CN112347423A - Data screening method and device for radius analysis original data and storage medium - Google Patents
Data screening method and device for radius analysis original data and storage medium Download PDFInfo
- Publication number
- CN112347423A CN112347423A CN202011167027.1A CN202011167027A CN112347423A CN 112347423 A CN112347423 A CN 112347423A CN 202011167027 A CN202011167027 A CN 202011167027A CN 112347423 A CN112347423 A CN 112347423A
- Authority
- CN
- China
- Prior art keywords
- data
- approximate
- standard deviation
- variance
- screening
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000012216 screening Methods 0.000 title claims abstract description 57
- 238000004458 analytical method Methods 0.000 title claims abstract description 27
- 238000004364 calculation method Methods 0.000 claims abstract description 58
- 238000001914 filtration Methods 0.000 claims description 29
- 238000004422 calculation algorithm Methods 0.000 claims description 23
- 238000001228 spectrum Methods 0.000 claims description 6
- 238000005516 engineering process Methods 0.000 abstract description 10
- 238000010586 diagram Methods 0.000 description 10
- 238000004590 computer program Methods 0.000 description 7
- 238000012986 modification Methods 0.000 description 6
- 230000004048 modification Effects 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
Abstract
The invention discloses a data screening method and a device for radius analysis original data and a computer readable storage medium, wherein the method comprises the following steps: acquiring data to be screened in real time and calculating an approximate average value of the data in a recursion calculation mode; calculating an approximate standard deviation or an approximate variance of the data based on the approximate average value in a recursive calculation mode; and screening the data by using the approximate mean, the approximate standard deviation or the approximate variance. The invention solves the problem that the data can not be screened in real time in the traditional technology, realizes the real-time screening of the data by calculating the standard deviation or the variance of the data in real time, and ensures the validity of the data.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data screening method and apparatus for radius analysis raw data, and a computer-readable storage medium.
Background
The increasing number of automobiles is caused by the promotion of modern technology, and the safety performance is always the most important guarantee part of the automobiles. The tire is an important part of an automobile, the main factor to be considered in the performance of the tire is the tire pressure of the tire, and certain hidden dangers can be caused by the overhigh or overlow tire pressure in the driving process of the automobile. Therefore, it is very important to monitor the tire pressure during the driving of the automobile. The tire pressure monitoring system, i.e. the TPMS, is a set of system for monitoring the tire pressure condition in real time, and alarming abnormal conditions such as low pressure condition and air leakage condition, thereby ensuring the driving safety of automobiles and drivers.
There are currently two main forms of systems for monitoring the tyre pressure of a motor vehicle: direct TPMS and indirect TPMS. The operating principle of the direct TPMS is that the sensor is arranged to monitor the specific value of the tire pressure, and when the tire pressure is reduced due to air leakage, the direct TPMS can give an alarm. The working principle of the indirect TPMS is that the running radius and the frequency spectrum of the wheels of the vehicle are calculated and analyzed through algorithm calculation, so that the monitoring of the tire pressure is realized. In comparison, the direct TPMS can know specific information in real time and can obtain the information of the automobile tire pressure more directly. However, the direct TPMS has disadvantages in that the sensor is easily damaged and the maintenance is relatively troublesome, so that the cost is high. The indirect TPMS is low in cost and easy to maintain, but the indirect tire pressure monitoring is based on the test of a large amount of data, and the accuracy of the result is that deletion of the large amount of data in the calculation process ensures that the calculated data are all valid data.
Disclosure of Invention
The data screening method and device for the radius analysis raw data and the computer readable storage medium solve the problem that data cannot be screened in real time in the traditional technology, achieve real-time screening of the data by calculating the standard deviation or variance of the data in real time, and guarantee the effectiveness of the data.
The embodiment of the application provides a data screening method for radius analysis raw data, which comprises the following steps:
acquiring data to be screened in real time and calculating an approximate average value of the data in a recursion calculation mode;
calculating an approximate standard deviation or an approximate variance of the data based on the approximate average value in a recursive calculation mode;
and screening the data by using the approximate mean, the approximate standard deviation or the approximate variance.
In one embodiment, the data is a running radius and a frequency spectrum of the wheel.
In one embodiment, the step of calculating the approximate average of the data by using recursive calculation includes:
and carrying out recursive calculation on the data and the average value obtained in the past by adopting a digital low-pass filtering algorithm to obtain an approximate average value of the data.
In one embodiment, the step of calculating an approximate standard deviation or an approximate variance of the data based on the approximate mean by means of recursive calculation comprises:
carrying out recursive calculation on the data and the average value and the standard deviation obtained in advance by adopting a digital low-pass filtering algorithm to obtain an approximate standard deviation of the data; or
And carrying out recursive calculation on the data and the average value and the variance obtained before by adopting a digital low-pass filtering algorithm to obtain the approximate variance of the data.
In one embodiment, the step of filtering the data using the approximate mean, the approximate standard deviation, or the approximate variance comprises:
setting a value range of data according to the approximate average value, the approximate standard deviation or the approximate variance;
and screening the data by utilizing the value range.
In an embodiment, the step of setting a value range of data according to the approximate average, the approximate standard deviation, or the approximate variance includes:
and taking the sum of the approximate average and the approximate standard deviation or the approximate variance as the upper value limit of the data, and taking the difference between the approximate average and the approximate standard deviation or the approximate variance as the lower value limit of the data.
In an embodiment, the step of setting a value range of data according to the approximate average, the approximate standard deviation, or the approximate variance further includes:
and updating the value range of the data by adding a specific value to the upper value limit and subtracting the specific value from the lower value limit.
In an embodiment, the method further comprises:
and updating the specific value according to the historical data screening result.
The embodiment of the present application further provides an apparatus, which includes a processor, a memory, and a data filtering program stored on the memory and operable on the processor, and when executed by the processor, the data filtering program implements the steps of the data filtering method for radius analysis of raw data as described above.
The embodiment of the present application also provides a computer-readable storage medium, on which a data filtering program is stored, and when the data filtering program is executed by a processor, the data filtering program implements the steps of the data filtering method for radius analysis of raw data as described above.
The technical scheme of the data screening method and device for radius analysis raw data and the computer readable storage medium provided in the embodiment of the application has at least the following technical effects:
because the data to be screened is obtained in real time and the approximate average value of the data is calculated in a recursion calculation mode; calculating an approximate standard deviation or an approximate variance of the data based on the approximate average value in a recursive calculation mode; and a technical means for screening the data by using the approximate mean, the approximate standard deviation or the approximate variance. Therefore, the problem that the data cannot be screened in real time in the traditional technology is effectively solved, the standard deviation or the variance of the data is calculated in real time, the data is screened in real time, and the effectiveness of the data is guaranteed.
Drawings
FIG. 1 is a schematic structural diagram of an apparatus according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of a data screening method for radius analysis of raw data according to a first embodiment of the present invention;
FIG. 3 is a schematic flow chart of a data screening method for radius analysis of raw data according to a second embodiment of the present invention;
fig. 4 is a schematic flowchart of a data screening method for radius analysis raw data according to a third embodiment of the present application.
Detailed Description
In order to solve the problem that data cannot be screened in real time in the traditional technology, the data to be screened are obtained in real time, and the approximate average value of the data is calculated in a recursion calculation mode; calculating an approximate standard deviation or an approximate variance of the data based on the approximate average value in a recursive calculation mode; and screening the data by using the approximate mean value, the approximate standard deviation or the approximate variance. The standard deviation or variance of the data is calculated in real time, so that the data is screened in real time, and the effectiveness of the data is guaranteed.
For a better understanding of the above technical solutions, exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Referring to fig. 1, it is a schematic diagram of a hardware structure of an apparatus involved in various embodiments of the present application, where the apparatus may include: processor 101, memory 102, input unit 103, output unit 104, and the like. Those skilled in the art will appreciate that the hardware configuration of the apparatus shown in fig. 1 does not constitute a limitation of the apparatus, which may include more or less components than those shown, or some components in combination, or a different arrangement of components.
The various components of the device are described in detail below with reference to fig. 1:
the processor 101 is a control center of the apparatus, connects various parts of the entire apparatus, and performs various functions of the apparatus or processes data by running or executing a program stored in the memory 102 and calling up the data stored in the memory 102, thereby monitoring the entire apparatus.
The memory 102 may be used to store various programs of the device as well as various data. The memory 102 mainly includes a program storage area and a data storage area, wherein the program storage area at least stores programs required for data screening; the storage data area may store various data of the device. Further, the memory 102 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The input unit 103 may be used to input data that needs to be subjected to data screening.
The output unit 104 may be used to output data that has been subjected to data screening.
In the embodiment of the present application, the processor 101 may be configured to call the data filter stored in the memory 102, and perform the following operations:
acquiring data to be screened in real time and calculating an approximate average value of the data in a recursion calculation mode;
calculating an approximate standard deviation or an approximate variance of the data based on the approximate average value in a recursive calculation mode;
and screening the data by using the approximate mean, the approximate standard deviation or the approximate variance.
In one embodiment, the data is a running radius and a frequency spectrum of the wheel.
In one embodiment, the processor 101 may be configured to invoke a data filter stored in the memory 102 and perform the following operations:
and carrying out recursive calculation on the data and the average value obtained in the past by adopting a digital low-pass filtering algorithm to obtain an approximate average value of the data.
In one embodiment, the processor 101 may be configured to invoke a data filter stored in the memory 102 and perform the following operations:
carrying out recursive calculation on the data and the average value and the standard deviation obtained in advance by adopting a digital low-pass filtering algorithm to obtain an approximate standard deviation of the data; or
And carrying out recursive calculation on the data and the average value and the variance obtained before by adopting a digital low-pass filtering algorithm to obtain the approximate variance of the data.
In one embodiment, the processor 101 may be configured to invoke a data filter stored in the memory 102 and perform the following operations:
setting a value range of data according to the approximate average value, the approximate standard deviation or the approximate variance;
and screening the data by utilizing the value range.
In one embodiment, the processor 101 may be configured to invoke a data filter stored in the memory 102 and perform the following operations:
and taking the sum of the approximate average and the approximate standard deviation or the approximate variance as the upper value limit of the data, and taking the difference between the approximate average and the approximate standard deviation or the approximate variance as the lower value limit of the data.
In one embodiment, the processor 101 may be configured to invoke a data filter stored in the memory 102 and perform the following operations:
and updating the value range of the data by adding a specific value to the upper value limit and subtracting the specific value from the lower value limit.
In one embodiment, the processor 101 may be configured to invoke a data filter stored in the memory 102 and perform the following operations:
and updating the specific value according to the historical data screening result.
According to the technical scheme, the data to be screened are obtained in real time, and the approximate average value of the data is calculated in a recursion calculation mode; calculating an approximate standard deviation or an approximate variance of the data based on the approximate average value in a recursive calculation mode; and a technical means for screening the data by using the approximate mean, the approximate standard deviation or the approximate variance. Therefore, the problem that the data cannot be screened in real time in the traditional technology is effectively solved, the standard deviation or the variance of the data is calculated in real time, the data is screened in real time, and the effectiveness of the data is guaranteed.
For better understanding of the above technical solutions, the following detailed descriptions will be provided in conjunction with the drawings and the detailed description of the embodiments.
Referring to fig. 2, in a first embodiment of the present application, a data screening method for radius analysis raw data specifically includes the following steps:
and step S110, acquiring data to be screened in real time and calculating an approximate average value of the data in a recursion calculation mode.
In this embodiment, the data may be the running radius and the frequency spectrum of the wheel, or may be other data that is updated in real time and needs to be filtered in real time. And by acquiring the running radius and the spectrum data of the wheels to be screened in real time and screening the data in real time, the data with overlarge deviation in the data can be removed, so that the accuracy in subsequent calculation is ensured, and the accuracy of the finally obtained tire pressure estimation result is ensured. The data screening method for radius analysis raw data adopts the method that the data is screened by calculating the mean value, standard deviation or variance of the data. However, the method for calculating the standard deviation and the variance, which is usually adopted in mathematics, needs to calculate all data at a time to obtain a final result, however, the data to be screened in the present application is data updated in real time, the data volume is always increased, and the direct adoption of the above calculation method is troublesome and not easy to implement. Therefore, the data screening method for the radius analysis original data adopts a method for calculating approximate values to conveniently obtain the standard deviation and the variance of the data, namely a recursion calculation method is used for replacing a common method for calculating the standard deviation and the variance at one time. By the method, the difficulty of calculation and the time of calculation can be reduced, so that the timeliness of a final result can be ensured. Before calculating the approximate standard deviation and the approximate variance, the approximate average of the data needs to be calculated in a recursive manner, that is, the approximate average of the current time is calculated by using the average obtained at the previous time and the currently obtained data.
And step S120, calculating the approximate standard deviation or the approximate variance of the data by adopting a recursion calculation mode based on the approximate average value.
In this embodiment, after the approximate average of the data is obtained through calculation, the approximate standard deviation or the approximate variance of the data may be further calculated through a recursive calculation based on the approximate average, that is, the approximate standard deviation at the current time is calculated by using the average, the standard deviation and the currently obtained data obtained at the previous time, or the approximate variance at the current time is calculated by using the average, the variance and the currently obtained data obtained at the previous time. The method for calculating the approximate standard deviation and the approximate variance is not a method for calculating the standard deviation and the variance of the standard in the general mathematical sense, but is a method for calculating the standard deviation and the variance relative to the approximate average based on the approximate average obtained in the past by adopting a recursion calculation mode. In one embodiment, the recursive computation may be a digital low pass filter algorithm.
And S130, screening the data by using the approximate average value, the approximate standard deviation or the approximate variance.
In this embodiment, the data is screened by using the approximate average, the approximate standard deviation or the approximate variance, so that data with an excessive deviation in the data can be removed, the calculation accuracy in the subsequent calculation by using the data is ensured, and the accuracy of a result obtained by final calculation is ensured. Generally, the data is filtered by the approximate mean, the approximate standard deviation or the approximate variance, and the data is selected by determining a value range of the data by the approximate mean and the approximate standard deviation or by the approximate mean and the approximate variance. For the selection of the value range, the approximate average value plus the approximate standard deviation or the approximate variance can be generally used as the upper value limit, and the approximate average value minus the approximate standard deviation or the approximate variance can be used as the lower value limit. In an embodiment, in order to ensure the selected amount of data, a specific value may be added to the upper value limit, and a specific value may be subtracted from the lower value limit.
The method has the advantages that the data to be screened are obtained in real time, and the approximate average value of the data is calculated in a recursion calculation mode; calculating an approximate standard deviation or an approximate variance of the data based on the approximate average value in a recursive calculation mode; and screening the data by using the approximate mean value, the approximate standard deviation or the approximate variance. Therefore, the problem that the data cannot be screened in real time in the traditional technology is effectively solved, the standard deviation or the variance of the data is calculated in real time, the data is screened in real time, and the effectiveness of the data is guaranteed.
Referring to fig. 3, in a second embodiment of the present application, a data screening method for radius analysis raw data specifically includes the following steps:
and S211, acquiring data to be screened in real time, and performing recursive calculation on the data and the average value obtained in the past by adopting a digital low-pass filtering algorithm to obtain an approximate average value of the data.
In this embodiment, after obtaining the data to be filtered in real time, a digital low-pass filtering algorithm may be used to perform recursive calculation on the data and the previously obtained average value to obtain an approximate average value of the data. By calculating the approximate average value of the data in real time by adopting a digital low-pass filtering algorithm, the approximate variation trend of the data can be obtained. In one embodiment, the formula of the digital low-pass filtering algorithm may be as follows:
S_Mean(i+1)＝[S-S_Mean(i)]/K+S_Mean(i)
wherein, S _ Mean (i +1) is an approximate average value obtained at the current time, S _ Mean (i) is an average value obtained at the previous time, S is data obtained at the current time, and K is a filter coefficient. For the value of the filter coefficient K, if the value is larger, the variation speed of the approximate average value is slow, but the approximate average value is more accurate; if the value of the approximate average value is small, the change speed of the approximate average value is high, but the fluctuation range of the approximate average value is larger. The approximate average finally obtained by the formula is a certain multiple of the mathematically obtained average, and is mainly related to the value of the filter coefficient, but the data screening method for the radius analysis of the original data can accept the calculation result as the approximate average to screen the data.
Step S221, a digital low-pass filtering algorithm is adopted to carry out recursion calculation on the data and the average value and the standard deviation which are obtained in advance, and the approximate standard deviation of the data is obtained.
In this embodiment, after the approximate average of the data is obtained through calculation, the data and the average and standard deviation obtained before may be subjected to recursive calculation by using a digital low-pass filtering algorithm to obtain the approximate standard deviation of the data. By calculating the approximate standard deviation of the data in real time using a digital low-pass filtering algorithm, the variation of the approximate fluctuation range of the data can be obtained. In one embodiment, the formula of the digital low-pass filtering algorithm may be as follows:
S_SD(i+1)＝[|S-S_Mean(i)|-S_SD(i)]/K+S_SD(i)
wherein, S _ SD (i +1) is an approximate standard deviation found at the current time, S _ mean (i) is an average value found at the previous time, S _ SD (i) is a standard deviation found at the previous time, S is data obtained at the current time, and K is a filter coefficient. The value of the filter coefficient K is the same as the filter coefficient whose average value is calculated in step S211. The approximate standard deviation finally obtained by the formula is a certain multiple of the mathematically obtained standard deviation, but the meaning of the data fluctuation in a certain time is the same as the mathematically obtained standard deviation, and it is acceptable to perform data screening by using the calculation result as the approximate standard deviation in the data screening method for analyzing the original data in radius according to the present application.
Step S222, performing recursive calculation on the data and the previously obtained average value and variance by using a digital low-pass filtering algorithm to obtain an approximate variance of the data.
In this embodiment, after the approximate average of the data is obtained through calculation, the data and the previously obtained average and variance may be recursively calculated through a digital low-pass filtering algorithm to obtain the approximate variance of the data. By calculating the approximate variance of the data in real time using a digital low pass filtering algorithm, the variation of the approximate fluctuation range of another angle of the data can be obtained. In one embodiment, the formula of the digital low-pass filtering algorithm may be as follows:
wherein, S _ Var (i +1) is an approximate variance obtained at the current time, S _ mean (i) is an average value obtained at the previous time, S _ Var (i) is a variance obtained at the previous time, S is data obtained at the current time, and K is a filter coefficient. The value of the filter coefficient K is determined by the same criterion as the criterion of the filter coefficient for calculating the approximate average value in step S211, and the weight of the variance obtained at the present time and the variance obtained at the previous time is determined. The meaning of the approximate variance finally obtained by the formula indicating that the data fluctuates in a certain period of time is the same as the mathematical variance, and it is acceptable to perform data screening by using the calculation result as the approximate variance in the data screening method for analyzing the raw data by radius according to the present application.
And step S230, screening the data by using the approximate average value, the approximate standard deviation or the approximate variance.
The method has the beneficial effect that the step of calculating the approximate average value, the approximate standard deviation or the approximate variance is refined. Therefore, the problem that the data cannot be screened in real time in the traditional technology is effectively solved, the standard deviation or the variance of the data is calculated in real time, the data is further screened in real time, and the effectiveness of the data is guaranteed.
Referring to fig. 4, in a third embodiment of the present application, a data screening method for radius analysis raw data specifically includes the following steps:
step S310, acquiring data needing to be screened in real time and calculating an approximate average value of the data in a recursion calculation mode.
And step S320, calculating the approximate standard deviation or the approximate variance of the data by adopting a recursion calculation mode based on the approximate average value.
And step S331, setting a value range of data according to the approximate average value, the approximate standard deviation or the approximate variance.
In this embodiment, after the approximate average, the approximate standard deviation, or the approximate variance is calculated, the value range of the data may be set according to the approximate average, the approximate standard deviation, or the approximate variance. For example, the sum of the approximate average and the approximate standard deviation or the approximate variance may be used as an upper limit of the data, and the difference between the approximate average and the approximate standard deviation or the approximate variance may be used as a lower limit of the data. However, such a value range is too narrow, and the filtered data is too strict, so that the subsequent calculation cannot be performed due to the small amount of the filtered data. In this case, the value range can be appropriately widened. In one embodiment. The value range of the data can be updated by adding a specific value to the upper value limit and subtracting the specific value from the lower value limit, so that the purpose of widening the value range of the data is achieved. The specific value may be set according to the amount of data desired to be obtained by the filtering.
And S332, screening the data by using the value range.
In this embodiment, when the value range of the data is obtained, the data may be screened by using the value range. That is, the data that is not within the value range is deleted, and the data within the value range is retained. In an embodiment, if a value range of the data is updated by adding a specific value to the upper value limit and subtracting the specific value from the lower value limit, the specific value may be updated according to a history data screening result after data screening is performed for a certain number of times. For example, if the percentage of data screening in the historical data screening result is lower than a preset threshold, the specific value may be increased so that the data screening does not filter out excessive data. By updating the specific value, the value range of the data can be ensured to be close to the optimal value, namely, the wrong and deviated data can be deleted, the number of correct data can be ensured, and the subsequent calculation is not influenced.
The method has the beneficial effect that the step of screening the data is refined on the basis of the first embodiment. Therefore, the problem that the data cannot be screened in real time in the traditional technology is effectively solved, the standard deviation or the variance of the data is calculated in real time, the data is further screened in real time, and the effectiveness of the data is guaranteed.
Based on the same inventive concept, an embodiment of the present application further provides an apparatus, where the apparatus includes a processor, a memory, and a data filtering program that is stored in the memory and can be run on the processor, and when the data filtering program is executed by the processor, the data filtering program implements each process of the data filtering method embodiment for radius analysis of original data, and can achieve the same technical effect, and is not described herein again to avoid repetition.
Since the apparatus provided in the embodiments of the present application is an apparatus used for implementing the method in the embodiments of the present application, based on the method described in the embodiments of the present application, a person skilled in the art can understand the specific structure and the variation of the apparatus, and thus details are not described herein again. All devices used in the methods of the embodiments of the present application are within the scope of the present application.
Based on the same inventive concept, an embodiment of the present application further provides a computer-readable storage medium, where a data filtering program is stored on the computer-readable storage medium, and when the data filtering program is executed by a processor, the data filtering program implements the processes of the data filtering method for radius analysis of original data, and can achieve the same technical effects, and is not described herein again to avoid repetition.
Since the computer-readable storage medium provided in the embodiments of the present application is a computer-readable storage medium used for implementing the method in the embodiments of the present application, based on the method described in the embodiments of the present application, those skilled in the art can understand the specific structure and modification of the computer-readable storage medium, and thus details are not described herein. Any computer-readable storage medium that can be used with the methods of the embodiments of the present application is intended to be within the scope of the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A data screening method for raw data of radius analysis, the method comprising:
acquiring data to be screened in real time and calculating an approximate average value of the data in a recursion calculation mode;
calculating an approximate standard deviation or an approximate variance of the data based on the approximate average value in a recursive calculation mode;
and screening the data by using the approximate mean, the approximate standard deviation or the approximate variance.
2. The data screening method for radius analysis raw data according to claim 1, wherein the data is a running radius of a wheel and a frequency spectrum.
3. The method for data screening of raw radius analysis data according to claim 1, wherein said step of calculating an approximate average of said data by recursive calculation comprises:
and carrying out recursive calculation on the data and the average value obtained in the past by adopting a digital low-pass filtering algorithm to obtain an approximate average value of the data.
4. The method for data screening of raw radius analysis data according to claim 3, wherein said step of calculating an approximate standard deviation or an approximate variance of said data by a recursive calculation based on said approximate mean comprises:
carrying out recursive calculation on the data and the average value and the standard deviation obtained in advance by adopting a digital low-pass filtering algorithm to obtain an approximate standard deviation of the data; or
And carrying out recursive calculation on the data and the average value and the variance obtained before by adopting a digital low-pass filtering algorithm to obtain the approximate variance of the data.
5. The method of claim 1, wherein the step of screening the data using the approximate mean, the approximate standard deviation, or the approximate variance comprises:
setting a value range of data according to the approximate average value, the approximate standard deviation or the approximate variance;
and screening the data by utilizing the value range.
6. The method for screening radially analyzed raw data according to claim 5, wherein said step of setting a value range of data according to said approximate mean, standard deviation or variance comprises:
and taking the sum of the approximate average and the approximate standard deviation or the approximate variance as the upper value limit of the data, and taking the difference between the approximate average and the approximate standard deviation or the approximate variance as the lower value limit of the data.
7. The method for screening radially analyzed raw data as set forth in claim 6, wherein said step of setting a value range of data according to said approximate mean, standard deviation or variance further comprises:
and updating the value range of the data by adding a specific value to the upper value limit and subtracting the specific value from the lower value limit.
8. The method for data screening of raw radius analysis data according to claim 7, wherein said method further comprises:
and updating the specific value according to the historical data screening result.
9. An apparatus comprising a processor, a memory, and a data filter stored on the memory and operable on the processor, the data filter when executed by the processor implementing the steps of the data filtering method of radius analysis raw data according to any one of claims 1 to 8.
10. A computer-readable storage medium, having stored thereon a data filtering program, which when executed by a processor, implements the steps of the data filtering method of radius analysis raw data according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011167027.1A CN112347423A (en) | 2020-10-27 | 2020-10-27 | Data screening method and device for radius analysis original data and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011167027.1A CN112347423A (en) | 2020-10-27 | 2020-10-27 | Data screening method and device for radius analysis original data and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112347423A true CN112347423A (en) | 2021-02-09 |
Family
ID=74359150
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011167027.1A Pending CN112347423A (en) | 2020-10-27 | 2020-10-27 | Data screening method and device for radius analysis original data and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112347423A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2004091942A1 (en) * | 2003-04-11 | 2004-10-28 | Continental Tire North America, Inc. | Tire status detection system and method |
| CN105758633A (en) * | 2016-02-26 | 2016-07-13 | 中国航空工业集团公司上海航空测控技术研究所 | Method for evaluating health conditions of various components of gearbox |
| CN108885239A (en) * | 2016-02-19 | 2018-11-23 | 江森自控科技公司 | The system and method for real time parameter estimation for rechargeable battery |
| CN109039833A (en) * | 2018-09-30 | 2018-12-18 | 网宿科技股份有限公司 | A kind of method and apparatus monitoring bandwidth status |
| CN111017136A (en) * | 2019-12-24 | 2020-04-17 | 上海船舶运输科学研究所 | Ship fouling monitoring and evaluating method and evaluating system |
-
2020
- 2020-10-27 CN CN202011167027.1A patent/CN112347423A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2004091942A1 (en) * | 2003-04-11 | 2004-10-28 | Continental Tire North America, Inc. | Tire status detection system and method |
| CN108885239A (en) * | 2016-02-19 | 2018-11-23 | 江森自控科技公司 | The system and method for real time parameter estimation for rechargeable battery |
| CN105758633A (en) * | 2016-02-26 | 2016-07-13 | 中国航空工业集团公司上海航空测控技术研究所 | Method for evaluating health conditions of various components of gearbox |
| CN109039833A (en) * | 2018-09-30 | 2018-12-18 | 网宿科技股份有限公司 | A kind of method and apparatus monitoring bandwidth status |
| CN111017136A (en) * | 2019-12-24 | 2020-04-17 | 上海船舶运输科学研究所 | Ship fouling monitoring and evaluating method and evaluating system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110995524B (en) | Flow data monitoring method and device, electronic equipment and computer readable medium | |
| US6675126B2 (en) | Method, computer program, and storage medium for estimating randomness of function of representative value of random variable by the use of gradient of same function | |
| CN111238492A (en) | Fixed point drift correction method and device, terminal equipment and storage medium | |
| CN111323848A (en) | Method for correcting vehicle rainfall sensor and storage medium | |
| CN111997709A (en) | On-line monitoring method and system for vehicle-mounted engine oil | |
| CN111459746B (en) | Alarm generation method, device, electronic equipment and readable storage medium | |
| CN112345869A (en) | Automobile electronic equipment testing method and system, electronic equipment and storage medium | |
| CN115349865A (en) | Driver electrocardiosignal analysis method and device and storage medium | |
| CN112347423A (en) | Data screening method and device for radius analysis original data and storage medium | |
| CN113704698A (en) | Event detection method and system for non-intrusive load identification | |
| CN112444820B (en) | Robot pose determining method and device, readable storage medium and robot | |
| CN113375775B (en) | Weight correction method and weight correction system for shared vehicle and electronic equipment | |
| CN112874528A (en) | Vehicle speed determination method, vehicle speed determination device, vehicle-mounted terminal and storage medium | |
| JP4279431B2 (en) | Operating state determination device | |
| CN112612844B (en) | Data processing method, device, equipment and storage medium | |
| CN111984624B (en) | Method and system for data migration through correction migration model | |
| CN113810743A (en) | Live broadcast room flow monitoring method and device | |
| CN111045875B (en) | Vehicle accident detection method and related equipment | |
| CN111750947B (en) | Calculation display method and calculation display system for instantaneous oil consumption of vehicle | |
| CN114327557A (en) | Vehicle OTA (over the air) upgrade time evaluation method and device | |
| CN109268492B (en) | Self-adaptive calibration method and device for gear of whole vehicle | |
| CN116448219B (en) | Oil level abnormality detection method, apparatus, device, and computer-readable storage medium | |
| CN116061612B (en) | Early warning method and system for slow air leakage of automobile tire | |
| CN111409647B (en) | Vehicle speed correction method and device and vehicle | |
| CN113282968B (en) | Method, device, equipment and storage medium for judging serial numbers of vehicle-mounted equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |