CN117688001B - Vehicle-mounted nitrogen oxide monitoring data processing method, system, device and medium - Google Patents
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 216
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 title claims abstract description 207
- 238000003672 processing method Methods 0.000 title claims abstract description 23
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Abstract
The application discloses a vehicle-mounted nitrogen oxide monitoring data processing method, a system, a device and a storage medium, wherein the method comprises the steps of obtaining the observed concentration and the measured temperature at the last monitoring moment, constructing a state equation and an observation equation of nitrogen oxide monitoring, carrying out state prediction at the current monitoring moment on monitoring data by applying Kalman filtering, carrying out innovation inspection, and carrying out state update on the current state by using standard Kalman filtering if the innovation inspection is normal; judging that observed data exist at the next monitoring time of the current monitoring time, applying Kalman filtering to the observed data at the next monitoring time to predict the state of the next monitoring time, carrying out innovation inspection, and if the innovation inspection is normal, carrying out state updating on the current state through standard Kalman filtering. The application can be widely applied to the technical field of data processing.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a vehicle-mounted nitrogen oxide monitoring data processing method, a system, a device and a storage medium.
Background
The on-board nox monitoring data may be affected by a number of external and internal complications such as traffic flow changes, weather conditions, geographic locations, sensor errors, etc. Aiming at the nitrogen oxide sensor based on the electrochemical principle, the influence of temperature change on the performance of the nitrogen oxide sensor is particularly remarkable, and the signal value of the sensor can have negative drift, continuous jump and other conditions, so that the quality of monitoring data is seriously influenced. Meanwhile, in continuous driving monitoring, equipment may be affected by vibration, wind speed and wind pressure in the running of a vehicle and interference of surrounding electromagnetic environments, so that the problems of missing and random abrupt change of monitoring data and the like occur.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art to a certain extent.
Therefore, an object of the embodiments of the present application is to provide a vehicle-mounted nitrogen oxide monitoring data processing method, system, device and storage medium, which can improve the reliability of vehicle-mounted nitrogen oxide monitoring data.
In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the application comprises the following steps: a vehicle-mounted nitrogen oxide monitoring data processing method comprises the following steps: acquiring an observation data set of the last monitoring moment, wherein the observation data set comprises an observation concentration and a measurement temperature, and constructing a state equation and an observation equation for monitoring nitrogen oxides; carrying out state prediction at the current monitoring moment by applying Kalman filtering to the observed concentration, carrying out innovation inspection, and carrying out state updating on the current state through standard Kalman filtering if the innovation inspection is normal; if the information is checked to be abnormal, updating the current state in a preset mode; judging whether observed data exist at the next monitoring time of the current monitoring time, if so, returning to execute the step of applying Kalman filtering to the observed concentration to predict the state of the current monitoring time and perform innovation inspection, and if not, distributing weight to the observed data around the next monitoring time according to the interval length between the observed data and the next monitoring time of the current monitoring time, weighting and summing the observed data, and performing data supplementation on the next monitoring time; judging whether the current monitoring time is a preset statistical time, if not, returning to execute the step of carrying out state prediction of the current monitoring time by applying Kalman filtering to the observed concentration and carrying out innovation inspection; if yes, distributing weights to all the monitoring moments according to the measured temperatures of all the monitoring moments in a preset statistical period, and calculating a weighted average value of the monitoring data in the statistical period.
In addition, the method for processing vehicle-mounted nitrogen oxide monitoring data according to the above embodiment of the present invention may further have the following additional technical features:
further, in the embodiment of the present application, the step of applying a kalman filter to the monitored data to predict a state at a current monitoring time and perform an innovation check, and if the innovation check is normal, performing a state update on the current state through a standard kalman filter specifically includes:
Constructing a state model and an observation model of vehicle-mounted nitrogen oxide concentration data monitoring, wherein a state equation of the vehicle-mounted nitrogen oxide data monitoring is expressed as follows:
，
Wherein, Is the a priori estimated concentration value at time k, A is the state transition matrix,/>Is the posterior estimated concentration value at the moment k-1, omega k-1 is Gaussian white noise with the mean value of zero and the variance of Q;
The observation model is as follows:
，
Wherein z k is the observation concentration, H is the observation matrix, v k is Gaussian white noise with the mean value of zero and the variance of R;
According to the state transition matrix and the observation matrix, calculating a filtering test term according to a recursive calculation formula of Kalman filtering;
if the check item is smaller than or equal to a preset critical value, determining that the innovation check is normal, and carrying out state update on the current state through standard Kalman filtering.
Further, in an embodiment of the present application, the step of updating the state in a preset manner includes:
if the check item is larger than a preset critical value, determining that the innovation check is abnormal, and calculating a first average value of the first five monitoring values at the current monitoring moment and a second average value of the last five monitoring values including the moment;
if the absolute value of the difference between the first average value and the second average value is larger than a preset critical value, performing standard Kalman filtering;
And if the absolute value of the difference between the first average value and the second average value is smaller than a preset critical value, scaling the observed noise covariance matrix, and then executing Kalman filtering updating.
Further, in the embodiment of the present application, the step of calculating the filtering test term according to the state transition matrix and the observation matrix and according to a recursive calculation formula of kalman filtering specifically includes:
calculating a filtering test term according to the state transition matrix, the observation matrix, a state prediction equation, a prediction covariance formula, an innovation calculation formula, an innovation covariance calculation formula and a test term calculation formula;
Wherein the state prediction equation is ，
The prediction covariance formula is，
The new information calculation formula is，
The new information covariance calculation formula is，
Calculation formula of test term，
Is the a priori estimated concentration value at time k, A is the state transition matrix,/>Is the posterior estimated concentration value at time k-1,/>For the prediction covariance matrix at time k,/>For the posterior estimated covariance matrix at time k-1, X T represents the transpose operation of matrix X, Q is the system process noise covariance, η k is the innovation vector at time k, z k is the observation concentration, H is the observation matrix,For the innovation covariance, R is the observed noise covariance, γ k is the test term at time k, () -1 is the inverse of the matrix.
Further, in the embodiment of the present application, the step of performing standard kalman filtering specifically includes;
Calculating a Kalman gain by a first formula; the first formula includes: ，
performing state estimation through a second formula; the second formula includes: ，
Estimating covariance by a third formula; the third formula includes: 。
wherein K k is Kalman gain, For the prediction covariance matrix at time k, H is the observation matrix, X T represents the transpose operation of matrix X,/>Is the innovation covariance, () -1 is the inverse of the matrix,/>Is the posterior estimated concentration value at time k/>Is the prior estimated concentration value at the moment k, eta k is the innovation vector at the moment k,/>A covariance matrix is estimated for the posterior at time k.
Further, in the embodiment of the present application, the scaling factor of the observed noise covariance matrix is calculated as follows:
，
Wherein, For a scaling factor of iteration number i, γ k is the check term at time k,/>For chi-square distribution critical value, m is the degree of freedom of the detection statistic, alpha is a preset probability confidence range, eta k is the new information vector at k moment, X T represents the transpose operation of matrix X, and/>For the new information covariance, () -1 is the inverse operation of the matrix, R is the observed noise covariance, the iteration initial value ψ k =1 is set, and the iteration termination condition is/>Or the iteration number reaches a set value.
Further, in the embodiment of the present application, the step of allocating weights to the observation data around the next monitoring time according to the interval length between the observation data and the next monitoring time of the current monitoring time and weighting and summing the observation data, and performing data replenishment on the next monitoring time specifically includes:
Selecting the data of the first five and the last five total ten times of the next monitoring time of the current monitoring time, carrying out weighted summation according to a weight calculation formula, and carrying out data filling on the next monitoring time, wherein the weight calculation formula in the weighted summation comprises:
，
where t j is the interval between this time and the 10 preceding and following times.
Further, in the embodiment of the present application, the step of assigning weights to the monitoring moments according to the measured temperatures of the monitoring moments in a preset statistical period specifically includes:
constructing a weight calculation formula, and acquiring the measured temperature of each monitoring moment and the first quantity of monitoring data in a preset statistical period;
Inputting the first quantity and the measured temperature into the weight calculation formula to obtain weights of all monitoring moments; wherein the weight calculation formula comprises
，
Where T s is the measured temperature and N is the first quantity.
On the other hand, the embodiment of the application also provides a vehicle-mounted nitrogen oxide monitoring data processing system, which comprises:
The first processing unit is used for obtaining the observed concentration and the measured temperature at the last monitoring moment and constructing a state equation and an observation equation for monitoring nitrogen oxides;
The second processing unit is used for carrying out state prediction at the current monitoring moment by applying Kalman filtering to the observed concentration and carrying out innovation inspection, and if the innovation inspection is normal, carrying out state update on the current state by using standard Kalman filtering; if the information is checked to be abnormal, updating the current state in a preset mode;
The third processing unit is used for judging whether observed data exist at the next monitoring time of the current monitoring time, if so, returning to execute the step of applying Kalman filtering to the observed concentration to predict the state of the current monitoring time and perform innovation inspection, and if not, distributing weight to the observed data around the next monitoring time according to the interval length between the observed data and the next monitoring time of the current monitoring time and performing weighted summation to perform data replenishment at the next monitoring time;
The fourth processing unit is used for judging whether the current monitoring time is a preset statistical time, if not, returning to execute the step of applying Kalman filtering to the observed concentration to predict the state of the current monitoring time and carrying out innovation inspection; if yes, distributing weights to all the monitoring moments according to the measured temperatures of all the monitoring moments in a preset statistical period, and calculating a weighted average value of the monitoring data in the statistical period.
On the other hand, the application also provides a vehicle-mounted nitrogen oxide monitoring data processing device, which comprises:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is caused to implement a vehicle-mounted nitrogen oxide monitoring data processing method according to any one of the summary of the invention.
Furthermore, the present application provides a computer readable storage medium, in which processor executable instructions are stored, which when executed by a processor are configured to perform a vehicle-mounted nitrogen oxide monitoring data processing method according to any one of the above.
The advantages and benefits of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
The application can effectively improve the problems of data drift, data loss, random fluctuation and the like of the vehicle-mounted nitrogen oxide monitoring sensor under the actions of temperature change and other environmental factors through Kalman filtering, and mainly considers the influence characteristic of temperature on the monitoring result, thereby improving the stability and the continuity of data. Therefore, the accuracy of the monitoring data is ensured, and more accurate monitoring statistical results are obtained.
Drawings
FIG. 1 is a schematic diagram illustrating steps of a method for processing vehicle-mounted NOx monitoring data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram showing a relationship between a temperature T and a temperature-dependent correction coefficient nT according to an embodiment of the present invention;
FIG. 3 is a graph showing the correspondence between sensor signals and actual gas concentrations in an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating dynamic monitoring of actual NOx in an embodiment of the present invention;
FIG. 5 is a graph illustrating statistics of NOx monitoring data according to an embodiment of the present invention;
Fig. 6 is a schematic structural diagram of a vehicle-mounted nitrogen oxide monitoring data processing device according to an embodiment of the present invention.
Detailed Description
The following describes the principle and process of the vehicle-mounted nitrogen oxide monitoring data processing method, system, device and storage medium according to the embodiments of the present invention with reference to the accompanying drawings.
First, the terms used in the present application will be explained:
NOx is an oxide of nitrogen, including oxides of nitrogen such as NO, NO 2, and N 2O5.
The application provides a vehicle-mounted nitrogen oxide monitoring data processing method, which comprises the following steps:
s101, acquiring an observation data set of the last monitoring moment, wherein the observation data set comprises an observation concentration and a measurement temperature, and constructing a state equation and an observation equation for monitoring nitrogen oxides;
S102, performing state prediction at the current monitoring moment on the observed concentration by applying Kalman filtering, performing innovation inspection, and if the innovation inspection is normal, performing state update on the current state by using standard Kalman filtering; if the information is checked to be abnormal, updating the current state in a preset mode;
S103, judging whether observed data exist at the next monitoring time of the current monitoring time, if so, returning to execute the step of applying Kalman filtering to the observed concentration to predict the state of the current monitoring time and perform innovation inspection, and if not, distributing weight to the observed data around the next monitoring time according to the length of the interval with the next monitoring time of the current monitoring time, weighting and summing, and performing data supplementation on the next monitoring time.
S104, judging whether the current monitoring time is a preset statistical time, if not, returning to execute the step of carrying out state prediction on the current monitoring time by applying Kalman filtering to the observed concentration and carrying out innovation inspection; if yes, distributing weights to all monitoring moments according to the measured temperatures of all monitoring moments in a preset statistical period, and calculating a weighted average value of monitoring data in a statistical period.
Further, the step of performing state prediction at the current monitoring time by applying kalman filtering to the monitored data and performing an innovation check, and if the innovation check is normal, performing state update on the current state by using standard kalman filtering specifically includes:
Constructing a state model and an observation model of vehicle-mounted nitrogen oxide concentration data monitoring, wherein a state equation of the vehicle-mounted nitrogen oxide data monitoring is expressed as follows:
，
Wherein, Is the a priori estimated concentration value at time k, A is the state transition matrix,/>Is the posterior estimated concentration value at the moment k-1, omega k-1 is Gaussian white noise with the mean value of zero and the variance of Q;
The observation model is as follows:
，
Wherein z k is the observation concentration, H is the observation matrix, v k is Gaussian white noise with the mean value of zero and the variance of R;
According to the state transition matrix and the observation matrix, calculating a filtering test term according to a recursive calculation formula of Kalman filtering;
if the check item is smaller than or equal to a preset critical value, determining that the innovation check is normal, and carrying out state update on the current state through standard Kalman filtering.
Further, the step of updating the state in a preset manner includes:
if the check item is larger than a preset critical value, determining that the innovation check is abnormal, and calculating a first average value of the first five monitoring values at the current monitoring moment and a second average value of the last five monitoring values including the moment;
if the absolute value of the difference between the first average value and the second average value is larger than a preset critical value, performing standard Kalman filtering;
If the absolute value of the difference between the first average value and the second average value is smaller than a preset critical value, scaling the observed noise covariance matrix, and then executing Kalman filtering updating.
Further, according to the state transition matrix and the observation matrix, according to a recursive calculation formula of Kalman filtering, calculating a filtering test term specifically includes:
calculating a filtering test term according to the state transition matrix, the observation matrix, the state prediction equation, the prediction covariance formula, the innovation covariance formula and the test term calculation formula;
Wherein the state prediction equation is ，
The prediction covariance formula is，
The new information calculation formula is，
The new information covariance calculation formula is，
Calculation formula of test term，
Is the a priori estimated concentration value at time k, A is the state transition matrix,/>Is the posterior estimated concentration value at time k-1,/>For the prediction covariance matrix at time k,/>For the posterior estimated covariance matrix at time k-1, X T represents the transpose operation of matrix X, Q is the system process noise covariance, η k is the innovation vector at time k, z k is the observation concentration, H is the observation matrix,For the innovation covariance, R is the observed noise covariance, γ k is the test term at time k, () -1 is the inverse of the matrix.
Further, the step of performing standard Kalman filtering specifically includes;
Calculating a Kalman gain by a first formula; the first formula includes: ，
performing state estimation through a second formula; the second formula includes: ，
estimating covariance by a third formula; the third formula includes: 。
wherein K k is Kalman gain, For the prediction covariance matrix at time k, H is the observation matrix, X T represents the transpose operation of matrix X,/>Is the innovation covariance, () -1 is the inverse of the matrix,/>Is the posterior estimated concentration value at time k/>Is the prior estimated concentration value at the moment k, eta k is the innovation vector at the moment k,/>A covariance matrix is estimated for the posterior at time k.
Further, the scaling factor of the observed noise covariance matrix is calculated as follows:
，
Wherein, For a scaling factor of iteration number i, γ k is the check term at time k,/>For chi-square distribution critical value, m is the degree of freedom of the detection statistic, alpha is a preset probability confidence range, eta k is the new information vector at k moment, X T represents the transpose operation of matrix X, and/>For the new information covariance, () -1 is the inverse operation of the matrix, R is the observed noise covariance, the iteration initial value ψ k =1 is set, and the iteration termination condition is/>Or the iteration number reaches a set value.
Further, the step of allocating weights to the observation data around the next monitoring time according to the length of the interval with the next monitoring time of the current monitoring time and weighting and summing the observation data, and performing data filling on the next monitoring time specifically includes:
selecting the data of the first five and the last five total ten times of the next monitoring time of the current monitoring time, carrying out weighted summation according to a weight calculation formula, and carrying out data filling on the next monitoring time, wherein the weight calculation formula in the weighted summation comprises:
，
where t j is the interval between this time and the 10 preceding and following times.
Further, the step of assigning weights to the monitoring moments according to the measured temperatures of the monitoring moments in a preset statistical period specifically includes:
constructing a weight calculation formula, and acquiring the measured temperature at each monitoring moment and the first quantity of monitoring data in a preset statistical period;
Inputting the first quantity and the measured temperature into a weight calculation formula to obtain weights of all monitoring moments; wherein the weight calculation formula comprises
，
Where T s is the measured temperature and N is the first quantity.
The following describes the specific calculation principle of the present application with reference to the drawings:
As shown in fig. 1, a vehicle-mounted nitrogen oxide monitoring data processing method includes the following steps:
Step S1, in the manufacturing stage of the vehicle-mounted monitoring equipment, adopting ALPHASENSE electrochemical sensors to monitor nitrogen oxides, obtaining a relation curve between temperature T and a temperature-related correction coefficient nT by changing the ambient temperature in a zero-atmosphere environment, and then obtaining a corresponding relation curve between sensor signals and actual gas concentration, wherein the specific steps are as follows:
s11, changing the ambient temperature under the zero-atmosphere environment, acquiring corresponding sensor signals, recording the ambient temperature, and calculating nT corresponding to different temperatures according to the following formula:
WEc =(WEu – WEe ) − nT * (AEu – AEe )，
in the above formula, WEc is a calibrated Working Electrode (WE) output signal, WEc corresponds to 0, WEu is an uncalibrated original WE output signal, corresponding to an AD1 channel value of the oxynitride sensor, AEu is an uncalibrated original Auxiliary Electrode (AE) output signal, WEe is WE electronic offset on an AFE or ISB board, namely WE output signal when the sensor is not inserted, 3999 is taken, AEe is AE electronic offset on the AFE or ISB board, namely AE output signal when the sensor is not inserted, 3998 is taken;
S12, as shown in FIG. 2, fitting the corresponding relation between the temperature T and the temperature-related correction coefficient nT, fitting by adopting a five-term equation, and reserving 15 decimal points;
S13, setting the corresponding relation between the sensor signal and the actual nitrogen oxide concentration as a linear relation, namely Y=aX+b, wherein X is a sensor output signal WEc, and Y is the concentration;
S14, placing the experiment cabin in a constant temperature and humidity box, introducing nitrogen oxide standard gas into the gas experiment cabin, simultaneously using a TE-42i nitrogen oxide analyzer and a nitrogen oxide sensor to be measured to measure the concentration of nitrogen oxide, carrying out data fitting on the nitrogen oxide sensor to be measured by taking the test data of the standard nitrogen oxide analyzer as a reference to derive a value and a value b, wherein R 2 in the fitting process is required to be higher than 0.99, and recalibrating after the reason is required to be checked if the R 2 is lower than 0.99, so as to finally obtain a corresponding relation curve between a sensor signal WEc and the actual gas concentration as shown in figure 3.
Step S2, in the data monitoring stage, a state equation and an observation equation for monitoring nitrogen oxides are constructed, state prediction is carried out on nitrogen oxide monitoring values shown in fig. 4 by applying Kalman filtering, and meanwhile, innovation inspection is carried out, if the inspection is normal, standard Kalman filtering is used for updating, and if the inspection is abnormal, a preset mode is selected for carrying out state updating according to different abnormal states, wherein the specific steps are as follows:
s21, constructing a state model and an observation model for monitoring the concentration data of the vehicle-mounted nitrogen oxides, wherein a state equation for monitoring the data of the vehicle-mounted nitrogen oxides is expressed as follows:
，
Wherein, Is the a priori estimated concentration value at time k, A is the state transition matrix,/>Is the posterior estimated concentration value at the moment k-1, omega k-1 is Gaussian white noise with the mean value of zero and the variance of Q;
The observation equation can be expressed as:
，
wherein z k is the observed concentration, H is the observed matrix, v k is the Gaussian white noise with the mean value of zero and the variance of R.
S22, calculating a filtering test term according to a recursive calculation formula of Kalman filtering by using the state transition matrix A and the observation matrix H：
State prediction，
Prediction covariance，
Calculating new information，
Calculating innovation covariance，
Calculating a test item，
If the test item is smaller than or equal to the preset critical value, continuing to execute standard Kalman filtering:
Calculating Kalman gain ，
State estimation，
Estimating covariance，
If the check item is greater than the preset critical value, executing S23;
S23, calculating an average value G 1 of the five monitoring values before the moment and an average value G 2 of the five monitoring values including the moment, if the absolute value of the difference between G 2 and G 1 is larger than a preset critical value, executing standard Kalman filtering, if the absolute value of the difference between G 2 and G 1 is smaller than the preset critical value, scaling R, then executing Kalman filtering update, and calculating a scaling factor for scaling R as follows:
，
Setting an iteration initial value psi k =1, i as the iteration number, and setting an iteration termination condition as Or the iteration times reach a set value; /(I)For iteration number/>M is the degree of freedom of the detection statistic, the value is 1, alpha is a preset probability confidence range, and the value is 99%/>Obtained by checking the chi-square distribution table.
Step S3, judging whether monitored data are missing at the next moment, if the monitored data are missing, continuing to process by using the filtering method in the step S2, and entering the step S4, if the monitored data are missing, distributing weights to surrounding data according to the interval length with the next moment, weighting and summing the weights, supplementing the data at the next moment, continuing to process by using the filtering method in the step S2, and entering the step S4, wherein the specific steps are as follows:
s31, judging whether monitoring data are missing at the next moment, if not, continuing to process by using the filtering method in the step S2, and entering the step S4, and if so, turning to the step S32;
s32, selecting the data of the first five times and the last five times which are summed up to ten times at the next time, carrying out weighted summation according to a weight calculation formula, complementing the observed data at the next time, turning to S4, and calculating the weight according to the following formula (t j is the interval between the time and the first 10 times):
，
the processing continues with the filtering method in step S2 and proceeds to step S4.
Step S4, the present embodiment sets the statistical time period to 10 minutes, determines whether the statistical time is reached, if the statistical time is not reached, returns to S2 to continue the filtering process, if the statistical time is reached, assigns weights to each time according to the measured temperature of each statistical time in the statistical period, obtains the monitored statistical value in the statistical time period, returns to S2 to continue the filtering process, and calculates the weights according to the following method (T j is the monitored temperature of each time in the statistical time period, and N is the number of monitored values in the statistical time period):
，
And finally normalizing the ownership values to obtain the final weight value of each monitoring value. Finally, a diagram of the nitrogen oxide monitoring data statistics shown in fig. 5 is obtained.
In addition, corresponding to the method, the embodiment of the application also provides a vehicle-mounted nitrogen oxide monitoring data processing system, which can comprise: a first processing unit 1001, a second processing unit 1002, a third processing unit, and a fourth processing unit 1004.
The first processing unit 1001 may be configured to obtain an observed concentration and a measured temperature at a previous monitoring time, and construct a state equation and an observed equation for monitoring nitrogen oxides;
The second processing unit 1002 may be configured to apply a kalman filter to the observed concentration to perform state prediction at the current monitoring time, perform an innovation check, and perform state update on the current state through a standard kalman filter if the innovation check is normal; if the information is checked to be abnormal, updating the current state in a preset mode;
The third processing unit 1003 may be configured to determine whether there is observation data at a next monitoring time of the current monitoring time, and if so, return to perform a step of performing state prediction of the current monitoring time by applying kalman filtering to the observation concentration, and perform innovation inspection, and if not, assign weights to observation data around the next monitoring time according to the length of an interval with the next monitoring time of the current monitoring time, and perform weighted summation, and perform data alignment for the next monitoring time;
the fourth processing unit 1004 may be configured to determine whether the current monitoring time is a preset statistical time, and if not, return to execute the step of performing state prediction of the current monitoring time by applying kalman filtering to the observed concentration, and perform innovation inspection; if yes, distributing weights to all monitoring moments according to the measured temperatures of all monitoring moments in a preset statistical period, and calculating the average value of monitoring data in a statistical period.
The acquiring unit may be any integrated circuit unit or a micro processor unit obtained by integrating a chip with a processing function and its peripheral circuit by the existing integration technology. The first processing unit and the second processing unit may be any integrated circuit module or a micro processor module obtained by integrating a chip with a processing function and a peripheral circuit thereof in the prior art. And the first processing unit and the second processing unit may further comprise one or more memories. One or more memories may be used to store specific algorithms in the present application.
In some embodiments of the application, the first processing unit 1001 may be provided in the same gateway or device with a processor as the second processing unit 1002. The specific device connection manner and device arrangement of the first processing unit 1001 and the second processing unit 1002, and the second processing unit 1002 and the third processing unit 1003 are not limited.
It should be noted that, the content in the above-mentioned embodiments of the method for processing vehicle-mounted nitrogen oxide monitoring data is applicable to the embodiments of the present vehicle-mounted nitrogen oxide monitoring data processing system, and the functions specifically implemented by the embodiments of the present vehicle-mounted nitrogen oxide monitoring data processing system are the same as those of the embodiments of the above-mentioned vehicle-mounted nitrogen oxide monitoring data processing method, and the beneficial effects achieved by the embodiments of the above-mentioned vehicle-mounted nitrogen oxide monitoring data processing method are the same as those achieved by the embodiments of the above-mentioned vehicle-mounted nitrogen oxide monitoring data processing method.
Correspondingly, the embodiment of the application also provides a vehicle-mounted nitrogen oxide monitoring data processing device, the specific structure of which can be referred to as fig. 6, comprising:
At least one processor 1011;
at least one memory 1012 for storing at least one program;
and when the at least one program is executed by the at least one processor, the at least one processor is enabled to realize the vehicle-mounted nitrogen oxide monitoring data processing method.
The content in the method embodiment is applicable to the embodiment of the device, and the functions specifically realized by the embodiment of the device are the same as those of the method embodiment, and the obtained beneficial effects are the same as those of the method embodiment.
Corresponding to the method of fig. 1, an embodiment of the present application further provides a computer readable storage medium having stored therein processor executable instructions which, when executed by a processor, are adapted to carry out the vehicle-mounted nitrogen oxide monitoring data processing method.
The content in the embodiment of the vehicle-mounted nitrogen oxide monitoring data processing method is applicable to the embodiment of the storage medium, the specific function of the embodiment of the storage medium is the same as that of the embodiment of the vehicle-mounted nitrogen oxide monitoring data processing method, and the achieved beneficial effects are the same as those of the embodiment of the vehicle-mounted nitrogen oxide monitoring data processing method.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations may be changed and in which sub-operations described as part of a larger operation may be performed independently.
Furthermore, while the application is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the functions and/or features may be integrated in a single physical device and/or software module or may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the application, which is to be defined in the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium, including several programs for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable programs for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with a program execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the programs from the program execution system, apparatus, or device and execute the programs. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the program execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the foregoing description of the present specification, reference has been made to the terms "one embodiment/example", "another embodiment/example", "certain embodiments/examples", and the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present application has been described in detail, the present application is not limited to the embodiments described above, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present application, and these equivalent modifications and substitutions are intended to be included in the scope of the present application as defined in the appended claims.
Claims (7)
1. The vehicle-mounted nitrogen oxide monitoring data processing method is characterized by comprising the following steps of:
acquiring an observation data set of the last monitoring moment, wherein the observation data set comprises an observation concentration and a measurement temperature, and constructing a state equation and an observation equation for monitoring nitrogen oxides;
carrying out state prediction at the current monitoring moment by applying Kalman filtering to the observed concentration, carrying out innovation inspection, and carrying out state updating on the current state through standard Kalman filtering if the innovation inspection is normal; if the information is checked to be abnormal, updating the current state in a preset mode;
Judging whether observed data exist at the next monitoring time of the current monitoring time, if so, returning to execute the step of applying Kalman filtering to the observed concentration to predict the state of the current monitoring time and perform innovation inspection, and if not, distributing weight to the observed data around the next monitoring time according to the interval length between the observed data and the next monitoring time of the current monitoring time, weighting and summing the observed data, and performing data supplementation on the next monitoring time;
Judging whether the current monitoring time is a preset statistical time, if not, returning to execute the step of carrying out state prediction of the current monitoring time by applying Kalman filtering to the observed concentration and carrying out innovation inspection; if yes, distributing weights to all the monitoring moments according to the measured temperatures of all the monitoring moments in a preset statistical period, and calculating a weighted average value of the monitoring data in the statistical period; and if the information is checked to be normal, the current state is updated through the standard Kalman filtering, which specifically comprises the following steps:
Constructing a state model and an observation model of vehicle-mounted nitrogen oxide concentration data monitoring, wherein a state equation of the vehicle-mounted nitrogen oxide data monitoring is expressed as follows:
，
Wherein, Is the a priori estimated concentration value at time k, A is the state transition matrix,/>Is the posterior estimated concentration value at the moment k-1, omega k-1 is Gaussian white noise with the mean value being zero and the variance being the system process noise covariance;
The observation model is as follows:
，
Wherein z k is the observation concentration, H is the observation matrix, v k is the Gaussian white noise with the mean value of zero and the variance of the observation noise covariance;
According to the state transition matrix and the observation matrix, calculating a filtering test term according to a recursive calculation formula of Kalman filtering;
If the check item is smaller than or equal to a preset critical value, determining that the innovation check is normal, and updating the current state through standard Kalman filtering; the step of updating the state in a preset manner comprises the following steps:
if the check item is larger than a preset critical value, determining that the innovation check is abnormal, and calculating a first average value of the first five monitoring values at the current monitoring moment and a second average value of the last five monitoring values including the moment;
if the absolute value of the difference between the first average value and the second average value is larger than a preset critical value, performing standard Kalman filtering;
If the absolute value of the difference between the first average value and the second average value is smaller than a preset critical value, scaling the observed noise covariance matrix, and then executing Kalman filtering updating; the step of calculating a filtering test term according to the state transition matrix and the observation matrix and a recursive calculation formula of Kalman filtering specifically comprises the following steps:
calculating a filtering test term according to the state transition matrix, the observation matrix, a state prediction equation, a prediction covariance formula, an innovation calculation formula, an innovation covariance calculation formula and a test term calculation formula;
Wherein the state prediction equation is ，
The prediction covariance formula is ，
The new information calculation formula is，
The new information covariance calculation formula is，
Calculation formula of test term，
Is the a priori estimated concentration value at time k, A is the state transition matrix,/>Is the a posteriori estimated concentration value at time k-1,For the prediction covariance matrix at time k,/>For the posterior estimated covariance matrix at time k-1, X T represents the transpose operation of matrix X, Q is the system process noise covariance, η k is the innovation vector at time k, z k is the observation concentration, H is the observation matrix,/>For the innovation covariance, R is the observed noise covariance, gamma k is the test term at time k, () -1 is the inverse of the matrix;
The step of performing standard Kalman filtering specifically comprises;
Calculating a Kalman gain by a first formula; the first formula includes: ，
performing state estimation through a second formula; the second formula includes: ，
Estimating covariance by a third formula; the third formula includes: ，
wherein K k is Kalman gain, For the prediction covariance matrix at time k, H is the observation matrix, X T represents the transpose operation of matrix X,/>Is the innovation covariance, () -1 is the inverse of the matrix,/>Is the posterior estimated concentration value at time k/>Is the prior estimated concentration value at the moment k, eta k is the innovation vector at the moment k,/>A covariance matrix is estimated for the posterior at time k.
2. The vehicle-mounted nitrogen oxide monitoring data processing method according to claim 1, wherein the scaling factor of the observed noise covariance matrix is calculated as follows:
，
Wherein, For a scaling factor of iteration number i, γ k is the check term at time k,/>For chi-square distribution critical value, m is the degree of freedom of the detection statistic, alpha is a preset probability confidence range, eta k is the new information vector at k moment, X T represents the transpose operation of matrix X, and/>For the new information covariance, () -1 is the inverse operation of the matrix, R is the observed noise covariance, the iteration initial value ψ k =1 is set, and the iteration termination condition is/>Or the iteration number reaches a set value.
3. The method for processing vehicle-mounted nitrogen oxide monitoring data according to claim 1, wherein the step of allocating weights to the observation data around the next monitoring time according to the interval length between the vehicle-mounted nitrogen oxide monitoring data and the next monitoring time of the current monitoring time and weighting and summing the observation data, and performing data replenishment on the next monitoring time specifically comprises the steps of:
Selecting the data of the first five and the last five total ten times of the next monitoring time of the current monitoring time, carrying out weighted summation according to a weight calculation formula, and carrying out data filling on the next monitoring time, wherein the weight calculation formula in the weighted summation comprises:
，
where t j is the interval between this time and the 10 preceding and following times.
4. The method for processing vehicle-mounted nitrogen oxide monitoring data according to claim 1, wherein the step of assigning weights to the monitoring moments according to the measured temperatures of the monitoring moments in a preset statistical period specifically comprises:
constructing a weight calculation formula, and acquiring the measured temperature of each monitoring moment and the first quantity of monitoring data in a preset statistical period;
Inputting the first quantity and the measured temperature into the weight calculation formula to obtain weights of all monitoring moments; wherein the weight calculation formula comprises
，
Where T s is the measured temperature and N is the first quantity.
5. A vehicle-mounted nitrogen oxide monitoring data processing system, comprising:
The first processing unit is used for obtaining the observed concentration and the measured temperature at the last monitoring moment and constructing a state equation and an observation equation for monitoring nitrogen oxides;
The second processing unit is used for carrying out state prediction at the current monitoring moment by applying Kalman filtering to the observed concentration and carrying out innovation inspection, and if the innovation inspection is normal, carrying out state update on the current state by using standard Kalman filtering; if the information is checked to be abnormal, updating the current state in a preset mode;
The third processing unit is used for judging whether observed data exist at the next monitoring time of the current monitoring time, if so, returning to execute the step of applying Kalman filtering to the observed concentration to predict the state of the current monitoring time and perform innovation inspection, and if not, distributing weight to the observed data around the next monitoring time according to the interval length between the observed data and the next monitoring time of the current monitoring time and performing weighted summation to perform data replenishment at the next monitoring time;
The fourth processing unit is used for judging whether the current monitoring time is a preset statistical time, if not, returning to execute the step of applying Kalman filtering to the observed concentration to predict the state of the current monitoring time and carrying out innovation inspection; if yes, distributing weights to all the monitoring moments according to the measured temperatures of all the monitoring moments in a preset statistical period, and calculating a weighted average value of the monitoring data in the statistical period; and if the information is checked to be normal, the current state is updated through the standard Kalman filtering, which specifically comprises the following steps:
Constructing a state model and an observation model of vehicle-mounted nitrogen oxide concentration data monitoring, wherein a state equation of the vehicle-mounted nitrogen oxide data monitoring is expressed as follows:
，
Wherein, Is the a priori estimated concentration value at time k, A is the state transition matrix,/>Is the posterior estimated concentration value at the moment k-1, omega k-1 is Gaussian white noise with the mean value being zero and the variance being the system process noise covariance;
The observation model is as follows:
，
Wherein z k is the observation concentration, H is the observation matrix, v k is the Gaussian white noise with the mean value of zero and the variance of the observation noise covariance;
According to the state transition matrix and the observation matrix, calculating a filtering test term according to a recursive calculation formula of Kalman filtering;
If the check item is smaller than or equal to a preset critical value, determining that the innovation check is normal, and updating the current state through standard Kalman filtering; the step of updating the state in a preset manner comprises the following steps:
if the check item is larger than a preset critical value, determining that the innovation check is abnormal, and calculating a first average value of the first five monitoring values at the current monitoring moment and a second average value of the last five monitoring values including the moment;
if the absolute value of the difference between the first average value and the second average value is larger than a preset critical value, performing standard Kalman filtering;
If the absolute value of the difference between the first average value and the second average value is smaller than a preset critical value, scaling the observed noise covariance matrix, and then executing Kalman filtering updating; the step of calculating a filtering test term according to the state transition matrix and the observation matrix and a recursive calculation formula of Kalman filtering specifically comprises the following steps:
calculating a filtering test term according to the state transition matrix, the observation matrix, a state prediction equation, a prediction covariance formula, an innovation calculation formula, an innovation covariance calculation formula and a test term calculation formula;
Wherein the state prediction equation is ，
The prediction covariance formula is ，
The new information calculation formula is，
The new information covariance calculation formula is，
Calculation formula of test term，
Is the a priori estimated concentration value at time k, A is the state transition matrix,/>Is the a posteriori estimated concentration value at time k-1,For the prediction covariance matrix at time k,/>For the posterior estimated covariance matrix at time k-1, X T represents the transpose operation of matrix X, Q is the system process noise covariance, η k is the innovation vector at time k, z k is the observation concentration, H is the observation matrix,/>For the innovation covariance, R is the observed noise covariance, gamma k is the test term at time k, () -1 is the inverse of the matrix;
The step of performing standard Kalman filtering specifically comprises;
Calculating a Kalman gain by a first formula; the first formula includes: ，
performing state estimation through a second formula; the second formula includes: ，
Estimating covariance by a third formula; the third formula includes: ，
wherein K k is Kalman gain, For the prediction covariance matrix at time k, H is the observation matrix, X T represents the transpose operation of matrix X,/>Is the innovation covariance, () -1 is the inverse of the matrix,/>Is the posterior estimated concentration value at time k/>Is the prior estimated concentration value at the moment k, eta k is the innovation vector at the moment k,/>A covariance matrix is estimated for the posterior at time k.
6. A vehicle-mounted nitrogen oxide monitoring data processing device, characterized by comprising:
at least one processor;
at least one memory for storing at least one program;
When the at least one program is executed by the at least one processor, the at least one processor is caused to implement a vehicle-mounted nitrogen oxide monitoring data processing method as claimed in any one of claims 1 to 4.
7. A computer readable storage medium, in which processor executable instructions are stored, characterized in that the processor executable instructions are for performing a vehicle-mounted nitrogen oxide monitoring data processing method according to any of the claims 1-4 when being executed by a processor.
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