CN114394099B - Method and device for identifying abnormal running of vehicle, computer equipment and storage medium - Google Patents
Method and device for identifying abnormal running of vehicle, computer equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of artificial intelligence, and discloses a vehicle driving abnormality identification method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring GPS data of a target vehicle and setting the GPS data as target data; acquiring GPS data of at least one comparison vehicle, setting the GPS data of the comparison vehicle with the content similar to that of the target data as similar data, constructing a mileage driving curve according to the target data and the similar data, identifying the position relation between the target mileage of the target data and the mileage driving curve, and judging whether the position relation accords with a preset abnormality judgment rule; if yes, judging that the target vehicle is in a mileage normal running state; if not, judging that the target vehicle is in the mileage abnormal driving state. The invention realizes the timely identification of whether the target vehicle has too short mileage due to poor operation or has abnormal running condition of long-distance continuous running of the vehicle due to the shortage of the vehicle in the vehicle team.
Description
Technical Field
The present invention relates to the field of artificial intelligence technologies, and in particular, to a method and apparatus for identifying abnormal driving of a vehicle, a computer device, and a storage medium.
Background
The current vehicles are very complex in the real driving process, and usually need to drive different routes or load and unload at some addresses, and currently, the driving route and the driving duration of the vehicles are usually controlled by adopting a mode of acquiring GPS data.
However, the inventors found that if the GPS data of the vehicle is directly analyzed, only the travel route and the travel time length of the vehicle are obtained, but it is not known whether the vehicle appears such as: the vehicle is too short in mileage due to poor operation in the month, or the vehicle is in shortage to cause abnormal conditions such as long-distance continuous running of the vehicle, so that the running abnormality of the vehicle cannot be recognized in time.
Disclosure of Invention
The invention aims to provide a vehicle driving abnormality identification method, a device, computer equipment and a storage medium, which are used for solving the problem that the driving abnormality of a vehicle cannot be known only by obtaining the driving route and the driving duration of the vehicle in the prior art.
In order to achieve the above object, the present invention provides a vehicle traveling abnormality recognition method including:
Acquiring GPS data of a target vehicle and setting the GPS data as target data; wherein, the GPS data records the position coordinates of the vehicle at each time point in the running path;
acquiring GPS data of at least one comparison vehicle, and setting the GPS data of the comparison vehicle with the content similar to the target data as similar data;
constructing a mileage driving curve according to the target data and the similar data, wherein the mileage driving curve represents the target mileage of the target vehicle and the distribution rule of the similar mileage of the comparison vehicle corresponding to the similar data;
identifying the position relation between the target driving mileage of the target data and the mileage driving curve, and judging whether the position relation accords with a preset abnormality judgment rule; if yes, judging that the target vehicle is in a mileage normal running state; if not, judging that the target vehicle is in the mileage abnormal driving state.
In the above aspect, before the acquiring the GPS data of the at least one comparison vehicle, the method further includes:
and acquiring GPS data of at least one other vehicle, setting GPS data of other vehicles with starting point coordinates and end point coordinates consistent with the target data as comparison data, and setting other vehicles corresponding to the comparison data as comparison vehicles, wherein the starting point coordinates refer to position coordinates representing starting points of a driving path in the GPS data, and the end point coordinates refer to position coordinates representing end points of the driving path in the GPS data.
In the above aspect, the setting the GPS data of the comparison vehicle, which has a content similar to the target data, as the similar data includes:
extracting a time point in the target data and a position coordinate corresponding to the time point to obtain a target driving characteristic;
identifying a target starting time in the target data, and setting a position coordinate corresponding to the target starting time in the target data as a target starting characteristic; identifying target termination time in the target data, and setting position coordinates corresponding to the target termination time in the target data as target termination characteristics;
extracting a plurality of target driving characteristics from target data, and carrying out clustering operation on the plurality of target driving characteristics, the target starting characteristics and the target ending characteristics to obtain at least one target clustering point;
extracting a time point in GPS data of the comparison vehicle and a position coordinate corresponding to the time point to obtain a comparison driving characteristic;
identifying a comparison starting time in the GPS data of the comparison vehicle, and setting a position coordinate corresponding to the comparison starting time in the GPS data of the comparison vehicle as a comparison starting characteristic; identifying a comparison termination time in the GPS data of the comparison vehicle, and setting a position coordinate corresponding to the comparison termination time in the GPS data of the comparison vehicle as a comparison termination characteristic;
Extracting a plurality of comparison driving features from GPS data of the comparison vehicle, and carrying out clustering operation on the comparison driving features, the comparison starting features and the comparison ending features to obtain at least one comparison clustering point;
calculating a comparison difference value between the target cluster point and the comparison cluster point; and if the comparison difference value is smaller than a preset similarity threshold value, judging that the comparison vehicle is similar to the target vehicle, and setting GPS data of the comparison vehicle as similar data.
In the above aspect, after setting the GPS data of the comparison vehicle whose content is similar to the target data as the similar data, the method further includes:
summarizing the comparison vehicles corresponding to the similar data and the target vehicle to obtain a similar set, and storing the similar set in a preset similar database;
and uploading the similar groups in the similar database to a blockchain.
In the above scheme, the constructing the mileage driving curve according to the target data and the similar data includes:
calculating a target driving mileage of the target data according to the target starting characteristic and the target ending characteristic of the target data, and calculating a target unit mileage of the target driving mileage according to a preset division granularity;
Calculating the similar driving mileage of the similar data according to the similar initial characteristics and the similar ending characteristics of the similar data, and calculating the similar unit mileage of the similar driving mileage according to the preset division granularity;
and constructing a normal distribution curve according to the target unit mileage and the similar unit mileage to serve as the mileage driving curve.
In the above aspect, after setting the GPS data of the comparison vehicle whose content is similar to the target data as the similar data, the method further includes:
constructing a duration driving curve according to the target data and the similar data, wherein the duration driving curve represents the target driving duration of the target vehicle and the distribution rule of the similar driving duration of the comparison vehicle corresponding to the similar data;
identifying the position relation between the target running duration of the target data and the duration running curve, and judging whether the position relation accords with a preset abnormality judgment rule; if yes, judging that the target vehicle is in a long-duration normal running state; if not, judging that the target vehicle is in a long-duration abnormal running state.
In the above scheme, the constructing a long-duration driving curve according to the target data and the similar data includes:
Identifying the time length of the position coordinates in the continuous moving state in the target data, setting the time length as the target running time length, calculating the target running time length of the target data, and calculating the target unit time length of the target running time length according to the preset dividing granularity;
identifying the time length of the position coordinates in the continuous moving state in the similar data, setting the time length as similar running time length, calculating the similar running time length of the similar data, and calculating the similar unit time length of the similar running time length according to preset dividing granularity;
and constructing a normal distribution curve according to the target unit duration and the similar unit duration to serve as the duration driving curve.
In order to achieve the above object, the present invention also provides a vehicle running abnormality recognition device including:
the target input module is used for acquiring GPS data of a target vehicle and setting the GPS data as target data; wherein, the GPS data records the position coordinates of the vehicle at each time point in the running path;
the similarity identification module is used for acquiring GPS data of at least one comparison vehicle and setting the GPS data of the comparison vehicle with the content similar to the target data as similar data;
The mileage curve construction module is used for constructing mileage traveling curves according to the target data and the similar data, wherein the mileage traveling curves represent the target mileage of the target vehicle and the distribution rule of the similar mileage of the comparison vehicle corresponding to the similar data;
the mileage abnormality recognition module is used for recognizing the position relation between the target mileage of the target data and the mileage driving curve and judging whether the position relation accords with a preset abnormality judgment rule; if yes, judging that the target vehicle is in a mileage normal running state; if not, judging that the target vehicle is in the mileage abnormal driving state.
In order to achieve the above object, the present invention also provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the steps of the vehicle driving abnormality identification method are implemented when the processor of the computer device executes the computer program.
In order to achieve the above object, the present invention further provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the steps of the vehicle running abnormality recognition method described above.
According to the vehicle driving abnormality identification method, device, computer equipment and storage medium, the GPS data of the target vehicle and the GPS data of the similar vehicle are compared by acquiring the similar vehicle similar to the target vehicle, so that whether the target vehicle has an abnormal driving condition that the mileage is too short due to poor operation or the vehicle continuously runs in a long distance due to the shortage of the vehicle in a vehicle team is timely identified, and the problem that the abnormal condition of the target vehicle cannot be identified due to the fact that the GPS data of the target vehicle is directly analyzed at present is solved.
Drawings
FIG. 1 is a flowchart of a method for identifying abnormal driving conditions of a vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic view illustrating an environment application of a method for identifying abnormal driving conditions of a vehicle according to a second embodiment of the present invention;
FIG. 3 is a flowchart showing a specific method of identifying a vehicle running abnormality in a second embodiment of the method for identifying a vehicle running abnormality according to the present invention;
FIG. 4 is a schematic diagram illustrating a program module of a third embodiment of a vehicle driving abnormality recognition device according to the present invention;
fig. 5 is a schematic hardware structure of a computer device in a fourth embodiment of the computer device of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a vehicle driving abnormality identification method, a device, computer equipment and a storage medium, which are suitable for the technical field of artificial intelligence and provide a vehicle driving abnormality identification method based on a target input module, a similar identification module, a mileage curve construction module and a mileage abnormality identification module. The invention obtains GPS data of at least one comparison vehicle by obtaining GPS data of a target vehicle and setting the GPS data as target data, and sets the GPS data of the comparison vehicle with content similar to the target data as similar data; constructing a mileage traveling curve according to the target data and the similar data; identifying the position relation between the target driving mileage of the target data and the mileage driving curve, and judging whether the position relation accords with a preset abnormality judgment rule; if yes, judging that the target vehicle is in a mileage normal running state; if not, judging that the target vehicle is in the mileage abnormal driving state.
Embodiment one:
referring to fig. 1, a method for identifying abnormal driving of a vehicle according to the present embodiment includes:
s101: acquiring GPS data of a target vehicle and setting the GPS data as target data; wherein, the GPS data records the position coordinates of the vehicle at each time point in the running path;
S103: acquiring GPS data of at least one comparison vehicle, and setting the GPS data of the comparison vehicle with the content similar to the target data as similar data;
s105: constructing a mileage driving curve according to the target data and the similar data, wherein the mileage driving curve represents the target mileage of the target vehicle and the distribution rule of the similar mileage of the comparison vehicle corresponding to the similar data;
s107: identifying the position relation between the target driving mileage of the target data and the mileage driving curve, and judging whether the position relation accords with a preset abnormality judgment rule; if yes, judging that the target vehicle is in a mileage normal running state; if not, judging that the target vehicle is in the mileage abnormal driving state.
In an exemplary embodiment, GPS data of the target vehicle within one month may be extracted to track the travel route of the target vehicle within one month. GPS data refers to the global positioning system (Global Positioning System, GPS), which is a high-precision radio navigation positioning system based on satellites that provides accurate geographic location, vehicle speed, and accurate time information anywhere in the world and near earth space.
Since the travel routes of other vehicles with consistent initial coordinates and final coordinates may still have a large difference from the travel route of the target vehicle and have no similar comparison value, the present step ensures that the travel routes between the target vehicle and the comparison vehicle for similarity comparison with the target vehicle are also similar by acquiring the GPS data of at least one comparison vehicle and setting the GPS data of the comparison vehicle with content similar to the target data as similar data, thereby ensuring the accuracy of the anomaly recognition of the subsequent target vehicle.
And representing the driving mileage of the target vehicle and the purpose of the distribution rule of the driving mileage of the comparison vehicle corresponding to the similar data by constructing a mileage driving curve reflecting the mathematical relationship between the target data and the similar data.
In order to identify the similarity degree between the target driving mileage of the target vehicle and the similar driving mileage of other similar vehicles and further infer whether the target vehicle has abnormal technical effects, the step is to identify the position of the target driving mileage of the target data on a normal distribution curve corresponding to the mileage driving curve; if the position of the target driving mileage on the normal distribution curve is within the probability threshold range in the abnormality judgment rule, judging that the target vehicle is in a mileage normal driving state; if the position of the target driving mileage on the normal distribution curve is out of the probability threshold range in the abnormality determination rule, determining the mileage abnormality driving state of the target vehicle, for example: according to mathematical expectations of normal distribution, the probability that the vehicle operation mileage is [ mu-2σ, mu+2σ ] is 95.44%, 95.44% is set as a probability threshold range, and when the vehicle daily average mileage exceeds the range, the abnormal risk of the vehicle is early-warned.
In summary, the method and the device for identifying the abnormal driving situation of the long-distance continuous driving of the target vehicle can be used for timely identifying whether the target vehicle has too short mileage due to poor operation or is caused by the shortage of the vehicle team vehicles by acquiring the similar vehicles similar to the target vehicle and comparing the GPS data of the target vehicle with the GPS data of the similar vehicles, so that the problem that the abnormal situation of the target vehicle cannot be identified due to the fact that the GPS data of the target vehicle is directly analyzed at present is solved.
In fig. 1, S107 is shown with the following labels:
s107-1: identifying a positional relationship between a target driving range of the target data and the range driving curve;
s107-2: judging whether the position relation accords with a preset abnormality judgment rule or not;
s107-3: if yes, judging that the target vehicle is in a mileage normal running state;
s107-4: if not, judging that the target vehicle is in the mileage abnormal driving state.
Embodiment two:
the present embodiment is a specific application scenario of the first embodiment, and by this embodiment, the method provided by the present invention can be more clearly and specifically described.
Next, the method provided in this embodiment will be specifically described by taking a comparison vehicle in which the GPS data content is similar to that of the target vehicle, and constructing a mileage traveling curve according to the GPS data of the target vehicle and the similar vehicle in a server running the vehicle traveling abnormality recognition method as an example, to recognize an abnormal traveling state of the target vehicle. It should be noted that the present embodiment is only exemplary, and does not limit the scope of protection of the embodiment of the present invention.
Fig. 2 schematically shows an environmental application diagram of a vehicle running abnormality recognition method according to a second embodiment of the present application.
In the exemplary embodiment, the server 2 in which the vehicle travel abnormality recognition method is located is connected to one target vehicle 3 and at least one other vehicle 4, respectively, through a network; the server 2 may provide services over one or more networks, which may include various network devices such as routers, switches, multiplexers, hubs, modems, bridges, repeaters, firewalls, proxy devices, and/or the like. The network may include physical links such as coaxial cable links, twisted pair cable links, fiber optic links, combinations thereof, and/or the like. The network may include wireless links, such as cellular links, satellite links, wi-Fi links, and/or the like; the network is connected to the GPS device of the target vehicle 3 to acquire the GPS data of the target vehicle 3, and the network is connected to the GPS device of the other vehicle 4 to acquire the GPS data of the other vehicle 4.
Fig. 3 is a flowchart of a specific method of identifying abnormal driving conditions of a vehicle according to an embodiment of the present invention, and the method specifically includes steps S201 to S208.
S201: acquiring GPS data of a target vehicle and setting the GPS data as target data; wherein, the GPS data records the position coordinates of the vehicle at each time point in the running path;
in this step, GPS data of the target vehicle within one month may be extracted to track the travel route of the target vehicle within one month. GPS data refers to the global positioning system (Global Positioning System, GPS), which is a high-precision radio navigation positioning system based on satellites that provides accurate geographic location, vehicle speed, and accurate time information anywhere in the world and near earth space.
S202: and acquiring GPS data of at least one other vehicle, setting GPS data of other vehicles with starting point coordinates and end point coordinates consistent with the target data as comparison data, and setting other vehicles corresponding to the comparison data as comparison vehicles, wherein the starting point coordinates refer to position coordinates representing starting points of a driving path in the GPS data, and the end point coordinates refer to position coordinates representing end points of the driving path in the GPS data.
In this step, GPS data of other vehicles in the same month corresponding to the target vehicle may be extracted to track the driving route of the target vehicle in the month. GPS data refers to the global positioning system (Global Positioning System, GPS), which is a high-precision radio navigation positioning system based on satellites that provides accurate geographic location, vehicle speed, and accurate time information anywhere in the world and near earth space.
S203: and acquiring GPS data of at least one comparison vehicle, and setting the GPS data of the comparison vehicle with the content similar to the target data as similar data.
Since the travel routes of other vehicles with consistent initial coordinates and final coordinates may still have a large difference from the travel route of the target vehicle and have no similar comparison value, the present step ensures that the travel routes between the target vehicle and the comparison vehicle for similarity comparison with the target vehicle are also similar by acquiring the GPS data of at least one comparison vehicle and setting the GPS data of the comparison vehicle with content similar to the target data as similar data, thereby ensuring the accuracy of the anomaly recognition of the subsequent target vehicle.
In a preferred embodiment, the setting the GPS data of the comparison vehicle having the content similar to the target data as similar data includes:
s31: extracting a time point in the target data and a position coordinate corresponding to the time point to obtain a target driving characteristic;
specifically, the extracting a time point in the target data and a position coordinate corresponding to the time point to obtain a target driving feature includes:
S311: and calculating the driving time of the target vehicle according to the target starting time and the target ending time in the target data, wherein the target starting time refers to a time point of the target data representing that the target vehicle is at the starting point of the driving path, and the ending time refers to a time point of the target data representing that the target vehicle is at the ending point of the driving path.
S312: dividing the running time length by a preset dividing number to obtain a plurality of equidistant running time periods, obtaining dividing points corresponding to the dividing number, and setting a time point corresponding to the dividing points in the target data as a dividing time point;
s313: and setting a position coordinate corresponding to a segmentation time point in the target data as a driving characteristic.
S32: identifying a target starting time in the target data, and setting a position coordinate corresponding to the target starting time in the target data as a target starting characteristic; identifying target termination time in the target data, and setting position coordinates corresponding to the target termination time in the target data as target termination characteristics;
s33: and extracting a plurality of target driving characteristics from target data, and carrying out clustering operation on the plurality of target driving characteristics, the target starting characteristics and the target ending characteristics to obtain at least one target clustering point.
In the step, a k-means clustering algorithm is driven in the clustering model.
Specifically, the clustering operation on the plurality of target driving features, the target starting feature and the target ending feature to obtain at least one target clustering point includes:
s331: setting target driving characteristics with consistent position coordinates in the plurality of target driving characteristics as pause characteristics, and integrating the pause characteristics with consistent position coordinates to form a pause set;
s332: descending order arrangement is carried out on the pause sets according to the number of pause features to obtain pause sequences;
s333: sequentially extracting pause sets from the first position of the pause sequence according to the preset stay quantity, setting the extracted pause sets as stay sets, and setting position coordinates in the stay sets as stay features;
s334: and recording the plurality of target driving features, the target starting features and the target ending features into the clustering model, taking the target starting features, the target ending features and the stay features corresponding to the stay quantity as initial center points of the clustering model, and driving the clustering model to obtain at least one target clustering point for the plurality of target driving features, the target starting features and the target ending features.
S34: extracting a time point in GPS data of the comparison vehicle and a position coordinate corresponding to the time point to obtain a comparison driving characteristic;
specifically, the extracting a time point in the GPS data of the comparison vehicle and a position coordinate corresponding to the time point to obtain a comparison driving feature includes:
s341: and calculating the driving time of the target vehicle according to the comparison starting time and the comparison ending time in the GPS data of the comparison vehicle, wherein the comparison starting time refers to a time point when the GPS data of the comparison vehicle represents that the target vehicle is at the starting point of the driving path, and the ending time refers to a time point when the GPS data of the comparison vehicle represents that the target vehicle is at the ending point of the driving path.
S342: dividing the running time length by a preset dividing number to obtain a plurality of equidistant running time periods, obtaining dividing points corresponding to the dividing number, and setting a time point corresponding to the dividing points in the GPS data of the comparison vehicle as a dividing time point;
s343: and setting a position coordinate corresponding to a segmentation time point in the GPS data of the comparison vehicle as a driving characteristic.
S35: identifying a comparison starting time in the GPS data of the comparison vehicle, and setting a position coordinate corresponding to the comparison starting time in the GPS data of the comparison vehicle as a comparison starting characteristic; identifying a comparison termination time in the GPS data of the comparison vehicle, and setting a position coordinate corresponding to the comparison termination time in the GPS data of the comparison vehicle as a comparison termination characteristic;
s36: and extracting a plurality of comparison driving features from the GPS data of the comparison vehicle, and carrying out clustering operation on the comparison driving features, the comparison starting features and the comparison ending features to obtain at least one comparison clustering point.
In the step, a k-means clustering algorithm is driven in the clustering model.
Specifically, the clustering operation is performed on the plurality of comparison driving features, the comparison starting feature and the comparison ending feature to obtain at least one comparison clustering point, which includes:
s361: setting the comparison driving characteristics with consistent position coordinates in the comparison driving characteristics as pause characteristics, and integrating the pause characteristics with consistent position coordinates to form a pause set;
s362: descending order arrangement is carried out on the pause sets according to the number of pause features to obtain pause sequences;
S363: sequentially extracting pause sets from the first position of the pause sequence according to the preset stay quantity, setting the extracted pause sets as stay sets, and setting position coordinates in the stay sets as stay features;
s364: and recording the plurality of comparison driving features, the comparison starting features and the comparison ending features into the clustering model, taking the comparison starting features, the comparison ending features and the stay features corresponding to the stay quantity as initial center points of the clustering model, and driving the clustering model to obtain at least one comparison clustering point for the plurality of comparison driving features, the comparison starting features and the comparison ending features.
S37: calculating a comparison difference value between the target cluster point and the comparison cluster point; and if the comparison difference value is smaller than a preset similarity threshold value, judging that the comparison vehicle is similar to the target vehicle, and setting GPS data of the comparison vehicle as similar data.
In the step, the target cluster points and the comparison cluster points are subtracted to obtain a difference value, and the square of the difference value is calculated to obtain the comparison difference value.
If the target clustering points and the comparison clustering points are multiple, sequencing according to the sequence of the target clustering points on the driving paths in the target data to obtain a target sequence, and sequencing according to the sequence of the comparison clustering points on the driving paths in the GPS data of the comparison vehicle to obtain a comparison sequence; and corresponding the target clustering points in the target sequence and the comparison clustering points in the comparison sequence one by one, sequentially subtracting the target clustering points from the comparison clustering points to obtain a plurality of difference values, sequentially squaring the plurality of difference values, and then summing the plurality of difference values to obtain the comparison difference value.
If the target data and the GPS data of the comparison vehicle are directly compared, not only the amount of power consumption is large, but also it is difficult to find similar data having a similarity with the target data.
Therefore, in the embodiment, the target key node on the driving route corresponding to the target data and the contrast key node on the driving route corresponding to the GPS data of the contrast vehicle are obtained by taking the starting point feature, the end point feature and the stay feature as initial clustering center points and performing clustering operation; therefore, only the target key node and the comparison key node are compared, a route similar to the driving route corresponding to the target data can be obtained, and the GPS data of the comparison vehicle corresponding to the similar route is set as similar data.
S204: and summarizing the comparison vehicles corresponding to the similar data and the target vehicle to obtain a similar set, and storing the similar set in a preset similar database.
In this step, the similar set is obtained by summarizing the comparison vehicles corresponding to the similar data and the target vehicle, and the similar set is stored in a preset similar database so as to facilitate subsequent retrieval and be used for carrying out anomaly identification on the running state of the target vehicle.
Further, uploading the similar consist in the similar database into a blockchain.
It should be noted that, the corresponding digest information is obtained based on the similar vehicle group, specifically, the digest information is obtained by hashing the similar vehicle group, for example, by using the sha256s algorithm. Uploading summary information to the blockchain can ensure its security and fair transparency to the user. The user device may download the summary information from the blockchain to verify that the similar consist has been tampered with. The blockchain referred to in this example is a novel mode of application for computer technology such as distributed data storage, point-to-point transmission, consensus mechanisms, encryption algorithms, and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
S205: and constructing a mileage driving curve according to the target data and the similar data, wherein the mileage driving curve represents the target mileage of the target vehicle and the distribution rule of the similar mileage of the comparison vehicle corresponding to the similar data.
In this step, the objective of characterizing the mileage of the target vehicle and the distribution rule of the mileage of the comparison vehicle corresponding to the similar data is achieved by constructing a mileage running curve reflecting the mathematical relationship between the target data and the similar data.
In a preferred embodiment, said constructing a mileage driving curve based on said target data and said similar data comprises:
s51: calculating a target driving mileage of the target data according to the target starting characteristic and the target ending characteristic of the target data, and calculating a target unit mileage of the target driving mileage according to a preset division granularity;
s52: calculating the similar driving mileage of the similar data according to the similar initial characteristics and the similar ending characteristics of the similar data, and calculating the similar unit mileage of the similar driving mileage according to the preset division granularity;
s53: and constructing a normal distribution curve according to the target unit mileage and the similar unit mileage to serve as the mileage driving curve.
Because the situation of shift and work break occurs when the vehicle runs, if the target data and the similar data are directly compared, the situation of shift and work break of the vehicle is easily identified as an abnormal situation; however, if the shift and the break are too long, it is also predicted that the abnormal situation occurs in the vehicle, so that the target mileage and the similar mileage are respectively divided into the target unit mileage and the similar unit mileage by the division granularity (for example, division into weeks, days, hours, etc.), so as to cover the situation that the mileage is not generated when the vehicle is shifted/broken normally, and the situation that the shift/break is too long or abnormal running occurs in the vehicle is shown by a normal distribution curve, for example: the vehicle is too short in mileage due to poor operation in the month, or the vehicle continuously runs for a long distance due to shortage of the vehicle in the fleet.
By way of example, assuming that the similar vehicles are 9, GPS data of one target vehicle and 9 similar vehicles are divided into one group, i.e., 1 target data and 9 similar data are divided into one group;
calculating the target driving mileage of the target data, and calculating the target unit mileage of the target driving mileage, namely: target average daily driving mileage;
calculating the similar driving mileage of the similar data, and calculating the similar unit mileage of the similar driving mileage, namely: mileage is averaged over similar days.
Calculating the sum of a target average daily driving distance and nine similar average daily driving distances, dividing the sum by 10 to obtain an average driving distance mu, obtaining a daily driving distance variance sigma 2 according to the average driving distance mu and the target average daily driving distance and nine similar average daily driving distances, and constructing a normal distribution curve according to the average driving distance and the daily driving distance variance.
Optionally, the calculating the similar driving mileage of the similar data according to the similar initial characteristic and the similar ending characteristic of the similar data includes:
s54: and calculating the mileage average of the target unit mileage and the similar unit mileage, and constructing a mileage average curve according to the mileage average.
S206: and constructing a duration driving curve according to the target data and the similar data, wherein the duration driving curve represents the target driving duration of the target vehicle and the distribution rule of the similar driving duration of the comparison vehicle corresponding to the similar data.
In the step, the purpose of representing the running duration of the target vehicle and the distribution rule of the running duration of the comparison vehicle corresponding to the similar data is achieved by constructing a duration running curve reflecting the mathematical relationship between the target data and the similar data.
In a preferred embodiment, said constructing a long-duration travel curve from said target data and said similar data comprises:
s61: identifying the time length of the position coordinates in the continuous moving state in the target data, setting the time length as the target running time length, calculating the target running time length of the target data, and calculating the target unit time length of the target running time length according to the preset dividing granularity;
s62: identifying the time length of the position coordinates in the continuous moving state in the similar data, setting the time length as similar running time length, calculating the similar running time length of the similar data, and calculating the similar unit time length of the similar running time length according to preset dividing granularity;
S63: and constructing a normal distribution curve according to the target unit duration and the similar unit duration to serve as the duration driving curve.
Because the situation of shift and work break occurs when the vehicle runs, if the target data and the similar data are directly compared, the situation of shift and work break of the vehicle is easily identified as an abnormal situation; however, if the shift and the rest are too long, it is also indicated that the abnormal situation occurs in the vehicle, so that the target running duration and the similar running duration are respectively divided into the target unit duration and the similar unit duration according to the division granularity (for example, division into weeks, days, hours, etc.), so as to cover the situation that the running duration is not generated when the vehicle is shifted/stopped normally, and the situation that the shift/rest is too long or abnormal running occurs is shown through a normal distribution curve, for example: the duration of the vehicle is too short in the month due to poor operation, or the vehicle continuously runs for a long distance due to shortage of the vehicle in the fleet.
By way of example, assuming that the similar vehicles are 9, GPS data of one target vehicle and 9 similar vehicles are divided into one group, i.e., 1 target data and 9 similar data are divided into one group;
Calculating the target running duration of the target data, and calculating the target unit duration of the target running duration, namely: target average day travel time length;
calculating the similar running duration of the similar data, and calculating the similar unit duration of the similar running duration, namely: and (5) driving duration on similar days.
Calculating the sum of a target average daily running time length and nine similar average daily running time lengths, dividing the sum by 10 to obtain an average running time length mu, obtaining a daily time length variance sigma 2 according to the average running time length mu and the target average daily running time length and nine similar average daily running time lengths, and constructing a normal distribution curve according to the average running time length and the daily time length variances.
Optionally, the calculating the similar driving duration of the similar data according to the similar start feature and the similar end feature of the similar data includes:
s64: calculating the average time length of the target unit time length and the similar unit time length, and constructing a time length average value curve according to the average time length.
S207: identifying the position relation between the target driving mileage of the target data and the mileage driving curve, and judging whether the position relation accords with a preset abnormality judgment rule; if yes, judging that the target vehicle is in a mileage normal running state; if not, judging that the target vehicle is in the mileage abnormal driving state.
In order to identify the similarity degree between the target driving mileage of the target vehicle and the similar driving mileage of other similar vehicles and further infer whether the target vehicle has abnormal technical effects, the step is to identify the position of the target driving mileage of the target data on a normal distribution curve corresponding to the mileage driving curve; if the position of the target driving mileage on the normal distribution curve is within the probability threshold range in the abnormality judgment rule, judging that the target vehicle is in a mileage normal driving state; if the position of the target driving mileage on the normal distribution curve is out of the probability threshold range in the abnormality determination rule, determining the mileage abnormality driving state of the target vehicle, for example: according to mathematical expectations of normal distribution, the probability that the vehicle operation mileage is [ mu-2σ, mu+2σ ] is 95.44%, 95.44% is set as a probability threshold range, and when the vehicle daily average mileage exceeds the range, the abnormal risk of the vehicle is early-warned.
Optionally, calculating a variance value of the target unit duration and an average corresponding to the mileage mean curve, and judging whether the variance value exceeds a preset mean variance threshold; if yes, judging that the target vehicle is in a mileage normal running state; if not, judging that the target vehicle is in the mileage abnormal driving state.
In the fig. 3, S207 is shown with the following labels:
s207-1: identifying a positional relationship between a target driving range of the target data and the range driving curve;
s207-2: judging whether the position relation accords with a preset abnormality judgment rule or not;
s207-3: if yes, judging that the target vehicle is in a mileage normal running state;
s207-4: if not, judging that the target vehicle is in the mileage abnormal driving state.
S208: identifying the position relation between the target running duration of the target data and the duration running curve, and judging whether the position relation accords with a preset abnormality judgment rule; if yes, judging that the target vehicle is in a long-duration normal running state; if not, judging that the target vehicle is in a long-duration abnormal running state.
In order to identify the similarity between the target running duration of the target vehicle and the similar running durations of other similar vehicles and further infer whether the target vehicle has an abnormal technical effect, the step is to identify the position of the target running duration of the target data on a normal distribution curve corresponding to the duration running curve; if the position of the target running duration on the normal distribution curve is within the probability threshold range in the abnormality judgment rule, judging that the target vehicle is in a duration normal running state; if the position of the target driving duration on the normal distribution curve is out of the probability threshold range in the abnormality determination rule, determining a duration abnormal driving state of the target vehicle, for example: according to mathematical expectations of normal distribution, the probability that the vehicle operation duration is [ mu-2σ, mu+2σ ] is 95.44%, 95.44% is set as a probability threshold range, and when the average vehicle daily duration exceeds the range, the abnormal risk of the vehicle is early warned.
Optionally, calculating a variance value of the average of the target unit time length and the average corresponding to the time length mean curve, and judging whether the variance value exceeds a preset mean variance threshold; if yes, judging that the target vehicle is in a long-duration normal running state; if not, judging that the target vehicle is in a long-duration abnormal running state.
In the fig. 3, the S208 is shown with the following steps:
s208-1: identifying the position relation between the target running duration of the target data and the duration running curve;
s208-2: judging whether the position relation accords with a preset abnormality judgment rule or not;
s208-3: if yes, judging that the target vehicle is in a long-duration normal running state;
s208-4: if not, judging that the target vehicle is in a long-duration abnormal running state.
Embodiment III:
referring to fig. 4, a vehicle travel abnormality identifying apparatus 1 of the present embodiment includes:
a target input module 11 for acquiring and setting as target data one GPS data of one target vehicle; wherein, the GPS data records the position coordinates of the vehicle at each time point in the running path;
a similarity identifying module 13, configured to obtain GPS data of at least one comparison vehicle, and set the GPS data of the comparison vehicle whose content is similar to the target data as similar data;
The mileage curve construction module 15 is configured to construct a mileage traveling curve according to the target data and the similar data, where the mileage traveling curve characterizes a target mileage of the target vehicle and a distribution rule of similar mileage of the comparison vehicle corresponding to the similar data;
the mileage abnormality identification module 17 is configured to identify a positional relationship between a target mileage of the target data and the mileage traveling curve, and determine whether the positional relationship meets a preset abnormality determination rule; if yes, judging that the target vehicle is in a mileage normal running state; if not, judging that the target vehicle is in the mileage abnormal driving state.
Optionally, the vehicle traveling abnormality identifying device 1 further includes:
the comparison and identification module 12 is configured to obtain GPS data of at least one other vehicle, set GPS data of the other vehicle, where the GPS data corresponds to the target data, and set a start point coordinate and an end point coordinate, where the start point coordinate is a position coordinate representing a start point of a driving path in the GPS data, and set the other vehicle corresponding to the comparison data as a comparison vehicle, and the end point coordinate is a position coordinate representing an end point of the driving path in the GPS data.
Optionally, the similarity identifying module 13 further includes:
a target driving feature unit 131, configured to extract a time point in the target data and a position coordinate corresponding to the time point to obtain a target driving feature;
a target start-stop feature unit 132 for identifying a target start time in the target data, and setting a position coordinate corresponding to the target start time in the target data as a target start feature; identifying target termination time in the target data, and setting position coordinates corresponding to the target termination time in the target data as target termination characteristics;
and a target clustering unit 133, configured to extract a plurality of the target driving features from the target data, and perform a clustering operation on the plurality of target driving features, the target starting feature and the target ending feature to obtain at least one target clustering point.
A comparison running feature unit 134, configured to extract a time point in the GPS data of the comparison vehicle and a position coordinate corresponding to the time point to obtain a comparison running feature;
a comparison start-stop feature unit 135, configured to identify a comparison start time in the GPS data of the comparison vehicle, and set a position coordinate corresponding to the comparison start time in the GPS data of the comparison vehicle as a comparison start feature; identifying a comparison termination time in the GPS data of the comparison vehicle, and setting a position coordinate corresponding to the comparison termination time in the GPS data of the comparison vehicle as a comparison termination characteristic;
A comparison clustering unit 136, configured to extract a plurality of the comparison driving features from the GPS data of the comparison vehicle, and perform a clustering operation on the plurality of comparison driving features, the comparison starting feature and the comparison ending feature to obtain at least one comparison cluster point;
a similarity identifying unit 137, configured to calculate a comparison difference value between the target cluster point and the comparison cluster point; and if the comparison difference value is smaller than a preset similarity threshold value, judging that the comparison vehicle is similar to the target vehicle, and setting GPS data of the comparison vehicle as similar data.
Optionally, the vehicle traveling abnormality identifying device 1 further includes:
and the similarity storage module 14 is used for summarizing the comparison vehicles corresponding to the similarity data and the target vehicles to obtain a similar set, and storing the similar set in a preset similarity database.
Optionally, the mileage curve construction module 15 further includes:
a target mileage dividing unit 151 for calculating a target mileage of the target data according to a target start feature and a target end feature of the target data, and calculating a target unit mileage of the target mileage according to a preset division granularity;
A similar mileage dividing unit 152, configured to calculate a similar driving mileage of the similar data according to a similar start feature and a similar end feature of the similar data, and calculate a similar unit mileage of the similar driving mileage according to a preset division granularity;
and a mileage normal curve unit 153 for constructing a normal distribution curve as the mileage traveling curve according to the target unit mileage and the similar unit mileage.
And a mileage average curve unit 154, configured to calculate mileage averages of the target unit mileage and the similar unit mileage, and construct a mileage average curve according to the mileage averages.
Optionally, the vehicle traveling abnormality identifying device 1 further includes:
and a duration curve construction module 16, configured to construct a duration running curve according to the target data and the similar data, where the duration running curve characterizes a target running duration of the target vehicle and a distribution rule of similar running durations of the comparison vehicle corresponding to the similar data.
Optionally, the time length curve construction module 16 further includes:
a target duration dividing unit 161, configured to identify a time duration of the target data when the position coordinate is in the continuous moving state, set the time duration as a target running duration, calculate a target running duration of the target data, and calculate a target unit duration of the target running duration according to a preset division granularity;
A similar duration dividing unit 162, configured to identify a time length of the similar data when the position coordinate is in the continuous moving state, set the time length as a similar running duration, calculate a similar running duration of the similar data, and calculate a similar unit duration of the similar running duration according to a preset division granularity;
a time length normal curve unit 163, configured to construct a normal distribution curve according to the target unit time length and the similar unit time length, as the time length driving curve.
And a time length average curve unit 164, configured to calculate a time length average of the target unit time length and the similar unit time length, and construct a time length average curve according to the time length average.
Optionally, the vehicle traveling abnormality identifying device 1 further includes:
a time length abnormality recognition module 18, configured to recognize a positional relationship between a target running time length of the target data and the time length running curve, and determine whether the positional relationship meets a preset abnormality determination rule; if yes, judging that the target vehicle is in a long-duration normal running state; if not, judging that the target vehicle is in a long-duration abnormal running state.
The technical scheme is applied to the intelligent decision field of artificial intelligence, GPS data of at least one comparison vehicle is obtained by obtaining GPS data of a target vehicle and setting the GPS data as target data, the GPS data of the comparison vehicle with the content similar to the target data is set as similar data through a clustering algorithm, a mileage driving curve is constructed according to the target data and the similar data to serve as a classification model, the position relation between the target mileage of the target data and the mileage driving curve is identified, and whether the position relation accords with a preset abnormal judgment rule is judged; if yes, judging that the target vehicle is in a mileage normal running state; if not, judging that the target vehicle is in the mileage abnormal driving state.
Embodiment four:
in order to achieve the above objective, the present invention further provides a computer device 5, where the components of the vehicle driving anomaly identification device of the third embodiment may be dispersed in different computer devices, and the computer device 5 may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a rack server (including a separate server or a server cluster formed by a plurality of application servers) that execute a program, or the like. The computer device of the present embodiment includes at least, but is not limited to: a memory 51, a processor 52, which may be communicatively coupled to each other via a system bus, as shown in fig. 5. It should be noted that fig. 5 only shows a computer device with components-but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead.
In the present embodiment, the memory 51 (i.e., readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 51 may be an internal storage module of a computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory 51 may also be an external storage device of a computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like. Of course, the memory 51 may also include both internal memory modules of the computer device and external memory devices. In the present embodiment, the memory 51 is generally used to store an operating system installed in a computer device and various types of application software, such as program codes of the vehicle running abnormality recognition device of the third embodiment. Further, the memory 51 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 52 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 52 is typically used to control the overall operation of the computer device. In the present embodiment, the processor 52 is configured to execute the program codes stored in the memory 51 or process data, such as running the vehicle running abnormality recognition device, to implement the vehicle running abnormality recognition methods of the first and second embodiments.
Fifth embodiment:
to achieve the above object, the present invention also provides a computer-readable storage medium such as a flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by the processor 52, performs the corresponding functions. The computer-readable storage medium of the present embodiment is for storing a computer program that implements the vehicle travel abnormality recognition method, which when executed by the processor 52 implements the vehicle travel abnormality recognition methods of the first and second embodiments.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (9)
1. A vehicle travel abnormality identification method characterized by comprising:
acquiring GPS data of a target vehicle and setting the GPS data as target data; wherein, the GPS data records the position coordinates of the vehicle at each time point in the running path;
acquiring GPS data of at least one comparison vehicle, extracting a time point in the target data, and obtaining a target driving characteristic corresponding to a position coordinate of the time point;
Identifying a target starting time in the target data, and setting a position coordinate corresponding to the target starting time in the target data as a target starting characteristic; identifying target termination time in the target data, and setting position coordinates corresponding to the target termination time in the target data as target termination characteristics;
extracting a plurality of target driving characteristics from target data, and carrying out clustering operation on the plurality of target driving characteristics, the target starting characteristics and the target ending characteristics to obtain at least one target clustering point;
extracting a time point in GPS data of the comparison vehicle and a position coordinate corresponding to the time point to obtain a comparison driving characteristic;
identifying a comparison starting time in the GPS data of the comparison vehicle, and setting a position coordinate corresponding to the comparison starting time in the GPS data of the comparison vehicle as a comparison starting characteristic; identifying a comparison termination time in the GPS data of the comparison vehicle, and setting a position coordinate corresponding to the comparison termination time in the GPS data of the comparison vehicle as a comparison termination characteristic;
extracting a plurality of comparison driving features from GPS data of the comparison vehicle, and carrying out clustering operation on the comparison driving features, the comparison starting features and the comparison ending features to obtain at least one comparison clustering point;
Calculating a comparison difference value between the target cluster point and the comparison cluster point; if the comparison difference value is smaller than a preset similarity threshold value, judging that the comparison vehicle is similar to the target vehicle, and setting GPS data of the comparison vehicle as similar data;
constructing a mileage driving curve according to the target data and the similar data, wherein the mileage driving curve represents the target mileage of the target vehicle and the distribution rule of similar mileage of a comparison vehicle corresponding to the similar data;
identifying the position relation between the target driving mileage of the target data and the mileage driving curve, and judging whether the position relation accords with a preset abnormality judgment rule; if yes, judging that the target vehicle is in a mileage normal running state; if not, judging that the target vehicle is in the mileage abnormal driving state.
2. The vehicle travel anomaly identification method of claim 1, wherein prior to the acquiring GPS data for at least one comparison vehicle, the method further comprises:
and acquiring GPS data of at least one other vehicle, setting GPS data of other vehicles with starting point coordinates and end point coordinates consistent with the target data as comparison data, and setting other vehicles corresponding to the comparison data as comparison vehicles, wherein the starting point coordinates refer to position coordinates representing starting points of a driving path in the GPS data, and the end point coordinates refer to position coordinates representing end points of the driving path in the GPS data.
3. The vehicle travel abnormality identifying method according to claim 1, characterized in that after the GPS data of the comparison vehicle is set to similar data, the method further includes:
summarizing the comparison vehicles corresponding to the similar data and the target vehicle to obtain a similar set, and storing the similar set in a preset similar database;
and uploading the similar groups in the similar database to a blockchain.
4. The vehicle travel abnormality identification method according to claim 1, characterized in that the constructing a mileage travel curve from the target data and the similar data includes:
calculating a target driving mileage of the target data according to the target starting characteristic and the target ending characteristic of the target data, and calculating a target unit mileage of the target driving mileage according to a preset division granularity;
calculating the similar driving mileage of the similar data according to the similar initial characteristics and the similar ending characteristics of the similar data, and calculating the similar unit mileage of the similar driving mileage according to the preset division granularity;
and constructing a normal distribution curve according to the target unit mileage and the similar unit mileage to serve as the mileage driving curve.
5. The vehicle travel abnormality identifying method according to claim 1, characterized in that after the GPS data of the comparison vehicle is set to similar data, the method further includes:
constructing a duration driving curve according to the target data and the similar data, wherein the duration driving curve represents the target driving duration of the target vehicle and the distribution rule of the similar driving duration of the comparison vehicle corresponding to the similar data;
identifying the position relation between the target running duration of the target data and the duration running curve, and judging whether the position relation accords with a preset abnormality judgment rule; if yes, judging that the target vehicle is in a long-duration normal running state; if not, judging that the target vehicle is in a long-duration abnormal running state.
6. The vehicle travel abnormality identification method according to claim 5, characterized in that said constructing a long-duration travel curve from said target data and said similar data includes:
identifying the time length of the position coordinates in the continuous moving state in the target data, setting the time length as the target running time length, calculating the target running time length of the target data, and calculating the target unit time length of the target running time length according to the preset dividing granularity;
Identifying the time length of the position coordinates in the continuous moving state in the similar data, setting the time length as similar running time length, calculating the similar running time length of the similar data, and calculating the similar unit time length of the similar running time length according to preset dividing granularity;
and constructing a normal distribution curve according to the target unit duration and the similar unit duration to serve as the duration driving curve.
7. A vehicle travel abnormality recognition device characterized by comprising:
the target input module is used for acquiring GPS data of a target vehicle and setting the GPS data as target data; wherein, the GPS data records the position coordinates of the vehicle at each time point in the running path;
the similar identification module is used for acquiring GPS data of at least one comparison vehicle, extracting a time point in the target data and a position coordinate corresponding to the time point to obtain a target driving characteristic; identifying a target starting time in the target data, and setting a position coordinate corresponding to the target starting time in the target data as a target starting characteristic; identifying target termination time in the target data, and setting position coordinates corresponding to the target termination time in the target data as target termination characteristics; extracting a plurality of target driving characteristics from target data, and carrying out clustering operation on the plurality of target driving characteristics, the target starting characteristics and the target ending characteristics to obtain at least one target clustering point; extracting a time point in GPS data of the comparison vehicle and a position coordinate corresponding to the time point to obtain a comparison driving characteristic; identifying a comparison starting time in the GPS data of the comparison vehicle, and setting a position coordinate corresponding to the comparison starting time in the GPS data of the comparison vehicle as a comparison starting characteristic; identifying a comparison termination time in the GPS data of the comparison vehicle, and setting a position coordinate corresponding to the comparison termination time in the GPS data of the comparison vehicle as a comparison termination characteristic; extracting a plurality of comparison driving features from GPS data of the comparison vehicle, and carrying out clustering operation on the comparison driving features, the comparison starting features and the comparison ending features to obtain at least one comparison clustering point; calculating a comparison difference value between the target cluster point and the comparison cluster point; if the comparison difference value is smaller than a preset similarity threshold value, judging that the comparison vehicle is similar to the target vehicle, and setting GPS data of the comparison vehicle as similar data;
The mileage curve construction module is used for constructing mileage traveling curves according to the target data and the similar data, wherein the mileage traveling curves represent the target traveling mileage of the target vehicle and the distribution rule of the similar traveling mileage of the comparison vehicle corresponding to the similar data;
the mileage abnormality recognition module is used for recognizing the position relation between the target mileage of the target data and the mileage driving curve and judging whether the position relation accords with a preset abnormality judgment rule; if yes, judging that the target vehicle is in a mileage normal running state; if not, judging that the target vehicle is in the mileage abnormal driving state.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor of the computer device implements the steps of the vehicle driving anomaly identification method of any one of claims 1 to 6 when the computer program is executed.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that the computer program stored on the readable storage medium, when executed by a processor, implements the steps of the vehicle running abnormality recognition method according to any one of claims 1 to 6.
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