CN116796140A - Abnormal analysis method, device, equipment and storage medium based on artificial intelligence - Google Patents
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Abstract
The embodiment of the application belongs to the field of artificial intelligence, and relates to an anomaly analysis method based on artificial intelligence, which comprises the following steps: acquiring historical vehicle driving data of a target user; analyzing the historical vehicle driving data to obtain a driving habit time period of the target user; acquiring track data of a target vehicle in a driving habit time period; constructing initial track time sequence data based on the track data; generating track timing data based on the initial track timing data; clustering the track time sequence data based on a clustering model to obtain a target clustering result of each track time sequence data; and generating an abnormal identification result of each track time sequence data based on the target clustering result. The application also provides an anomaly analysis device, computer equipment and a storage medium based on the artificial intelligence. In addition, the application also relates to a blockchain technology, and an abnormality identification result can be stored in the blockchain. The method improves the processing efficiency of the driving track abnormality analysis and ensures the accuracy of the generated abnormality identification result.
Description
Technical Field
The present application relates to the field of artificial intelligence development, and in particular, to an anomaly analysis method, an anomaly analysis device, a computer device, and a storage medium based on artificial intelligence.
Background
Currently, in the vehicle insurance claim management business in the field of financial insurance, there is generally a business requirement that a driving track of a customer needs to be analyzed. The existing method for analyzing the driving track of the customer usually adopts a manual analysis mode, and the claimant manually analyzes the driving track data in the claimant application behavior of the customer according to own professional experience to obtain a corresponding analysis result, however, the manual analysis mode of the driving track data has the defects of large workload, low processing efficiency and lower accuracy of the generated analysis result of the driving track data due to different professional levels of the claimant.
Disclosure of Invention
The embodiment of the application aims to provide an anomaly analysis method, an anomaly analysis device, computer equipment and a storage medium based on artificial intelligence so as to solve the existing technical problems.
In order to solve the technical problems, the embodiment of the application provides an anomaly analysis method based on artificial intelligence, which adopts the following technical scheme:
Acquiring historical vehicle driving data of a target user corresponding to a target vehicle to be analyzed;
analyzing the historical vehicle driving data to obtain a driving habit time period corresponding to the target user;
acquiring track data of the target vehicle in the driving habit time period;
constructing corresponding initial track time sequence data based on the track data; wherein the number of track timing data includes a plurality;
normalizing the initial track time sequence data to obtain processed track time sequence data;
clustering all the track time sequence data based on a preset clustering model to obtain target clustering results respectively corresponding to the track time sequence data; the clustering model is a model constructed and generated based on a k-shape algorithm;
and generating an abnormal identification result of each track time sequence data based on the target clustering result.
Further, the step of analyzing the historical vehicle driving data to obtain a driving habit time period corresponding to the target user specifically includes:
analyzing the historical vehicle running data to obtain the vehicle running times of the target user in each unit time period in a preset historical time period;
Screening out appointed unit time periods of which the running times of the vehicle are greater than a preset time threshold value from all the unit time periods;
and integrating all the appointed unit time periods to obtain the driving habit time period of the target user.
Further, the step of constructing corresponding initial track time sequence data based on the track data specifically includes:
acquiring a preset time division period;
the time division period is used as a time slice to carry out time sequence division processing on the track data, so as to obtain processed track data;
and taking the processed track data as the initial track time sequence data.
Further, the step of clustering all the track time sequence data based on a preset clustering model to obtain clustering results respectively corresponding to the track time sequence data specifically includes:
clustering all the track time sequence data through the clustering model, and randomly selecting a preset number of first track time sequence data from all the track time sequence data to serve as clustering centroids;
traversing and calculating the shape distance between the cluster centroid and the second track time sequence data; the second track time sequence data are track time sequence data except the first track time sequence data in all the track time sequence data;
Based on the shape distance and a preset proximity principle, distributing the second track time sequence data to clusters where cluster centroids closest to the second track time sequence data are located;
and for each allocated cluster, calculating the average value of all points in the cluster, taking the average value as a new centroid, and repeating the centroid updating process until the clustering result converges to obtain the target clustering result.
Further, the clustering result comprises a first cluster corresponding to a normal track category and a second cluster corresponding to an abnormal track category; the step of generating an anomaly identification result of each track time sequence data based on the target clustering result specifically comprises the following steps:
acquiring first track time sequence data contained in the first cluster;
generating a first anomaly identification result corresponding to the first track time sequence data; the content of the first abnormal identification result comprises that the first track time sequence data belongs to an abnormal track;
acquiring second track time sequence data contained in the second cluster;
generating a second anomaly identification result corresponding to the second track time sequence data; the content of the second abnormal recognition result includes that the second track time sequence data belongs to a normal track.
Further, after the step of generating the anomaly identification result of each of the track time series data based on the target clustering result, the method further includes:
screening third track time sequence data belonging to an abnormal track from all the track time sequence data based on the abnormal identification result;
acquiring an abnormal analysis result corresponding to the third track time sequence data, which is input by a first management user;
and generating a target abnormality identification result corresponding to the third track time sequence data based on the abnormality analysis result.
Further, after the step of generating the anomaly identification result of each of the track time series data based on the target clustering result, the method further includes:
acquiring a preset report template;
acquiring preset treatment measure information;
generating a driving analysis report corresponding to the target user based on the abnormality recognition result and the report template;
acquiring communication information of a second management user;
and pushing the driving analysis report and the processing measure information to the second management user based on the communication information.
In order to solve the technical problems, the embodiment of the application also provides an anomaly analysis device based on artificial intelligence, which adopts the following technical scheme:
The first acquisition module is used for acquiring historical vehicle driving data of a target user corresponding to a target vehicle to be analyzed;
the analysis module is used for analyzing the historical vehicle driving data to obtain a driving habit time period corresponding to the target user;
the second acquisition module is used for acquiring track data of the target vehicle in the driving habit time period;
the construction module is used for constructing corresponding initial track time sequence data based on the track data; wherein the number of track timing data includes a plurality;
the first processing module is used for carrying out normalization processing on the initial track time sequence data to obtain processed track time sequence data;
the second processing module is used for clustering all the track time sequence data based on a preset clustering model to obtain target clustering results respectively corresponding to the track time sequence data; the clustering model is a model constructed and generated based on a k-shape algorithm;
the first generation module is used for generating an abnormal identification result of each track time sequence data based on the target clustering result.
In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:
Acquiring historical vehicle driving data of a target user corresponding to a target vehicle to be analyzed;
analyzing the historical vehicle driving data to obtain a driving habit time period corresponding to the target user;
acquiring track data of the target vehicle in the driving habit time period;
constructing corresponding initial track time sequence data based on the track data; wherein the number of track timing data includes a plurality;
normalizing the initial track time sequence data to obtain processed track time sequence data;
clustering all the track time sequence data based on a preset clustering model to obtain target clustering results respectively corresponding to the track time sequence data; the clustering model is a model constructed and generated based on a k-shape algorithm;
and generating an abnormal identification result of each track time sequence data based on the target clustering result.
In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:
acquiring historical vehicle driving data of a target user corresponding to a target vehicle to be analyzed;
analyzing the historical vehicle driving data to obtain a driving habit time period corresponding to the target user;
Acquiring track data of the target vehicle in the driving habit time period;
constructing corresponding initial track time sequence data based on the track data; wherein the number of track timing data includes a plurality;
normalizing the initial track time sequence data to obtain processed track time sequence data;
clustering all the track time sequence data based on a preset clustering model to obtain target clustering results respectively corresponding to the track time sequence data; the clustering model is a model constructed and generated based on a k-shape algorithm;
and generating an abnormal identification result of each track time sequence data based on the target clustering result.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
firstly, acquiring historical vehicle driving data of a target user corresponding to a target vehicle to be analyzed; then analyzing the historical vehicle driving data to obtain a driving habit time period corresponding to the target user; then, track data of the target vehicle in the driving habit time period is acquired; constructing corresponding initial track time sequence data based on the track data; carrying out normalization processing on the initial track time sequence data to obtain processed track time sequence data, and clustering all the track time sequence data based on a preset clustering model to obtain target clustering results respectively corresponding to the track time sequence data; and finally, generating an abnormal identification result of each track time sequence data based on the target clustering result. According to the embodiment of the application, the driving habit time period of the target user can be intelligently determined by carrying out data analysis on the historical vehicle driving data of the target user, so that the abnormal identification of the driving track of the user can be realized only by analyzing the track data of the target vehicle in the driving habit time period, the track data of the target vehicle in all time periods is not needed to be analyzed, the time pertinence of identifying the track data can be effectively improved, the workload of the abnormal analysis of the driving track of the target user is greatly reduced, and the processing efficiency of the abnormal analysis of the driving track is improved. In addition, the clustering model based on the k-shape algorithm is used for clustering track time sequence data of the target vehicle in a driving habit time period, so that the time sequence characteristics of the driving track can be considered, the change trend of the track time sequence data can be effectively clustered, the accurate analysis of the dynamic change of the driving track is realized, and the accuracy of the generated abnormal recognition result of the track time sequence data of the vehicle driving is effectively improved.
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In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of an artificial intelligence based anomaly analysis method in accordance with the present application;
FIG. 3 is a schematic diagram of one embodiment of an artificial intelligence based anomaly analysis device in accordance with the present application;
FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the anomaly analysis method based on artificial intelligence provided by the embodiment of the application is generally executed by a server/terminal device, and correspondingly, the anomaly analysis device based on artificial intelligence is generally arranged in the server/terminal device.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow chart of one embodiment of an artificial intelligence based anomaly analysis method in accordance with the present application is shown. The anomaly analysis method based on artificial intelligence comprises the following steps:
Step S201, acquiring historical vehicle travel data of a target user corresponding to a target vehicle to be analyzed.
In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the artificial intelligence-based anomaly analysis method operates may acquire the historical vehicle travel data through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection. The method comprises the steps of acquiring user information of a target user, and inquiring historical vehicle driving data corresponding to the target user from a pre-constructed vehicle information database based on the user information. The user information may include a user name or a user ID. The vehicle information database is a database which is built in advance and stores vehicle information of each customer. The vehicle information includes at least vehicle travel data, trajectory data of the vehicle, and the like.
Step S202, analyzing the historical vehicle driving data to obtain a driving habit time period corresponding to the target user.
In this embodiment, the above-mentioned analysis of the historical vehicle driving data to obtain a specific implementation process of the driving habit period corresponding to the target user will be described in further detail in the following specific embodiments, which will not be described herein.
Step S203, acquiring track data of the target vehicle in the driving habit period.
In this embodiment, a verification time interval corresponding to the target vehicle may be determined first, and then track data of the target vehicle in the driving habit time period may be queried from the vehicle information database according to the verification time interval. The selection of the verification time interval may be determined according to actual service requirements, for example, in the first two days from the current time.
Step S204, constructing corresponding initial track time sequence data based on the track data; wherein the number of track timing data includes a plurality.
In this embodiment, the time sequence data, specifically, time sequence data, refers to a series of values of the same statistical index arranged according to the time sequence of occurrence, so that the driving behavior track of the user can be better reflected. Wherein the time series data are time-varying sequence data which are collected according to predefined variables and at fixed intervals, the most important feature of the time series data is that the order thereof is very critical. The time sequence is to find out the evolution mode from the time sequence of the predicted index, establish the mathematical model, make the quantitative estimation to the future development trend of the predicted index, in addition, the above-mentioned specific implementation process of constructing the corresponding initial track time sequence data based on the track data, the present application will be further detailed in the following specific embodiments, and will not be described here too much.
Step S205, performing normalization processing on the initial track time sequence data to obtain processed track time sequence data.
In this embodiment, since the time clustering is performed according to a time period, and the driving track is discontinuous, discrete and greatly differentiated from the dimension of the time end, the normalization processing needs to be performed on the initial track time sequence data, specifically, the normalization processing may be performed on the initial track time sequence data by adopting z-normalized, that is, the feature in the time period is subjected to the normalization processing, so as to obtain the processed track time sequence data.
Step S206, clustering all the track time sequence data based on a preset clustering model to obtain target clustering results respectively corresponding to the track time sequence data.
In this embodiment, the clustering model is a model constructed and generated based on a k-shape algorithm. The specific implementation process of clustering all the track time series data based on the preset clustering model to obtain the target clustering result corresponding to each track time series data is described in further detail in the following specific embodiments, which are not described herein. Additionally, the training generation process of the cluster model may include: firstly, time sequence data arrangement is carried out on open track data of a sample vehicle, and partial track data in the open track data is marked by using labels of normal driving track data or abnormal driving track data; then, carrying out time sequence data marking on the marked two parts of data, namely, marking whether the sample vehicle drives normally according to time periods, so as to obtain driving track data of each time sequence of the sample vehicle; then training the clustering model by using the open track data arranged by the time sequence data as a sample to obtain a classification result of the clustered open track data; and performing result verification on the classification result of the open track data and the marked part of track data, and judging that the clustering model training is finished if the accuracy of the classification result of the open track data and the marked part of track data is greater than a preset accuracy threshold.
Step S207, generating an abnormal recognition result of each track time sequence data based on the target clustering result.
In this embodiment, the foregoing specific implementation process of generating the anomaly identification result of each track time series data based on the target clustering result will be described in further detail in the following specific embodiments, which will not be described herein.
Firstly, acquiring historical vehicle driving data of a target user corresponding to a target vehicle to be analyzed; then analyzing the historical vehicle driving data to obtain a driving habit time period corresponding to the target user; then, track data of the target vehicle in the driving habit time period is acquired; constructing corresponding initial track time sequence data based on the track data; carrying out normalization processing on the initial track time sequence data to obtain processed track time sequence data, and clustering all the track time sequence data based on a preset clustering model to obtain target clustering results respectively corresponding to the track time sequence data; and finally, generating an abnormal identification result of each track time sequence data based on the target clustering result. According to the method and the device for identifying the driving track abnormality of the target user, the driving habit time period of the target user can be intelligently determined by carrying out data analysis on the historical driving data of the target user, so that the abnormal identification of the driving track of the user can be realized only by analyzing the track data of the target vehicle in the driving habit time period, the track data of the target vehicle in all time periods is not needed to be analyzed, the time pertinence of identifying the track data can be effectively improved, the workload of the driving track abnormality analysis of the target user is greatly reduced, and the processing efficiency of the driving track abnormality analysis is improved. In addition, the clustering model based on the k-shape algorithm is used for clustering track time sequence data of the target vehicle in a driving habit time period, so that the time sequence characteristics of the driving track can be considered, the change trend of the track time sequence data can be effectively clustered, the accurate analysis of the dynamic change of the driving track is realized, and the accuracy of the generated abnormal recognition result of the track time sequence data of the vehicle driving is effectively improved.
In some alternative implementations, step S202 includes the steps of:
and analyzing the historical vehicle running data to obtain the vehicle running times of the target user in each unit time period in a preset historical time period.
In the present embodiment, the above unit time period may be 1 hour. The value of the preset historical time period is not particularly limited, and may be set according to actual use requirements, for example, within the first half year from the current time. The method and the device only analyze the running times of the target user in each unit time period in the preset historical time period, so that the abnormal identification of the driving track of the user is realized according to the latest and effective running times data of the vehicle, the running times data of the vehicle of the target user are not needed to be analyzed, the workload of the abnormal analysis of the driving track of the target user is greatly reduced, and the processing efficiency of the abnormal analysis of the driving track is effectively improved.
And screening out appointed unit time periods of which the running times of the vehicle are larger than a preset time threshold value from all the unit time periods.
In this embodiment, the value of the frequency threshold is not specifically limited, and may be determined according to an actual service test result. The greater the number of vehicle runs per unit time period, the more the user is accustomed to having a habit of vehicle running per unit time period.
And integrating all the appointed unit time periods to obtain the driving habit time period of the target user.
In the present embodiment, all of the specified unit time periods are included in the driving habit time period described above. The driving habit period refers to a period of time in which the target user frequently uses/drives the target vehicle.
According to the method, the historical vehicle running data are analyzed, so that the number of vehicle running times of the target user in each unit time period in a preset historical time period is obtained; then screening out appointed unit time periods of which the running times of the vehicle are greater than a preset time threshold value from all the unit time periods; and integrating all the appointed unit time periods to obtain the driving habit time period of the target user. According to the method and the device, the driving habit time period of the target user can be intelligently determined by carrying out data analysis on the vehicle driving times of the target user in each unit time period in the preset historical time period, so that the abnormal identification of the driving track of the user can be realized only by analyzing the track data of the target vehicle in the driving habit time period, the track data of the target vehicle in all time periods is not needed to be analyzed, the time pertinence of identifying the track data can be improved, the workload of the abnormal analysis of the driving track of the target user is greatly reduced, and the processing efficiency of the abnormal analysis of the driving track is effectively improved.
In some alternative implementations of the present embodiment, step S204 includes the steps of:
and acquiring a preset time division period.
In this embodiment, the value of the time division period is not particularly limited, and may be determined according to the actual service usage requirement, for example, an hour may be used as the time division period.
And carrying out time sequence division processing on the track data by taking the time division period as a time slice to obtain the processed track data.
And taking the processed track data as the initial track time sequence data.
The method comprises the steps of obtaining a preset time division period; then, the time division period is used as a time slice to carry out time sequence division processing on the track data, so as to obtain processed track data; and taking the processed track data as the initial track time sequence data. The method and the device for carrying out time sequence division processing on the track data based on the use of time division periods can realize rapid generation of the needed initial track time sequence data, so that the time sequence characteristics of the driving track can be considered in the subsequent abnormal analysis of the track time sequence data, thereby realizing accurate analysis on dynamic changes of the driving track and being beneficial to improving the accuracy of the generated abnormal analysis result on the driving track.
In some alternative implementations, step S206 includes the steps of:
clustering all the track time sequence data through the clustering model, and randomly selecting a preset number of first track time sequence data from all the track time sequence data to serve as clustering centroids.
In this embodiment, the preset number is specifically 2.
And traversing and calculating the shape distance between the cluster centroid and the second track time sequence data.
In this embodiment, the shape distance between the cluster centroid and the second track timing data may be calculated based on a shape distance calculation formula. The second track time sequence data are track time sequence data except the first track time sequence data in all the track time sequence data.
And distributing the second track time sequence data to a cluster where the cluster centroid closest to the second track time sequence data is located based on the shape distance and a preset nearby principle.
In the present embodiment of the present invention, in the present embodiment,
and for each allocated cluster, calculating the average value of all points in the cluster, taking the average value as a new centroid, and repeating the centroid updating process until the clustering result converges to obtain the target clustering result.
In this embodiment, by inputting: a matrix with X being n X m (n track time sequence data with the length of m, the number k of clusters being 2, namely a normal track and an abnormal track), outputs a vector with the length of IDX being n (namely n time sequences are gathered to 2 clusters), randomly initializing a matrix with the length of C being k X m (namely k circle centers are m), initializing all vectors to be 0, selecting 2 objects (track time sequence data) as centroids of initial clusters, performing track allocation and clustering for each track time sequence data (namely each same time period) by adopting an iterative process which is the same as k-means, taking each track time sequence data of a target vehicle as an object to calculate a vector between centroids of the two clusters, simultaneously comparing each vehicle track time sequence with the centroid vector of the two clusters, distributing each track time sequence data of each target vehicle to each cluster on the basis of a nearby principle, repeating the actions above, nearby dividing each track time sequence data into each cluster, continuously updating and calculating the two clusters until the centroids have no change, and obtaining a final clustering result.
Clustering all the track time sequence data through the clustering model, and randomly selecting a preset number of first track time sequence data from all the track time sequence data to serve as a clustering centroid; then traversing and calculating the shape distance between the cluster centroid and the second track time sequence data; then, based on the shape distance and a preset nearby principle, the second track time sequence data is distributed to clusters where cluster centroids closest to the second track time sequence data are located; and calculating the average value of all points in each allocated cluster, taking the average value as a new centroid, and repeating the centroid updating process until the clustering result converges to obtain the target clustering result. According to the method, the clustering model based on the k-shape algorithm is used for clustering the track time sequence data of the target vehicle, the time sequence characteristics of the driving track can be considered, and the change trend of the track time sequence data can be effectively clustered, so that the accurate analysis of the dynamic change of the driving track is realized, and the generated accuracy of the clustering result of the driving track is improved.
In some optional implementations, the clustering result includes a first cluster corresponding to a normal track category and includes a second cluster corresponding to an abnormal track category; step S207 includes the steps of:
And acquiring first track time sequence data contained in the first cluster.
In this embodiment, the clustering result includes a first cluster corresponding to a normal track category and includes a second cluster corresponding to an abnormal track category. The first track timing data refers to all track timing data allocated in the first cluster.
And generating a first abnormal recognition result corresponding to the first track time sequence data.
In this embodiment, the content of the first anomaly identification result includes that the first track time sequence data belongs to an anomaly track.
And acquiring second track time sequence data contained in the second cluster.
In this embodiment, the second track timing data refers to all track timing data allocated in the second cluster.
And generating a second abnormal recognition result corresponding to the second track time sequence data.
In this embodiment, the content of the second anomaly identification result includes that the second track time sequence data belongs to a normal track.
The method comprises the steps of obtaining first track time sequence data contained in a first cluster; then generating a first abnormal recognition result corresponding to the first track time sequence data; acquiring second track time sequence data contained in the second cluster; and generating a second abnormal recognition result corresponding to the second track time sequence data. According to the method, the clustering results generated by clustering all the track time sequence data through the clustering model are analyzed, so that the abnormal recognition result of each track time sequence data can be quickly and accurately generated, and the accuracy of the generated abnormal recognition result of the driving track is improved.
In some optional implementations of this embodiment, after step S207, the electronic device may further perform the following steps:
and screening third track time sequence data belonging to the abnormal track from all the track time sequence data based on the abnormal identification result.
In this embodiment, the abnormal recognition result of the track timing data includes that the track timing data belongs to an abnormal track or that the track timing data belongs to a normal track.
And acquiring an abnormal analysis result corresponding to the third track time sequence data, which is input by the first management user.
In this embodiment, the first management user may refer to a vehicle claimant.
And generating a target abnormality identification result corresponding to the third track time sequence data based on the abnormality analysis result.
In this embodiment, the abnormality recognition result of the third track timing data may be updated based on the abnormality analysis result to generate the target abnormality recognition result corresponding to the third track timing data.
The application screens out third track time sequence data belonging to an abnormal track from all track time sequence data based on the abnormal identification result; then obtaining an abnormal analysis result corresponding to the third track time sequence data, which is input by a first management user; and generating a target abnormality identification result corresponding to the third track time sequence data based on the abnormality analysis result. According to the method and the device, the abnormal recognition result of the track time sequence data is generated, the abnormal analysis result corresponding to the track time sequence data, which is input by the management user, can be intelligently received, the abnormal recognition result of the track time sequence data is adjusted based on the abnormal analysis result so as to generate the final target abnormal recognition result, further confirmation processing of the abnormal recognition result of the track time sequence data according to personal requirements of the management user is realized, the accuracy of the generated target abnormal recognition result is further improved, and the use experience of the management user is improved.
In some optional implementations of this embodiment, after step S207, the electronic device may further perform the following steps:
and acquiring a preset report template.
In this embodiment, the report template is a template file pre-constructed according to actual service requirements.
And acquiring preset treatment measure information.
In the present embodiment, the above-described processing measure information is information of processing advice applied to a driving locus where an abnormality exists, which is generated in advance based on actual business processing experience.
And generating a driving analysis report corresponding to the target user based on the abnormal recognition result and the report template.
In this embodiment, the driving analysis report associated with the target user may be generated by correspondingly populating the abnormality recognition result into the report template.
And acquiring communication information of the second management user.
In this embodiment, the communication information may include a phone number or mail information.
And pushing the driving analysis report and the processing measure information to the second management user based on the communication information.
The method comprises the steps of obtaining a preset report template and obtaining preset treatment measure information; then generating a driving analysis report corresponding to the target user based on the abnormal recognition result and the report template; then obtaining the communication information of the second management user; and pushing the driving analysis report and the processing measure information to the second management user based on the communication information. According to the method and the system, after the abnormal recognition result of the track time sequence data is generated based on the use of the clustering model, the driving analysis report corresponding to the abnormal recognition result is intelligently generated, and meanwhile, the driving analysis report and the corresponding processing measure information are pushed to the management user, so that the management user can clearly look up the abnormal situation of the vehicle driving track of the target user based on the driving analysis report, and can carry out subsequent corresponding processing according to the processing measure information, thereby being beneficial to improving the working experience of the management user and improving the working efficiency of the management user.
It should be emphasized that, to further ensure the privacy and security of the anomaly identification result, the anomaly identification result may also be stored in a node of a blockchain.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm 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.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an anomaly analysis device based on artificial intelligence, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device is particularly applicable to various electronic devices.
As shown in fig. 3, the artificial intelligence based anomaly analysis device 300 according to the present embodiment includes: a first acquisition module 301, an analysis module 302, a second acquisition module 303, a construction module 304, a first processing module 305, a second processing module 306, and a first generation module 307. Wherein:
a first obtaining module 301, configured to obtain historical vehicle driving data of a target user corresponding to a target vehicle to be analyzed;
the analysis module 302 is configured to analyze the historical vehicle driving data to obtain a driving habit time period corresponding to the target user;
a second obtaining module 303, configured to obtain track data of the target vehicle in the driving habit period;
a construction module 304, configured to construct corresponding initial track timing data based on the track data; wherein the number of track timing data includes a plurality;
the first processing module 305 is configured to perform normalization processing on the initial track time sequence data to obtain processed track time sequence data;
The second processing module 306 is configured to cluster all the track time sequence data based on a preset cluster model, so as to obtain target clustering results corresponding to each track time sequence data respectively; the clustering model is a model constructed and generated based on a k-shape algorithm;
a first generating module 307, configured to generate an anomaly identification result of each of the track time series data based on the target clustering result.
In this embodiment, the operations performed by the modules or units are respectively corresponding to the steps of the artificial intelligence based anomaly analysis method in the foregoing embodiment, and are not described herein again.
In some alternative implementations of the present embodiment, the analysis module 302 includes:
the analysis sub-module is used for analyzing the historical vehicle running data and acquiring the vehicle running times of the target user in each unit time period in a preset historical time period;
the screening sub-module is used for screening out appointed unit time periods of which the running times of the vehicle are larger than a preset time threshold value from all the unit time periods;
and the first generation sub-module is used for integrating all the appointed unit time periods to obtain the driving habit time period of the target user.
In this embodiment, the operations performed by the modules or units are respectively corresponding to the steps of the artificial intelligence based anomaly analysis method in the foregoing embodiment, and are not described herein again.
In some alternative implementations of the present embodiment, the building block 304 includes:
the first acquisition submodule is used for acquiring a preset time division period;
the processing submodule is used for carrying out time sequence division processing on the track data by taking the time division period as a time slice to obtain processed track data;
and the first determining submodule is used for taking the processed track data as the initial track time sequence data.
In this embodiment, the operations performed by the modules or units are respectively corresponding to the steps of the artificial intelligence based anomaly analysis method in the foregoing embodiment, and are not described herein again.
In some alternative implementations of the present embodiment, the second processing module 306 includes:
the selecting sub-module is used for clustering all the track time sequence data through the clustering model, and randomly selecting a preset number of first track time sequence data from all the track time sequence data to serve as a clustering centroid;
The calculation sub-module is used for traversing and calculating the shape distance between the cluster centroid and the second track time sequence data; the second track time sequence data are track time sequence data except the first track time sequence data in all the track time sequence data;
the distribution sub-module is used for distributing the second track time sequence data to clusters where cluster centroids closest to the second track time sequence data are located based on the shape distance and a preset nearby principle;
and the second determining module is used for calculating the average value of all points in each allocated cluster, taking the average value as a new centroid, and repeating the centroid updating process until the clustering result converges to obtain the target clustering result.
In this embodiment, the operations performed by the modules or units are respectively corresponding to the steps of the artificial intelligence based anomaly analysis method in the foregoing embodiment, and are not described herein again.
In some optional implementations of this embodiment, the clustering result includes a first cluster corresponding to a normal track category and includes a second cluster corresponding to an abnormal track category; the first generation module 307 includes:
A second acquisition sub-module, configured to acquire first track timing data included in the first cluster;
the second generation sub-module is used for generating a first abnormal identification result corresponding to the first track time sequence data; the content of the first abnormal identification result comprises that the first track time sequence data belongs to an abnormal track;
a third acquisition sub-module, configured to acquire second track timing data included in the second cluster;
a third generation sub-module, configured to generate a second anomaly identification result corresponding to the second track timing data; the content of the second abnormal recognition result includes that the second track time sequence data belongs to a normal track.
In this embodiment, the operations performed by the modules or units are respectively corresponding to the steps of the artificial intelligence based anomaly analysis method in the foregoing embodiment, and are not described herein again.
In some optional implementations of the present embodiment, the artificial intelligence based anomaly analysis device further includes:
the screening module is used for screening third track time sequence data belonging to the abnormal track from all the track time sequence data based on the abnormal identification result;
The third acquisition module is used for acquiring an abnormal analysis result corresponding to the third track time sequence data, which is input by the first management user;
and the second generation module is used for generating a target abnormality identification result corresponding to the third track time sequence data based on the abnormality analysis result.
In this embodiment, the operations performed by the modules or units are respectively corresponding to the steps of the artificial intelligence based anomaly analysis method in the foregoing embodiment, and are not described herein again.
In some optional implementations of the present embodiment, the artificial intelligence based anomaly analysis device further includes:
the fourth acquisition module is used for acquiring a preset report template;
a fifth acquisition module, configured to acquire preset processing measure information;
a third generation module, configured to generate a driving analysis report corresponding to the target user based on the abnormality recognition result and the report template;
a sixth acquisition module, configured to acquire communication information of the second management user;
and the pushing module is used for pushing the driving analysis report and the processing measure information to the second management user based on the communication information.
In this embodiment, the operations performed by the modules or units are respectively corresponding to the steps of the artificial intelligence based anomaly analysis method in the foregoing embodiment, and are not described herein again.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, 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. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of an anomaly analysis method based on artificial intelligence. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as executing computer readable instructions of the anomaly analysis method based on artificial intelligence.
The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
in the embodiment of the application, firstly, historical vehicle running data of a target user corresponding to a target vehicle to be analyzed is obtained; then analyzing the historical vehicle driving data to obtain a driving habit time period corresponding to the target user; then, track data of the target vehicle in the driving habit time period is acquired; constructing corresponding initial track time sequence data based on the track data; carrying out normalization processing on the initial track time sequence data to obtain processed track time sequence data, and clustering all the track time sequence data based on a preset clustering model to obtain target clustering results respectively corresponding to the track time sequence data; and finally, generating an abnormal identification result of each track time sequence data based on the target clustering result. According to the embodiment of the application, the driving habit time period of the target user can be intelligently determined by carrying out data analysis on the historical vehicle driving data of the target user, so that the abnormal identification of the driving track of the user can be realized only by analyzing the track data of the target vehicle in the driving habit time period, the track data of the target vehicle in all time periods is not needed to be analyzed, the time pertinence of identifying the track data can be effectively improved, the workload of the abnormal analysis of the driving track of the target user is greatly reduced, and the processing efficiency of the abnormal analysis of the driving track is improved. In addition, the clustering model based on the k-shape algorithm is used for clustering track time sequence data of the target vehicle in a driving habit time period, so that the time sequence characteristics of the driving track can be considered, the change trend of the track time sequence data can be effectively clustered, the accurate analysis of the dynamic change of the driving track is realized, and the accuracy of the generated abnormal recognition result of the track time sequence data of the vehicle driving is effectively improved.
The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the artificial intelligence-based anomaly analysis method as described above.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
in the embodiment of the application, firstly, historical vehicle running data of a target user corresponding to a target vehicle to be analyzed is obtained; then analyzing the historical vehicle driving data to obtain a driving habit time period corresponding to the target user; then, track data of the target vehicle in the driving habit time period is acquired; constructing corresponding initial track time sequence data based on the track data; carrying out normalization processing on the initial track time sequence data to obtain processed track time sequence data, and clustering all the track time sequence data based on a preset clustering model to obtain target clustering results respectively corresponding to the track time sequence data; and finally, generating an abnormal identification result of each track time sequence data based on the target clustering result. According to the embodiment of the application, the driving habit time period of the target user can be intelligently determined by carrying out data analysis on the historical vehicle driving data of the target user, so that the abnormal identification of the driving track of the user can be realized only by analyzing the track data of the target vehicle in the driving habit time period, the track data of the target vehicle in all time periods is not needed to be analyzed, the time pertinence of identifying the track data can be effectively improved, the workload of the abnormal analysis of the driving track of the target user is greatly reduced, and the processing efficiency of the abnormal analysis of the driving track is improved. In addition, the clustering model based on the k-shape algorithm is used for clustering track time sequence data of the target vehicle in a driving habit time period, so that the time sequence characteristics of the driving track can be considered, the change trend of the track time sequence data can be effectively clustered, the accurate analysis of the dynamic change of the driving track is realized, and the accuracy of the generated abnormal recognition result of the track time sequence data of the vehicle driving is effectively improved.
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. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.
Claims (10)
1. An anomaly analysis method based on artificial intelligence is characterized by comprising the following steps:
acquiring historical vehicle driving data of a target user corresponding to a target vehicle to be analyzed;
analyzing the historical vehicle driving data to obtain a driving habit time period corresponding to the target user;
acquiring track data of the target vehicle in the driving habit time period;
constructing corresponding initial track time sequence data based on the track data; wherein the number of track timing data includes a plurality;
normalizing the initial track time sequence data to obtain processed track time sequence data;
clustering all the track time sequence data based on a preset clustering model to obtain target clustering results respectively corresponding to the track time sequence data; the clustering model is a model constructed and generated based on a k-shape algorithm;
and generating an abnormal identification result of each track time sequence data based on the target clustering result.
2. The abnormality analysis method based on artificial intelligence according to claim 1, wherein the step of analyzing the historical vehicle running data to obtain a driving habit period corresponding to the target user specifically includes:
Analyzing the historical vehicle running data to obtain the vehicle running times of the target user in each unit time period in a preset historical time period;
screening out appointed unit time periods of which the running times of the vehicle are greater than a preset time threshold value from all the unit time periods;
and integrating all the appointed unit time periods to obtain the driving habit time period of the target user.
3. The anomaly analysis method based on artificial intelligence of claim 1, wherein the step of constructing corresponding initial trajectory timing data based on the trajectory data specifically comprises:
acquiring a preset time division period;
the time division period is used as a time slice to carry out time sequence division processing on the track data, so as to obtain processed track data;
and taking the processed track data as the initial track time sequence data.
4. The abnormal analysis method based on artificial intelligence according to claim 1, wherein the step of clustering all the trajectory timing data based on a preset clustering model to obtain clustering results respectively corresponding to the trajectory timing data specifically comprises:
Clustering all the track time sequence data through the clustering model, and randomly selecting a preset number of first track time sequence data from all the track time sequence data to serve as clustering centroids;
traversing and calculating the shape distance between the cluster centroid and the second track time sequence data; the second track time sequence data are track time sequence data except the first track time sequence data in all the track time sequence data;
based on the shape distance and a preset proximity principle, distributing the second track time sequence data to clusters where cluster centroids closest to the second track time sequence data are located;
and for each allocated cluster, calculating the average value of all points in the cluster, taking the average value as a new centroid, and repeating the centroid updating process until the clustering result converges to obtain the target clustering result.
5. The artificial intelligence based anomaly analysis method of claim 1, wherein the clustering result includes a first cluster corresponding to a normal track category and a second cluster corresponding to an anomaly track category; the step of generating an anomaly identification result of each track time sequence data based on the target clustering result specifically comprises the following steps:
Acquiring first track time sequence data contained in the first cluster;
generating a first anomaly identification result corresponding to the first track time sequence data; the content of the first abnormal identification result comprises that the first track time sequence data belongs to an abnormal track;
acquiring second track time sequence data contained in the second cluster;
generating a second anomaly identification result corresponding to the second track time sequence data; the content of the second abnormal recognition result includes that the second track time sequence data belongs to a normal track.
6. The artificial intelligence based anomaly analysis method of claim 1, further comprising, after the step of generating anomaly identification results for each of the trajectory timing data based on the target cluster results:
screening third track time sequence data belonging to an abnormal track from all the track time sequence data based on the abnormal identification result;
acquiring an abnormal analysis result corresponding to the third track time sequence data, which is input by a first management user;
and generating a target abnormality identification result corresponding to the third track time sequence data based on the abnormality analysis result.
7. The artificial intelligence based anomaly analysis method of claim 1, further comprising, after the step of generating anomaly identification results for each of the trajectory timing data based on the target cluster results:
acquiring a preset report template;
acquiring preset treatment measure information;
generating a driving analysis report corresponding to the target user based on the abnormality recognition result and the report template;
acquiring communication information of a second management user;
and pushing the driving analysis report and the processing measure information to the second management user based on the communication information.
8. An artificial intelligence based anomaly analysis device, comprising:
the first acquisition module is used for acquiring historical vehicle driving data of a target user corresponding to a target vehicle to be analyzed;
the analysis module is used for analyzing the historical vehicle driving data to obtain a driving habit time period corresponding to the target user;
the second acquisition module is used for acquiring track data of the target vehicle in the driving habit time period;
the construction module is used for constructing corresponding initial track time sequence data based on the track data; wherein the number of track timing data includes a plurality;
The first processing module is used for carrying out normalization processing on the initial track time sequence data to obtain processed track time sequence data;
the second processing module is used for clustering all the track time sequence data based on a preset clustering model to obtain target clustering results respectively corresponding to the track time sequence data; the clustering model is a model constructed and generated based on a k-shape algorithm;
the first generation module is used for generating an abnormal identification result of each track time sequence data based on the target clustering result.
9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the artificial intelligence based anomaly analysis method of any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the artificial intelligence based anomaly analysis method of any one of claims 1 to 7.
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