CN115221218A - Quality evaluation method and device for vehicle data, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a quality evaluation method and device of vehicle data, computer equipment and a storage medium. The quality of the vehicle data collected by the vehicle-mounted terminal can be evaluated in the development stage, so that the vehicle-mounted terminal can be used in the development stage when having problems, and the server does not need to analyze the data collected by the vehicle-mounted terminal in the use stage so as to evaluate the data quality of the vehicle-mounted terminal. In addition, the data collected by the vehicle-mounted terminal does not need to be analyzed by the server, so that the processing load of the server can be reduced. Finally, when the quality of the vehicle data collected by the vehicle-mounted terminal is evaluated, a plurality of evaluation indexes are adopted for comprehensive evaluation, so that the accuracy of the evaluation of the quality of the vehicle data can be improved.
Description
Technical Field
The present application relates to the field of computer network technologies, and in particular, to a method and an apparatus for quality assessment of vehicle data, a computer device, a storage medium, and a computer program product.
Background
Along with the continuous acceleration of the networking process of the vehicles, all host factories are matched with networking intelligent terminal equipment for new vehicle types so as to obtain real-time data of the vehicles, and then the real-time data are stored and processed to serve the digital transformation of enterprises or improve the user experience. Therefore, the quality of data collected by the vehicle-mounted networking intelligent terminal directly influences the quality of a series of digital products formed on the basis of the data. At present, after a vehicle provided with a vehicle-mounted networking intelligent terminal is put into use, the vehicle-mounted networking intelligent terminal uploads vehicle data to a server, and then the server evaluates the quality of the vehicle data. Since the evaluation process is performed after the vehicle is put into use, many problems may occur in the use of the in-vehicle terminal.
Disclosure of Invention
In view of the above, it is necessary to provide a quality evaluation method, apparatus, computer device, storage medium, and computer program product of vehicle data that can be evaluated in advance, in view of the above technical problems.
On one hand, the application provides a quality evaluation method of vehicle data, which is applied to a target vehicle, wherein the target vehicle is provided with CAN network data acquisition equipment and a vehicle-mounted terminal; the method comprises the following steps:
acquiring first CAN network data, GPS data and driving statistical data acquired by a vehicle-mounted terminal in the driving process of a target vehicle, and acquiring second CAN network data acquired by CAN network data acquisition equipment;
acquiring a first evaluation index for representing the abnormal degree of data type quality in target data, wherein the target data comprises at least one of first CAN network data, GPS data or driving statistical data;
comparing the first CAN network data with the second CAN network data to obtain a second evaluation index for representing the abnormal degree of the data quality in the first CAN network data;
acquiring a third evaluation index for representing the data compliance degree according to data specification evaluation results aiming at respective data types in the first CAN network data and the GPS data;
and obtaining an evaluation result for representing the quality of the data acquired by the vehicle-mounted terminal according to the first evaluation index, the second evaluation index and the third evaluation index.
In one embodiment, the target data includes first CAN network data, GPS data, and driving statistics; the method for obtaining the first evaluation index for representing the abnormal degree of the data type quality in the target data comprises the following steps:
determining a quality abnormal data type in data types covered by the first CAN network data and the second CAN network data according to the data difference degree between the first CAN network data and the second CAN network data acquired at the same acquisition time;
determining a quality abnormal data type in data types covered by the GPS data according to the data difference degree between the GPS data acquired at different acquisition moments;
comparing the driving statistical data acquired at different sampling moments with the statistical result of the vehicle driving statistical model, and determining the quality abnormal data type in the data types covered by the driving statistical data;
and determining a first evaluation index according to the quality abnormal data type in the data types covered by the first CAN network data, the GPS data and the driving statistical data.
In one embodiment, determining a quality abnormal data type in data types covered by first CAN network data and second CAN network data according to a data difference degree between the first CAN network data and the second CAN network data acquired at the same acquisition time includes:
for any data type covered by the first CAN network data and the second CAN network data, respectively acquiring first target data corresponding to the data type in the first CAN network data acquired at the same acquisition time and second target data corresponding to the data type in the second CAN network data;
respectively acquiring difference degree values between first target data and second target data according to the first target data and the second target data acquired at the same sampling moment;
and determining the quality abnormal degree value of the data type according to the obtained difference degree value and the correspondingly obtained quantity, and obtaining a judgment result of the data type according to the quality abnormal degree value of the data type, wherein the judgment result is used for indicating whether the data type is the quality abnormal data type.
In one embodiment, determining the quality abnormal data type in the data types covered by the GPS data according to the degree of data difference between the GPS data acquired at different acquisition times includes:
for any data type covered by the GPS data, determining whether the data of the data type acquired at each acquisition time is quality abnormal data or not according to the data of the data type acquired at the last acquisition time of each acquisition time, each acquisition time and the next acquisition time of each acquisition time;
and determining whether the data type is the quality abnormal data type according to the quantity ratio of the quality abnormal data of the data type in all the collected data of the data type.
In one embodiment, the number of the first CAN network data and the second CAN network data is multiple; comparing the first CAN network data with the second CAN network data to obtain a second evaluation index for representing the abnormal degree of the data quality in the first CAN network data, and the method comprises the following steps:
the method comprises the steps that a plurality of first CAN network data are normalized, so that the acquisition frequency presented by the normalized first CAN network data is consistent with the acquisition frequency of the first CAN network data;
aligning the respective acquisition time of the plurality of normalized first CAN network data with the respective acquisition time of the plurality of second CAN network data, and determining the first CAN network data with errors at the acquisition time in the plurality of normalized first CAN network data;
and acquiring the quantity ratio of the first CAN network data with errors at the acquisition moment in the plurality of normalized first CAN network data as a second evaluation index.
In one embodiment, obtaining a third evaluation index for characterizing the data compliance degree according to the data specification evaluation result for the data type covered by each of the first CAN network data and the GPS data includes:
comparing the data specification used by the data type covered by the first CAN network data and the GPS data with a preset data specification, and determining an abnormal data type which does not conform to the preset data specification;
and acquiring the quantity ratio of the abnormal data types which do not meet the preset data specification in the data types covered by the first CAN network data and the GPS data respectively as a third evaluation index.
In another aspect, the present application also provides a quality evaluation apparatus of vehicle data, the apparatus including:
the first acquisition module is used for acquiring first CAN network data, GPS data and driving statistical data acquired by a vehicle-mounted terminal in the driving process of a target vehicle and acquiring second CAN network data acquired by CAN network data acquisition equipment.
The second acquisition module is used for acquiring a first evaluation index for representing the abnormal degree of data type quality in target data, wherein the target data comprises at least one of first CAN network data, GPS data or driving statistical data;
the comparison module is used for comparing the first CAN network data with the second CAN network data to obtain a second evaluation index for representing the abnormal degree of the data quality in the first CAN network data;
the third acquisition module is used for acquiring a third evaluation index for representing the data compliance degree according to data standard evaluation results aiming at respective data types in the first CAN network data and the GPS data;
and the fourth acquisition module is used for acquiring an evaluation result for representing the quality of the data acquired by the vehicle-mounted terminal according to the first evaluation index, the second evaluation index and the third evaluation index.
In one embodiment, the target data includes first CAN network data, GPS data, and driving statistics; the first acquisition module is used for determining the quality abnormal data type in the data types covered by the first CAN network data and the second CAN network data according to the data difference degree between the first CAN network data and the second CAN network data acquired at the same acquisition time; determining a quality abnormal data type in data types covered by the GPS data according to the data difference degree between the GPS data acquired at different acquisition moments; comparing the driving statistical data acquired at different sampling moments with the statistical result of the vehicle driving statistical model, and determining the quality abnormal data type in the data types covered by the driving statistical data; and determining a first evaluation index according to the quality abnormal data type in the data types covered by the first CAN network data, the GPS data and the driving statistical data.
In one embodiment, the first obtaining module is further configured to, for any data type covered by the first CAN network data and the second CAN network data, respectively obtain first target data corresponding to the data type in the first CAN network data and second target data corresponding to the data type in the second CAN network data, which are collected at the same collection time; respectively acquiring difference degree values between first target data and second target data according to the first target data and the second target data acquired at the same sampling moment; and determining the quality abnormal degree value of the data type according to the obtained difference degree value and the correspondingly obtained quantity, and obtaining a judgment result of the data type according to the quality abnormal degree value of the data type, wherein the judgment result is used for indicating whether the data type is the quality abnormal data type or not.
In one embodiment, the first obtaining module is further configured to, for any data type covered by the GPS data, determine whether the data of the data type acquired at each acquisition time is quality abnormal data according to the data of the data type acquired at a previous acquisition time of each acquisition time, and a next acquisition time of each acquisition time; and determining whether the data type is the quality abnormal data type according to the quantity ratio of the quality abnormal data of the data type in all the collected data of the data type.
In one embodiment, the number of the first CAN network data and the number of the second CAN network data are both multiple; the comparison module is used for normalizing the plurality of first CAN network data so that the acquisition frequency presented by the normalized plurality of first CAN network data is consistent with the acquisition frequency of the first CAN network data; aligning the respective acquisition time of the plurality of normalized first CAN network data with the respective acquisition time of the plurality of second CAN network data, and determining the first CAN network data with errors at the acquisition time in the plurality of normalized first CAN network data; and acquiring the quantity ratio of the first CAN network data with errors at the acquisition moment in the structured plurality of first CAN network data as a second evaluation index.
In one embodiment, the third obtaining module is configured to compare a data specification used by a data type covered by each of the first CAN network data and the GPS data with a preset data specification, and determine an abnormal data type that does not meet the preset data specification; and acquiring the quantity ratio of the abnormal data types which do not meet the preset data specification in the data types covered by the first CAN network data and the GPS data respectively as a third evaluation index.
In another aspect, the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps in the above-mentioned method for evaluating the quality of vehicle data when executing the computer program.
In another aspect, the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in the above-described method for quality assessment of vehicle data.
In another aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the above-described method for quality assessment of vehicle data.
According to the vehicle data quality evaluation method, the vehicle data quality evaluation device, the computer equipment, the storage medium and the computer program product, the vehicle-mounted terminal and the CAN network data acquisition equipment are arranged on the target vehicle in a development stage before the vehicle-mounted terminal is put into use to acquire data, the abnormal degree evaluation index for representing the data acquired by the vehicle-mounted terminal is determined through data comparison, and the data quality acquired by the vehicle-mounted terminal is evaluated based on the evaluation index. The quality of the vehicle data collected by the vehicle-mounted terminal can be evaluated in the development stage, so that the problem of the vehicle-mounted terminal can be solved in the development stage, and the data quality of the vehicle-mounted terminal can be evaluated without being completely analyzed by the server only according to the data collected by the vehicle-mounted terminal in the use stage. In addition, the data collected by the vehicle-mounted terminal does not need to be analyzed by the server, so that the processing load of the server can be reduced. Finally, when the quality of the vehicle data collected by the vehicle-mounted terminal is evaluated, a plurality of evaluation indexes are adopted for comprehensive evaluation, so that the accuracy of the evaluation of the quality of the vehicle data can be improved.
Drawings
FIG. 1 is a diagram illustrating an exemplary embodiment of a method for evaluating vehicle data quality;
FIG. 2 is a schematic flow chart diagram of a method for quality assessment of vehicle data according to one embodiment;
FIG. 3 is a flowchart illustrating a method for quality assessment of vehicle data according to another embodiment;
FIG. 4 is a block diagram showing the construction of a quality evaluation apparatus for vehicle data according to an embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In some embodiments, the method for evaluating the quality of the vehicle data provided by the embodiment of the present application may be applied to an application environment as shown in fig. 1. The in-vehicle terminal 102 may communicate with the server 104 directly or indirectly through a wired or wireless network, which is not particularly limited in this embodiment of the present application. The in-vehicle terminal 102 and the server 104 may cooperatively perform the quality evaluation method of the vehicle data in the embodiment of the present application. One implementation procedure when the vehicle-mounted terminal 102 and the server 104 cooperatively execute the quality evaluation method of the vehicle data is taken as an example.
Specifically, the in-vehicle terminal 102 may collect first CAN network data, GPS data, and driving statistical data collected by the in-vehicle terminal during driving of the target vehicle, and send the collected data to the server 104. The in-vehicle terminal 10 may also acquire the second CAN network data acquired by the CAN network data acquisition device, and also transmit to the server 104 at the same time.
The server 104 acquires a first evaluation index for representing the abnormal degree of the data type quality in the target data; comparing the first CAN network data with the second CAN network data to obtain a second evaluation index for representing the abnormal degree of the data quality in the first CAN network data; acquiring a third evaluation index for representing the data compliance degree according to data specification evaluation results aiming at respective data types in the first CAN network data and the GPS data; and acquiring a quality evaluation result for representing the data acquired by the vehicle-mounted terminal according to the first evaluation index, the second evaluation index and the third evaluation index. It is to be appreciated that the server 104 can be integrated in the cloud.
Among them, the in-vehicle terminal 102 may be mounted on a target vehicle. The server 104 may be a background server corresponding to software, a web page, an applet, or the like, or a server specially used for vehicle data quality evaluation, which is not limited in this embodiment of the present disclosure. The server 104 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
In some embodiments, described in conjunction with the above-described implementation environment, a method for quality assessment of vehicle data is provided, as shown in fig. 2. Taking the example that the method is applied to a computer device (the computer device may specifically be the vehicle-mounted terminal or the server in fig. 1), the method includes the following steps:
step 202, acquiring first CAN network data, GPS data and driving statistical data acquired by a vehicle-mounted terminal in the driving process of a target vehicle, and acquiring second CAN network data acquired by CAN network data acquisition equipment.
The first CAN network data and the second CAN network data are mainly used for distinguishing two acquisition sources, namely a vehicle-mounted terminal and CAN network data acquisition equipment. The first CAN network data, the second CAN network data, the GPS data, and the driving statistic data may be a general data name, that is, four data names may all cover various types of data, which is not specifically limited in this embodiment of the present application. For example, the GPS data may include at least one of latitude and longitude, GPS vehicle speed, GPS heading, or GPS altitude, and the driving statistics may include at least one of vehicle acceleration, mileage, or number of brakes. For the CAN network data, all signal data generated by the target vehicle during driving may be included.
The CAN network data acquisition device may be a CANoe device, the CAN network data acquisition device and the vehicle-mounted terminal may be set to have the same acquisition frequency, and the acquired data may be recorded.
And 204, acquiring a first evaluation index for representing the abnormal degree of the data type quality in target data, wherein the target data comprises at least one of first CAN network data, GPS data or driving statistical data.
Specifically, the first evaluation index in this step may be only to evaluate the data quality of any one of the first CAN network data, the GPS data, or the driving statistic data, or may be to evaluate the data quality of any two of them, or may be to evaluate the data quality of three kinds of data, which is not specifically limited in this embodiment of the present application. In addition, the first evaluation index may be obtained by comparing the data with a preset value range, so as to determine which data types are quality abnormal data types in the data types covered by the target data. And determining the quantity proportion of the quality abnormal data type in the data type covered by the target data to serve as a first evaluation index.
And step 206, comparing the first CAN network data with the second CAN network data to obtain a second evaluation index for representing the abnormal degree of the data quality in the first CAN network data.
It CAN be understood that the second CAN network data collected by the CAN network data collecting device CAN be used as a comparison object to determine which data in the first CAN network data have abnormal quality. The quantity ratio of the data with abnormal quality in the first CAN network data CAN be directly used as a second evaluation index.
And 208, acquiring a third evaluation index for representing the data compliance degree according to data specification evaluation results aiming at respective data types in the first CAN network data and the GPS data.
The data specification acquired by the manufacturer of the vehicle-mounted terminal needs to be matched with the preset data specification specified by the vehicle manufacturer, so that which data in the first CAN network data and the GPS data acquired by the vehicle-mounted terminal do not accord with the data specification specified by the vehicle manufacturer CAN be determined. Therefore, the data specifications presented by the first CAN network data and the GPS data acquired by the vehicle-mounted terminal are compared with the preset data specifications specified by a vehicle manufacturer, so that which data types in the first CAN network data and the GPS data acquired by the vehicle-mounted terminal do not accord with the preset data specifications CAN be determined, and a third evaluation index is obtained.
And 210, obtaining an evaluation result for representing the quality of the data acquired by the vehicle-mounted terminal according to the first evaluation index, the second evaluation index and the third evaluation index.
As is clear from the above explanation, the first evaluation index, the second evaluation index, and the third evaluation index may be actually ratios. And the evaluation result for representing the quality of the data collected by the vehicle-mounted terminal can be obtained according to the score by converting the proportion into the score. And the evaluation result can be used for guiding relevant workers to carry out problem troubleshooting on the vehicle-mounted terminal. Of course, in the actual implementation process, the first evaluation index, the second evaluation index, and the third evaluation index have the same guiding significance, that is, the problem may also be checked according to the three indexes, and this is not specifically limited in the embodiment of the present application.
According to the quality evaluation method of the vehicle data, the vehicle-mounted terminal and the CAN network data acquisition equipment are arranged on the target vehicle in a development stage before the vehicle-mounted terminal is put into use to acquire the data, the abnormal degree evaluation index for representing the data acquired by the vehicle-mounted terminal is determined through data comparison, and the quality of the data acquired by the vehicle-mounted terminal is evaluated based on the evaluation index. The quality of the vehicle data collected by the vehicle-mounted terminal can be evaluated in the development stage, so that the problem of the vehicle-mounted terminal can be solved in the development stage, and the data quality of the vehicle-mounted terminal can be evaluated without being completely analyzed by the server only according to the data collected by the vehicle-mounted terminal in the use stage. In addition, the data collected by the vehicle-mounted terminal does not need to be analyzed by the server, so that the processing load of the server can be reduced. Finally, when the quality of the vehicle data collected by the vehicle-mounted terminal is evaluated, a plurality of evaluation indexes are adopted for comprehensive evaluation, so that the accuracy of the evaluation of the quality of the vehicle data can be improved.
In some embodiments, the target data includes first CAN network data, GPS data, and driving statistics; the method for acquiring the first evaluation index for representing the abnormal degree of the data type quality in the target data comprises the following steps:
determining a quality abnormal data type in data types covered by the first CAN network data and the second CAN network data according to the data difference degree between the first CAN network data and the second CAN network data acquired at the same acquisition time; determining a quality abnormal data type in data types covered by the GPS data according to the data difference degree between the GPS data acquired at different acquisition moments; comparing the driving statistical data acquired at different sampling moments with the statistical result of the vehicle driving statistical model, and determining the quality abnormal data type in the data types covered by the driving statistical data; and determining a first evaluation index according to the quality abnormal data type in the data types covered by the first CAN network data, the GPS data and the driving statistical data.
The vehicle-mounted terminal and the CAN network data acquisition equipment acquire data simultaneously in the driving process of a target vehicle and CAN use the same acquisition frequency. Thus, it can be appreciated that the two acquisition processes are time synchronized. In the embodiment of the application, the first CAN network data and the second CAN network data acquired at the synchronous time, that is, the same acquisition time are compared, so that how large the difference between the first CAN network data acquired by the vehicle-mounted terminal and the second CAN network data serving as a comparison object is, is determined, and the data difference degree is determined. And for those data types with larger data difference degree, the data type can be determined as the data type with abnormal quality.
Similarly, the quality anomaly data type in the data types covered by the GPS data can also be determined based on the similar process. While driving statistics, such as mileage and the like, can generally be predicted through modeling. Similarly, by comparing the running statistic data with the statistic result of the vehicle running statistic model, the quality abnormality data type in the data types covered by the running statistic data can also be determined.
Through the above process, the quality abnormal data type among the data types covered by each of the first CAN network data, the GPS data, and the driving statistic data CAN be determined. Counting a first total number of quality abnormal data types in the three data types; and then counting the sum of the total number of the data types covered by the three data types respectively to obtain a second total number, and calculating the ratio of the first total number to the second total number to be used as a first evaluation index.
In the above embodiment, the first evaluation index may be obtained, and the first evaluation index is used to represent the data type quality abnormal degree of at least one of the first CAN network data, the GPS data, or the driving statistical data, that is, the multiple data collected by the vehicle-mounted terminal may be comprehensively evaluated to obtain the quality evaluation result of the vehicle data collected by the vehicle-mounted terminal, so that the accuracy of the vehicle data quality evaluation may be improved.
In some embodiments, determining, according to a data difference degree between the first CAN network data and the second CAN network data acquired at the same acquisition time, a quality abnormal data type in data types covered by the first CAN network data and the second CAN network data includes:
for any data type covered by the first CAN network data and the second CAN network data, respectively acquiring first target data corresponding to the data type in the first CAN network data acquired at the same acquisition time and second target data corresponding to the data type in the second CAN network data; respectively acquiring difference degree values between first target data and second target data according to the first target data and the second target data acquired at the same sampling moment; and determining the quality abnormal degree value of the data type according to the obtained difference degree value and the correspondingly obtained quantity, and obtaining a judgment result of the data type according to the quality abnormal degree value of the data type, wherein the judgment result is used for indicating whether the data type is the quality abnormal data type.
Specifically, for ease of understanding, the data type a is taken as an example, and the acquisition time is a, b, c, and d. Respectively acquiring first target data corresponding to the type A in first CAN network data acquired at four acquisition moments of a, b, c and d, and recording the first target data as x i1 . Wherein i represents the ith acquisition time, "1"The representation is a first target data in the first CAN-network data. Meanwhile, second target data corresponding to the type A in second CAN network data acquired at four acquisition moments a, b, c and d CAN be acquired respectively and CAN be recorded as x i2 . Wherein, i represents the ith acquisition time, and "2" represents the second target data in the second CAN network data.
Wherein the difference degree value can be obtained by calculating the difference between two data, or a squared difference. Of course, in the actual implementation process, other methods may be used to perform the calculation, and this is not specifically limited in the embodiment of the present application. In the embodiment of the present application, the following formula can be used for calculation:
in the above formula, x i1 First target data x representing that the data type is A in first CAN network data acquired at the ith acquisition moment i2 And the second target data represent the second CAN network data with the data type A in the ith acquisition time, and n represents n acquisition times. δ represents the quality anomaly measure value for data type A.
And then
Representing a difference measure value between the first target data and the second target data for data type a. And calculating the ratio between the difference degree value and the number of the obtained difference degree values (namely the number of the acquisition moments), wherein the obtained ratio delta is the quality abnormal degree value of the data type A. By judging whether the delta is larger than a preset threshold value or not, if so, the data type A can be judged to be a quality abnormal data type.
In the above embodiment, the second CAN network data is used as a reference, and the data difference degree between the first CAN network data and the second CAN network data of each data type, which are acquired at the same time, is accumulated to obtain a determination result of whether the quality of each data type is abnormal, and whether each data type is an abnormal quality data type is determined based on the determination result. Because the difference degree between the data collected by the vehicle-mounted terminal and the data of the CAN network data collection equipment at the same collection time CAN be accumulated aiming at each data type, the abnormal degree of the data quality of the vehicle-mounted terminal when the data of each data type in the CAN network data is collected CAN be effectively evaluated, and the data quality collected by the vehicle-mounted terminal CAN be accurately evaluated.
In some embodiments, determining the quality abnormal data type in the data types covered by the GPS data according to the degree of data difference between the GPS data acquired at different acquisition moments includes:
for any data type covered by the GPS data, determining whether the data of the data type acquired at each acquisition time is abnormal quality data or not according to the data of the data type acquired at the last acquisition time of each acquisition time, the data of the data type acquired at each acquisition time and the data of the data type acquired at the next acquisition time of each acquisition time; and determining whether the data type is the quality abnormal data type according to the quantity ratio of the quality abnormal data of the data type in all the collected data of the data type.
Specifically, taking a certain data type of the GPS data and a certain collection time as an example, if the degree of difference between the data type of the data collected at the collection time and the data type of the data collected at the previous collection time of the collection time is Δ x1, and the degree of difference between the data type of the data collected at the collection time and the data type of the data collected at the next collection time of the collection time is Δ x2, it may be determined whether the data type of the data collected at the collection time is abnormal quality data according to Δ x1 and Δ x 2.
The determination process may be to determine whether Δ x1 and Δ x2 are greater than a predetermined threshold. If the data types are larger than the preset data types, the possibility of data mutation of the data types acquired at the acquisition time is higher, and therefore the data of the data types acquired at the acquisition time can be determined to be quality abnormal data. Since how many data are collected for the data type can be known, and the quantity of the quality abnormal data for the data type can also be known, the ratio of the quantity of the quality abnormal data of the data type in all the collected data of the data type can be obtained by calculating the ratio of the quantity of the quality abnormal data of the data type and the quantity of the quality abnormal data of the data type.
In the above embodiment, the GPS data itself is used as a reference, that is, for the same data type, the difference degree between the data of the data type acquired at different acquisition times is compared, so as to obtain whether the data acquired at each acquisition time is quality abnormal data. Therefore, by accumulating the occurrence times of the quality abnormal data and calculating the quantity ratio of all data, the abnormal degree of the data quality when the vehicle-mounted terminal collects the data of each data type in the GPS data can be effectively evaluated, and the data quality collected by the vehicle-mounted terminal can be accurately evaluated.
In some embodiments, the first CAN network data and the second CAN network data are both multiple in number; comparing the first CAN network data with the second CAN network data to obtain a second evaluation index for representing the abnormal degree of the data quality in the first CAN network data, and the method comprises the following steps:
the method comprises the steps that a plurality of first CAN network data are normalized, so that the acquisition frequency presented by the normalized first CAN network data is consistent with the acquisition frequency of the first CAN network data; aligning the respective acquisition time of the plurality of normalized first CAN network data with the respective acquisition time of the plurality of second CAN network data, and determining the first CAN network data with errors at the acquisition time in the plurality of normalized first CAN network data; and acquiring the quantity ratio of the first CAN network data with errors at the acquisition moment in the structured plurality of first CAN network data as a second evaluation index.
According to the content of the embodiment, the vehicle-mounted terminal and the CAN network data acquisition equipment CAN use the same acquisition frequency. Even if the same acquisition frequency is used, the respective acquisition moments of the vehicle-mounted terminal and the CAN network data acquisition equipment are inconsistent. For example, when the vehicle-mounted terminal collects data, problems such as re-collection, missing collection, and cycle jump may occur, which may cause the collection time of the data collected by the vehicle-mounted terminal to be inconsistent with the collection time of the data collected by the CAN network data collection device. Therefore, in order to make the collection time appear consistent, in the embodiment of the present application, the first CAN network data may be structured first.
The regulating process may delete data retransmitted or missed by the vehicle-mounted terminal, or delete data collected more than the collection frequency of the CAN network data collection device due to the jump of the collection period, which is not specifically limited in the embodiment of the present application. It CAN be understood that the rule is mainly to make the acquisition time of the first CAN network data substantially aligned with the acquisition time of the second CAN network data. In practical implementation, the acquisition time of the two devices may still have an error in a slight time interval. With the IC, the time of acquisition of the first CAN network data CAN be aligned with the time of acquisition of the second CAN network data, thereby determining which of the plurality of first CAN network data have an error over the time interval relative to the time of acquisition of the second CAN network data. By counting the number of the data with errors in the time interval, the number ratio of the data in the normalized plurality of first CAN network data CAN be calculated and CAN be used as a second evaluation index.
In the above embodiment, the second CAN network data is used as a reference, and the acquisition time of the first CAN network data is aligned with the acquisition time of the second CAN network data, so as to determine the number of the first CAN network data with the acquisition time error. The proportion of the number of the first CAN network data with errors in the total number of the first CAN network data CAN be calculated, so that the data quality of the vehicle-mounted terminal in the process of collecting the CAN network data CAN be effectively evaluated in a time dimension.
In addition, before the evaluation process, the second CAN network data CAN be normalized firstly, so that the accident condition in the acquisition process CAN be avoided, the evaluation process of acquiring the CAN network data by the vehicle-mounted terminal is influenced, and the accuracy of the evaluation result is improved.
In some embodiments, obtaining a third evaluation index for characterizing the degree of data compliance according to the data specification evaluation result for the data type covered by each of the first CAN network data and the GPS data includes:
comparing the data specification used by the data type covered by the first CAN network data and the GPS data with a preset data specification, and determining an abnormal data type which does not conform to the preset data specification; and acquiring the quantity ratio of the abnormal data types which do not meet the preset data specification in the data types covered by the first CAN network data and the GPS data respectively as a third evaluation index.
The preset data specification may include a collected data precision specification, a collected data value range specification, a data format specification, a measurement unit specification, and the like, which is not specifically limited in the embodiment of the present application. The preset data standard mainly requires that data acquired by the vehicle-mounted terminal need to meet the data compliance requirements of vehicle manufacturers, and if the data acquired by the vehicle-mounted terminal does not meet the preset data standard, the vehicle-mounted terminal cannot be used after a target vehicle is installed due to data incompatibility. And determining the number of data types which do not meet the preset data specification in the first CAN network data and the GPS data, and calculating the number ratio of the number in all the data types in the first CAN network data and the GPS data to obtain a third evaluation index.
In the embodiment, whether the data collected by the vehicle-mounted terminal meets the preset data or not CAN be judged, and the data type number ratio which does not meet the preset data specification is counted to be used as one evaluation index, so that the data quality of the vehicle-mounted terminal when the CAN network data and the GPS data are collected CAN be effectively evaluated, and the vehicle-mounted terminal is compatible with a target vehicle in the subsequent use process.
For convenience of understanding, as shown in fig. 3, the description will be made by referring to the contents of the above embodiment, and the method for evaluating the quality of vehicle data mentioned in the present application includes the following steps:
step 302, acquiring first CAN network data, GPS data and driving statistical data acquired by a vehicle-mounted terminal in the driving process of a target vehicle, and acquiring second CAN network data acquired by CAN network data acquisition equipment.
Step 304, determining a quality abnormal data type in data types covered by the first CAN network data and the second CAN network data according to the data difference degree between the first CAN network data and the second CAN network data acquired at the same acquisition time; and determining the quality abnormal data type in the data types covered by the GPS data according to the data difference degree between the GPS data acquired at different acquisition moments.
Step 306, comparing the driving statistical data acquired at different sampling moments with the statistical result of the vehicle driving statistical model, and determining the quality abnormal data type in the data types covered by the driving statistical data; and determining a first evaluation index according to the quality abnormal data type in the data types covered by the first CAN network data, the GPS data and the driving statistical data.
Step 308, the plurality of first CAN network data are normalized, so that the acquisition frequency presented by the normalized plurality of first CAN network data is consistent with the acquisition frequency of the first CAN network data; aligning the respective acquisition time of the plurality of normalized first CAN network data with the respective acquisition time of the plurality of second CAN network data, and determining the first CAN network data with errors at the acquisition time in the plurality of normalized first CAN network data; and acquiring the quantity ratio of the first CAN network data with errors at the acquisition moment in the structured plurality of first CAN network data as a second evaluation index.
Step 310, comparing data specifications used by data types covered by the first CAN network data and the GPS data with preset data specifications, and determining abnormal data types which do not accord with the preset data specifications; and acquiring the quantity ratio of the abnormal data types which do not meet the preset data specification in the data types covered by the first CAN network data and the GPS data respectively as a third evaluation index.
And step 312, obtaining an evaluation result for representing the quality of the data acquired by the vehicle-mounted terminal according to the first evaluation index, the second evaluation index and the third evaluation index.
In the embodiment, the vehicle-mounted terminal and the CAN network data acquisition equipment are arranged on the target vehicle to acquire data in a development stage before the vehicle-mounted terminal is put into use, and an abnormal degree evaluation index for representing the data acquired by the vehicle-mounted terminal is determined through data comparison so as to evaluate the quality of the data acquired by the vehicle-mounted terminal based on the evaluation index. The quality of the vehicle data collected by the vehicle-mounted terminal can be evaluated in the development stage, so that the problem of the vehicle-mounted terminal can be solved in the development stage, and the data quality of the vehicle-mounted terminal can be evaluated without being completely analyzed by the server only according to the data collected by the vehicle-mounted terminal in the use stage. In addition, the data collected by the vehicle-mounted terminal does not need to be analyzed by the server, so that the processing load of the server can be reduced. Finally, when the quality of the vehicle data collected by the vehicle-mounted terminal is evaluated, a plurality of evaluation indexes are adopted for comprehensive evaluation, so that the accuracy of the evaluation of the quality of the vehicle data can be improved.
It should be understood that, although the steps in the flowcharts related to the above embodiments are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the above embodiments may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides a vehicle data quality evaluation device for realizing the vehicle data quality evaluation method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so that specific limitations in one or more embodiments of the vehicle data quality assessment device provided below can be referred to the limitations in the vehicle data quality assessment method above, and are not described herein again.
In some embodiments, as shown in fig. 4, there is provided a vehicle data quality assessment apparatus, which may be a part of a computer device using a software module or a hardware module, or a combination of the two, and specifically includes: a first obtaining module 402, a second obtaining module 404, a comparing module 406, a third obtaining module 408, and a fourth obtaining module 410, wherein:
the first acquiring module 402 is configured to acquire first CAN network data, GPS data, and driving statistical data, which are acquired by a vehicle-mounted terminal during a driving process of a target vehicle, and acquire second CAN network data, which is acquired by a CAN network data acquiring device.
A second obtaining module 404, configured to obtain a first evaluation index used for representing a degree of data type quality abnormality in target data, where the target data includes at least one of first CAN network data, GPS data, or driving statistics data;
the comparison module 406 is configured to compare the first CAN network data with the second CAN network data, and obtain a second evaluation index used for representing a data quality abnormal degree in the first CAN network data;
a third obtaining module 408, configured to obtain a third evaluation index used for representing a data compliance degree according to a data specification evaluation result for each data type in the first CAN network data and the GPS data;
the fourth obtaining module 410 is configured to obtain an evaluation result used for representing quality of data collected by the vehicle-mounted terminal according to the first evaluation index, the second evaluation index, and the third evaluation index.
In some embodiments, the target data includes first CAN network data, GPS data, and driving statistics; the first acquiring module 402 is configured to determine a quality abnormal data type in data types covered by the first CAN network data and the second CAN network data according to a data difference degree between the first CAN network data and the second CAN network data acquired at the same acquisition time; determining a quality abnormal data type in data types covered by the GPS data according to the data difference degree between the GPS data acquired at different acquisition moments; comparing the driving statistical data acquired at different sampling moments with the statistical result of the vehicle driving statistical model, and determining the quality abnormal data type in the data types covered by the driving statistical data; and determining a first evaluation index according to the quality abnormal data type in the data types covered by the first CAN network data, the GPS data and the driving statistical data.
In some embodiments, the first obtaining module 402 is further configured to, for any data type covered by the first CAN network data and the second CAN network data, respectively obtain first target data corresponding to the data type in the first CAN network data and second target data corresponding to the data type in the second CAN network data, which are collected at the same collection time; respectively acquiring difference degree values between first target data and second target data according to the first target data and the second target data acquired at the same sampling moment; and determining the quality abnormal degree value of the data type according to the obtained difference degree value and the correspondingly obtained quantity, and obtaining a judgment result of the data type according to the quality abnormal degree value of the data type, wherein the judgment result is used for indicating whether the data type is the quality abnormal data type.
In some embodiments, the first obtaining module 402 is further configured to, for any data type covered by the GPS data, determine whether the data of the data type acquired at each acquisition time is quality abnormal data according to data of the data type respectively acquired at a previous acquisition time of each acquisition time, at each acquisition time, and at a next acquisition time of each acquisition time; and determining whether the data type is the quality abnormal data type according to the quantity proportion of the quality abnormal data of the data type in all the collected data of the data type.
In some embodiments, the first CAN network data and the second CAN network data are both multiple in number; the comparison module 406 is configured to normalize the plurality of first CAN network data, so that the collection frequency presented by the normalized plurality of first CAN network data is consistent with the collection frequency of the first CAN network data; aligning the respective acquisition time of the plurality of normalized first CAN network data with the respective acquisition time of the plurality of second CAN network data, and determining the first CAN network data with errors at the acquisition time in the plurality of normalized first CAN network data; and acquiring the quantity ratio of the first CAN network data with errors at the acquisition moment in the structured plurality of first CAN network data as a second evaluation index.
In some embodiments, the third obtaining module 408 is configured to compare a data specification used by a data type covered by each of the first CAN network data and the GPS data with a preset data specification, and determine an abnormal data type that does not meet the preset data specification; and acquiring the quantity ratio of the abnormal data types which do not meet the preset data specification in the data types covered by the first CAN network data and the GPS data respectively as a third evaluation index.
For specific definition of the quality evaluation device for the vehicle data, reference may be made to the above definition of the quality evaluation method for the vehicle data, which is not described herein again. The respective modules in the above-described vehicle data quality evaluation device may be entirely or partially realized by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal or a server, and its internal structure diagram may be as shown in fig. 5. The computer device includes a processor, a memory, an Input/Output interface (I/O for short), and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing data collected by the vehicle-mounted terminal and the CAN network data collection equipment. The input/output interface of the computer device is used for exchanging information between the processor and an external device. The communication interface of the computer device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a method of quality assessment of vehicle data.
It will be appreciated by those skilled in the art that the configuration shown in fig. 5 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is further provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, in which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, carries out the steps in the method embodiments described above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation. The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A quality evaluation method of vehicle data is characterized in that the method is applied to a target vehicle, and CAN network data acquisition equipment and a vehicle-mounted terminal are installed on the target vehicle; the method comprises the following steps:
acquiring first CAN network data, GPS data and driving statistical data acquired by the vehicle-mounted terminal in the driving process of the target vehicle, and acquiring second CAN network data acquired by the CAN network data acquisition equipment;
acquiring a first evaluation index for representing the abnormal degree of data type quality in target data, wherein the target data comprises at least one of first CAN network data, GPS data or driving statistical data;
comparing the first CAN network data with the second CAN network data to obtain a second evaluation index for representing the abnormal degree of data quality in the first CAN network data;
acquiring a third evaluation index for representing the data compliance degree according to data specification evaluation results aiming at respective data types in the first CAN network data and the GPS data;
and obtaining an evaluation result for representing the quality of the data acquired by the vehicle-mounted terminal according to the first evaluation index, the second evaluation index and the third evaluation index.
2. The method of claim 1, wherein the target data includes first CAN network data, GPS data, and driving statistics; the acquiring of the first evaluation index for representing the degree of data type quality anomaly in the target data includes:
determining a quality abnormal data type in data types covered by first CAN network data and second CAN network data according to the data difference degree between the first CAN network data and the second CAN network data acquired at the same acquisition time;
determining a quality abnormal data type in data types covered by the GPS data according to the data difference degree between the GPS data acquired at different acquisition moments;
comparing the driving statistical data acquired at different sampling moments with the statistical result of the vehicle driving statistical model, and determining the quality abnormal data type in the data types covered by the driving statistical data;
and determining a first evaluation index according to the quality abnormal data type in the data types covered by the first CAN network data, the GPS data and the driving statistical data.
3. The method of claim 2, wherein determining the quality anomaly data type from the data types covered by the first CAN network data and the second CAN network data according to the degree of data difference between the first CAN network data and the second CAN network data acquired at the same acquisition time comprises:
for any data type covered by the first CAN network data and the second CAN network data, respectively acquiring first target data corresponding to any data type in the first CAN network data and second target data corresponding to any data type in the second CAN network data, which are acquired at the same acquisition time;
respectively acquiring a difference degree value between first target data and second target data aiming at the first target data and the second target data which are acquired at the same sampling moment;
determining the quality abnormal degree value of any data type according to the acquired difference degree value and the correspondingly acquired quantity, and acquiring a judgment result of any data type according to the quality abnormal degree value of any data type, wherein the judgment result is used for indicating whether the data type is the quality abnormal data type or not.
4. The method according to claim 2, wherein the determining the quality abnormal data type in the data types covered by the GPS data according to the degree of data difference between the GPS data acquired at different acquisition time includes:
for any data type covered by the GPS data, determining whether the data of any data type acquired at each acquisition time is quality abnormal data or not according to the data of any data type respectively acquired at the last acquisition time of each acquisition time, each acquisition time and the next acquisition time of each acquisition time;
and determining whether any data type is the quality abnormal data type according to the quantity ratio of the quality abnormal data of any data type in all the collected data of any data type.
5. The method of claim 1 wherein the first and second CAN network data are each plural in number; the comparing the first CAN network data with the second CAN network data to obtain a second evaluation index for representing the degree of data quality abnormality in the first CAN network data includes:
the method comprises the steps that a plurality of first CAN network data are normalized, so that the acquisition frequency presented by the normalized first CAN network data is consistent with the acquisition frequency of the first CAN network data;
aligning the respective acquisition time of the plurality of normalized first CAN network data with the respective acquisition time of the plurality of second CAN network data, and determining the first CAN network data with errors at the acquisition time in the plurality of normalized first CAN network data;
and acquiring the quantity ratio of the first CAN network data with errors at the acquisition moment in the structured plurality of first CAN network data as a second evaluation index.
6. The method according to claim 1, wherein the obtaining a third evaluation index for characterizing a data compliance degree according to the data specification evaluation result for the data type covered by each of the first CAN network data and the GPS data comprises:
comparing the data specification used by the data type covered by the first CAN network data and the GPS data with a preset data specification, and determining an abnormal data type which does not conform to the preset data specification;
and acquiring the quantity ratio of the abnormal data types which do not meet the preset data specification in the data types covered by the first CAN network data and the GPS data respectively as a third evaluation index.
7. A quality evaluation apparatus of vehicle data, characterized by comprising:
the first acquisition module is used for acquiring first CAN network data, GPS data and driving statistical data acquired by the vehicle-mounted terminal in the driving process of the target vehicle and acquiring second CAN network data acquired by the CAN network data acquisition equipment;
the second acquisition module is used for acquiring a first evaluation index for representing the abnormal degree of the data type quality in target data, wherein the target data comprises at least one of first CAN network data, GPS data or driving statistical data;
the comparison module is used for comparing the first CAN network data with the second CAN network data to obtain a second evaluation index for representing the abnormal degree of data quality in the first CAN network data;
the third acquisition module is used for acquiring a third evaluation index for representing the data compliance degree according to data specification evaluation results aiming at respective data types in the first CAN network data and the GPS data;
and the fourth obtaining module is used for obtaining an evaluation result for representing the quality of the data acquired by the vehicle-mounted terminal according to the first evaluation index, the second evaluation index and the third evaluation index.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115766514A (en) * | 2022-11-02 | 2023-03-07 | 中国第一汽车股份有限公司 | Full link quality monitoring method and device of Internet of vehicles, storage medium and vehicle |
| CN118011132A (en) * | 2024-04-07 | 2024-05-10 | 徐州徐工汽车制造有限公司 | Test data analysis method, system and computer readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115766514A (en) * | 2022-11-02 | 2023-03-07 | 中国第一汽车股份有限公司 | Full link quality monitoring method and device of Internet of vehicles, storage medium and vehicle |
| CN118011132A (en) * | 2024-04-07 | 2024-05-10 | 徐州徐工汽车制造有限公司 | Test data analysis method, system and computer readable storage medium |
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