CN117668600A - Method, device and equipment for processing abnormal operation data of Internet of vehicles and storage medium - Google Patents
Method, device and equipment for processing abnormal operation data of Internet of vehicles and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for processing abnormal operation data of the Internet of vehicles. Wherein the method comprises the following steps: and acquiring the vehicle network data to be processed, wherein the vehicle network data to be processed comprises various vehicle operation data. Determining abnormal operation data in the vehicle operation data, determining a plurality of target associated data corresponding to the abnormal operation data in the to-be-processed vehicle networking data, and constructing a query data set based on the plurality of target associated data; inquiring a pre-established data comparison table based on the inquiry data set to obtain a comparison data set matched with the inquiry data set; and acquiring standard operation data corresponding to the abnormal operation data in the comparison data set as target replacement data, and replacing the abnormal operation data in the to-be-processed internet of vehicles data with the target replacement data. The problem that the value of data is excavated because the accuracy of the abnormal data processing method in the prior art is low is solved. The beneficial effect of improving the optimization accuracy of the abnormal data of the Internet of vehicles is achieved.
Description
Technical Field
The present invention relates to the field of vehicle data processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for processing abnormal operation data of a vehicle networking.
Background
With the rapid development of internet of vehicles big data technology, potential values in data are gradually mined and applied by people. However, the abnormal data in the data directly affects the analysis accuracy of the data application, and how to effectively process the abnormal data in the data is a problem to be solved at present.
When the traditional mode is used for processing abnormal data, the data is filled in modes such as mode, a box division method and the like. The method has the problem of low accuracy of filling data, and when the signal value changes greatly, the gap between the filled data and the true value by using the method is also larger.
Therefore, the processing method in the prior art is used for processing the abnormal data, and the physical characteristics represented by the data cannot be truly reflected. Further, problems that the application analysis of the data is interfered and even the value mining of the subsequent data is affected easily occur.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for processing abnormal operation data of the Internet of vehicles, which are used for solving the technical problems that the processing precision of the abnormal operation data of the Internet of vehicles is low and the application analysis of the data is easy to interfere in the prior art.
According to an aspect of the present invention, there is provided a vehicle networking abnormal operation data processing method, including:
acquiring vehicle network data to be processed, wherein the vehicle network data to be processed comprises various vehicle operation data, and the vehicle operation data comprises vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters;
determining abnormal operation data in the vehicle operation data, determining a plurality of target associated data corresponding to the abnormal operation data in the to-be-processed vehicle networking data, and constructing a query data set based on the plurality of target associated data;
inquiring a pre-established data comparison table based on the inquiry data set to obtain a comparison data set matched with the inquiry data set, wherein the data comparison table is used for storing a plurality of comparison data sets corresponding to vehicle operation data, and each comparison data set comprises a plurality of standard operation data corresponding to associated vehicle operation data;
and acquiring standard operation data corresponding to the abnormal operation data in the comparison data set as target replacement data, and replacing the abnormal operation data in the to-be-processed internet of vehicles data with the target replacement data.
According to another aspect of the present invention, there is provided an abnormal operation data processing apparatus for internet of vehicles, the apparatus comprising:
The system comprises a to-be-processed data acquisition module, a processing module and a processing module, wherein the to-be-processed data acquisition module is used for acquiring to-be-processed vehicle network data, the to-be-processed vehicle network data comprise various vehicle operation data, and the vehicle operation data comprise vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters;
the query data set construction module is used for determining abnormal operation data in vehicle operation data, determining a plurality of target associated data corresponding to the abnormal operation data in the to-be-processed vehicle networking data and constructing a query data set based on the plurality of target associated data;
the control data set determining module is used for inquiring a pre-established data control table based on the inquiry data set to obtain a control data set matched with the inquiry data set, wherein the data control table is used for storing a plurality of control data sets corresponding to vehicle operation data, and each control data set comprises a plurality of standard operation data corresponding to associated vehicle operation data;
the abnormal operation data replacing module is used for acquiring standard operation data corresponding to the abnormal operation data in the comparison data set as target replacement data and replacing the abnormal operation data in the to-be-processed internet of vehicles data with the target replacement data. According to another aspect of the present invention, there is provided an electronic apparatus including:
At least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the vehicle networking abnormal operation data processing method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a method for processing abnormal operation data of the internet of vehicles according to any embodiment of the present invention.
According to the technical scheme, firstly, vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters are obtained by obtaining the to-be-processed vehicle networking data comprising various vehicle operation data. And then determining abnormal operation data in the vehicle operation data, determining a plurality of target associated data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data, and constructing a query data set based on the plurality of target associated data. And then, inquiring a pre-established data comparison table based on the inquiry data set to acquire a comparison data set matched with the inquiry data set, acquiring standard operation data corresponding to the abnormal operation data in the comparison data set as target replacement data, and replacing the abnormal operation data in the to-be-processed internet-of-vehicles data with the target replacement data. The problem of the abnormal data processing method in the prior art that the data value mining is interfered due to low accuracy is solved, and the beneficial effects of improving the optimization accuracy of abnormal data of the Internet of vehicles and further improving the data application accuracy are achieved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for processing abnormal operation data of internet of vehicles according to a first embodiment of the present invention;
fig. 2 is a flowchart of a method for processing abnormal operation data of internet of vehicles according to a second embodiment of the present invention;
fig. 3 is a flowchart of a preferred abnormal operation data processing method for internet of vehicles according to a third embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an abnormal operation data processing device for internet of vehicles according to a fourth embodiment of the present invention;
fig. 5 is a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and "object" in the description of the present invention and the claims and the above drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for processing abnormal operation data of internet of vehicles, where the method may be executed by an abnormal operation data processing device of internet of vehicles, the abnormal operation data processing device of internet of vehicles may be implemented in a form of hardware and/or software, and the abnormal operation data processing device of internet of vehicles may be configured in an electronic device. As shown in fig. 1, the method includes:
s110, acquiring vehicle networking data to be processed, wherein the vehicle networking data to be processed comprises various vehicle operation data, and the vehicle operation data comprises vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters.
In this embodiment, the to-be-processed internet of vehicles data may be to-be-processed data that is returned to the server through the vehicle-mounted device in the running process of the vehicle. The vehicle network data to be processed may include a variety of vehicle operation data. Further, the vehicle operating data may include various types of vehicle operating parameters. The vehicle operating parameters are understood to be parameters for distinguishing vehicle travel data of different data dimensions or for distinguishing different types of vehicle travel data. For example, vehicle operation data may include one or more of vehicle operating parameters such as engine speed, travel speed, and exhaust temperature of the vehicle. The vehicle operation value corresponding to the vehicle operation parameter may be understood as a specific value adopted when the vehicle operation parameter is operated, and is associated with the vehicle running information. For example, when the vehicle operating parameter is engine speed, its corresponding vehicle operating value may be a revolutions per minute; when the vehicle running parameter is running speed, the corresponding vehicle running value can be B kilometers per hour; when the vehicle operation parameter is the exhaust temperature, the corresponding vehicle operation value can be C ℃; etc.
S120, determining abnormal operation data in the vehicle operation data, determining a plurality of target associated data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data, and constructing a query data set based on the plurality of target associated data.
In this embodiment, the abnormal operation data in the vehicle operation data may be numerical value abnormal data obtained in the case of operation failure, communication interruption, acquisition missing or acquisition error in the data acquisition process of the vehicle-mounted device. The plurality of target-related data corresponding to the abnormal operation data may be operation data corresponding to a parameter associated with a parameter of the abnormal operation data. The query data set may be constructed based on a plurality of target association data, which are associated with the abnormal operation data as one data set, for querying the database for the numerical value of the abnormal operation data.
For example, a vehicle gear D is set 0 Lower engine speed R 0 With vehicle speed V 0 Presenting the associated characteristics. Thus, the data set (D 0 ，V 0 ) Can be used as the engine speed R 0 Query data set in case of data anomalies. It will be appreciated that during vehicle operation, the engine speed increases rapidly as the engine output power increases, while the vehicle speed increases relatively slowly with respect to the engine speed, with a lag response time Δt. Thus, at T 0 When abnormal operation data occurs at any time and an inquiry data set associated with the abnormal operation data is constructed, T can be selected 0 -constructing a query data set from a corresponding plurality of operational data at time Δt as a plurality of target-associated data.
Optionally, the determining the target associated data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data includes: determining associated operation data corresponding to abnormal operation data in the to-be-processed internet of vehicles data as reference operation data; for each reference operation data, determining change delay data between the reference operation data and the abnormal operation data, and acquiring historical operation data corresponding to the reference operation data from historical vehicle network data based on the change delay data as target associated data corresponding to the abnormal operation data.
In the present embodiment, the reference operation data may be a vehicle operation value corresponding to a vehicle operation parameter associated with the abnormal operation data in the case where the vehicle operation parameter of the abnormal operation data is known. For example, in Table 1 below, at T 6 At that time, when abnormality occurs in the shift data D, the vehicle speed and engine speed data set (V, R) may be used as reference running data. As can be seen from the example of Table 1 below, the gear is at T 2 The time varies, and the vehicle speed and the engine speed vary with different degrees of delay. The change delay data may delay the change in the value of one vehicle operating parameter with the change in the value of the associated vehicle operating parameter, and then sum the delay times as the change approaches stability. In the following table, the change delay data may be T for vehicle speed 4 -T 1 . The variable delay data may be T for engine speed 5 -T 1 。
The historical internet of vehicles data may be all internet of vehicles data that is up to the time of occurrence of the abnormal operation data. The historical operating data may be vehicle operating data in historical internet of vehicles data. The historical operation data corresponding to the reference operation data is obtained from the historical vehicle network data based on the change delay data, and can be used as target associated data corresponding to the abnormal operation data, and corresponding data in the historical operation data can be obtained as target associated data according to the maximum value in all the change delay data. In the following table, T may be taken as 5 Vehicle speed V at time 5 And engine speed data R 5 As target association data. By the technical scheme of the embodiment, unsteady state time data sources (for example, T 2 T is as follows 3 The operation data source under the moment) on the abnormal operation data processing of the internet of vehicles, and the accuracy of the abnormal data processing is improved.
| Time of day/operation data | Gear position | Vehicle speed | Engine speed |
| T 1 | D 1 | V 1 (10km/h) | R 1 (1500r/min) |
| T 2 | D 2 | V 2 (10km/h) | R 2 (1500r/min) |
| T 3 | D 2 | V 3 (30km/h) | R 3 (1500r/min) |
| T 4 | D 2 | V 4 (30km/h) | R 4 (1000r/min) |
| T 5 | D 2 | V 5 (30km/h) | R 5 (1000r/min) |
| T 6 | D 9999 (abnormality) | V 6 | R 6 |
TABLE 1
Optionally, determining the associated operation data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data includes: and inquiring the associated operation data corresponding to the abnormal operation data based on a pre-established associated relation table, and determining the associated operation data corresponding to the abnormal operation data from the to-be-processed internet of vehicles data based on the vehicle operation parameters corresponding to the associated operation data, wherein the associated relation table stores the association degree between the vehicle operation parameters.
In the present embodiment, the association relation table may be a table indicating association relations between respective vehicle operation parameters in the vehicle operation data. For example, for one vehicle operation parameter, a regression analysis prediction model or other modes may be used to extract the association relationship between operation parameters. Then, all the operation parameters and the corresponding association degrees which have association relation with the operation parameters can be identified and stored in an association relation table. In the vehicle networking data to be processed, the operation data which is not associated with the abnormal operation data can not be stored in the association relation table, so that the query speed and the data processing efficiency can be improved.
Optionally, determining the change delay data between the reference operation data and the abnormal operation data includes: and acquiring the variable delay data between the reference operation data and the abnormal operation data by inquiring a pre-constructed delay data table.
In this embodiment, the delay data table may be a data table commonly constructed by all the reference operation data obtained from the historical internet of vehicles data in the foregoing embodiments and the time node corresponding to the reference operation data until the occurrence time of the abnormal operation data.
S130, inquiring a pre-established data comparison table based on the inquiry data set to obtain a comparison data set matched with the inquiry data set, wherein the data comparison table is used for storing a plurality of comparison data sets corresponding to vehicle operation data, and each comparison data set comprises a plurality of standard operation data corresponding to associated vehicle operation data.
In this embodiment, the standard operation data corresponding to the vehicle operation data may be vehicle operation data in an ideal case. For example, ideal data of the gear, the vehicle speed, and the engine speed may be used as the standard operation data. Alternatively, the standard operation data may be vehicle operation data meeting ideal working conditions and unified standards obtained by means of mean value calculation and the like after large-scale sampling of internet of vehicles is performed. The plurality of standard operating data may constitute a comparison data set, wherein the plurality of standard operating data are associated with each other, corresponding to operating parameters of the plurality of associated vehicle operating data, respectively. The plurality of control data sets may be stored by a data lookup table. In the pre-established data comparison table, the query is performed based on the query data set, and a comparison data set matched with the query data set can be obtained. Further, standard operation data corresponding to the query data group is queried in the comparison data group.
S140, standard operation data corresponding to the abnormal operation data in the comparison data set is obtained to serve as target replacement data, and the abnormal operation data in the to-be-processed internet of vehicles data is replaced with the target replacement data.
In this embodiment, the target replacement data may be standard operation data in which the operation parameter is the same as the operation parameter of the abnormal operation data in the comparison data group. For example, in the case where the abnormal operation data is the engine speed R, the query data set may be (D, V), and if the comparison data set matching (D, V) is (D 1 ，V 1 ，R 1 ) At this time, (D) 1 ，V 1 ，R 1 ) R in (a) 1 Can be the target replacement data corresponding to the abnormal operation data R. Abnormal operation data R in the to-be-processed internet-of-vehicles data can be replaced by target replacement data R 1 . That is, the vehicle network data to be processed becomes (D, V, R) 1 )。
According to the technical scheme, vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters are acquired by acquiring the to-be-processed internet-of-vehicles data. Then determining abnormal operation data in the vehicle operation data; inquiring associated operation data corresponding to the abnormal operation data based on a pre-established associated relation table, and determining the associated operation data corresponding to the abnormal operation data from the to-be-processed internet of vehicles data based on vehicle operation parameters corresponding to the associated operation data as reference operation data; for each reference operation data, determining change delay data between the reference operation data and the abnormal operation data, acquiring historical operation data corresponding to the reference operation data from historical vehicle network data based on the change delay data, serving as target association data corresponding to the abnormal operation data, and constructing a query data set based on a plurality of target association data. And then inquiring a pre-established data comparison table based on the inquiry data set so as to acquire a comparison data set matched with the inquiry data set. And finally, standard operation data corresponding to the abnormal operation data in the comparison data set is obtained to serve as target replacement data, and the abnormal operation data in the to-be-processed internet of vehicles data is replaced with the target replacement data. The standard operation data is obtained by sampling the large data of the Internet of vehicles in a large scale, and accords with the ideal working condition and the unified standard. Therefore, the abnormal operation data processing method of the Internet of vehicles solves the problem that the abnormal data processing method in the prior art is low in accuracy so as to interfere with data value mining, and has the beneficial effects of improving the optimization accuracy of abnormal data of the Internet of vehicles and further improving the data application accuracy.
Example two
Fig. 2 is a flowchart of a method for processing abnormal operation data of internet of vehicles according to a second embodiment of the present invention, where the method for constructing an initial comparison data set based on the key operation data and the associated operation data is specifically described based on the above embodiments. Reference is made to the description of this example for a specific implementation. The technical features that are the same as or similar to those of the foregoing embodiments are not described herein.
As shown in fig. 2, the method includes:
s210, acquiring the to-be-processed internet-of-vehicles data.
S220, determining abnormal operation data in the vehicle operation data, determining a plurality of target associated data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data, and constructing a query data set based on the plurality of target associated data.
S230, acquiring sample vehicle network data corresponding to a plurality of vehicles, wherein the sample vehicle network data comprises various vehicle operation data, and the vehicle operation data comprises vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters.
In this embodiment, since the vehicle condition of each vehicle in the same kind of vehicle may be different, the vehicle operation parameters and the vehicle operation values may be different between different kinds of vehicles. In order to ensure the reliability of the abnormal data processing method, vehicles can be classified according to vehicle types, and vehicle networking data of a plurality of vehicles can be obtained as sample vehicle networking data aiming at each vehicle type.
S240, respectively constructing an initial data table corresponding to the vehicles based on the sample Internet of vehicles data corresponding to each vehicle, and determining a data comparison table based on a plurality of initial data tables.
In this embodiment, the initial data table may be a vehicle operation data table generated by using internet of vehicles data of one vehicle as a data source. Based on a plurality of initial data tables, the vehicle operation data in the initial data tables are subjected to modes of median solving, expected value solving, mean solving and the like, standard operation data corresponding to the vehicle operation data can be determined, and then a data comparison table is constructed based on the standard operation data.
Optionally, the constructing an initial data table based on the sample internet of vehicles data corresponding to the vehicle includes: acquiring the vehicle operation data to be analyzed in the sample internet of vehicles data as key operation data, and taking other vehicle operation data except the key operation data in the internet of vehicles data to be processed as operation data to be processed; determining the data association degree between the key operation data and the operation data to be processed, and determining associated operation data corresponding to the key operation data based on the data association degree; and constructing an initial comparison data set based on the key operation data and the associated operation data, and establishing an initial data table based on the initial comparison data set.
In this embodiment, the key operation data may be one of the vehicle operation parameters in the vehicle operation data to be analyzed. The operation data to be processed may be the remaining vehicle operation data except for the vehicle operation parameters corresponding to the key operation data. The control data set is constructed based on the key operation data and the associated operation data, and the initial control data set can be constructed based on the key operation data and all the associated operation data obtained after the data association degree calculation.
Optionally, the determining the data association degree between the key operation data and the operation data to be processed includes: and determining the data association degree between the key operation data and the operation data to be processed by adopting a stepwise regression method.
In this embodiment, a stepwise regression method is used to determine the data association degree between the key operation data and the operation data to be processed, which may be that the key operation data and the operation data to be processed are extracted by using a stepwise regression method to extract the association relationship between fields. The operational data to be processed can then be evaluated by t-test and f-test, with the evaluation index being AIC (Akaike information criterion, red pool information content criterion).
Specifically, the associated operation data corresponding to the key operation data is determined based on the data association degree, and the vehicle operation data is taken as the associated operation data corresponding to the key operation data when the data association degree is smaller than or equal to a preset association degree threshold value. Along the above example, an AIC threshold may be set according to the actual situation, and it is determined that the correlation between the to-be-processed operation data and the critical operation data is strong when AIC is lower than the threshold. And then all relevant operation data in the operation data to be processed can be screened out.
It should be noted that other existing algorithms may be used to determine the degree of data association between the critical operation data and the operation data to be processed. In some cases, the vehicle operation data is used as the associated operation data corresponding to the key operation data when the data association degree is larger than or equal to a preset association degree threshold value. And the vehicle operation data with higher correlation with the key operation data is selected as the correlation operation data corresponding to the key operation data. Typically, the associated operational data varies as critical operational data varies.
Optionally, after determining the associated operation data corresponding to the key operation data based on the data association degree, constructing an association relation table according to the key operation data and the associated operation data and the data association degree. The method has the advantages that repeated determination of the data association degree between the same vehicle operation data can be avoided, and the efficiency of acquiring the data association degree between the vehicle operation data in an application scene is improved.
Optionally, the constructing an initial control data set based on the key operation data and the associated operation data includes: respectively determining the change delay data between each key operation data and the associated operation data, and determining an acquisition time window based on the maximum value in the change delay data; and acquiring the key operation data and the associated operation data based on the acquisition time window, and constructing an initial comparison data group based on the standard operation data, wherein the key operation data and the associated operation data are used as standard operation data corresponding to the vehicle operation data.
In this embodiment, the collection time window may be a plurality of values of associated operation data, where a delay change occurs along with a change in a value of one key operation data, and then when all the values of the associated operation data change tend to be stable, the maximum delay time elapsed, that is, the maximum value in the change delay data. Exemplary, the key operation data and the associated operation data are acquired based on the acquisition time window, and the key operation data and the associated operation data are used as standard operation data corresponding to the vehicle operation data, which may be in the process of eliminating the acquisition time window The data is used as standard operation data corresponding to the vehicle operation data. For example, T in Table 1 above may be used 1 And T 5 And the key operation data and the associated operation data at the moment are used as standard operation data corresponding to the vehicle operation data. T which can be based on 1 And T 5 And constructing an initial comparison data set by the corresponding standard operation data at the moment.
Optionally, after determining the variable delay data between each of the key operation data and the associated operation data, storing the variable delay data correspondingly based on the key operation data, the associated operation data and the variable delay data, so as to obtain a delay data table.
In this embodiment, as shown in table 2 below, the delay data table may be a data table formed by key operation data, associated operation data and variable delay data after removing irrelevant data in the process of collecting a time window.
| Varying delay data/run data | Gear position | Vehicle speed | Engine speed |
| D 1 | V 1 (10km/h) | R 1 (1500r/min) | |
| ΔT 1 | D 2 | V 4 (30km/h) | R 4 (1000r/min) |
| ΔT 2 | D 2 | V 5 (30km/h) | R 5 (1000r/min) |
TABLE 2
S250, inquiring a pre-established data comparison table based on the inquiring data set to obtain a comparison data set matched with the inquiring data set, wherein the data comparison table is used for storing a plurality of comparison data sets corresponding to the vehicle operation data, and each comparison data set comprises a plurality of associated standard operation data corresponding to the vehicle operation data.
S260, standard operation data corresponding to the abnormal operation data in the comparison data set is obtained to serve as target replacement data, and the abnormal operation data in the to-be-processed internet of vehicles data is replaced by the target replacement data.
According to the technical scheme, vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters are acquired by acquiring the to-be-processed internet-of-vehicles data. And then determining abnormal operation data in the vehicle operation data, determining a plurality of target associated data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data, and constructing a query data set based on the plurality of target associated data. Further, sample internet of vehicles data corresponding to a plurality of vehicles are obtained, an initial data table corresponding to the vehicles is built based on the sample internet of vehicles data corresponding to each vehicle, and a data comparison table is determined based on the initial data tables; and inquiring a pre-established data comparison table based on the inquiry data set so as to acquire a comparison data set matched with the inquiry data set. And finally, standard operation data corresponding to the abnormal operation data in the comparison data set is obtained to serve as target replacement data, and the abnormal operation data in the to-be-processed internet of vehicles data is replaced by the target replacement data. The problem of the abnormal data processing method in the prior art that the data value mining is interfered due to low accuracy is solved, and the beneficial effects of improving the optimization accuracy of abnormal data of the Internet of vehicles and further improving the data application accuracy are achieved.
Example III
Fig. 3 is a flowchart of a method for processing abnormal operation data of internet of vehicles according to a third embodiment of the present invention. As shown in fig. 3, the process flow includes:
s301: and (3) deriving vehicle operation data from the data lake or the database, and storing the vehicle operation data as sample internet of vehicles data as a new data table.
The sample internet of vehicles data can comprise all data acquired by vehicle operation. The sample internet of vehicles data can preserve the original format of the data. In this embodiment, the data lake or database may be equivalent to the data to be processed that is returned to the server through the vehicle-mounted device during the running of the vehicle in each of the foregoing embodiments.
S302: and determining key operation data in the new data table, acquiring a to-be-processed field y corresponding to the key operation data, and determining the association relation between the to-be-processed field y and fields corresponding to other vehicle operation data in the new data table. Specifically, a stepwise regression method can be adopted to determine relevance of all fields acquired by the Internet of vehicles, fields corresponding to other vehicle operation data are introduced one by one while the field y to be processed is introduced, the introduced fields are evaluated through t test and f test, the evaluation index is AIC, and the AIC is considered to have strong relevance if lower than a preset relevance threshold. Screening out the related field x 1 ,x 2 ,x 3 ...x n And performing the next data processing. The preset association threshold value can be adjusted according to actual conditions. The critical operational data may be any vehicle operational data in the sample internet of vehicles data. Specifically, each vehicle operation parameter in the new data table can be respectively transported to the corresponding vehicleThe line data serves as key operation data so as to process abnormal operation data corresponding to each kind of vehicle operation data.
In this embodiment, the field y to be processed may be equivalent to the key operation data in the foregoing embodiments. Related field x 1 ,x 2 ,x 3 ...x n May correspond to the associated operational data in the previous embodiments.
S303: determining a field y to be processed and a related field x according to the selected related field, namely the related operation data 1 ,x 2 ,x 3 ...x n Is a time delay of (a). By single factor analysis, i.e. under the condition of unchanged other parameters, the y and the x are independently studied 1 ,x 2 ,x 3 ...x n The specific delay relation of each field is determined by the following specific method:
screening the increment interval of the y value, and searching the corresponding time t of the change rate delta y of the y value less than or equal to 0 s1 And x 1 The rate of change Δx of (a) 1 Time t less than or equal to 0 s2 Time difference t of (2) 1 ，t 1 I.e. the field y to be processed and the associated field x 1 Is a time delay of (a). Similarly, the associated field x can be obtained 1 The delay time of all the associated fields except the one, namely the change delay data.
S304, determining the actual relation between the field y to be processed and other fields in a stable state. First, a steady state determination window Δt=t is determined max Wherein t is max ＝max(t 1 ,t 2 ...), i.e. the maximum t of the delay times of y and other associated fields determined in the previous step max . Based on the collected sample Internet of vehicles data, statistics x is performed within a time window delta t 1 ,x 3 ...x m And (3) corresponding to the y value under the combination of the field values to form a data comparison table under a steady state, and then carrying out abnormal operation data processing based on the table.
In this embodiment, the steady state determination window may correspond to the acquisition time window in each of the foregoing embodiments. The data lookup table in steady state may correspond to the control data set or the initial control data set in the previous embodiments.
S305: acquiring to-be-processed Internet of vehiclesWhen the data identifies that abnormal operation data occurs in the field y to be processed, determining the time t of the occurrence of the abnormal operation data a According to the field y to be processed and other fields x 1 ,x 3 ...x m Time delay relation t of (2) 1 、t 3 .., recombining the data values to form a new data listThat is, the data set is searched, and the data lookup table is searched by using the data set to obtain the corresponding field y value in the state.
According to the technical scheme, vehicle operation data are derived from the data lake or the database, and the to-be-processed Internet of vehicles data are obtained. And then determining the association relation between the field y of the abnormal data and other acquired fields by adopting a stepwise regression method. And determining the delay time of the field y to be processed and other related fields according to the screened fields. And the maximum value in the delay time of the determined y and other associated fields is used as a steady state judgment window. And in the time window, counting the corresponding y value under the combination of the related field values to form a data comparison table under a steady state. And recombining the data values according to the delay relation between the field y to be processed and other fields to form a new data list, and inquiring a data comparison table by using the data list to obtain the corresponding field y value in the state. The problem of the abnormal data processing method in the prior art that the data value mining is interfered due to low accuracy is solved, and the beneficial effects of improving the optimization accuracy of abnormal data of the Internet of vehicles and further improving the data application accuracy are achieved.
Example IV
Fig. 4 is a schematic structural diagram of an abnormal operation data processing device for internet of vehicles according to a fourth embodiment of the present invention. As shown in fig. 4, the apparatus includes: a pending data acquisition module 410, a query data set construction module 420, a control data set determination module 430, and an abnormal operation data replacement module 440.
The system comprises a to-be-processed data acquisition module 410, a processing module and a processing module, wherein the to-be-processed data acquisition module is used for acquiring to-be-processed internet-of-vehicles data, wherein the to-be-processed internet-of-vehicles data comprises various vehicle operation data, and the vehicle operation data comprises vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters;
a query data set construction module 420, configured to determine abnormal operation data in the vehicle operation data, determine a plurality of target associated data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data, and construct a query data set based on the plurality of target associated data;
a comparison data set determining module 430, configured to query a pre-established data comparison table based on the query data set to obtain a comparison data set that matches the query data set, where the data comparison table is configured to store a plurality of comparison data sets corresponding to the vehicle operation data, and each of the comparison data sets includes a plurality of associated standard operation data corresponding to the vehicle operation data;
And an abnormal operation data replacing module 440, configured to obtain standard operation data corresponding to the abnormal operation data in the comparison data set as target replacement data, and replace the abnormal operation data in the to-be-processed internet of vehicles data with the target replacement data.
According to the technical scheme, firstly, vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters are obtained by obtaining the to-be-processed vehicle networking data comprising various vehicle operation data. And then determining abnormal operation data in the vehicle operation data, determining a plurality of target associated data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data, and constructing a query data set based on the plurality of target associated data. And then, inquiring a pre-established data comparison table based on the inquiry data set to acquire a comparison data set matched with the inquiry data set, acquiring standard operation data corresponding to the abnormal operation data in the comparison data set as target replacement data, and replacing the abnormal operation data in the to-be-processed internet-of-vehicles data with the target replacement data. The problem of the abnormal data processing method in the prior art that the data value mining is interfered due to low accuracy is solved, and the beneficial effects of improving the optimization accuracy of abnormal data of the Internet of vehicles and further improving the data application accuracy are achieved.
On the basis of the above technical solution, optionally, the query data set construction module 420 includes a target association data determining unit.
The target associated data determining unit is used for determining associated operation data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data as reference operation data; and determining change delay data between the reference operation data and the abnormal operation data according to each piece of reference operation data, and acquiring historical operation data corresponding to the reference operation data from historical vehicle network data based on the change delay data to serve as target associated data corresponding to the abnormal operation data.
Based on the above technical solution, further, the target associated data determining unit is specifically configured to: and inquiring the associated operation data corresponding to the abnormal operation data based on a pre-constructed associated relation table, and determining the associated operation data corresponding to the abnormal operation data from the to-be-processed internet-of-vehicles data based on the vehicle operation parameters corresponding to the associated operation data, wherein the associated relation table stores the association degree between the vehicle operation parameters.
Based on the above technical solution, further, the target associated data determining unit is specifically configured to: and acquiring the variable delay data between the reference operation data and the abnormal operation data by inquiring a pre-constructed delay data table.
Based on the above technical solution, optionally, the query data set construction module 420 includes a data look-up table determining unit.
The system comprises a data comparison table determining unit, a data comparison table determining unit and a data processing unit, wherein the data comparison table determining unit is used for obtaining sample vehicle network data corresponding to a plurality of vehicles, the sample vehicle network data comprise various vehicle operation data, and the vehicle operation data comprise vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters; and respectively constructing an initial data table corresponding to the vehicles based on the sample Internet of vehicles data corresponding to each vehicle, and determining a data comparison table based on a plurality of initial data tables.
On the basis of the technical scheme, the data comparison table determining unit further comprises an initial data table determining subunit.
The initial data table determining subunit is used for acquiring the vehicle operation data to be analyzed in the sample internet of vehicles data as key operation data, and taking other vehicle operation data except the key operation data in the internet of vehicles data to be processed as operation data to be processed; determining the data association degree between the key operation data and the operation data to be processed, and determining associated operation data corresponding to the key operation data based on the data association degree; and constructing an initial comparison data set based on the key operation data and the associated operation data, and establishing an initial data table based on the initial comparison data set.
On the basis of the technical scheme, optionally, the target associated data determining unit is used for determining the data association degree between the key operation data and the operation data to be processed by adopting a stepwise regression method.
On the basis of the technical scheme, the initial data table determining subunit is further specifically configured to determine change delay data between each piece of key operation data and the associated operation data, and determine an acquisition time window based on a maximum value in the change delay data; and acquiring the key operation data and the associated operation data based on the acquisition time window, and constructing an initial comparison data group based on the standard operation data, wherein the key operation data and the associated operation data are used as standard operation data corresponding to the vehicle operation data.
On the basis of the above technical solution, optionally, the target associated data determining unit is further configured to:
after the related operation data corresponding to the key operation data is determined based on the data association degree, constructing an association relation table according to the key operation data and the related operation data and the data association degree; and/or after determining the change delay data between each key operation data and the associated operation data respectively, correspondingly storing the key operation data, the associated operation data and the change delay data to obtain a delay data table.
The device for processing the abnormal operation data of the Internet of vehicles provided by the embodiment of the invention can execute the method for processing the abnormal operation data of the Internet of vehicles provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example five
Fig. 5 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as the internet of vehicles abnormal operation data processing method.
In some embodiments, the internet of vehicles abnormal operation data processing method may be implemented as a computer program tangibly embodied on a computer readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the internet of vehicles abnormal operation data processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the vehicle networking abnormal operation data processing method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The method for processing the abnormal operation data of the Internet of vehicles is characterized by comprising the following steps of:
acquiring vehicle network data to be processed, wherein the vehicle network data to be processed comprises various vehicle operation data, and the vehicle operation data comprises vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters;
determining abnormal operation data in the vehicle operation data, determining a plurality of target associated data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data, and constructing a query data set based on the plurality of target associated data;
Inquiring a pre-established data comparison table based on the inquiry data set to obtain a comparison data set matched with the inquiry data set, wherein the data comparison table is used for storing a plurality of comparison data sets corresponding to the vehicle operation data, and each comparison data set comprises a plurality of associated standard operation data corresponding to the vehicle operation data;
and acquiring standard operation data corresponding to the abnormal operation data in the comparison data set as target replacement data, and replacing the abnormal operation data in the to-be-processed internet of vehicles data with the target replacement data.
2. The method according to claim 1, wherein the determining target association data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data includes:
determining associated operation data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data as reference operation data;
and determining change delay data between the reference operation data and the abnormal operation data according to each piece of reference operation data, and acquiring historical operation data corresponding to the reference operation data from historical vehicle network data based on the change delay data to serve as target associated data corresponding to the abnormal operation data.
3. The method according to claim 2, wherein the determining the associated operation data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data includes:
and inquiring the associated operation data corresponding to the abnormal operation data based on a pre-constructed associated relation table, and determining the associated operation data corresponding to the abnormal operation data from the to-be-processed internet-of-vehicles data based on the vehicle operation parameters corresponding to the associated operation data, wherein the associated relation table stores the association degree between the vehicle operation parameters.
4. The method of claim 2, wherein said determining the change delay data between the reference operational data and the abnormal operational data comprises:
and acquiring the variable delay data between the reference operation data and the abnormal operation data by inquiring a pre-constructed delay data table.
5. The method of claim 1, further comprising, prior to said querying a pre-established data lookup table based on said query data set:
acquiring sample vehicle networking data corresponding to a plurality of vehicles, wherein the sample vehicle networking data comprises various vehicle operation data, and the vehicle operation data comprises vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters;
And respectively constructing an initial data table corresponding to the vehicles based on the sample Internet of vehicles data corresponding to each vehicle, and determining a data comparison table based on a plurality of initial data tables.
6. The method of claim 5, wherein the constructing an initial data table based on the sample internet of vehicles data corresponding to the vehicle comprises:
acquiring the vehicle operation data to be analyzed in the sample internet of vehicles data as key operation data, and taking other vehicle operation data except the key operation data in the internet of vehicles data to be processed as operation data to be processed;
determining the data association degree between the key operation data and the operation data to be processed, and determining associated operation data corresponding to the key operation data based on the data association degree;
and constructing an initial comparison data set based on the key operation data and the associated operation data, and establishing an initial data table based on the initial comparison data set.
7. The method of claim 6, wherein the determining a degree of data association between the critical operational data and the operational data to be processed comprises:
And determining the data association degree between the key operation data and the operation data to be processed by adopting a stepwise regression method.
8. The method of claim 6, wherein said constructing an initial set of control data based on said critical operational data and said associated operational data comprises:
respectively determining the change delay data between each key operation data and the associated operation data, and determining an acquisition time window based on the maximum value in the change delay data;
and acquiring the key operation data and the associated operation data based on the acquisition time window, and constructing an initial comparison data group based on the standard operation data, wherein the key operation data and the associated operation data are used as standard operation data corresponding to the vehicle operation data.
9. The method as recited in claim 8, further comprising:
after the related operation data corresponding to the key operation data is determined based on the data association degree, constructing an association relation table according to the key operation data and the related operation data and the data association degree; and/or the number of the groups of groups,
after the change delay data between each key operation data and the associated operation data are respectively determined, correspondingly storing the key operation data, the associated operation data and the change delay data to obtain a delay data table.
10. An abnormal operation data processing device for internet of vehicles, comprising:
the system comprises a to-be-processed data acquisition module, a processing module and a processing module, wherein the to-be-processed data acquisition module is used for acquiring to-be-processed vehicle network data, the to-be-processed vehicle network data comprise various vehicle operation data, and the vehicle operation data comprise vehicle operation parameters and vehicle operation values corresponding to the vehicle operation parameters;
the query data set construction module is used for determining abnormal operation data in the vehicle operation data, determining a plurality of target associated data corresponding to the abnormal operation data in the to-be-processed internet of vehicles data, and constructing a query data set based on the plurality of target associated data;
a comparison data set determining module, configured to query a pre-established data comparison table based on the query data set to obtain a comparison data set that matches the query data set, where the data comparison table is configured to store a plurality of comparison data sets corresponding to the vehicle operation data, and each of the comparison data sets includes a plurality of associated standard operation data corresponding to the vehicle operation data;
the abnormal operation data replacement module is used for acquiring standard operation data corresponding to the abnormal operation data in the comparison data set as target replacement data and replacing the abnormal operation data in the to-be-processed internet of vehicles data with the target replacement data.
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