences, each travel trajectory sequence is interpolated to ensure that the number of latitude and longitude position points of each travel trajectory is the same;

[0071] Number the travel trajectories from 1 to N, define travel trajectory 1 as cluster center 1, calculate the Euclidean distance between all points in travel trajectory 2 and all points in travel trajectory 1, and if all distances are less than or equal to the set threshold, travel trajectory 2 and travel trajectory 1 are classified into the same category;

[0072] At the same time, the average value of each latitude and longitude position point of travel trajectory 1 and travel trajectory 2 is taken to replace the original cluster center 1. If the distance is greater than the set threshold, travel trajectory 2 is used as cluster center 2, and all travel trajectories are traversed in turn to complete the cluster analysis of all travel trajectories;

[0073] The cluster center with the largest number of corresponding trajectory sequences after being solved by the fast bundle algorithm is the most frequent travel trajectory sequence of each new energy vehicle.

[0074] In other words, in one example, the new energy vehicle has a total of n travel trajectory sequences, and the corresponding number of cluster centers is m, which means that the new energy vehicle has m different travel routes, and the maximum number of cluster center trajectories is k, which means that there are k trajectories that coincide with the most frequent travel trajectory sequence of the new energy vehicle.

[0075] Step 103 , based on the most frequent travel trajectory sequence of each new energy vehicle and in combination with the longest common subsequence algorithm, obtain the similarity between the current driving trajectory sequence and the most frequent travel trajectory sequence, and determine whether the current trajectory is an abnormal trajectory.

[0076] Specifically, the most frequent travel trajectory sequence of each new energy vehicle is combined with the longest common subsequence algorithm to obtain the similarity between the current driving trajectory sequence and the most frequent travel trajectory sequence, and determine whether the current trajectory is an abnormal trajectory, including:

[0077] Since the longest common subsequence algorithm is used to solve the longest common subsequence of two one-dimensional sequences, it is necessary to convert the longitude and latitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence into a one-dimensional data sequence;

[0078] Based on the longest common subsequence algorithm, the one-dimensional data sequence transformed from the longitude and latitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence is solved to obtain the longest common subsequence of the current driving trajectory sequence and the most frequent travel trajectory sequence;

[0079] Determine the number of common trajectory points according to the longest common subsequence of the current driving trajectory sequence and the most frequent travel trajectory sequence;

[0080] The number of common trajectory points is divided by the number of longitude and latitude position points in the current driving trajectory sequence to obtain the trajectory similarity α. A threshold θ is set. When α is less than θ, the current trajectory is determined to be an abnormal trajectory.

[0081] In one example, the above converting the latitude and longitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence into a one-dimensional data sequence includes:

[0082] Based on the Geohash algorithm, the longitude and latitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence are converted into a one-dimensional Base32 encoded data sequence.

[0083] It is worth mentioning that the longest common subsequence algorithm is based on a dynamic programming solution algorithm, setting subsequences, common subsequences, and longest common subsequences, where a subsequence is defined as "given a sequence x =<x1,x2,x3,x4…,xm> ; Another sequence z =<z1,z2,z3…,zk> ; If there is a strictly increasing sequence of subscripts of x<i1,i2,i3…,ik> ; for all 1, 2, 3…, k; x(ik)=zk; then z is called a subsequence of x". A common subsequence is defined as: "If z is both a subsequence of x and a subsequence of y, then z is called a common subsequence of x and y". The longest common subsequence is the longest one among all common subsequences. The relation matrix method is used to solve the maximum common subsequence. For example, for the sequence "X=<A,B,C,B,D,A,B> ” and “Y=<B,D,C,A,B,A> "Solve for the largest common subsequence.

[0084] Step 104 , based on the pre-defined electronic fence and in combination with the result of determining whether the current trajectory is an abnormal trajectory, determine whether the new energy vehicle is driven under the influence of alcohol.

[0085] In the embodiment of the present application, the above-mentioned determination of drunk driving of a new energy vehicle based on the pre-defined electronic fence and the determination result of whether the current trajectory is an abnormal trajectory includes:

[0086] According to the actual situation of the drinking business district in the jurisdiction, a number of non-repeating electronic fences are delineated on the electronic map, and each electronic fence is composed of at least three non-repeating latitude and longitude location points;

[0087] The vehicle status information uploaded in real time by new energy vehicles before the current time t is defined as historical data, and the vehicle status information uploaded in real time by new energy vehicles after the current time t is defined as real-time data;

[0088] The most frequent travel trajectory sequence of each new energy vehicle determined based on historical data is defined as sequence A, and the current travel trajectory sequence determined based on real-time data is defined as sequence B;

[0089] Dynamically update sequence B, add each trajectory data after the current time t to sequence B, and delete all trajectory data before the newly collected trajectory data in sequence B if the time interval between the newly collected trajectory data and the previous trajectory data exceeds 180 minutes;

[0090] Set the calculation period T. If the current trajectory is judged to be normal, the calculation ends and the drunk driving judgment is performed again after waiting for T time period.

[0091] If the current trajectory is determined to be abnormal, the vehicle status information uploaded by the new energy vehicle in real time will be continuously monitored from the moment the current trajectory is determined to be abnormal, to determine whether the new energy vehicle has entered the designated electronic fence, and if the detention time exceeds 120 minutes, the new energy vehicle will be determined to be suspected of drunk driving, and the drunk driving determination will be made again after the T period of time;

[0092] In response to the continuous monitoring time exceeding 180 minutes, during which the new energy vehicle has not entered the designated electronic fence or the new energy vehicle has stayed in the electronic fence for less than 120 minutes, the current calculation is terminated and another drunk driving determination is made after the T time period.

[0093] It can be understood that the above calculation cycle T is a time period, which means that the sequence B is judged once every T time period whether the current trajectory is an abnormal trajectory.

[0094] Figure 3 The structure block diagram of a new energy vehicle drunk driving determination device provided by an exemplary embodiment of the present invention is shown. The new energy vehicle drunk driving determination device is applicable to the new energy vehicle drunk driving determination method as described above. The device includes:

[0095] Monitoring module 301, used to monitor vehicle status information uploaded by new energy vehicles in real time;

[0096] A determination module 302 is used to determine the most frequent travel trajectory sequence of each new energy vehicle based on the vehicle status information uploaded in real time by the new energy vehicle and in combination with the fast bundle algorithm;

[0097] The determination module 303 is used to determine whether the current trajectory is an abnormal trajectory by obtaining the similarity between the current driving trajectory sequence and the most frequent travel trajectory sequence based on the most frequent travel trajectory sequence of each new energy vehicle and in combination with the longest common subsequence algorithm;

[0098] The determination module 302 is also used to determine whether a new energy vehicle is driven under the influence of alcohol based on a pre-defined electronic fence and in combination with a determination result of whether the current trajectory is an abnormal trajectory.

[0099] In some embodiments, the vehicle status information includes at least:

[0100] Timestamp, which records the specific time point when the vehicle status data is generated or collected, and is used to ensure the time sequence and synchronization of the data;

[0101] Longitude and latitude location points, which provide the vehicle's current geographic location information and are used to track the vehicle's location and driving trajectory;

[0102] Vehicle speed, which records the current speed of the vehicle and is used to monitor the vehicle's driving status and speed changes;

[0103] Vehicle operating status, including vehicle startup status information, shutdown status information, driving status information, and fault information;

[0104] Battery status, including battery remaining capacity information, battery temperature information, and charging status information;

[0105] Motor status, including motor operating status information, speed information, and temperature information;

[0106] Mileage, which records the vehicle's cumulative mileage;

[0107] Environmental parameters, which provide information about the temperature inside the vehicle and the temperature of the external environment;

[0108] Driving behavior, which provides the vehicle's sudden acceleration status information and sudden braking status information.

[0109] In some embodiments, the method of determining the most frequent travel trajectory sequence of each new energy vehicle based on the vehicle status information uploaded in real time by the new energy vehicle and in combination with the fast bundle algorithm includes:

[0110] Obtain vehicle status information corresponding to each new energy vehicle;

[0111] Arrange in ascending order according to the timestamps, starting from the first longitude and latitude position point, and divide once in response to the time stamp interval between two consecutive longitude and latitude position points being greater than 180 minutes;

[0112] If the vehicle status information of the new energy vehicle is divided n-1 times to form n travel trajectory sequences, each of the travel trajectory sequences is interpolated to ensure that the number of latitude and longitude position points of each travel trajectory is the same;

[0113] The travel trajectories are numbered from 1 to N, travel trajectory 1 is defined as cluster center 1, and the Euclidean distances between all points in travel trajectory 2 and all points in travel trajectory 1 are calculated. If all distances are less than or equal to a set threshold, travel trajectory 2 and travel trajectory 1 are classified into one category;

[0114] At the same time, the average value of each latitude and longitude position point of travel trajectory 1 and travel trajectory 2 is taken to replace the original cluster center 1. If the distance is greater than the set threshold, travel trajectory 2 is used as cluster center 2, and all travel trajectories are traversed in turn to complete the cluster analysis of all travel trajectories;

[0115] The cluster center with the largest number of corresponding trajectory sequences after being solved by the fast bundle algorithm is the most frequent travel trajectory sequence of each new energy vehicle.

[0116] In some embodiments, the determining whether the current trajectory is an abnormal trajectory by obtaining the similarity between the current driving trajectory sequence and the most frequent travel trajectory sequence based on the most frequent travel trajectory sequence of each new energy vehicle in combination with the longest common subsequence algorithm includes:

[0117] Convert the latitude and longitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence into a one-dimensional data sequence;

[0118] Solving the one-dimensional data sequence converted from the longitude and latitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence based on the longest common subsequence algorithm to obtain the longest common subsequence of the current driving trajectory sequence and the most frequent travel trajectory sequence;

[0119] Determining the number of common trajectory points according to the longest common subsequence of the current driving trajectory sequence and the most frequent travel trajectory sequence;

[0120] The number of common trajectory points is divided by the number of longitude and latitude position points in the current driving trajectory sequence to obtain the trajectory similarity α, and a threshold θ is set. When α is less than θ, the current trajectory is determined to be an abnormal trajectory.

[0121] In a possible implementation, converting the latitude and longitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence into a one-dimensional data sequence includes:

[0122] Based on the Geohash algorithm, the longitude and latitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence are converted into a one-dimensional Base32 encoded data sequence.

[0123] In some embodiments, the determination of drunk driving of a new energy vehicle based on a pre-defined electronic fence and in combination with the determination result of whether the current trajectory is an abnormal trajectory includes:

[0124] According to the actual situation of the drinking business district in the jurisdiction, a number of non-repeating electronic fences are delineated on the electronic map, and each electronic fence is composed of at least three non-repeating latitude and longitude location points;

[0125] The vehicle status information uploaded in real time by new energy vehicles before the current time t is defined as historical data, and the vehicle status information uploaded in real time by new energy vehicles after the current time t is defined as real-time data;

[0126] The most frequent travel trajectory sequence of each new energy vehicle determined based on the historical data is defined as sequence A, and the current travel trajectory sequence determined based on the real-time data is defined as sequence B;

[0127] Dynamically update the sequence B, add each trajectory data after the current time t to the sequence B, and if the time stamp interval between the newly collected trajectory data and the previous trajectory data exceeds 180 minutes, delete all trajectory data before the newly collected trajectory data in the sequence B;

[0128] Set the calculation period T. If the current trajectory is judged to be normal, the calculation ends and the drunk driving judgment is performed again after waiting for T time period.

[0129] If the current trajectory is determined to be abnormal, the vehicle status information uploaded by the new energy vehicle in real time will be continuously monitored from the moment the current trajectory is determined to be abnormal, to determine whether the new energy vehicle has entered the designated electronic fence, and if the detention time exceeds 120 minutes, the new energy vehicle will be determined to be suspected of drunk driving, and the drunk driving determination will be made again after the T period of time;

[0130] In response to the continuous monitoring time exceeding 180 minutes, during which the new energy vehicle has not entered the designated electronic fence or the new energy vehicle has stayed in the electronic fence for less than 120 minutes, the current calculation is terminated and another drunk driving determination is made after the T time period.

[0131] In some embodiments, the calculation cycle T is a time period, indicating that the sequence B is determined once every T time period to determine whether the current trajectory is an abnormal trajectory.

[0132] It should be noted that the new energy vehicle drunk driving determination device provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0133] Figure 4 A schematic diagram of the structure of a computer device for executing a method for determining drunk driving of a new energy vehicle provided by an exemplary embodiment of the present invention is shown, and the computer device includes:

[0134] The processor 401 includes one or more processing cores. The processor 401 executes various functional applications and data processing by running software programs and modules.

[0135] The receiver 402 and the transmitter 403 can be implemented as a communication component, and the communication component can be a communication chip. Optionally, the communication component can be implemented to include a signal transmission function. That is, the transmitter 403 can be used to transmit a control signal to the image acquisition device and the scanning device, and the receiver 402 can be used to receive the corresponding feedback instruction.

[0136] The memory 404 is connected to the processor 401 via a bus 405 .

[0137] The memory 404 may be used to store at least one instruction, and the processor 401 may be used to execute the at least one instruction to implement each step in the above method embodiment.

[0138] An embodiment of the present invention also provides a computer-readable storage medium, which stores at least one instruction, at least one program, code set or instruction set, which is loaded and executed by a processor to implement the above-mentioned new energy vehicle drunk driving determination method.

[0139] The present invention also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the new energy vehicle drunk driving determination method described in any of the above embodiments.

[0140] Optionally, the computer readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a solid state drive (SSD), or an optical disk. Among them, the random access memory may include a resistance random access memory (ReRAM) and a dynamic random access memory (DRAM). The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0141] It should be understood that the specific examples herein are only intended to help those skilled in the art to better understand the present disclosure, rather than to limit the scope of the present invention.

[0142] It is understood that in the various implementations of this specification, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the present disclosure.

[0143] It can be understood that the various implementation modes described in this specification can be implemented individually or in combination, and the present disclosure is not limited to this.

[0144] Unless otherwise stated, all technical and scientific terms used in this disclosure have the same meaning as those generally understood by those skilled in the art of the technical field of this specification. The terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the scope of this specification. The term "and / or" used in this specification includes any and all combinations of one or more related listed items. The singular forms of "a", "above", and "the" used in this disclosure and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0145] It can be understood that the processor of the present disclosure can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method implementation method can be completed by the hardware integrated logic circuit or software instructions in the processor. The above processor can be a general processor, a digital signal processor (DigitalSignalProcessor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the present disclosure can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the present disclosure can be directly embodied as a hardware decoding processor to perform, or the hardware and software modules in the decoding processor can be combined to perform. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0146] It is understood that the memory in the present disclosure may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (programmable ROM, PROM), an erasable programmable read-only memory (erasable PROM, EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory may be a random access memory (RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0147] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this specification.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method implementation methods and will not be repeated here.

[0149] In the several embodiments provided in this specification, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device implementation described above is only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0150] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0151] In addition, each functional unit in each embodiment of the present specification may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0152] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this specification, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of this specification. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0153] The above is only a specific implementation of this specification, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in this specification, which should be included in the protection scope of this specification. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for determining drunk driving of new energy vehicles, characterized in that: The method comprises: Monitor vehicle status information uploaded by new energy vehicles in real time; Based on the vehicle status information uploaded in real time by the new energy vehicles, and in combination with the fast bundle algorithm, the most frequent travel trajectory sequence of each new energy vehicle is determined; According to the most frequent travel trajectory sequence of each new energy vehicle, the similarity between the current travel trajectory sequence and the most frequent travel trajectory sequence is obtained in combination with the longest common subsequence algorithm, and whether the current trajectory is an abnormal trajectory is determined; Based on the pre-delineated electronic fence and in combination with the determination result of whether the current trajectory is an abnormal trajectory, drunk driving of a new energy vehicle is determined.

2. The method for determining drunk driving of new energy vehicles according to claim 1 is characterized in that: The vehicle status information includes at least: Timestamp, which records the specific time point when the vehicle status data is generated or collected, and is used to ensure the time sequence and synchronization of the data; Longitude and latitude location points, which provide the vehicle's current geographic location information and are used to track the vehicle's location and driving trajectory; Vehicle speed, which records the current speed of the vehicle and is used to monitor the vehicle's driving status and speed changes; Vehicle operating status, including vehicle startup status information, shutdown status information, driving status information, and fault information; Battery status, including battery remaining capacity information, battery temperature information, and charging status information; Motor status, including motor operating status information, speed information, and temperature information; Mileage, which records the vehicle's cumulative mileage; Environmental parameters, which provide information about the temperature inside the vehicle and the temperature of the external environment; Driving behavior, which provides the vehicle's sudden acceleration status information and sudden braking status information.

3. The method for determining drunk driving of new energy vehicles according to claim 1 is characterized in that: The vehicle status information uploaded in real time by the new energy vehicle is combined with the fast bundle algorithm to determine the most frequent travel trajectory sequence of each new energy vehicle, including: Obtain vehicle status information corresponding to each new energy vehicle; Arrange in ascending order according to the timestamps, starting from the first longitude and latitude position point, and divide once in response to the time stamp interval between two consecutive longitude and latitude position points being greater than 180 minutes; If the vehicle status information of the new energy vehicle is divided n-1 times to form n travel trajectory sequences, each of the travel trajectory sequences is interpolated to ensure that the number of latitude and longitude position points of each travel trajectory is the same; The travel trajectories are numbered from 1 to N, travel trajectory 1 is defined as cluster center 1, and the Euclidean distances between all points in travel trajectory 2 and all points in travel trajectory 1 are calculated. If all distances are less than or equal to a set threshold, travel trajectory 2 and travel trajectory 1 are classified into one category; At the same time, the average value of each latitude and longitude position point of travel trajectory 1 and travel trajectory 2 is taken to replace the original cluster center 1. If the distance is greater than the set threshold, travel trajectory 2 is used as cluster center 2, and all travel trajectories are traversed in turn to complete the cluster analysis of all travel trajectories; The cluster center with the largest number of corresponding trajectory sequences after being solved by the fast bundle algorithm is the most frequent travel trajectory sequence of each new energy vehicle.

4. The method for determining drunk driving of new energy vehicles according to claim 1 is characterized in that: The determining whether the current trajectory is an abnormal trajectory by obtaining the similarity between the current driving trajectory sequence and the most frequent travel trajectory sequence according to the most frequent travel trajectory sequence of each new energy vehicle and combining the longest common subsequence algorithm includes: Convert the latitude and longitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence into a one-dimensional data sequence; Solving the one-dimensional data sequence converted from the longitude and latitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence based on the longest common subsequence algorithm to obtain the longest common subsequence of the current driving trajectory sequence and the most frequent travel trajectory sequence; Determining the number of common trajectory points according to the longest common subsequence of the current driving trajectory sequence and the most frequent travel trajectory sequence; The number of common trajectory points is divided by the number of longitude and latitude position points in the current driving trajectory sequence to obtain the trajectory similarity α, and a threshold θ is set. When α is less than θ, the current trajectory is determined to be an abnormal trajectory.

5. The method for determining drunk driving of new energy vehicles according to claim 4 is characterized in that: The step of converting the latitude and longitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence into a one-dimensional data sequence includes: Based on the Geohash algorithm, the longitude and latitude position points in the current driving trajectory sequence and the most frequent travel trajectory sequence are converted into a one-dimensional Base32 encoded data sequence.

6. The method for determining drunk driving of new energy vehicles according to claim 1 is characterized in that: The method of determining drunk driving of a new energy vehicle based on the pre-defined electronic fence and combining the determination result of whether the current trajectory is an abnormal trajectory includes: According to the actual situation of the drinking business district in the jurisdiction, a number of non-repeating electronic fences are delineated on the electronic map, and each electronic fence is composed of at least three non-repeating latitude and longitude location points; The vehicle status information uploaded in real time by new energy vehicles before the current time t is defined as historical data, and the vehicle status information uploaded in real time by new energy vehicles after the current time t is defined as real-time data; The most frequent travel trajectory sequence of each new energy vehicle determined based on the historical data is defined as sequence A, and the current travel trajectory sequence determined based on the real-time data is defined as sequence B; Dynamically update the sequence B, add each trajectory data after the current time t to the sequence B, and if the time stamp interval between the newly collected trajectory data and the previous trajectory data exceeds 180 minutes, delete all trajectory data before the newly collected trajectory data in the sequence B; Set the calculation period T. If the current trajectory is judged to be normal, the calculation ends and the drunk driving judgment is performed again after waiting for T time period. If the current trajectory is determined to be abnormal, the vehicle status information uploaded by the new energy vehicle in real time will be continuously monitored from the moment the current trajectory is determined to be abnormal, to determine whether the new energy vehicle has entered the designated electronic fence, and if the detention time exceeds 120 minutes, the new energy vehicle will be determined to be suspected of drunk driving, and the drunk driving determination will be made again after the T period of time; In response to the continuous monitoring time exceeding 180 minutes, during which the new energy vehicle has not entered the designated electronic fence or the new energy vehicle has stayed in the electronic fence for less than 120 minutes, the current calculation is terminated and another drunk driving determination is made after the T time period.

7. The method for determining drunk driving of new energy vehicles according to claim 6 is characterized in that: The calculation cycle T is a time period, which means that the sequence B is judged once every T time period whether the current trajectory is an abnormal trajectory.

8. A new energy vehicle drunk driving determination device, characterized in that: The device is applicable to the drunk driving determination method for new energy vehicles according to any one of claims 1 to 7, and the device comprises: Monitoring module, used to monitor vehicle status information uploaded in real time by new energy vehicles; A determination module, used to determine the most frequent travel trajectory sequence of each new energy vehicle based on the vehicle status information uploaded in real time by the new energy vehicle and in combination with a fast bundle algorithm; A determination module, used to determine whether the current trajectory is an abnormal trajectory based on the most frequent travel trajectory sequence of each new energy vehicle and the similarity between the current travel trajectory sequence and the most frequent travel trajectory sequence in combination with the longest common subsequence algorithm; The determination module is also used to determine whether the new energy vehicle is driven under the influence of alcohol based on a pre-defined electronic fence and in combination with the determination result of whether the current trajectory is an abnormal trajectory.

9. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the method for determining drunk driving of new energy vehicles as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The readable storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the method for determining drunk driving of new energy vehicles as described in any one of claims 1 to 7.

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