CN106289794A - The data processing method of car load test and device - Google Patents
The data processing method of car load test and device Download PDFInfo
- Publication number
- CN106289794A CN106289794A CN201510271685.8A CN201510271685A CN106289794A CN 106289794 A CN106289794 A CN 106289794A CN 201510271685 A CN201510271685 A CN 201510271685A CN 106289794 A CN106289794 A CN 106289794A
- Authority
- CN
- China
- Prior art keywords
- data
- point
- sampling
- car load
- sampled
- 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.)
- Granted
Links
Landscapes
- Combined Controls Of Internal Combustion Engines (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The data processing method of a kind of car load test and device, the method comprise the steps that acquisition car load original sampling data；Described car load original sampling data is compared with preset data format standard respectively, when described car load test data are inconsistent with preset data format standard, described car load test data is carried out corresponding regular process.By described method and apparatus, the test data in complete vehicle test can be processed, it is achieved the rapidly and efficiently analysis of data.
Description
Technical field
The present invention relates to technical field of data processing, particularly relate to the data processing method of a kind of car load test
And device.
Background technology
Car load test is according to testing standard, simulating vehicle working condition under certain working condition.Logical
Cross and vehicle carried out car load test, can to the dynamic property of vehicle, ride comfort, braking, economy with
And stability is estimated, thus follow-up exploitation design.Specifically, the detection of car load test
Content may include that inspection outside vehicle, Vehicle Chassis Dynamic Tests, automobile exhaust detection, vehicle fuel consumption
Amount detection, the detection of draft hitch performance, engine performance detection, wheel balance degree detect, turn to
Wheel steering locking angle detection, wheel alignment detection, chassis gap detection, the inspection of speedometer index error
Survey, automobile braking performance detection, the detection of skid amount, light detection, loudspeaker noise level detection etc..
In the analysis of test data, the sampled data obtained may be complicated various, thus is difficult to
Fast and effeciently data analysis.
Summary of the invention
The problem that the embodiment of the present invention solves is how to process the test data in complete vehicle test, real
The rapidly and efficiently analysis of existing data.
For solving the problems referred to above, the embodiment of the present invention provides the data processing method of a kind of car load test, bag
Include:
Obtain car load original sampling data；
Described car load original sampling data is compared with preset data format standard respectively, when described whole
When car test data are inconsistent with preset data format standard, described car load test data are advised accordingly
Whole process.
Optionally, described described car load original sampling data is compared with preset data format standard respectively
Relatively, when described car load test data are inconsistent with preset data format standard, described car load is tested number
According to carrying out corresponding regular process, including:
Detect the sampling period of different parameters in described original sampling data the most identical；
When in described original sampling data, the sampling period of different parameters differs, by described crude sampling
The sampling time of data is divided into the standard sample cycle, and calculates different parameters in described original sampling data
Sampled data y1 of sampled point is:
Wherein, xiFor the sampling instant of sampled point i, x in original sampling datajPhase for described sampled point i
The sampling instant of adjacent sampled point j, yiFor described sampling instant x corresponding in described original sampling dataiSampling
Data, yjCorresponding described sampling instant x in described original sampling datajSampled data, x is standard sample
In the cycle, described sampling number is according to y1Corresponding sampling optimization is in described sampled point i and described sampled point j
Between.
Optionally, described described car load original sampling data is compared with preset data format standard respectively
Relatively, when described car load test data are inconsistent with preset data format standard, described car load is tested number
According to carrying out corresponding regular process, including:
Judge in described original sampling data, whether the unit of parameter is default standard unit；
When in described original sampling data, the unit of parameter is not default standard unit, by described parameter
Numerical value be multiplied by conversion coefficient, and be described presetting by the Conversion of measurement unit of parameter in described original sampling data
Standard block.
Optionally, described described car load original sampling data is compared with preset data format standard respectively
Relatively, when described car load test data are inconsistent with preset data format standard, described car load is tested number
Include according to carrying out corresponding regular process:
By the Conversion of measurement unit of oil consumption cumulative in described original sampling data for presetting unit；
The sampled data of described cumulative oil consumption sampled point is deducted the sampled data of previous sampled point, and works as
When the sampled data of described sampled point is less than the sampled data of previous sampled point, by the sampling of described sampled point
Data deduct the sampled data of described previous sampled point and add the range of described oil consumption, obtain described sampling
The sampled data of point.
Optionally, described described car load original sampling data is compared with preset data format standard respectively
Relatively, when described car load test data are inconsistent with preset data format standard, described car load is tested number
According to carrying out corresponding regular process, including:
Whether the sampled point detecting described original sampling data is wild point；
When the sampled point judging described original sampling data is put as open country, delete adopting corresponding to described wild point
Sample data, and calculate the sampled data corresponding to described wild point and be:
Wherein, xaFor the sampling instant of described wild some neighbouring sample point a, xbFor described wild some neighbouring sample point
The sampling instant of the neighbouring sample point b of b, yaFor described sampling instant corresponding in described original sampling data
xaSampled data, ybCorresponding described sampling instant x in described original sampling databSampled data, x
For the standard sample cycle.
Optionally, described described car load original sampling data is compared with preset data format standard respectively
Relatively, when described car load test data are inconsistent with preset data format standard, described car load is tested number
According to carrying out corresponding regular process, including:
Whether the sampled point detecting described original sampling data is noise point；
When the sampled point judging described original sampling data is noise point, smoothing techniques is taken advantage of to update institute by two
State the sampled data corresponding to noise point.
Optionally, described described car load original sampling data is compared with preset data format standard respectively
Relatively, when described car load test data are inconsistent with preset data format standard, described car load is tested number
According to carrying out corresponding regular process, including:
Detect whether the parameter in described original sampling data comprises instantaneous oil consumption；
When parameter does not comprises described instantaneous oil consumption in described original sampling data, calculate described instantaneous oil consumption:
FC=0.1554 (0.866HC+0.429CO+0.273CO2)/D；
Wherein, FC is instantaneous oil consumption, HC be the discharge capacity of Hydrocarbon, CO be nitric oxide production row
High-volume, CO2For the discharge capacity of nitrogen dioxide, D represents the density of gasoline.
Optionally, described described car load original sampling data is compared with preset data format standard respectively
Relatively, when described car load test data are inconsistent with preset data format standard, described car load is tested number
According to carrying out corresponding regular process, including:
It is right the row name of parameter row in described original sampling data and the row name of the canonical parameter row preset to be carried out
Ratio；
When in described original sampling data, the row name of parameter row is inconsistent with the row name of the canonical parameter row preset
Time, the row name of parameter row in described original sampling data is revised as the row of described default canonical parameter row
Name.
Optionally, described described car load original sampling data is compared with preset data format standard respectively
Relatively, when described car load test data are inconsistent with preset data format standard, described car load is tested number
According to carrying out corresponding regular process, including:
Choose first sampling point detection range；
Described detection range is divided into many parts according to the standard sample cycle, and calculates every a instruction carriage speed
Line；
It is respectively compared the goodness of fit between described many parts of test speed lines and standard speed line, and selects described
The starting point of the test speed line that the goodness of fit is the highest is as first sampling point；
Described standard sample cycle of being added up by described first sampling point determines sampling terminating point.
Optionally, described in be respectively compared described many parts test speed lines and standard speed line between the goodnesses of fit,
And select the starting point of test speed line that the described goodness of fit is the highest to include as first sampling point:
By calculatingBetween the most described many parts of test speed lines and standard speed line
The goodness of fit, and select the starting point of the minimum test speed line of described difference as first sampling point；
Wherein, fi (t) is the change function of the speed t in time change being actual samples point with sampled point xi,
G (t) is the change function of standard speed t in time change, and x is the testing time, and i is described test speed line
Number.
In order to solve above-mentioned technical problem, the embodiment of the invention also discloses the data of a kind of car load test
Processing means, including:
Acquiring unit, is used for obtaining car load original sampling data；
Comparing unit, for carrying out described car load original sampling data with preset data format standard respectively
Relatively；
Regular unit, for when described car load test data are inconsistent with preset data format standard, right
Described car load test data carry out corresponding regular process.
Optionally, described comparing unit is for detecting the sampling week of different parameters in described original sampling data
Phase is the most identical；
Described regular unit is used for when in described original sampling data, the sampling period of different parameters differs
Time, the sampling time of described original sampling data is divided into the standard sample cycle, and calculates described original
Sampled data y of different parameters sampled point in sampled data1For:
Wherein, xiFor the sampling instant of sampled point i, x in original sampling datajPhase for described sampled point i
The sampling instant of adjacent sampled point j, yiFor described sampling instant x corresponding in described original sampling dataiSampling
Data, yjCorresponding described sampling instant x in described original sampling datajSampled data, x is standard sample
In the cycle, described sampling number is according to y1Corresponding sampling optimization is in described sampled point i and described sampled point j
Between.
Optionally, described comparing unit is for judging in described original sampling data that whether the unit of parameter is
The standard unit preset；
Described regular unit is used for when in described original sampling data, the unit of parameter is not default standard list
During position, the numerical value of described parameter is multiplied by conversion coefficient, and by the list of parameter in described original sampling data
Position is converted to described default standard block.
Optionally, described regular unit is for the Conversion of measurement unit by oil consumption cumulative in described original sampling data
For default unit, and the sampled data of described cumulative oil consumption sampled point is deducted the hits of previous sampled point
According to, and when the sampled data of described sampled point is less than the sampled data of previous sampled point, adopt described
The sampled data of sampling point deducts the sampled data of described previous sampled point and adds the range of described oil consumption,
Sampled data to described sampled point.
Optionally, whether described comparing unit is wild point for detecting the sampled point of described original sampling data；
When described regular unit is for putting as open country when the sampled point judging described original sampling data, delete described open country
Sampled data corresponding to Dian, and calculate the sampled data corresponding to described wild point and be:
Wherein, xaFor the sampling instant of described wild some neighbouring sample point a, xbFor described wild some neighbouring sample point
The sampling instant of the neighbouring sample point b of b, yaFor described sampling instant corresponding in described original sampling data
xaSampled data, ybCorresponding described sampling instant x in described original sampling databSampled data, x
For the standard sample cycle.
Optionally, whether described comparing unit is noise for detecting the sampled point of described original sampling data
Point；Described regular unit, for when the sampled point judging described original sampling data is noise point, passes through
Two take advantage of smoothing techniques to update the sampled data corresponding to described noise point.
Optionally, whether described comparing unit comprises wink for the parameter detected in described original sampling data
Time oil consumption；Described regular unit is used for when in described original sampling data, parameter does not comprise described instantaneous oil consumption
Time, calculate described instantaneous oil consumption: FC=0.1554 (0.866HC+0.429CO+0.273CO2)/D；Wherein,
FC is instantaneous oil consumption, HC be the discharge capacity of Hydrocarbon, CO be nitric oxide production discharge capacity, CO2
For the discharge capacity of nitrogen dioxide, D represents the density of gasoline.
Optionally, described comparing unit for row name parameter in described original sampling data arranged and is preset
Canonical parameter row row name contrast；Described regular unit is for when joining in described original sampling data
When the row name of ordered series of numbers is inconsistent with the row name of the canonical parameter row preset, will described original sampling data be joined
The row name of ordered series of numbers is revised as the row name of described default canonical parameter row.
Optionally, described regular unit is used for:
Choose first sampling point detection model, and described detection range is divided into many according to the standard sample cycle
Part, and calculate every a instruction carriage speed line；
It is respectively compared the goodness of fit between described many parts of test speed lines and standard speed line, and selects described
The starting point of the test speed line that the goodness of fit is the highest is as first sampling point；
Described standard sample cycle of being added up by described first sampling point determines sampling terminating point.
Optionally, described regular unit is additionally operable to by calculatingRelatively described many parts of tests
The goodness of fit between speed line and standard speed line, and select the minimum test speed line of described difference
Initial point is as first sampling point；
Wherein, fiT () is with sampled point xiThe change function changed for the speed t in time of actual samples point,
G (t) is the change function of standard speed t in time change, and x is the testing time, and i is described test speed line
Number.
Compared with prior art, the technical scheme of the embodiment of the present invention has the advantages that
When car load test data are inconsistent with preset data format standard, described car load test data are entered
The corresponding regular process of row, it is simple to follow-up data Treatment Analysis, such that it is able to improve the efficiency of data analysis,
Strengthen the effectiveness of data results.
Further, different in the detection original sampling data sampling periods is the most identical.When the sampling period not
Meanwhile, the sampling time of described original sampling data is arranged and repartitions according to the standard sample cycle, obtain
Need the sampled point carrying out calculating, by described calculative sampled point adjacent both ends initial data
Sampled point carry out interpolation calculation, obtain the sampled data of described calculative sampled point, thus complete
The regular unification of the sample frequency of sampled data, it is simple to follow-up data Treatment Analysis, can improve data and divide
The efficiency of analysis, strengthens the effectiveness of data results.
Further, by the Parameter units in described original sampling data is contrasted with standard unit,
And it is multiplied by conversion coefficient, it is achieved and the regular unification to sampled data unit, consequently facilitating follow-up data processes
Analyze, improve the efficiency of data analysis, strengthen the effectiveness of data results.
Further, limit, when the data value of cumulative fuel consumption parameters reaches certain due to the specification of testing tool
After threshold value, the threshold transition that can it be reached is range value, therefore when calculating instantaneous oil consumption, so that it may
Can occur that the sample magnitude of the previous sampling instant of cumulative fuel consumption parameters is more than the hits of a rear sampling instant
Value, the most instantaneous oil consumption is negative value.By when calculating instantaneous oil consumption, supplement described range value, can keep away
Exempt to occur the situation that sampled data is negative value of the instantaneous oil consumption obtained, it is to avoid error in data occurs, thus
It is easy to follow-up data statistical analysis, is quickly obtained analysis result accurately and effectively.
Further, when the sampled data judging described sampled point is put as open country, by adjacent to described wild point
Sampled point in the initial data of two ends carries out interpolation calculation, obtains the sampled data of described wild point, thus picks
Except the wild point in sampled data, it is to avoid error in data occurs in the interference of wild point, consequently facilitating follow-up data system
Meter is analyzed, and is quickly obtained analysis result accurately and effectively.
Further, when judging that the sampled point of described original sampling data is as noise point, take advantage of smooth by two
Method updates the sampled data corresponding to described noise point, it is to avoid noise point interference occurs that error in data affects
Test, consequently facilitating follow-up data statistical analysis, is quickly obtained analysis result accurately and effectively.
Further, when described crude sampling parameter does not includes instantaneous fuel consumption parameters, by detection vehicle
Discharge gas in Hydrocarbon, nitric oxide and the content of nitrogen dioxide and the density of gasoline,
Calculate the instantaneous oil consumption of vehicle, thus supplement instantaneous fuel consumption parameters, convenient at follow-up data analysis
In calculation process to data, thus be quickly obtained analysis result accurately and effectively.
Further, by the row name of parameter row in described original sampling data is arranged with the canonical parameter preset
Row name contrast, and when inconsistent, by the row name amendment of parameter row in described original sampling data
Row name for described default canonical parameter row, it is achieved that the unification to parameter name is regular, thus convenient
Calculation process to data in follow-up data analysis, is quickly obtained analysis result accurately and effectively.
Further, determine the detection range of first sampling point, and can be by described according to the standard sample cycle
Detection range is divided into multistage, thus obtains many parts accordingly using different sampled points as first sampling point
Test speed line.By comparing the goodness of fit between these test speed line and standard speed lines, can obtain
To one with standard speed line closest to test speed line, so that it is determined that its starting point be car load test
First sampling point, and then according to sample duration obtain sample terminating point, standard can be quickly obtained
The most effective analysis result.
Accompanying drawing explanation
Fig. 1 is the data processing method of a kind of car load test of the embodiment of the present invention；
Fig. 2 is the data processing method of the another kind of car load test of the embodiment of the present invention；
Fig. 3 is the data processing method of another car load test of the embodiment of the present invention；
Fig. 4 is the data processing method of another car load test of the embodiment of the present invention；
Fig. 5 is the data processing method of the another kind of car load test of the embodiment of the present invention；
Fig. 6 is the data processing method of another car load test of the embodiment of the present invention；
Fig. 7 is the data processing method of another car load test of the embodiment of the present invention；
Fig. 8 is the data processing method of the another kind of car load test of the embodiment of the present invention；
Fig. 9 is the data processing method of another car load test of the embodiment of the present invention；
Figure 10 is the structural representation of the data processing equipment of a kind of car load test of the embodiment of the present invention.
Detailed description of the invention
In the analysis of test data, the sampled data obtained may be complicated various, thus is difficult to
Fast and effeciently data analysis.Such as in vehicle testing, it is presently mainly based on new Europe driving cycle
Car load is tested by (New European Driving Cycle, NEDC) standard.Owing to test is original
Sampled data needs to be acquired by numerous different data acquisition equipments, and data acquisition equipment
Producer is possibly different from, so the form gathering data can have the multiformity of complexity, therefore gives data
Analyze work and cause inconvenience, delay the process of test analysis.
The embodiment of the invention discloses the data processing method of a kind of car load test, to solve above-mentioned technology
Problem.Understandable for enabling the above-mentioned purpose of the present invention, feature and advantage to become apparent from, below in conjunction with attached
The specific embodiment of the present invention is described in detail by figure.
With reference to Fig. 1, the data processing method of a kind of car load test of the embodiment of the present invention, may include that
Step S101, obtains described car load original sampling data.
In being embodied as, car load can be performed according to default standard condition survey according to the needs of test
Examination, and obtain corresponding car load
Original sampling data.For example, it may be new European Driving Cycle (New European Driving
Cycle, NEDC) standard condition, U.S.'s driving cycle (United State Driving Cycle, USDC)
Standard condition, or Japan's driving cycle (Japan Driving Cycle, JDC) standard condition.
Described car load test data are compared by step S102 respectively with preset data format standard, when
When described car load test data are inconsistent with preset data format standard, described car load test data are carried out
Corresponding regular process.
Use above-mentioned data processing method, by will obtain car load original sampling data respectively with present count
Compare according to format standard, when described car load test data are inconsistent with preset data format standard,
Described car load test data are carried out corresponding regular process, with the data rule of unified described original sampling data
Lattice, reduce the inconvenience during follow-up data processes.
In being embodied as, can use multiple method that data are carried out regular process, for making this area skill
Art personnel are more fully understood that the embodiment of the present invention, illustrate the present invention below by way of specific embodiment
Embodiment is how data are carried out regular process.
The embodiment of the invention also discloses the data processing method of another kind of car load test.Relative to Fig. 1 institute
Showing the data processing method of the car load test of embodiment, the present embodiment can be used for described car load crude sampling
The sample frequency of data regular.This is owing to the sampling period of initial data is true by data acquisition equipment
Fixed, in such as initial data, the sampling period of instantaneous oil consumption may be through fuel consumption meter and determines sample frequency
's.The sampling period of visible instrument is unstable, and the sampling period that can cause different parameters is different, so
Need it is entered frequency normalization operation.
As in figure 2 it is shown, the data processing method of described car load test can comprise the steps:
Step S201, obtains car load original sampling data.
Step S202, detects the sampling period of different parameters in described original sampling data the most identical.
When the sampling period of different parameters is identical in described original sampling data, perform step S203；When
When the sampling period of different parameters differs in described original sampling data, perform step S204.
Step S203, keeps the described sampling period.
Step S204, is divided into the standard sample cycle by the sampling time of described original sampling data, and leads to
Cross the centre line the sampled data of different parameters sampled point in original sampling data described in interpolation calculation.
In being embodied as, the described standard sample cycle can be the required precision according to Parameter analysis in advance
Set.
In being embodied as, the hits of different parameters sampled point in described original sampling data can be calculated
According to y1For:
Wherein, xiFor the sampling instant of sampled point i, x in original sampling datajPhase for described sampled point i
The sampling instant of adjacent sampled point j, yiFor described sampling instant x corresponding in described original sampling dataiSampling
Data, yjCorresponding described sampling instant x in described original sampling datajSampled data, x is standard sample
In the cycle, described sampling number is according to y1Corresponding sampling optimization is in described sampled point i and described sampled point j
Between.
Such as, in original sampling data, the sampling period of different parameters is the most different, such as the sampling of parameter A
Cycle is 0.15s, and the sampling period of parameter B is 0.1s, and the standard sample cycle is 0.1s, it is therefore desirable to
Parameter A is carried out the regular of sample frequency.In coordinate axes as shown in Figure 3, x-axis is time shaft, y-axis
For data axle, wherein, the parameter A sampling period after regular is Time [1]=0, Time [2]=0.1,
Time [3]=0.2.......Illustrate how to calculate described as a example by second sampled point corresponding to Time [2]
The sampled data of two sampled points.Described second sampling optimization sampled point i in original sampling data
Between (0.05,0.1) and j (0.2,0.4), therefore can be calculated according to above-mentioned formula (1)
The sampled data of described second sampled point is 0.1.
To sum up, by the data processing method in above-described embodiment, it is possible to achieve by different sample frequencys
Parameter is regular for identical sample frequency, such that it is able to realize the ratio in identical sampling period down-sampling parameter
Relatively, it is simple to follow-up data Treatment Analysis, the efficiency of data analysis can be improved, strengthen data results
Effectiveness.
With reference to Fig. 3, the embodiment of the invention also discloses the data processing method of another kind of car load test, this reality
Execute example to can be used for carrying out regular to the unit of parameter in described car load original sampling data, specifically can include as
Lower step:
Step S301, obtains car load original sampling data.
Step S302, it is judged that in described original sampling data, whether the unit of parameter is default standard unit.
When the unit of parameter is default standard unit in described original sampling data, perform step S303,
Otherwise perform step S304.
Step S303, keeps the unit of parameter in described original sampling data.
Step S304, is multiplied by conversion coefficient by the numerical value of described parameter, and by described original sampling data
The Conversion of measurement unit of parameter is described default standard block.
Such as, as shown in table 1, if the unit of speed is thousand ms/h in described original sampling data, and
The standard unit preset is meter per second, then by retrieving Conversion of measurement unit table as shown in the table, obtain km/
Hour and meter per second between conversion coefficient be 3.6, can be by the number of sampled data in described original sampling data
Value is multiplied by described conversion coefficient, and is described presetting by the Conversion of measurement unit of parameter in described original sampling data
Standard block.The unit thousand of speed parameter can also be obtained accordingly from the Conversion of measurement unit table described in following table
M/h and miles per hour between conversion coefficient, or in the unit of instantaneous oil consumption, l/h respectively with
Conversion coefficient between ml/hour, l/h and liter/second.
Table 1
To sum up, said method is used data to be processed, according to parameter in described original sampling data
Conversion coefficient between unit and default standard unit, it is possible to achieve to parameter list in described sampled data
Position regular, consequently facilitating follow-up data Treatment Analysis, improves the efficiency of data analysis, strengthens data
The effectiveness of analysis result.
With reference to Fig. 4, the embodiment of the invention also discloses the data processing method of a kind of car load test, can be used for
The parameter counted by range, such as cumulative oil consumption are carried out unit conversion.At the data of described car load test
Reason method may include that
Step S401, obtains car load original sampling data.
Step S402, by the Conversion of measurement unit of oil consumption cumulative in described original sampling data for presetting unit.
Step S403, it is judged that whether the sampled data of described cumulative oil consumption sampled point is less than previous sampled point
Sampled data.
In actual applications, the parameter counted by range, as cumulative oil consumption reaches in current sampled data
During to the limit of accumulating values, current range value can be added 1, and current sampled data is reset, weight
New count, so may result in when carrying out the Conversion of measurement unit of parameter, in fact it could happen that a rear sampling instant
Sampled data is less than the problem of the sampled data of previous sampling instant.
When the sampled data of described cumulative oil consumption sampled point is less than or equal to the sampled data of previous sampled point
Time, perform step S404, otherwise perform step S405.
Step S404, deducts the sampled data of described previous sampled point also by the sampled data of described sampled point
Range plus described oil consumption.
Step S405, deducts the sampled data of described previous sampled point by the sampled data of described sampled point.
In actual applications, it is limited by the restriction of sample devices, when sampled data reaches the tired of sample magnitude
When adding the limit, the sample magnitude of current sampling point will be reset, and range be added up, to avoid exceeding
Detection range.Therefore when calculating instantaneous oil consumption, it is possible to when the previous sampling of cumulative fuel consumption parameters occurs
The sample magnitude carved is more than the sample magnitude of a rear sampling instant, and the most instantaneous oil consumption is negative value.Use above-mentioned
Data are processed by method, by when calculating instantaneous oil consumption, supplement described range value, can avoid
The situation that sampled data is negative value of instantaneous oil consumption obtained occurs, it is to avoid error in data occurs, thus just
In follow-up data statistical analysis, it is quickly obtained analysis result accurately and effectively.
The embodiment of the invention also discloses the data processing method of a kind of car load test, can be used for described former
The wild detection put and process in beginning sampled data.As it is shown in figure 5, the data process side of described car load test
Method may include that
Step S501, obtains car load original sampling data.
Step S502, whether the sampled point detecting described original sampling data is wild point.
In being embodied as, can be by detecting whether described original sampling data is mess code or whether wraps
Include 0x0, judge that the sampled point of described original sampling data is whether as wild point.
When judging that described original sampling data is put as open country, perform step S503, otherwise perform step S504.
Step S503, deletes the sampled data corresponding to described wild point, and calculates corresponding to described wild point
Sampled data.
In being embodied as, the sampled data corresponding to the described wild point of described calculating can be by original
The data value of sampled data carries out interpolation operation, can carry out calculating the sampling of described wild point by following formula
Data y2:
Wherein, xaFor the sampling instant of described wild some neighbouring sample point a, xbFor described wild some neighbouring sample point
The sampling instant of the neighbouring sample point b of b, yaFor described sampling instant corresponding in described original sampling data
xaSampled data, ybCorresponding described sampling instant x in described original sampling databSampled data, x
For the standard sample cycle.
Such as, in original sampling data, in coordinate axes as shown in Figure 1, x-axis is time shaft, and y-axis is
Data axle, wherein, the parameter A sampling period after regular is Time [1]=0, Time [2]=0.1,
Time [3]=0.2.......If second sampled point corresponding to Time [2] is wild point, and described wild point is positioned at
In original sampling data between sampled point i (0.05,0.1) and j (0.2,0.4), therefore according to above-mentioned
It is 0.1 that formula (1) can be calculated the sampled data of described wild point.
Step S504, the sampled point keeping described original sampling data is constant.
To sum up, use said method that data are processed, by detecting in described original sampling data
Whether sampled point is wild point, and when being judged to wild point, by described wild some adjacent both ends original number
Sampled point according to carries out interpolation calculation, obtains the sampled data of described wild point, thus eliminates hits
Wild point according to, it is to avoid error in data occurs in the interference of wild point, consequently facilitating follow-up data statistical analysis, soon
Obtain analysis result accurately and effectively fastly.
The embodiment of the invention also discloses the data processing method of another kind of car load test.As shown in Figure 6,
The data processing method of described car load test may include that
Step S601, obtains car load original sampling data.
Step S602, whether the sampled point detecting described original sampling data is noise point.
In being embodied as, described noise point is owing to the response time of detection equipment is oversize or sensitivity
Problem, may measure some sampled points not meeting practical situation during measuring, so that test
Computational accuracy is affected.
In being embodied as, can be by sampling between neighbouring sample point in described original sampling data
The mode that data compare detects whether sampled point is noise point, i.e. compares the hits of neighbouring sample point
Whether the difference between according to is beyond threshold value, and the image being reflected in parameter shows for there is zigzag, or
During the steep phenomenon dropped that skyrockets, i.e. can determine that sampled point is noise point.
Step S603, takes advantage of smoothing techniques to update the sampled data corresponding to described noise point by two.
Step S604, the sampled data keeping sampled point is constant.
To sum up, carry out data process by said method, smoothing techniques can be taken advantage of to update described noise by two
Sampled data corresponding to Dian, it is to avoid noise point interference occurs that error in data affects test, consequently facilitating
Follow-up data statistical analysis, is quickly obtained analysis result accurately and effectively.
The embodiment of the invention also discloses the data processing method of another kind of car load test.As it is shown in fig. 7,
The data processing method of described car load test may include that
Step S701, obtains car load original sampling data.
Step S702, detects whether the parameter in described original sampling data comprises instantaneous oil consumption.
When parameter comprises described instantaneous oil consumption in described original sampling data, perform step S703, otherwise
Perform step S704.
Step S703, retains the instantaneous oil consumption in described original sampling data.
Step S704, calculates described instantaneous oil consumption.
In being embodied as, can calculate by detecting the emission of vehicle according to Carbon balance principle
Described instantaneous oil consumption:
FC=0.1554 (0.866HC+0.429CO+0.273CO2)/D； (3)
Wherein, FC is instantaneous oil consumption, HC be the discharge capacity of Hydrocarbon, CO be nitric oxide production row
High-volume, CO2 be the discharge capacity of nitrogen dioxide, D represents the density of gasoline.
To sum up, above-mentioned method of sampling carries out data process, by Carbon balance principle, by detection vehicle
Discharge gas in Hydrocarbon, nitric oxide and the content of nitrogen dioxide and the density of gasoline,
Can calculate the oil consumption of instantaneous oil consumption, thus the instantaneous oil of disappearance in original sampling data described in completion
Consumption parameter, convenient calculation process in follow-up data analysis to data, thus be quickly obtained accurately
Effective analysis result.
The embodiment of the invention also discloses the data processing method of another kind of car load test.As shown in Figure 8,
The data processing method of described car load test may include that
Step S801, obtains car load original sampling data.
Step S802, it is judged that the row name of parameter row and the canonical parameter row preset in described original sampling data
Row name the most consistent.
When in described original sampling data, the row name of parameter row is consistent with the row name of the canonical parameter row preset
Time, perform step S803, otherwise perform step S804.
Step S803, retains the row name of parameter row in described original sampling data.
Step S804, is revised as described default standard by the row name of parameter row in described original sampling data
The row name of parameter row.
To sum up, above-mentioned method of sampling carries out data process, can be to parameter in described original sampling data
The row name of row carries out unifying regular, so that it keeps consistent with the row name of the canonical parameter row preset, square
The continuous analysis to sampled data after an action of the bowels.
The embodiment of the invention also discloses the data processing method of another kind of car load test.As it is shown in figure 9,
The data processing method of described car load test may include that
Step S901, obtains car load original sampling data.
Step S902, chooses first sampling point detection range.
When the detection range of described first sampling point is for carrying out car load test, the possible range of first sampling point.
Such as, in being embodied as, for the car load test carried out based on NEDC testing standard, by present
The test observation at existing test data analysis and scene, it is possible to determine that detection equipment record start moment
Error is all within 2s with before and after driver NEDC test start time, meanwhile, again due to NEDC
Standard condition specifies that test vehicle starts the dead time that must have 11s, the most just can be by first sampling point
Hunting zone is scheduled on this district of 13s to front 9s before the measurement point that in test vehicle speed data, first is not zero
Within between.
Step S903, is divided into many parts by described detection range according to the standard sample cycle, and calculates each
Part test speed line.
Such as, with first sampling point detection range first measurement point being not zero in test vehicle speed data
As a example by within front this interval of 13s to front 9s, if the sampling period is 0.1s, the most possible sampling rises
Just before the measurement point that first speed is not zero, 130 points are between front 90 points, namely for initial point
Say and have 40 different test speed lines.According to these 40 different test speed lines, can obtain
40 corresponding speed function fi(t), i=40.
Step S904, is respectively compared the goodness of fit between described many parts of test speed lines and standard speed line,
And select the starting point of test speed line that the described goodness of fit is the highest as first sampling point.
In being embodied as, equation below can be used to calculate, the most described many parts of test speed lines
And the goodness of fit between standard speed line, and the starting point of the test speed line of described difference minimum is selected to make
For first sampling point:
Wherein, fiT () is the change function of the speed t in time change being actual samples point with sampled point xi,
G (t) is the change function of standard speed t in time change, and x is the testing time, and i is described test speed line
Number.
As a example by i=40, it is only necessary to obtain 40 groups of above-mentioned integrations, and therefrom find out the integrated value pair of minimum
The f answeredi(t), fiT first sampled point corresponding to () is the starting point of described sampling.
Step S905, the described standard sample cycle of being added up by described first sampling point determines sampling terminating point.
As a example by NEDC car load is tested, in being embodied as, owing to NEDC standard specifies whole test
Continue 1180s, therefore when by described step S904, after being calculated described first sampling point, then
The 11800th measurement point after sampling enlightenment point is the sampling terminating point of car load test.
In actual test, due to it is difficult to ensure that the moment of the beginning of equipment sampling and end drive exactly
Member carries out the initial of car load test and end time, so needing the starting point and ending point to data record
Judge.Above-mentioned method of sampling carries out data process, can be determined by the detection of first sampling point
Scope, and it is divided into a plurality of test speed line according to the sampling period, and judge to test speed line and standard vehicle
The goodness of fit between speed line, it may be determined that first sampling point, and then determine the terminating point of sampling, can be fast
Obtain analysis result accurately and effectively fastly.
It is understood that in being embodied as, according to practical situation, can be in above-described embodiment
The various sampled datas that sampling obtains carry out regular process, and the data acquisition that therefore, it can obtain sampling is used
Sampled data is processed by the method in said one embodiment, it would however also be possible to employ above-mentioned multiple embodiments
In method data are carried out respective handling, as required, it is also possible to use other regular method to adopting
Sample data carry out regular process, in order to carry out fast and effeciently car load test and Data Analysis Services.
The embodiment of the invention also discloses the data processing equipment of a kind of car load test.As shown in Figure 10, institute
The data processing equipment stating car load test may include that
Acquiring unit 1001, is used for obtaining car load original sampling data；
Comparing unit 1002, for by described car load original sampling data respectively with preset data format standard
Compare；
Regular unit 1003, is used for when described car load test data are inconsistent with preset data format standard,
Described car load test data are carried out corresponding regular process.
In being embodied as, described comparing unit 1002 can be used for detecting in described original sampling data different
The sampling period of parameter is the most identical；
Described regular unit 1003 is for when the sampling period of different parameters not phase in described original sampling data
Meanwhile, the sampling time of described original sampling data is divided into the standard sample cycle, and calculates described former
Sampled data y of different parameters sampled point in beginning sampled data1For:
Wherein, xi is the sampling instant of sampled point i, x in original sampling datajPhase for described sampled point i
The sampling instant of adjacent sampled point j, yiFor described sampling instant x corresponding in described original sampling dataiSampling
Data, yjCorresponding described sampling instant x in described original sampling datajSampled data, x is standard sample
In the cycle, described sampling number is according to y1Corresponding sampling optimization is in described sampled point i and described sampled point j
Between.
In being embodied as, described comparing unit 1002 can be used for judging parameter in described original sampling data
Unit whether be default standard unit；
Described regular unit 1003 is used for when in described original sampling data, the unit of parameter is not default mark
During quasi-unit, the numerical value of described parameter is multiplied by conversion coefficient, and by parameter in described original sampling data
Conversion of measurement unit be described default standard block.
In being embodied as, described regular unit 1003 can be used for oil cumulative in described original sampling data
The Conversion of measurement unit of consumption is for presetting unit, and the sampled data of described cumulative oil consumption sampled point is deducted previous adopting
The sampled data of sampling point, and when the sampled data of described sampled point is less than the sampled data of previous sampled point
Time, the sampled data of described sampled point is deducted the sampled data of described previous sampled point and plus described oil
The range of consumption, obtains the sampled data of described sampled point.
In being embodied as, described comparing unit 1002 can be used for detecting the sampling of described original sampling data
Whether point is wild point.Described regular unit 1003 can be used for when the sampled point judging described original sampling data
During for open country point, delete the sampled data corresponding to described wild point, and calculate the sampling corresponding to described wild point
Data are:
Wherein, xaFor the sampling instant of described wild some neighbouring sample point a, xbFor described wild some neighbouring sample point
The sampling instant of the neighbouring sample point b of b, yaFor described sampling instant corresponding in described original sampling data
xaSampled data, ybCorresponding described sampling instant x in described original sampling databSampled data, x
For the standard sample cycle.
In being embodied as, described comparing unit 1002 is for detecting the sampled point of described original sampling data
Whether it is noise point；Described regular unit 1003 is for when the sampled point judging described original sampling data being
During noise point, smoothing techniques is taken advantage of to update the sampled data corresponding to described noise point by two.
In being embodied as, described comparing unit 1002 may be used for detecting in described original sampling data
Whether parameter comprises instantaneous oil consumption；
Described regular unit 1003 may be used for when in described original sampling data, parameter does not comprise described instantaneous
During oil consumption, calculate described instantaneous oil consumption:
FC=0.1554 (0.866HC+0.429CO+0.273CO2)/D；
Wherein, FC is instantaneous oil consumption, HC be the discharge capacity of Hydrocarbon, CO be nitric oxide production row
High-volume, CO2 be the discharge capacity of nitrogen dioxide, D represents the density of gasoline.
In being embodied as, described comparing unit 1002 can be used for arranging parameter in described original sampling data
Row name with preset canonical parameter row row name contrast；
Described regular unit 1003 can be used for when in described original sampling data parameter row row name with preset
When the row name of canonical parameter row is inconsistent, the row name of parameter row in described original sampling data is revised as institute
State the row name of default canonical parameter row.
In being embodied as, described regular unit 1003 can be used for: chooses first sampling point detection model, and
Described detection range is divided into many parts according to the standard sample cycle, and calculates every a instruction carriage speed line；
It is respectively compared the goodness of fit between described many parts of test speed lines and standard speed line, and selects described coincideing
Spend the starting point of the highest test speed line as first sampling point；By cumulative for described first sampling point described
The standard sample cycle determines sampling terminating point.
In above-mentioned being embodied as, described regular unit 1003 can be also used for using equation below
Calculate, the goodness of fit between the most described many parts of test speed lines and standard speed line, and select institute
State the starting point of the minimum test speed line of difference as first sampling point:
Wherein, fiT () is with sampled point xiThe change function changed for the speed t in time of actual samples point,
G (t) is the change function of standard speed t in time change, and x is the testing time, and i is described test speed line
Number.
One of ordinary skill in the art will appreciate that all or part of step in the various methods of above-described embodiment
Suddenly the program that can be by completes to instruct relevant hardware, and this program can be stored in computer-readable
In storage medium, storage medium may include that ROM, RAM, disk or CD etc..
Although present disclosure is as above, but the present invention is not limited to this.Any those skilled in the art,
Without departing from the spirit and scope of the present invention, all can make various changes or modifications, therefore the guarantor of the present invention
The scope of protecting should be as the criterion with claim limited range.
Claims (20)
1. the data processing method of a car load test, it is characterised in that including:
Obtain car load original sampling data；
Described car load original sampling data is compared with preset data format standard respectively, when described car load
When test data are inconsistent with preset data format standard, described car load test data are advised accordingly
Whole process.
2. the data processing method of car load test as claimed in claim 1, it is characterised in that described by described
Car load original sampling data compares with preset data format standard respectively, when number tested by described car load
According to time inconsistent with preset data format standard, described car load test data are carried out corresponding regular process,
Including:
Detect the sampling period of different parameters in described original sampling data the most identical；
When in described original sampling data, the sampling period of different parameters differs, by described crude sampling number
According to sampling time be divided into the standard sample cycle, and calculate different parameters in described original sampling data
Sampled data y of sampled point1For:
Wherein, xiFor the sampling instant of sampled point i, x in original sampling datajAdjacent for described sampled point i
The sampling instant of sampled point j, yiFor described sampling instant x corresponding in described original sampling dataiAdopt
Sample data, yjCorresponding described sampling instant x in described original sampling datajSampled data, x for mark
In the quasi-sampling period, described sampling number is according to y1Corresponding sampling optimization in described sampled point i with described
Between sampled point j.
3. the data processing method of car load test as claimed in claim 1, it is characterised in that described by described
Car load original sampling data compares with preset data format standard respectively, when number tested by described car load
According to time inconsistent with preset data format standard, described car load test data are carried out corresponding regular process,
Including:
Judge in described original sampling data, whether the unit of parameter is default standard unit；
When in described original sampling data, the unit of parameter is not default standard unit, by described parameter
Numerical value is multiplied by conversion coefficient, and is described presetting by the Conversion of measurement unit of parameter in described original sampling data
Standard block.
4. the data processing method of car load test as claimed in claim 1, it is characterised in that described by described
Car load original sampling data compares with preset data format standard respectively, when number tested by described car load
According to time inconsistent with preset data format standard, described car load test data are carried out corresponding regular process
Including:
By the Conversion of measurement unit of oil consumption cumulative in described original sampling data for presetting unit；
The sampled data of described cumulative oil consumption sampled point is deducted the sampled data of previous sampled point, and works as institute
When stating the sampled data of sampled point less than the sampled data of previous sampled point, by the sampling of described sampled point
Data deduct the sampled data of described previous sampled point and plus the range of described oil consumption, obtain described in adopt
The sampled data of sampling point.
5. the data processing method of car load test as claimed in claim 1, it is characterised in that described by described
Car load original sampling data compares with preset data format standard respectively, when number tested by described car load
According to time inconsistent with preset data format standard, described car load test data are carried out corresponding regular process,
Including:
Whether the sampled point detecting described original sampling data is wild point；
When the sampled point judging described original sampling data is put as open country, delete the sampling corresponding to described wild point
Data, and calculate the sampled data corresponding to described wild point and be:
Wherein, xaFor the sampling instant of described wild some neighbouring sample point a, xbFor described wild some neighbouring sample point
The sampling instant of the neighbouring sample point b of b, yaDuring for the described sampling of correspondence in described original sampling data
Carve xaSampled data, ybCorresponding described sampling instant x in described original sampling databSampled data,
X is the standard sample cycle.
6. the data processing method of car load test as claimed in claim 1, it is characterised in that described by described
Car load original sampling data compares with preset data format standard respectively, when number tested by described car load
According to time inconsistent with preset data format standard, described car load test data are carried out corresponding regular process,
Including:
Whether the sampled point detecting described original sampling data is noise point；
When the sampled point judging described original sampling data is noise point, smoothing techniques is taken advantage of to update by two described
Sampled data corresponding to noise point.
7. the data processing method of car load test as claimed in claim 1, it is characterised in that described by described
Car load original sampling data compares with preset data format standard respectively, when number tested by described car load
According to time inconsistent with preset data format standard, described car load test data are carried out corresponding regular process,
Including:
Detect whether the parameter in described original sampling data comprises instantaneous oil consumption；
When parameter does not comprises described instantaneous oil consumption in described original sampling data, calculate described instantaneous oil consumption:
FC=0.1554 (0.866HC+0.429CO+0.273CO2)/D；
Wherein, FC is instantaneous oil consumption, HC be the discharge capacity of Hydrocarbon, CO be nitric oxide production row
High-volume, CO2For the discharge capacity of nitrogen dioxide, D represents the density of gasoline.
8. the data processing method of car load test as claimed in claim 1, it is characterised in that described by described
Car load original sampling data compares with preset data format standard respectively, when number tested by described car load
According to time inconsistent with preset data format standard, described car load test data are carried out corresponding regular process,
Including:
It is right the row name of parameter row in described original sampling data and the row name of the canonical parameter row preset to be carried out
Ratio；
When in described original sampling data, the row name of parameter row is inconsistent with the row name of the canonical parameter row preset
Time, the row name of parameter row in described original sampling data is revised as described default canonical parameter row
Row name.
9. the data processing method of the car load test as described in any one of claim 1-8, it is characterised in that institute
State and described car load original sampling data is compared with preset data format standard respectively, when described whole
When car test data are inconsistent with preset data format standard, described car load test data are carried out accordingly
Regular process, including:
Choose first sampling point detection range；
Described detection range is divided into many parts according to the standard sample cycle, and calculates every a instruction carriage speed line；
It is respectively compared the goodness of fit between described many parts of test speed lines and standard speed line, and selects described kiss
The starting point of the highest right test speed line is as first sampling point；
Described standard sample cycle of being added up by described first sampling point determines sampling terminating point.
10. the data processing method of car load test as claimed in claim 9, it is characterised in that described respectively than
The goodnesses of fit between more described many parts of test speed lines and standard speed line, and select the described goodness of fit
The starting point of high test speed line includes as first sampling point:
By calculatingKiss between the most described many parts of test speed lines and standard speed line
Right, and select the starting point of the minimum test speed line of described difference as first sampling point；
Wherein, fiT () is with sampled point xiThe change function changed for the speed t in time of actual samples point, g (t)
For the change function of standard speed t in time change, x is the testing time, and i is described test speed line
Number.
The data processing equipment of 11. 1 kinds of car load tests, it is characterised in that including:
Acquiring unit, is used for obtaining car load original sampling data；
Comparing unit, for comparing described car load original sampling data with preset data format standard respectively
Relatively；
Regular unit, for when described car load test data are inconsistent with preset data format standard, to institute
State car load test data and carry out corresponding regular process.
The data processing equipment of 12. car load as claimed in claim 11 tests, it is characterised in that
Described comparing unit is for detecting in described original sampling data the sampling period of different parameters whether phase
With；
Described regular unit is used for when in described original sampling data, the sampling period of different parameters differs,
The sampling time of described original sampling data is divided into the standard sample cycle, and calculates described original adopt
Sampled data y of different parameters sampled point in sample data1For:
Wherein, xiFor the sampling instant of sampled point i, x in original sampling datajAdjacent for described sampled point i
The sampling instant of sampled point j, yiFor described sampling instant x corresponding in described original sampling dataiAdopt
Sample data, yjCorresponding described sampling instant x in described original sampling datajSampled data, x for mark
In the quasi-sampling period, described sampling number is according to y1Corresponding sampling optimization in described sampled point i with described
Between sampled point j.
The data processing equipment of 13. car load as claimed in claim 11 tests, it is characterised in that described the most single
Unit is for judging in described original sampling data, whether the unit of parameter is default standard unit；
Described regular unit is used for when in described original sampling data, the unit of parameter is not default standard unit
Time, the numerical value of described parameter is multiplied by conversion coefficient, and by the list of parameter in described original sampling data
Position is converted to described default standard block.
The data processing equipment of 14. car load as claimed in claim 11 tests, it is characterised in that described regular list
Unit is used for the Conversion of measurement unit of oil consumption cumulative in described original sampling data as presetting unit, and by described
The sampled data of cumulative oil consumption sampled point deducts the sampled data of previous sampled point, and when described sampling
When the sampled data of point is less than the sampled data of previous sampled point, the sampled data of described sampled point is subtracted
Go the sampled data of described previous sampled point and add the range of described oil consumption, obtaining described sampled point
Sampled data.
The data processing equipment of 15. car load as claimed in claim 11 tests, it is characterised in that described the most single
Whether unit is wild point for detecting the sampled point of described original sampling data；
When described regular unit is for putting as open country when the sampled point judging described original sampling data, delete described
Sampled data corresponding to wild point, and calculate the sampled data corresponding to described wild point and be:
Wherein, xaFor the sampling instant of described wild some neighbouring sample point a, xbFor described wild some neighbouring sample point
The sampling instant of the neighbouring sample point b of b, yaDuring for the described sampling of correspondence in described original sampling data
Carve xaSampled data, ybCorresponding described sampling instant x in described original sampling databSampled data,
X is the standard sample cycle.
The data processing equipment of 16. car load as claimed in claim 11 tests, it is characterised in that
Whether described comparing unit is noise point for detecting the sampled point of described original sampling data；
Described regular unit is for when the sampled point judging described original sampling data is noise point, by two
Smoothing techniques is taken advantage of to update the sampled data corresponding to described noise point.
The data processing equipment of 17. car load as claimed in claim 11 tests, it is characterised in that
Whether described comparing unit comprises instantaneous oil consumption for the parameter detected in described original sampling data；
Described regular unit, for when in described original sampling data, parameter does not comprises described instantaneous oil consumption, is counted
Calculate described instantaneous oil consumption: FC=0.1554 (0.866HC+0.429CO+0.273CO2)/D；
Wherein, FC is instantaneous oil consumption, HC be the discharge capacity of Hydrocarbon, CO be nitric oxide production row
High-volume, CO2For the discharge capacity of nitrogen dioxide, D represents the density of gasoline.
The data processing equipment of 18. car load as claimed in claim 11 tests, it is characterised in that
Described comparing unit is for by the row name of parameter row in described original sampling data and the canonical parameter preset
The row name of row contrasts；
Described regular unit is for when the row name of parameter row and the canonical parameter preset in described original sampling data
When the row name of row is inconsistent, the row name of parameter row in described original sampling data is revised as described presetting
Canonical parameter row row name.
The data processing equipment of the 19. car load tests as described in any one of claim 11-18, it is characterised in that
Described regular unit is used for:
Choose first sampling point detection model, and described detection range be divided into many parts according to the standard sample cycle,
And calculate every a instruction carriage speed line；
It is respectively compared the goodness of fit between described many parts of test speed lines and standard speed line, and selects described kiss
The starting point of the highest right test speed line is as first sampling point；
Described standard sample cycle of being added up by described first sampling point determines sampling terminating point.
The data processing equipment of 20. car load as claimed in claim 19 tests, it is characterised in that described regular list
Unit is additionally operable to by calculatingThe most described many parts of test speed lines and standard speed line
Between the goodness of fit, and select the starting point of the minimum test speed line of described difference to initiate as sampling
Point；Wherein, fiT () is with sampled point xiThe change function changed for the speed t in time of actual samples point,
G (t) is the change function of standard speed t in time change, and x is the testing time, and i is described test speed
The number of line.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510271685.8A CN106289794B (en) | 2015-05-25 | 2015-05-25 | The data processing method and device of vehicle test |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510271685.8A CN106289794B (en) | 2015-05-25 | 2015-05-25 | The data processing method and device of vehicle test |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106289794A true CN106289794A (en) | 2017-01-04 |
| CN106289794B CN106289794B (en) | 2019-08-13 |
Family
ID=57634687
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510271685.8A Active CN106289794B (en) | 2015-05-25 | 2015-05-25 | The data processing method and device of vehicle test |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106289794B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106653092A (en) * | 2017-01-05 | 2017-05-10 | 上海剑桥科技股份有限公司 | Processing method and processing system of DDR (Double Data Rate) test waveform data file |
| CN109615648A (en) * | 2018-12-07 | 2019-04-12 | 深圳前海微众银行股份有限公司 | Depth of field data conversion method, device, equipment and computer readable storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1170164A (en) * | 1996-03-05 | 1998-01-14 | 株式会社数码显示实验室 | Data processing system and data processing method |
| WO2004104604A1 (en) * | 2003-05-23 | 2004-12-02 | Daimlerchrysler Ag | Diagnostic system |
| JP4400576B2 (en) * | 2006-01-10 | 2010-01-20 | 株式会社デンソー | Vehicle diagnostic system |
| CN102252856A (en) * | 2011-07-01 | 2011-11-23 | 深圳市航盛电子股份有限公司 | System for testing electric automobile power system assembly |
| CN102410936A (en) * | 2011-08-19 | 2012-04-11 | 奇瑞汽车股份有限公司 | Complete vehicle economy test system and test method for electric automobile |
| CN102445350A (en) * | 2010-10-13 | 2012-05-09 | 上海汽车集团股份有限公司 | System and method for measuring overall vehicle driving quality |
| CN104410607A (en) * | 2014-11-06 | 2015-03-11 | 上海汽车集团股份有限公司 | Thread pool-based engine test control method |
| CN104462604A (en) * | 2014-12-31 | 2015-03-25 | 成都市卓睿科技有限公司 | Data processing method and system |
-
2015
- 2015-05-25 CN CN201510271685.8A patent/CN106289794B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1170164A (en) * | 1996-03-05 | 1998-01-14 | 株式会社数码显示实验室 | Data processing system and data processing method |
| WO2004104604A1 (en) * | 2003-05-23 | 2004-12-02 | Daimlerchrysler Ag | Diagnostic system |
| JP4400576B2 (en) * | 2006-01-10 | 2010-01-20 | 株式会社デンソー | Vehicle diagnostic system |
| CN102445350A (en) * | 2010-10-13 | 2012-05-09 | 上海汽车集团股份有限公司 | System and method for measuring overall vehicle driving quality |
| CN102252856A (en) * | 2011-07-01 | 2011-11-23 | 深圳市航盛电子股份有限公司 | System for testing electric automobile power system assembly |
| CN102410936A (en) * | 2011-08-19 | 2012-04-11 | 奇瑞汽车股份有限公司 | Complete vehicle economy test system and test method for electric automobile |
| CN104410607A (en) * | 2014-11-06 | 2015-03-11 | 上海汽车集团股份有限公司 | Thread pool-based engine test control method |
| CN104462604A (en) * | 2014-12-31 | 2015-03-25 | 成都市卓睿科技有限公司 | Data processing method and system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106653092A (en) * | 2017-01-05 | 2017-05-10 | 上海剑桥科技股份有限公司 | Processing method and processing system of DDR (Double Data Rate) test waveform data file |
| CN109615648A (en) * | 2018-12-07 | 2019-04-12 | 深圳前海微众银行股份有限公司 | Depth of field data conversion method, device, equipment and computer readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106289794B (en) | 2019-08-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105092261B (en) | Road load method of testing and system | |
| CN107403482B (en) | A kind of method, apparatus and system of determining VMT Vehicle-Miles of Travel number | |
| JP2018025539A (en) | Vehicle test system, program for vehicle test system, vehicle test method, and travel resistance setting device | |
| CN106524967A (en) | Automotive wheel center actual driving displacement measuring and extracting method | |
| CN103786731B (en) | Apparatus and method for estimating car speed | |
| CN109916488B (en) | Dynamic vehicle weighing method and device | |
| CN109916485B (en) | Dynamic vehicle weighing method and device | |
| JP2011047675A (en) | Tire rubber index calculating method, device, and program | |
| CN110222421B (en) | Method and system for evaluating concentration of driving route | |
| CN105513405A (en) | Vehicle trajectory analysis method and device | |
| CN105205461A (en) | Signal noise reducing method for modal parameter identification | |
| JP2011203134A (en) | Program, device and method for detection of location | |
| CN103424167B (en) | Utilize the method that car load measurement vehicle weighing apparatus calculates axle weight | |
| CN106289794A (en) | The data processing method of car load test and device | |
| CN104089667B (en) | Vehicle oil consumption measuring method | |
| CN104880172A (en) | Road surface flatness measuring method and device based on Kalman filtering | |
| CN116702096A (en) | Method and device for measuring and calculating road sliding resistance of vehicle plateau environment | |
| CN110530589B (en) | Beam bridge impact coefficient detection method considering continuous traffic flow coupling effect | |
| CN113091746B (en) | Course turning analysis method and great circle course generation method | |
| CN106446320B (en) | Derrick steel structures damnification recognition method based on IMF instantaneous energy curvature difference | |
| EP2689229B1 (en) | Method of determining the stress that should be applied to a tyre during a high-efficiency indoor endurance test | |
| CN115935129A (en) | Method and device for determining soil scale heavy metal concentration value | |
| CN113340605B (en) | Tractor whole vehicle carbon accumulation test method and system | |
| Astarita et al. | EcoSmart: An application for smartphones for monitoring driving economy | |
| CN206146647U (en) | Automobile exhaust flow measurement control system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |