CN114528904A - Abnormality detection method, apparatus, device, and storage medium - Google Patents
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Abstract
The invention discloses an anomaly detection method, an anomaly detection device, anomaly detection equipment and a storage medium, wherein the method comprises the following steps: acquiring vehicle operation data to be detected; processing the vehicle operation data to be detected to obtain characteristic indexes with strong business knowledge correlation; determining outliers and outliers of the outliers based on the feature indicators and the basic vehicle information; determining whether operation of the outlier vehicle is abnormal based on the outlier. According to the technical scheme, after the outlier detection model is used for outputting the outlier vehicles and the outliers of the outlier vehicles, the problems of high randomness of the outliers and inaccurate results can occur, so that the outliers need to be verified to determine whether the operation of the outlier vehicles is abnormal or not, and the false alarm rate is reduced; meanwhile, the outlier detection model is easy to train, and the calculated amount is greatly reduced.
Description
Technical Field
The invention belongs to the technical field of vehicle detection, and particularly relates to an abnormality detection method, an abnormality detection device, abnormality detection equipment and a storage medium.
Background
At present, the fault treatment of the vehicle mainly depends on field professionals, even special experts are needed, when the vehicle has a fault, the vehicle can not be maintained in time, the result is unpredictable, and the maintenance cost is high. In view of the problem of manual troubleshooting of transformer faults, there are also professional system studies, such as a pure data-driven anomaly detection model, which can help maintenance personnel to find out the cause and the fault maintenance scheme more quickly.
However, the anomaly detection model driven by pure data has the defects of much interference information, difficulty in training and complex model.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. To this end, an object of the present invention is to provide an abnormality detection method, apparatus, device, and storage medium.
In order to solve the above technical problem, an embodiment of the present invention provides the following technical solutions:
an anomaly detection method comprising:
acquiring vehicle operation data to be detected;
processing the vehicle operation data to be detected to obtain characteristic indexes with strong business knowledge correlation;
determining outlier vehicles and the outliers of the outlier vehicles based on the characteristic indexes and the basic information of the vehicles;
determining whether operation of the outlier vehicle is abnormal based on the outlier.
Optionally, the characteristic indicators strongly related to business knowledge include:
the total heat exchange coefficient, the cell impedance, the cell information entropy and the equivalent short-circuit current.
Optionally, determining an outlier vehicle and an outlier of the outlier vehicle based on the feature index and the basic vehicle information includes:
inputting the characteristic index and the basic vehicle information into an outlier detection module as input characteristics;
the outlier detection module determines the outlier vehicle and an outlier of the outlier vehicle based on the input features.
Optionally, the outlier detecting module determines the outliers of the outlier vehicle and the outliers of the outlier vehicle based on the input features, including:
the outlier detection module detects the input features based on an outlier detection model to obtain a detection result;
determining the outlier vehicle and an outlier of the outlier vehicle based on the detection result.
Optionally, the determining the outlier of the outlier vehicle and the outlier of the outlier vehicle based on the detection result includes:
determining the degree of outlier based on the detection result;
determining the outlier vehicle based on the outlier.
Optionally, the determining whether the operation of the outlier vehicle is abnormal based on the outlier includes:
obtaining historical outliers of the outlier vehicle;
obtaining a threshold value of the outlier based on the historical outlier;
comparing the degree of outlier to a threshold value for the degree of outlier;
if the outlier is larger than the threshold of the outlier, the operation of the outlier vehicle is abnormal;
and if the outlier is smaller than or equal to the threshold of the outlier, the operation of the outlier vehicle is normal.
Optionally, if the outlier is greater than the threshold of the outlier, the method, after the operation of the outlier vehicle is abnormal, includes:
generating an early warning signal;
and carrying out early warning based on the early warning signal.
An embodiment of the present invention further provides an abnormality detection apparatus, including:
the acquisition unit is used for acquiring vehicle operation data to be detected;
the processing unit is used for processing the vehicle operation data to be detected to obtain characteristic indexes with strong business knowledge correlation;
a first determination unit configured to determine an outlier vehicle and an outlier of the outlier vehicle based on the feature index and the vehicle basic information;
a second determination unit configured to determine whether or not the operation of the outlier vehicle is abnormal, based on the outlier.
Embodiments of the present invention also provide an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the method as described above when executing the computer program.
Embodiments of the present invention also provide a computer-readable storage medium comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method as described above.
The embodiment of the invention has the following technical effects:
according to the technical scheme, 1) the characteristic indexes strongly related to the business knowledge are obtained based on the vehicle running data to be detected, and the subsequent abnormity detection work is carried out based on the characteristic indexes strongly related to the business knowledge, so that an abnormity detection model is greatly simplified.
2) After the outlier detection model outputs the outlier vehicles and the outliers of the outlier vehicles, the problems of high randomness of the outliers and inaccurate results may occur, so the outliers need to be verified to determine whether the operation of the outlier vehicles is abnormal or not, and the false alarm rate is reduced; meanwhile, the outlier detection model is easy to train, and the calculated amount is greatly reduced.
3) Compared with the prior art that the early warning signal is directly generated, the early warning signal is generated based on the checking result of the threshold value of the degree of outlier, the false alarm rate is greatly reduced, and the alarm reliability is improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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Fig. 1 is a schematic flow chart of an anomaly detection method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an abnormality detection apparatus according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
To facilitate understanding of the embodiments by those skilled in the art, some terms are explained:
(1) RTM, a set of system for monitoring the working data of the new energy vehicle in real time and sending the real-time data to a background database.
(2) Oneplasssvm: in the unsupervised learning algorithm, the training data has only one classification.
(3) Isolation Forest: and (5) isolating the forest, and detecting abnormal points by using an integrated learning idea.
(4) Local Outlier Factor: local outlier factor, classical algorithm based on density.
The embodiment of the invention provides an abnormity detection system, which comprises a processor, a memory, a diagnotor and an alarm, wherein the processor is used for processing an abnormal signal;
specifically, the system and the vehicle are in communication connection based on a network; the system acquires vehicle operation data in real time based on a network and stores a large amount of acquired vehicle operation data into a memory;
the processor calls the data of the memory, and the vehicle operation data are processed based on the trained algorithm model to obtain a processing result;
the algorithm model can be any One of a One-Class SVM, Isolation Forest, Local outer Factor and an Outlier calculation model based on angular variance, and is used for calculating the real-time Outlier of each vehicle;
after the processor obtains the outlier, the processor simultaneously outputs the vehicle identification ID of the outlier vehicle corresponding to the outlier; wherein, the processor can also be connected with the display device in a communication way, such as: the processor can send the vehicle identification ID of the vehicle corresponding to the output degree of separation and the degree of separation to the display screen for real-time display, so that a user or a worker can know the running state of the vehicle in real time.
The processor sends the acquired outliers and the outliers corresponding to the outliers to the diagnotor, the diagnotor calls the historical outliers from the memory, and whether the outliers are abnormal in operation is determined based on the historical outliers and the outliers;
if the outlier vehicle runs abnormally, the diagnotor feeds the information back to the system, and the system sends a control signal to the alarm to control the alarm to give an alarm.
Furthermore, the vehicle comprises a plurality of components such as a battery pack, an integrated circuit board and a battery cell, and the plurality of components such as the battery pack, the integrated circuit and the battery cell are monitored in real time during the running process of the vehicle, so that real-time data of the battery pack, the integrated circuit and the battery cell can be acquired; these real-time data are then uploaded to the system.
As shown in fig. 1, an embodiment of the present invention further provides an anomaly detection method, including:
step S1: acquiring vehicle operation data to be detected;
in an actual application scene, the system acquires a plurality of vehicle operation data to be detected in real time; specifically, the vehicle comprises a plurality of components such as a battery pack, an integrated circuit board and a battery cell, and the plurality of components such as the battery pack, the integrated circuit and the battery cell are monitored in real time during the running process of the vehicle, so that real-time data of the battery pack, the integrated circuit and the battery cell can be acquired; and then, the vehicle uploads the acquired real-time data to the system for processing.
Step S2: processing the vehicle operation data to be detected to obtain characteristic indexes with strong business knowledge correlation;
specifically, the characteristic index module calculates the RTM data of the vehicle group as a related characteristic index according to the service knowledge in a sliding window mode.
According to the embodiment of the invention, in order to reduce the calculation amount, a large amount of real-time data of the acquired vehicle group is preprocessed and converted into the characteristic indexes with strong correlation of business knowledge, so that the subsequent calculation is simplified.
Further, the characteristic indexes with strong correlation of business knowledge include, but are not limited to:
the total heat exchange coefficient, the cell impedance, the cell information entropy and the equivalent short-circuit current.
In an actual application scenario, the feature index module may further include other types of data according to actual detection needs.
Further, for the total heat exchange coefficient, the system can preset the range of the total heat exchange index, the system obtains the total heat exchange index of each vehicle in real time, the total heat exchange index is compared with the range of the total heat exchange index, and if the total heat exchange index exceeds the range of the preset total heat exchange index, the battery core can be determined to be abnormal; specifically, when the electric core generates heat abnormally, the overall heat exchange index is larger at low temperature, and is smaller at high temperature.
And the overall heat exchange index is a heat exchange coefficient between the battery cell and the temperature control module.
Further, the cell impedance is calculated based on the voltage and the current of the vehicle operation state over a period of time.
Further, the cell information entropy is obtained by calculation based on the cell voltage and the probe temperature and is used for measuring the problem of inconsistency between the voltage and the temperature.
And further, the equivalent short-circuit current is used for measuring the equivalent short-circuit current of the battery cell.
Further, in the actual running process of the vehicle, when any one of the overall heat exchange coefficient, the cell impedance, the cell information entropy and the equivalent short-circuit current of the characteristic indexes is abnormal, the running of the vehicle is abnormal.
In addition, the specific obtaining manner of the overall heat exchange coefficient, the cell impedance, the cell information entropy and the equivalent short-circuit current is not within the protection scope of the present invention, and the embodiment of the present invention is not specifically limited thereto.
In an optional embodiment of the present invention, the feature index module is set to a structure supporting plugging, so that algorithms related to other types of feature indexes can be flexibly added and configured to the feature index module subsequently without replacing the whole feature index module, thereby reducing the operation and maintenance cost.
According to the embodiment of the invention, the characteristic indexes strongly related to the business knowledge are obtained based on the vehicle running data to be detected, and the subsequent abnormity detection work is carried out based on the characteristic indexes strongly related to the business knowledge, so that the abnormity detection model is greatly simplified.
Step S3: determining outlier vehicles and the outliers of the outlier vehicles based on the characteristic indexes and the basic information of the vehicles;
specifically, determining an outlier vehicle and an outlier of the outlier vehicle based on the characteristic index and the basic vehicle information includes:
inputting the characteristic index and the basic vehicle information into an outlier detection module as input characteristics;
the outlier detection module determines the outlier vehicle and an outlier of the outlier vehicle based on the input features.
In an actual application scene, the system integrates the acquired characteristic indexes (1 or more) of the vehicle and basic information of the vehicle, and the characteristic indexes correspond to the running state of the vehicle.
Further, the outlier detection module determines the outliers of the outlier vehicle and the outliers of the outlier vehicle based on the input features, comprising:
the outlier detection module detects the input features based on an outlier detection model to obtain a detection result;
determining the outlier vehicle and an outlier of the outlier vehicle based on the detection result.
In an actual application scene, any One algorithm model in One-Class SVM, Isolation Forest, Local outer Factor or an Outlier calculation method based on angle variance can be used as an Outlier detection model;
for example, embodiments of the present invention output outliers and their outliers based on One-Class SVM models;
specifically, a training set is obtained, wherein the training set includes a large number of input feature samples.
And training the One-Class SVM model based on a large number of input feature samples to obtain a target One-Class SVM model.
According to the embodiment of the invention, the input characteristics are input into a target One-Class SVM model, and the target One-Class SVM model judges whether the input characteristics are consistent with the running state of the vehicle generated by the corresponding characteristic sample; for example: if the input features are similar to the running states of the vehicles corresponding to the input feature samples and belong to the same Class, the target One-Class SVM model does not output the degree of outlier corresponding to the input features; and if the input features are not similar to the input feature samples and do not belong to the same Class, outputting the corresponding outlier of the input features by the target One-Class SVM model.
Further, the determining the outlier vehicle and the degree of outlier of the outlier vehicle based on the detection result comprises:
determining the degree of outlier based on the detection result;
determining the outlier vehicle based on the outlier.
In an actual application scene, if the target One-Class SVM model judges that some or a certain input feature is abnormal, the One-Class SVM model outputs the outlier aiming at the certain input feature, and outputs the information of the outlier corresponding to the outlier according to the information contained in the input feature.
Further, the basic information of the vehicle includes, but is not limited to, information of vehicle identification ID, mileage, season, and region.
In an actual application scenario, after the outlier detection model outputs the outlier, the vehicle identification ID of the vehicle corresponding to the outlier is simultaneously output.
Step S4: determining whether operation of the outlier vehicle is abnormal based on the outlier.
Specifically, the determining whether the operation of the outlier vehicle is abnormal based on the outlier includes:
obtaining historical outliers of the outlier vehicle;
obtaining a threshold value of the outlier based on the historical outlier;
comparing the degree of outlier to a threshold value for the degree of outlier;
if the outlier is larger than the threshold of the outlier, the operation of the outlier vehicle is abnormal;
and if the outlier is smaller than or equal to the threshold of the outlier, the operation of the outlier vehicle is normal.
Further, the threshold value of the outlier is μ +3 σ of the historical outlier, wherein μ is the mean value of the historical outlier and σ is the standard deviation of the historical outlier;
that is, after the outlier is obtained, calculating to obtain mu +3 sigma of the historical outlier based on the mean value and the standard deviation of the historical outlier stored by the system;
comparing the outlier with the mu +3 sigma, and if the outlier is greater than the mu +3 sigma, the operation of the outlier vehicle is abnormal;
and if the outlier is less than or equal to mu +3 sigma, the operation of the outlier vehicle is normal.
According to the embodiment of the invention, after the outlier detection model is used for outputting the outlier vehicles and the outliers of the outlier vehicles, the problems of high randomness of the outliers and inaccurate results can occur, so that the outliers need to be verified to determine whether the operation of the outlier vehicles is abnormal or not, and the false alarm rate is reduced; meanwhile, the outlier detection model is easy to train, and the calculated amount is greatly reduced.
In an optional embodiment of the present invention, if the outlier is greater than the threshold of the outlier, the method, after the operation of the outlier vehicle is abnormal, includes:
generating an early warning signal;
and carrying out early warning based on the early warning signal.
In an actual application scene, when determining that the outlier vehicle corresponding to the outlier normally runs based on the threshold value of the outlier, the system does not generate an early warning signal;
when determining that the outlier vehicle corresponding to the outlier runs abnormally based on the threshold value of the outlier, the system generates an early warning signal, gives an alarm and timely informs workers to maintain the outlier vehicle.
According to the embodiment of the invention, the early warning signal is generated based on the checking result of the threshold value of the outlier, compared with the prior art that the early warning signal is directly generated, the false alarm rate is greatly reduced, and the alarm reliability is improved.
As shown in fig. 2, an embodiment of the present invention further provides an abnormality detection apparatus 200, including:
an acquisition unit 201, configured to acquire vehicle operation data to be detected;
the processing unit 202 is configured to process the vehicle operation data to be detected to obtain a characteristic index with strong business knowledge correlation;
a first determining unit 203 for determining an outlier vehicle and an outlier of the outlier vehicle based on the feature index and the vehicle basic information;
a second determination unit 204 for determining whether the operation of the outlier vehicle is abnormal based on the outlier.
Optionally, the characteristic indicators strongly related to business knowledge include:
the total heat exchange coefficient, the cell impedance, the cell information entropy and the equivalent short-circuit current.
Optionally, determining an outlier vehicle and an outlier of the outlier vehicle based on the feature index and the basic vehicle information includes:
inputting the characteristic index and the basic vehicle information into an outlier detection module as input characteristics;
the outlier detection module determines the outlier vehicle and an outlier of the outlier vehicle based on the input features.
Optionally, the outlier detecting module determines the outliers of the outlier vehicle and the outliers of the outlier vehicle based on the input features, including:
the outlier detection module detects the input features based on an outlier detection model to obtain a detection result;
determining the outlier vehicle and an outlier of the outlier vehicle based on the detection result.
Optionally, the determining the outlier of the outlier vehicle and the outlier of the outlier vehicle based on the detection result includes:
determining the degree of outlier based on the detection result;
determining the outlier vehicle based on the outlier.
Optionally, the determining whether the operation of the outlier vehicle is abnormal based on the outlier includes:
obtaining historical outliers of the outlier vehicle;
obtaining a threshold value of the outlier based on the historical outlier;
comparing the degree of outlier to a threshold value for the degree of outlier;
if the outlier is larger than the threshold of the outlier, the operation of the outlier vehicle is abnormal;
and if the outlier is smaller than or equal to the threshold of the outlier, the operation of the outlier vehicle is normal.
Optionally, if the outlier is greater than the threshold of the outlier, the method, after the operation of the outlier vehicle is abnormal, includes:
generating an early warning signal;
and carrying out early warning based on the early warning signal.
Embodiments of the present invention also provide an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the method as described above when executing the computer program.
Embodiments of the present invention also provide a computer-readable storage medium comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method as described above.
In addition, other configurations and functions of the apparatus according to the embodiment of the present invention are known to those skilled in the art, and are not described herein for reducing redundancy.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood according to specific situations by those of ordinary skill in the art.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
Although embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and not to be construed as limiting the present invention, and that changes, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. An abnormality detection method characterized by comprising:
acquiring vehicle operation data to be detected;
processing the vehicle operation data to be detected to obtain characteristic indexes with strong business knowledge correlation;
determining outlier vehicles and the outliers of the outlier vehicles based on the characteristic indexes and the basic information of the vehicles;
determining whether operation of the outlier vehicle is abnormal based on the outlier.
2. The method of claim 1, wherein the characteristic indicators that are strongly related to business knowledge comprise:
the total heat exchange coefficient, the cell impedance, the cell information entropy and the equivalent short-circuit current.
3. The method of claim 1, wherein determining outlier vehicles and an outlier of the outlier vehicles based on the feature index and vehicle ground information comprises:
inputting the characteristic index and the basic vehicle information into an outlier detection module as input characteristics;
the outlier detection module determines the outlier vehicle and an outlier of the outlier vehicle based on the input features.
4. The method of claim 3, wherein the outlier detection module determines the outlier vehicle and an outlier of the outlier vehicle based on the input features, comprising:
the outlier detection module detects the input features based on an outlier detection model to obtain a detection result;
determining the outlier vehicle and an outlier of the outlier vehicle based on the detection result.
5. The method of claim 4, wherein the determining the outlier vehicle and the degree of outlier of the outlier vehicle based on the detection comprises:
determining the degree of outlier based on the detection result;
determining the outlier vehicle based on the outlier.
6. The method of claim 1, wherein said determining whether operation of the outlier vehicle is abnormal based on the outlier comprises:
obtaining historical outliers of the outlier vehicle;
obtaining a threshold value of the outlier based on the historical outlier;
comparing the degree of outlier to a threshold value for the degree of outlier;
if the outlier is larger than the threshold of the outlier, the operation of the outlier vehicle is abnormal;
and if the outlier is smaller than or equal to the threshold of the outlier, the operation of the outlier vehicle is normal.
7. The method of claim 6, wherein the outlier vehicle operation being abnormal if the outlier is greater than the threshold for the outlier comprises:
generating an early warning signal;
and carrying out early warning based on the early warning signal.
8. An abnormality detection device characterized by comprising:
the acquisition unit is used for acquiring vehicle operation data to be detected;
the processing unit is used for processing the vehicle operation data to be detected to obtain characteristic indexes with strong business knowledge correlation;
a first determination unit configured to determine an outlier vehicle and an outlier of the outlier vehicle based on the feature index and the vehicle basic information;
a second determination unit configured to determine whether or not the operation of the outlier vehicle is abnormal, based on the outlier.
9. An electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method of any of claims 1-7.
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