CN110599620B - Data processing method and device, computer equipment and readable storage medium - Google Patents
Data processing method and device, computer equipment and readable storage medium Download PDFInfo
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Abstract
The invention relates to a data processing method, a data processing device, computer equipment and a readable storage medium, wherein the method comprises the following steps: acquiring characteristic data of the vehicle-mounted intelligent equipment and vehicle data sent by the vehicle-mounted intelligent equipment; the characteristic data represents a growth coefficient of the data volume of the vehicle data sent by the vehicle-mounted intelligent equipment; inputting the characteristic data and the vehicle data into a data correction model, and correcting the vehicle data to obtain corrected data; the data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment. In the method, the characteristic data of the vehicle-mounted intelligent device can represent the growth coefficient of the data volume of the vehicle data sent by the vehicle-mounted intelligent device, so that the characteristic data of the vehicle-mounted intelligent device and the vehicle data are input into the data correction model, the vehicle data can be corrected more accurately, and the accuracy of the obtained corrected data is improved.
Description
Technical Field
The present invention relates to the field of big data, and in particular, to a data processing method, apparatus, computer device, and readable storage medium.
Background
In some output systems of mass data, when data is abnormal, such as missing, error, etc., the data cannot be queried, displayed, calculated, etc., so that the prediction of abnormal data is very important.
In the conventional technology, a prediction method for abnormal data mainly predicts the abnormal data by using a prediction model established by using historical data, for example, predicting current missing data by using yesterday data so as to obtain complete output data, and inquiring, displaying, calculating and the like the output data. However, in the conventional technology, a prediction model is only based on modeling performed by historical data, if the historical data itself is abnormal, the predicted data is also abnormal data, if the historical data is abnormal for many consecutive days, the difference between the predicted data and an actual value is larger, and in addition, because the data itself has the growth property, a model which is formed by using the historical data to predict the abnormal data is not established in real time, the situation of data growth is not considered, so that the formed model cannot relatively accurately predict normal data in the future, only the requirement of one time can be met, and support cannot be continuously provided.
Therefore, the method for predicting the abnormal data in the traditional technology has the problem of low prediction accuracy.
Disclosure of Invention
Based on this, it is necessary to provide a data processing method, an apparatus, a computer device, and a readable storage medium for solving the problem of low prediction accuracy in the conventional abnormal data prediction method.
In a first aspect, an embodiment of the present invention provides a data processing method, where the method includes:
acquiring characteristic data of vehicle-mounted intelligent equipment and vehicle data sent by the vehicle-mounted intelligent equipment; the characteristic data represent a growth coefficient of the data volume of the vehicle data sent by the vehicle-mounted intelligent device;
inputting the characteristic data and the vehicle data into a data correction model, and correcting the vehicle data to obtain corrected data; the data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment.
In one embodiment, the data correction model includes a growth coefficient model and a polynomial regression model, and the inputting the feature data and the vehicle data into the data correction model to correct the vehicle data to obtain corrected data includes:
inputting the characteristic data into the growth coefficient model to obtain a growth coefficient of the vehicle data;
and inputting the growth coefficient and the vehicle data into the polynomial regression model, and correcting the vehicle data to obtain the corrected data.
In one embodiment, the inputting the growth coefficient and the vehicle data into the polynomial regression model for correction to obtain the corrected data includes:
acquiring a statistical characteristic value of the vehicle data; the statistical characteristic value comprises kurtosis, mean, variance and skewness of the data;
and inputting the growth coefficient, the vehicle data and the statistical characteristic value into the polynomial regression model, and correcting the vehicle data to obtain corrected data.
In one embodiment, before the inputting the characteristic data and the vehicle data into a data correction model and correcting the vehicle data to obtain corrected data, the method further includes:
judging whether abnormal data exist in the vehicle data or not by using a preset abnormal data judgment algorithm;
if so, inputting the characteristic data and the vehicle data into a data correction model, and correcting the vehicle data to obtain corrected data.
In one embodiment, the data correction model is obtained by training a preset data correction model by using vehicle sample data and sample feature data of the vehicle-mounted intelligent device, and includes:
acquiring sample characteristic data of the vehicle-mounted intelligent equipment and vehicle sample data sent by the vehicle-mounted intelligent equipment; the sample characteristic data represents a growth coefficient of the data volume of vehicle sample data sent by the vehicle-mounted intelligent equipment;
training a preset growth coefficient model by using the sample characteristic data of the vehicle-mounted intelligent equipment to obtain a growth coefficient of the vehicle sample data and the growth coefficient model;
and training a preset polynomial regression model by using the growth coefficient of the vehicle sample data and the vehicle sample data to obtain the polynomial regression model.
In one embodiment, the training a preset growth coefficient model by using the sample feature data of the vehicle-mounted intelligent device to obtain the growth coefficient of the vehicle sample data and the growth coefficient model includes:
inputting the sample characteristic data of the vehicle-mounted intelligent equipment into the preset growth coefficient model to obtain a predicted growth coefficient of the vehicle sample data;
obtaining a value of a loss function of the preset growth coefficient model according to the predicted growth coefficient of the vehicle sample data and a preset sample growth coefficient;
and training the preset growth coefficient model by using the value of the loss function of the preset growth coefficient model to obtain the growth coefficient model.
In one embodiment, the training a preset polynomial regression model by using the growth coefficient of the vehicle sample data and the vehicle sample data to obtain the polynomial regression model includes:
acquiring a statistical characteristic value of the vehicle sample data; the statistical characteristic value of the vehicle sample data comprises kurtosis, average, variance and skewness of the vehicle sample data;
inputting the growth coefficient of the vehicle sample data, the statistical characteristic value of the vehicle sample data and the vehicle sample data into the preset polynomial regression model, and correcting the vehicle sample data to obtain corrected vehicle sample data;
obtaining a value of a loss function of the preset polynomial regression model according to the corrected vehicle sample data and preset vehicle reference data;
and training the preset polynomial regression model by using the value of the loss function of the preset polynomial regression model to obtain the polynomial regression model.
In a second aspect, an embodiment of the present invention provides a data processing apparatus, where the apparatus includes:
the data acquisition module is used for acquiring characteristic data of the vehicle-mounted intelligent equipment and vehicle data sent by the vehicle-mounted intelligent equipment; the characteristic data represent a growth coefficient of the data volume of the vehicle data sent by the vehicle-mounted intelligent device;
the processing module is used for inputting the characteristic data and the vehicle data into a data correction model and correcting the vehicle data to obtain corrected data; the data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment.
In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the following steps when executing the computer program:
acquiring characteristic data of vehicle-mounted intelligent equipment and vehicle data sent by the vehicle-mounted intelligent equipment; the characteristic data represent a growth coefficient of the data volume of the vehicle data sent by the vehicle-mounted intelligent device;
inputting the characteristic data and the vehicle data into a data correction model, and correcting the vehicle data to obtain corrected data; the data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the following steps:
acquiring characteristic data of vehicle-mounted intelligent equipment and vehicle data sent by the vehicle-mounted intelligent equipment; the characteristic data represent a growth coefficient of the data volume of the vehicle data sent by the vehicle-mounted intelligent device;
inputting the characteristic data and the vehicle data into a data correction model, and correcting the vehicle data to obtain corrected data; the data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment.
In the data processing method and apparatus, the computer device, and the readable storage medium provided in the above embodiments, the computer device obtains feature data of the vehicle-mounted intelligent device and vehicle data sent by the vehicle-mounted intelligent device, where the feature data of the vehicle-mounted intelligent device represents a growth coefficient of a data volume of the vehicle data sent by the vehicle-mounted intelligent device; inputting the characteristic data of the vehicle-mounted intelligent equipment and the vehicle data sent by the vehicle-mounted intelligent equipment into a data correction model, and correcting the vehicle data to obtain corrected data; the data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment. In the method, the computer equipment firstly obtains the vehicle data sent by the vehicle-mounted intelligent equipment and the characteristic data of the vehicle-mounted intelligent equipment for representing the growth coefficient of the data quantity of the vehicle data sent by the vehicle-mounted intelligent equipment, the characteristic data of the vehicle-mounted intelligent equipment and the vehicle data sent by the vehicle-mounted intelligent equipment are input into a data correction model, the vehicle data are corrected to obtain corrected data, because the characteristic data of the vehicle-mounted intelligent equipment can represent the growth coefficient of the data quantity of the vehicle data sent by the vehicle-mounted intelligent equipment, the influence of the growth coefficient of the data quantity of the vehicle data on the correction capability of the data correction model is considered in the process, the characteristic data of the vehicle-mounted intelligent equipment and the vehicle data are input into the data correction model, the growth condition of the data can be considered, and the data correction model can correct the vehicle data more accurately, the accuracy of the resulting corrected data is improved.
Drawings
FIG. 1 is a diagram of an application environment of a data processing method according to an embodiment;
FIG. 2 is a flow diagram illustrating a data processing method, according to an embodiment;
FIG. 3 is a schematic flow chart diagram illustrating a data processing method according to another embodiment;
FIG. 4 is a schematic flow chart diagram illustrating a data processing method according to another embodiment;
FIG. 5 is a flowchart illustrating a data processing method according to another embodiment;
FIG. 6 is a block diagram of a data processing apparatus according to an embodiment;
FIG. 7 is a block diagram of a data processing apparatus according to an embodiment;
fig. 8 is a schematic internal structural diagram 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.
The data processing method provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The terminal 102 may be, but is not limited to, various vehicle-mounted intelligent devices, and the server 104 may be implemented by an independent server or a server cluster composed of a plurality of servers.
The following describes the technical solution of the present invention and how to solve the above technical problems with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 2 is a schematic flowchart of a data processing method according to an embodiment. The embodiment relates to a specific implementation process of acquiring feature data of each vehicle-mounted intelligent device and vehicle data sent by each vehicle-mounted intelligent device by computer equipment, inputting the feature data and the vehicle data into a data correction model, and correcting the vehicle data to obtain corrected data. As shown in fig. 2, the method may include:
s201, acquiring characteristic data of the vehicle-mounted intelligent device and vehicle data sent by the vehicle-mounted intelligent device; the characteristic data represents a growth coefficient of the data volume of the vehicle data transmitted by the vehicle-mounted intelligent device.
Specifically, the computer device obtains characteristic data of the vehicle-mounted intelligent device and vehicle data sent by the vehicle-mounted intelligent device, wherein the characteristic data of the vehicle-mounted intelligent device represents a growth coefficient of data volume of the vehicle data sent by the vehicle-mounted intelligent device. The computer device and the vehicle-mounted intelligent device are communicated through a network, the vehicle-mounted intelligent device can upload vehicle data to the computer device in real time, the computer device can acquire characteristic data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device within a preset time interval, and the vehicle data sent by the vehicle-mounted intelligent device can be data such as ignition times of a vehicle, coolant temperature of the vehicle, power of a vehicle engine, oil consumption of the vehicle and the like. Optionally, the feature data of the vehicle-mounted intelligent device includes a daily average active value of the vehicle-mounted intelligent device that transmits the vehicle data in a preset time interval, a total number of the vehicle-mounted intelligent devices, and an increase coefficient of the total number of the vehicle-mounted intelligent devices, and the feature data of the vehicle-mounted intelligent device includes a daily average active value of the vehicle-mounted intelligent device that transmits the vehicle data in seven days, a total number of the vehicle-mounted intelligent devices, and an increase coefficient of the total number of the vehicle-mounted intelligent devices. The daily average activity value of the vehicle-mounted intelligent equipment is the average value of the starting times of the vehicle-mounted intelligent equipment on the day, and the increase coefficient of the total number of the vehicle-mounted intelligent equipment is the number of the total number of the vehicle-mounted intelligent equipment which increases along with the time. Optionally, the feature data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device obtained by the computer device may be feature data of one vehicle-mounted intelligent device and vehicle data sent by one vehicle-mounted intelligent device, or feature data of a plurality of vehicle-mounted intelligent devices and vehicle data sent by a plurality of vehicle-mounted intelligent devices.
S202, inputting the characteristic data and the vehicle data into a data correction model, and correcting the vehicle data to obtain corrected data; the data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment.
Specifically, the computer device inputs the characteristic data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device into the data correction model, and corrects the vehicle data to obtain corrected data. The data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment. Optionally, the computer device may input the feature data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device into the data correction model according to a preset time interval, and correct the vehicle data to obtain corrected data, for example, the computer device may input the feature data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device into the data correction model within seven days, and correct the vehicle data within seven days to obtain corrected data, or may input the feature data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device into the data correction model within eight days, and correct the vehicle data within eight days to obtain corrected data. The data input into the data correction model are the characteristic data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device, the characteristic data of the vehicle-mounted intelligent device can represent the growth coefficient of the data volume of the vehicle data sent by the vehicle-mounted intelligent device, the factor of the growth of the data volume of the vehicle data is considered, the data correction model can correct the vehicle data more accurately, and the accuracy of the obtained corrected data is improved. Optionally, the computer device may input the vehicle sample data and the sample characteristic data of the vehicle-mounted intelligent device into a preset data correction model to obtain corrected vehicle sample data, obtain a loss function value of the preset data correction model according to the corrected vehicle sample data and the preset vehicle sample data, and determine the corresponding preset data correction model when the loss function value of the preset data correction model reaches a stable value as the data correction model.
In the embodiment, the computer device firstly obtains the vehicle data sent by the vehicle-mounted intelligent device and the characteristic data of the vehicle-mounted intelligent device for representing the growth coefficient of the data quantity of the vehicle data sent by the vehicle-mounted intelligent device, inputs the characteristic data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device into the data correction model, corrects the vehicle data to obtain the corrected data, because the characteristic data of the vehicle-mounted intelligent device can represent the growth coefficient of the data quantity of the vehicle data sent by the vehicle-mounted intelligent device, the influence of the growth coefficient of the data quantity of the vehicle data on the correction capability of the data correction model is considered in the process, the characteristic data of the vehicle-mounted intelligent device and the vehicle data are input into the data correction model, the growth condition of the data can be considered, and the data correction model can correct the vehicle data more accurately, the accuracy of the resulting corrected data is improved.
In the above scenario in which the feature data of the in-vehicle intelligent device and the vehicle data sent by the in-vehicle intelligent device are input into the data correction model, the data correction model includes a growth coefficient model and a polynomial regression model. Fig. 3 is a schematic flowchart of a data processing method according to another embodiment. The embodiment relates to a specific implementation process of inputting characteristic data of a vehicle-mounted intelligent device and vehicle data sent by the vehicle-mounted intelligent device into a data correction model by a computer device, correcting the vehicle data and obtaining corrected data. As shown in fig. 3, on the basis of the foregoing embodiment, as an optional implementation manner, the foregoing S202 includes:
and S301, inputting the characteristic data into the growth coefficient model to obtain the growth coefficient of the vehicle data.
Specifically, the computer device inputs the feature data of the vehicle-mounted intelligent device into the growth coefficient model to obtain a growth coefficient of the vehicle data sent by the vehicle-mounted intelligent device, that is, the computer device inputs the daily average activity value of the vehicle-mounted intelligent device, the total number of the vehicle-mounted intelligent devices and the growth coefficient of the total number of the vehicle-mounted intelligent devices into the growth coefficient model in a preset time interval to obtain the growth coefficient of the vehicle data sent by the vehicle-mounted intelligent device.
And S302, inputting the growth coefficient and the vehicle data into a polynomial regression model, and correcting the vehicle data to obtain corrected data.
Specifically, the computer device inputs the growth coefficient of the obtained vehicle data and the vehicle data into a polynomial regression model, and corrects the vehicle data to obtain corrected data. Optionally, the computer device may obtain a statistical characteristic value of the vehicle data, input the growth coefficient of the vehicle data, and the statistical characteristic value of the vehicle data into the polynomial regression model, and correct the vehicle data according to the growth coefficient of the vehicle data and the statistical characteristic value of the vehicle data to obtain the statistical characteristic value of the vehicle dataThe corrected data, wherein the statistical characteristic value of the vehicle data comprises the kurtosis, the average, the variance and the skewness of the vehicle data, so that the vehicle data is corrected according to the growth coefficient of the vehicle data and the statistical characteristic value of the vehicle data, the vehicle data can be corrected more accurately, and the accuracy of the obtained corrected vehicle data is improved. Alternatively, the computer device may be based on a formula
Obtaining kurtosis of vehicle data, wherein n is the size of the vehicle data, D is the variance of the vehicle data, and xiFor the ith data value in the above-mentioned vehicle data,
the arithmetic mean value of the vehicle data is used for reflecting the degree of kurtosis or the degree of peak convexity of the vehicle data distribution graph; the computer equipment can be according to formula
Obtaining an average of vehicle data, wherein n is the size of the vehicle data and xiThe ith data value in the vehicle data is obtained, wherein the average number of the vehicle data is used for reflecting the general level of the population or the centralized trend of the distribution; the computer equipment can be according to formula
Obtaining the variance of the vehicle data, wherein n is the size of the vehicle data, and xiIs the ith data value, x in the vehicle dataavgThe mean of the vehicle data, wherein the variance of the vehicle data is used for describing the variation condition or the discrete degree of the overall distribution of the vehicle data; the computer equipment can be according to formula
Obtaining the skewness of the vehicle data, wherein n is the size of the vehicle data and xiThe ith number in the vehicle dataAccording to the value of the one or more parameters,
is an arithmetic average of the above-mentioned vehicle data, wherein the skewness of the vehicle data is used to reflect the direction and degree of asymmetry of the vehicle data distribution.
In the embodiment, the computer device inputs the characteristic data into the growth coefficient model to obtain the growth coefficient of the vehicle data, inputs the growth coefficient of the vehicle data and the vehicle data into the polynomial regression model, corrects the vehicle data to obtain corrected data, and because the polynomial regression model inputs the growth coefficient of the vehicle data and the vehicle data, the vehicle data can be corrected more accurately in consideration of the growth coefficient of the vehicle data, and the accuracy of the obtained corrected data is improved.
In the above scenario where the feature data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device are input into the data correction model, the vehicle data needs to be determined first to determine whether there is abnormal data in the vehicle data, and if there is abnormal data in the vehicle data, the feature data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device are input into the data correction model to correct the vehicle data, so as to obtain corrected data. Fig. 4 is a schematic flowchart of a data processing method according to another embodiment. The embodiment relates to a specific implementation process for judging whether abnormal data exist in vehicle data sent by vehicle-mounted intelligent equipment through computer equipment. As shown in fig. 4, on the basis of the foregoing embodiment, as an optional implementation manner, the foregoing method further includes:
s401, judging whether abnormal data exist in the vehicle data or not by using a preset abnormal data judgment algorithm according to the statistical characteristics of the vehicle data.
Specifically, the computer device judges whether abnormal data exist in the vehicle data sent by the vehicle-mounted intelligent device or not by using a preset abnormal data judgment algorithm according to the statistical characteristics of the vehicle data. Optionally, when the vehicle data obeys normal distribution, the statistical characteristic of the vehicle data may be the number of the vehicle data and the vehicle dataAnd when the vehicle data do not conform to the normal distribution, the statistical characteristic of the vehicle data can be an average value of the vehicle data. Optionally, if the vehicle data sent by the vehicle-mounted intelligent device obeys the mathematical expectation of μ, the variance is σ2Normal distribution, the computer device may then count according to the formula P (| x- μ |)>3 delta) is less than or equal to delta, whether abnormal data exist in vehicle data sent by the vehicle-mounted intelligent device is judged, in the formula, x is the vehicle data sent by the vehicle-mounted intelligent device, delta is a preset probability threshold value, and optionally the preset probability threshold value can be 0.003. Illustratively, taking the ignition frequency of the vehicle as an example, if the ignition frequency data of the vehicle sent by the vehicle-mounted intelligent device is mu according to the mathematical expectation and the variance is sigma2Normal distribution according to the formula P (| x-mu |)>And 3 delta) is less than or equal to delta, judging that ignition frequency data with the probability value of the difference between the ignition frequency data and the mathematical expectation mu being greater than 3 sigma and the probability value being smaller than a preset probability threshold exist in the vehicle data sent by the vehicle-mounted intelligent device, and determining that abnormal data exists in the ignition frequency data of the vehicle sent by the vehicle-mounted intelligent device through the computer device. Optionally, when the vehicle data sent by the vehicle-mounted intelligent device does not comply with the normal distribution, it may be determined whether an average value of the vehicle data sent by the vehicle-mounted intelligent device is greater than a preset threshold, and it is determined whether abnormal data exists in the vehicle data sent by the vehicle-mounted intelligent device, optionally, the preset threshold may be a multiple of a standard deviation σ of the vehicle data, for example, the preset threshold may be 4 σ, and if an average distance of data in the vehicle data sent by the vehicle-mounted intelligent device is greater than 4 σ, it is determined that abnormal data exists in the vehicle data sent by the vehicle-mounted intelligent device.
And S402, if yes, inputting the characteristic data and the vehicle data into a data correction model, and correcting the vehicle data to obtain corrected data.
Specifically, if the computer device determines that abnormal data exists in the vehicle data sent by the vehicle-mounted intelligent device, the characteristic data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device are input into the data correction model, and the vehicle data sent by the vehicle-mounted intelligent device is corrected to obtain corrected data. It can be understood that the computer device determines whether the vehicle data sent by the vehicle-mounted intelligent device has abnormal data or not, only inputs the vehicle data with the abnormal data into the data correction model, and only corrects the vehicle data with the abnormal data, so that the processing efficiency of the vehicle data is improved.
In this embodiment, the computer device determines whether abnormal data exists in the vehicle data by using a preset abnormal data determination algorithm, and if the abnormal data exists, the feature data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device are input into the data correction model to correct the vehicle data, so as to obtain corrected data.
In the above scenario where the characteristic data of the vehicle-mounted intelligent device and the vehicle data sent by the vehicle-mounted intelligent device are input into the data correction model, the preset data correction model needs to be trained first to obtain the data correction model. Fig. 5 is a schematic flowchart of a data processing method according to another embodiment. The embodiment relates to a specific implementation process of training a preset data correction model by computer equipment. As shown in fig. 5, on the basis of the foregoing embodiment, as an optional implementation manner, the data correction model is obtained by training a preset data correction model by using vehicle sample data and sample feature data of the vehicle-mounted intelligent device, and includes:
s501, obtaining sample characteristic data of the vehicle-mounted intelligent equipment and vehicle sample data sent by the vehicle-mounted intelligent equipment; the sample characteristic data represents a growth coefficient of the data volume of vehicle sample data sent by the vehicle-mounted intelligent device.
Specifically, the computer device obtains sample characteristic data of the vehicle-mounted intelligent device and vehicle sample data sent by the vehicle-mounted intelligent device, wherein the sample characteristic data of the vehicle-mounted intelligent device represents a growth coefficient of data volume of the vehicle sample data sent by the vehicle-mounted intelligent device. Optionally, the computer device may obtain the sample characteristic data of the vehicle-mounted intelligent device and the vehicle sample data sent by the vehicle-mounted intelligent device from a database in which the sample characteristic data of the vehicle-mounted intelligent device and the vehicle sample data sent by the vehicle-mounted intelligent device are stored, or may obtain the sample characteristic data of the vehicle-mounted intelligent device and the vehicle sample data sent by the vehicle-mounted intelligent device in real time through network communication between the computer device and the vehicle-mounted intelligent device.
And S502, training a preset growth coefficient model by using the sample characteristic data of the vehicle-mounted intelligent equipment to obtain a growth coefficient and a growth coefficient model of vehicle sample data.
Specifically, the computer device trains a preset growth coefficient model by using sample characteristic data of the vehicle-mounted intelligent device to obtain a growth coefficient and a growth coefficient model of vehicle sample data. Optionally, the computer device may input the sample characteristic data of the vehicle-mounted intelligent device into a preset growth coefficient model to obtain a predicted growth coefficient of the vehicle sample data, obtain a value of a loss function of the preset growth coefficient model according to the predicted growth coefficient of the obtained vehicle sample data and the preset sample growth coefficient, and train the preset growth coefficient model by using the value of the loss function of the preset growth coefficient model to obtain the growth coefficient model. Optionally, the computer device may determine the corresponding growth coefficient model when the value of the loss function of the preset growth coefficient model reaches a stable value, as the growth coefficient model. Alternatively, the preset growth coefficient model may be a support vector machine model.
And S503, training the preset polynomial regression model by using the growth coefficient of the vehicle sample data and the vehicle sample data to obtain the polynomial regression model.
Specifically, the computer device trains a preset polynomial regression model by using the growth coefficient of the obtained vehicle sample data and the vehicle sample data to obtain the polynomial regression model. Optionally, the computer device may obtain a statistical characteristic value of the vehicle sample data, where the statistical characteristic value of the vehicle sample data includes a kurtosis, a mean, a variance, and a skewness of the vehicle sample data, input a growth coefficient of the obtained vehicle sample data, the statistical characteristic value of the vehicle sample data, and the vehicle sample data into the preset polynomial regression model, correct the vehicle sample data to obtain a corrected vehicle sample data, obtain a value of a loss function of the polynomial regression model according to the corrected vehicle sample data and preset vehicle reference data, and train the preset polynomial regression model by using a value of the loss function of the polynomial regression model to obtain the polynomial regression model. Optionally, the computer device may determine the corresponding polynomial regression model as the polynomial regression model when the value of the loss function of the preset polynomial regression model reaches a stable value.
In this embodiment, the computer device obtains the sample characteristic data of the vehicle-mounted intelligent device and the vehicle sample data sent by the vehicle-mounted intelligent device, trains the preset growth coefficient model by using the sample characteristic data of the vehicle-mounted intelligent device data to obtain the growth coefficient and the growth coefficient model of the vehicle sample data, trains the preset polynomial regression model by using the growth coefficient and the vehicle sample data of the vehicle sample data to obtain the polynomial regression model, so that the data growth condition can be considered to simulate the real change condition of the data along with time as much as possible, the numerical characteristics of the data can be represented more truly, and the accuracy of the obtained growth coefficient model and the polynomial regression model is improved.
It should be understood that although the various steps in the flow charts of fig. 2-5 are shown in order as indicated by the arrows, the steps are not necessarily performed in order 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 some of the steps in fig. 2-5 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
Fig. 6 is a schematic structural diagram of a data processing apparatus according to an embodiment. As shown in fig. 6, the apparatus may include: a data acquisition module 10 and a processing module 11.
Specifically, the data obtaining module 10 is configured to obtain feature data of the vehicle-mounted intelligent device and vehicle data sent by the vehicle-mounted intelligent device; the characteristic data represents a growth coefficient of the data volume of the vehicle data sent by the vehicle-mounted intelligent equipment;
the processing module 11 is configured to input the feature data and the vehicle data into the data correction model, and correct the vehicle data to obtain corrected data; the data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment.
The data processing apparatus provided in this embodiment may execute the method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
Fig. 7 is a schematic structural diagram of a data processing apparatus according to an embodiment. On the basis of the foregoing embodiment, the data correction model includes a growth coefficient model and a polynomial regression model, and optionally, as shown in fig. 7, the processing module 11 includes: a first processing unit 111 and a second processing unit 112.
Specifically, the first processing unit 111 is configured to input the feature data into a growth coefficient model to obtain a growth coefficient of the vehicle data;
and the second processing unit 112 is configured to input the growth coefficient and the vehicle data into the polynomial regression model, and correct the vehicle data to obtain corrected data.
The data processing apparatus provided in this embodiment may execute the method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
With reference to fig. 7, on the basis of the foregoing embodiment, optionally, the second processing unit 112 is specifically configured to obtain a statistical characteristic value of the vehicle data; the statistical characteristic value comprises kurtosis, average, variance and skewness of the data; and inputting the growth coefficient, the vehicle data and the statistical characteristic value into a polynomial regression model, and correcting the vehicle data to obtain corrected data.
The data processing apparatus provided in this embodiment may execute the method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
With continuing reference to fig. 7, based on the foregoing embodiment, optionally, as shown in fig. 7, the apparatus further includes: a judging module 12 and a correcting module 13.
Specifically, the determining module 12 is configured to determine whether abnormal data exists in the vehicle data according to the statistical characteristics of the vehicle data by using a preset abnormal data determining algorithm;
and the correction module 13 is configured to, if yes, input the feature data and the vehicle data into the data correction model, and correct the vehicle data to obtain corrected data.
The data processing apparatus provided in this embodiment may execute the method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
With continuing reference to fig. 7, based on the foregoing embodiment, optionally, as shown in fig. 7, the apparatus further includes: a sample data acquisition module 14, a first training module 15 and a second training module 16.
Specifically, the sample data obtaining module 14 is configured to obtain sample characteristic data of the vehicle-mounted intelligent device and vehicle sample data sent by the vehicle-mounted intelligent device; the sample characteristic data represents a data volume increase coefficient of vehicle sample data sent by the vehicle-mounted intelligent equipment;
the first training module 15 is configured to train a preset growth coefficient model by using sample characteristic data of the vehicle-mounted intelligent device to obtain a growth coefficient and a growth coefficient model of vehicle sample data;
and the second training module 16 is configured to train the preset polynomial regression model by using the growth coefficient of the vehicle sample data and the vehicle sample data to obtain the polynomial regression model.
The data processing apparatus provided in this embodiment may execute the method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
With continuing reference to fig. 7, based on the foregoing embodiment, optionally, as shown in fig. 7, the first training module 15 includes: an operation unit 151, a first acquisition unit 152, and a second acquisition unit 152.
Specifically, the operation unit 151 is configured to input sample characteristic data of the vehicle-mounted intelligent device into a preset growth coefficient model to obtain a predicted growth coefficient of vehicle sample data;
a first obtaining unit 152, configured to obtain a value of a loss function of a preset growth coefficient model according to a predicted growth coefficient of vehicle sample data and a preset sample growth coefficient;
the second obtaining unit 152 is configured to train the preset growth coefficient model by using the value of the loss function of the preset growth coefficient model, so as to obtain the growth coefficient model.
The data processing apparatus provided in this embodiment may execute the method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
With continuing reference to fig. 7, based on the foregoing embodiment, optionally, as shown in fig. 7, the second training module 16 includes: a third acquisition unit 161, a correction unit 162, a fourth acquisition unit 163, and a fifth acquisition unit 164.
Specifically, the third obtaining unit 161 is configured to obtain a statistical characteristic value of the vehicle sample data; the statistical characteristic value of the vehicle sample data comprises the kurtosis, the average, the variance and the skewness of the vehicle sample data;
the correcting unit 162 is configured to input the growth coefficient of the vehicle sample data, the statistical characteristic value of the vehicle sample data, and the vehicle sample data into a preset polynomial regression model, and correct the vehicle sample data to obtain corrected vehicle sample data;
a fourth obtaining unit 163, configured to obtain a value of a loss function of the preset polynomial regression model according to the corrected vehicle sample data and preset vehicle reference data;
a fifth obtaining unit 164, configured to train the preset polynomial regression model by using the value of the loss function of the preset polynomial regression model, so as to obtain the polynomial regression model.
The data processing apparatus provided in this embodiment may execute the method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
For specific limitations of the data processing apparatus, reference may be made to the above limitations of the data processing method, which are not described herein again. The various modules in the data processing apparatus described above may be implemented in whole or in part by software, hardware, and combinations 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.
The data processing method provided by the embodiment of the application can be applied to the computer device shown in fig. 8, and the internal structure diagram of the data processing method can be shown in fig. 8. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The database of the computer equipment is used for storing the data in the data processing method. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a data processing method.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring characteristic data of the vehicle-mounted intelligent equipment and vehicle data sent by the vehicle-mounted intelligent equipment; the characteristic data represents a growth coefficient of the data volume of the vehicle data sent by the vehicle-mounted intelligent equipment;
inputting the characteristic data and the vehicle data into a data correction model, and correcting the vehicle data to obtain corrected data; the data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment.
The implementation principle and technical effect of the computer device provided by the above embodiment are similar to those of the above method embodiment, and are not described herein again.
In one embodiment, a readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring characteristic data of the vehicle-mounted intelligent equipment and vehicle data sent by the vehicle-mounted intelligent equipment; the characteristic data represents a growth coefficient of the data volume of the vehicle data sent by the vehicle-mounted intelligent equipment;
inputting the characteristic data and the vehicle data into a data correction model, and correcting the vehicle data to obtain corrected data; the data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment.
The implementation principle and technical effect of the readable storage medium provided by the above embodiment are similar to those of the above method embodiment, and are not described herein again.
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, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within 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 invention, 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 inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (7)
1. A method of data processing, the method comprising:
acquiring characteristic data of vehicle-mounted intelligent equipment and vehicle data sent by the vehicle-mounted intelligent equipment; the characteristic data represent a growth coefficient of the data volume of the vehicle data sent by the vehicle-mounted intelligent device; the characteristic data comprise a daily average active value of vehicle-mounted intelligent equipment for sending vehicle data in a preset time interval, the total number of the vehicle-mounted intelligent equipment and a growth coefficient of the total number of the vehicle-mounted intelligent equipment; the vehicle data includes a number of ignition times of the vehicle, a coolant temperature of the vehicle, a power of an engine of the vehicle, and a fuel consumption of the vehicle;
judging whether abnormal data exist in the vehicle data or not by using a preset abnormal data judgment algorithm according to the statistical characteristics of the vehicle data;
if so, inputting the characteristic data and the vehicle data into a data correction model, and correcting the vehicle data to obtain corrected data; the data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment;
the data correction model comprises a growth coefficient model and a polynomial regression model, the characteristic data and the vehicle data are input into the data correction model, the vehicle data are corrected, and corrected data are obtained, and the method comprises the following steps:
inputting the characteristic data into the growth coefficient model to obtain a growth coefficient of the vehicle data;
acquiring a statistical characteristic value of the vehicle data; the statistical characteristic value comprises kurtosis, mean, variance and skewness of the vehicle data; the kurtosis of the vehicle data is used for reflecting the tapering degree or the peak convex degree of a vehicle data distribution graph; the average of the vehicle data is used to reflect a general level of population or a central tendency of distribution; the variance of the vehicle data is used for describing the variation condition or the discrete degree of the overall distribution of the vehicle data; the skewness of the vehicle data is used for reflecting the asymmetric direction and degree of the vehicle data distribution;
and inputting the growth coefficient, the vehicle data and the statistical characteristic value into the polynomial regression model, and correcting the vehicle data to obtain corrected data.
2. The method of claim 1, wherein the data correction model is obtained by training a preset data correction model by using vehicle sample data and sample characteristic data of an on-board intelligent device, and comprises:
acquiring sample characteristic data of the vehicle-mounted intelligent equipment and vehicle sample data sent by the vehicle-mounted intelligent equipment; the sample characteristic data represents a growth coefficient of the data volume of vehicle sample data sent by the vehicle-mounted intelligent equipment;
training a preset growth coefficient model by using the sample characteristic data of the vehicle-mounted intelligent equipment to obtain a growth coefficient of the vehicle sample data and the growth coefficient model;
and training a preset polynomial regression model by using the growth coefficient of the vehicle sample data and the vehicle sample data to obtain the polynomial regression model.
3. The method according to claim 2, wherein the training a preset growth coefficient model by using the sample feature data of the vehicle-mounted intelligent device to obtain the growth coefficient of the vehicle sample data and the growth coefficient model comprises:
inputting the sample characteristic data of the vehicle-mounted intelligent equipment into the preset growth coefficient model to obtain a predicted growth coefficient of the vehicle sample data;
obtaining a value of a loss function of the preset growth coefficient model according to the predicted growth coefficient of the vehicle sample data and a preset sample growth coefficient;
and training the preset growth coefficient model by using the value of the loss function of the preset growth coefficient model to obtain the growth coefficient model.
4. The method according to claim 3, wherein the training a preset polynomial regression model by using the growth coefficient of the vehicle sample data and the vehicle sample data to obtain the polynomial regression model comprises:
acquiring a statistical characteristic value of the vehicle sample data; the statistical characteristic value of the vehicle sample data comprises kurtosis, average, variance and skewness of the vehicle sample data;
inputting the growth coefficient of the vehicle sample data, the statistical characteristic value of the vehicle sample data and the vehicle sample data into the preset polynomial regression model, and correcting the vehicle sample data to obtain corrected vehicle sample data;
obtaining a value of a loss function of the preset polynomial regression model according to the corrected vehicle sample data and preset vehicle reference data;
and training the preset polynomial regression model by using the value of the loss function of the preset polynomial regression model to obtain the polynomial regression model.
5. A data processing apparatus, characterized in that the apparatus comprises:
the data acquisition module is used for acquiring characteristic data of the vehicle-mounted intelligent equipment and vehicle data sent by the vehicle-mounted intelligent equipment; the characteristic data represent a growth coefficient of the data volume of the vehicle data sent by the vehicle-mounted intelligent device; the characteristic data comprise a daily average active value of vehicle-mounted intelligent equipment for sending vehicle data in a preset time interval, the total number of the vehicle-mounted intelligent equipment and a growth coefficient of the total number of the vehicle-mounted intelligent equipment; the vehicle data includes a number of ignition times of the vehicle, a coolant temperature of the vehicle, a power of an engine of the vehicle, and a fuel consumption of the vehicle;
the processing module is used for judging whether the vehicle data has abnormal data or not by utilizing a preset abnormal data judgment algorithm according to the statistical characteristics of the vehicle data; if so, inputting the characteristic data and the vehicle data into a data correction model, and correcting the vehicle data to obtain corrected data; the data correction model is obtained by training a preset data correction model by utilizing vehicle sample data and sample characteristic data of the vehicle-mounted intelligent equipment;
the data correction model comprises a growth coefficient model and a polynomial regression model, the characteristic data and the vehicle data are input into the data correction model, the vehicle data are corrected, and corrected data are obtained, and the method comprises the following steps:
inputting the characteristic data into the growth coefficient model to obtain a growth coefficient of the vehicle data;
acquiring a statistical characteristic value of the vehicle data; the statistical characteristic value comprises kurtosis, mean, variance and skewness of the vehicle data; the kurtosis of the vehicle data is used for reflecting the tapering degree or the peak convex degree of a vehicle data distribution graph; the average of the vehicle data is used to reflect a general level of population or a central tendency of distribution; the variance of the vehicle data is used for describing the variation condition or the discrete degree of the overall distribution of the vehicle data; the skewness of the vehicle data is used for reflecting the asymmetric direction and degree of the vehicle data distribution;
and inputting the growth coefficient, the vehicle data and the statistical characteristic value into the polynomial regression model, and correcting the vehicle data to obtain corrected data.
6. 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 according to any of claims 1-4.
7. 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 according to any one of claims 1 to 4.
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