CN106908095A - A kind of extraction of sensing data alignment features and appraisal procedure - Google Patents
A kind of extraction of sensing data alignment features and appraisal procedure Download PDFInfo
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Abstract
The extraction of sensing data alignment features and appraisal procedure, step is：1., for static scene, multisensor cross-beta platform, multiple sensors are built, it is necessary to be calibrated the corresponding high-precision measuring instrument of sensor.For mobile context, the sensing node of integrated design multiple sensors and communication module is deployed in the mobile vehicle of the environment, and real-time data collection simultaneously passes high in the clouds back.2. the velocity estimation under mobile context, the measured value of different sensors module is merged by Kalman filtering, come to optimal velocity estimation value.3. pair data are pre-processed, and need to be calibrated sensor for every kind of, using the data of other sensors as feature, using the data of correspondence high precision instrument as mark, consistent data sample on generation space-time, with correlation technique to feature importance appraisal procedure.Linear appraisal procedure and nonlinear appraisal procedure.
Description
Technical field
Extraction and appraisal procedure the present invention relates to a kind of sensing data alignment features, particularly static and dynamic environment
Deployment, the importance appraisal procedure of speed and linear character and nonlinear characteristic is sought using data fusion.
Background technology
City based on extensive sensing network is perceived, and the data letter of fine granularity and various dimensions is provided for smart city
Breath, and it is increasingly becoming the main source of city big data.It is of new generation with the rise of sensor technology, it is inexpensive, it is portable
Formula sensor has gradually come into the life of people.These sensors based on the sensor of electrochemistry, progressively by industrial quarters and
Art circle has been used in the middle of the perception of the city of large-scale wireless network.Yet with by time migration, environmental factor, mobile context
Etc. aspect influence, these sensors have a low precision, weak dependence of measured value skew and sensor reading and actual value etc.
Problem.These characteristics show, even if there is the calibration dispatched from the factory, in order to ensure the accuracy of data, regularly recalibration is necessary.
In the tasks such as urban air-quality monitoring, reliable data can help people's preferably decision-making and planning, therefore sensor
Data are calibrated to for a very important problem.We need to calibrate the data of sensor, the work of calibration
Be exactly in order to minimize deviation.
“Pre-Deployment Testing,Augmentation and Calibration of Cross-
Point out that there is correlation between the data of different sensors in Sensitive Sensors ", the test for having built multisensor is put down
Platform verifies the sensors such as the correlation between different sensors data, including humiture, ozone, nitrogen dioxide, PM2.5.It is real
Test result and show that the data and humiture of most of gas sensor have correlation, sensor and the ozone sensing of nitrogen dioxide
The reading of device has apparent correlation.Based on data dependence, author is proposed based on polynary least square method (MLS)
Calibration method is calibrated merging the data of multisensor.The cross-beta platform wherein built is not directed to mobile context
Under, also alignment features are not extracted and assessed.
For " Pre-Deployment Testing, Augmentation and Calibration of Cross-
Shortcoming in Sensitive Sensors ", this paper presents for static environment and mobile environment, deployment and is commented feature extraction
Estimate method.
The content of the invention
The present invention will overcome the disadvantages mentioned above of prior art, a kind of extraction there is provided sensing data alignment features and comment
Estimate method.
To realize object above, the technical solution used in the present invention is：A kind of extraction of sensing data alignment features
And appraisal procedure, comprise the following steps：
Step 1, cross-beta platform is built, including：
(1.1) selection equipment, it is necessary to the corresponding high precision measuring instrument of sensor array and each of which being calibrated with
And other related sensor arrays.
(1.2) deployed with devices, it is main to include static deployment and Dynamical Deployment.
(1.2.1) static deployment, in static scene lower sensor array, together with high-precision reference instrument and other
Sensor deployment is in a static environment.
(1.2.1) Dynamical Deployment, for being integrated in mobile context lower sensor array and together with other sensors,
It is deployed on mobile vehicle together with corresponding high-precision reference instrument.
(1.3) data acquisition, the data that sensor and reference instrument are gathered are passed back high in the clouds by corresponding communication module.
Velocity characteristic estimation under step 2, mobile context, including：
(2.1) integrated motion sensor, including accelerometer module and GPS module.
(2.2) equation of accelerometer and GPS module on estimating speed is set up respectively.
(2.2.1) seeks speed by acceleration, and integration of the speed by acceleration in time is obtained.
(2.2.2) seeks speed by GPS, and speed can derivation be obtained in time by GPS displacements
(2.3) speed is asked by data fusion, the estimation using data fusion method to two speed is melted
Close to obtain optimal estimate.
Step 3, feature extraction and assessment, including：
(3.1) data prediction, main standardization and normalization including data, denoising.
(3.2) space-time consistency data sample is generated, for the sensor that each needs is calibrated, by other sensors
Data generate data sample consistent on space-time as feature, the test of sensor correspondence high precision instrument as mark.
(3.3) for every group of data sample, the importance of feature is estimated with related method.Including linear special
Levy appraisal procedure and nonlinear characteristic appraisal procedure.
(3.2.1) linear character is assessed, and the importance assessment of linear character tries to achieve Monomial coefficient using least square
It is estimated with Pearson's coefficient.
(3.2.2) nonlinear characteristic is assessed, and the importance assessment of nonlinear characteristic is carried out using the method for categorised decision tree
Assessment.
The beneficial effects of the invention are as follows：This method is directed to static situation and mobile environment, and the portion of relevant device is carried out respectively
Administration.To the velocity characteristic under mobile context, accurate estimation has been carried out.Linear character and nonlinear characteristic for calibrating are distinguished
Carry out importance assessment.
Brief description of the drawings
Fig. 1 is the workflow diagram of the inventive method.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Specific embodiment of the invention is as follows：
Step 1, cross-beta platform is built, including：
(1.1) selection equipment, it is necessary to the corresponding high precision measuring instrument of sensor array and each of which being calibrated with
And other related sensor arrays.Related sensor includes temperature, humidity, air pressure, illumination, the observation phenomenon such as pressure and sound
Sensor.
(1.2) deployed with devices, it is main to include static deployment and Dynamical Deployment., it is necessary to integrated GPS sensor under mobile context
And acceleration transducer.
(1.2.1) static deployment, in static scene lower sensor array, together with high-precision reference instrument and other
Sensor deployment is in a static environment.
(1.2.1) Dynamical Deployment, for being integrated in mobile context lower sensor array and together with other sensors,
It is deployed on mobile vehicle together with corresponding high-precision reference instrument.
(1.3) data acquisition, the data that sensor and reference instrument are gathered are passed back high in the clouds by corresponding communication module.
Data transfer mode is transmitted by GPRS module.
Velocity characteristic estimation under step 2, mobile context, including：
(2.1) integrated motion sensor, including accelerometer module and GPS module.
(2.2) equation of accelerometer and GPS module on estimating speed is set up respectively.
(2.2.1) seeks speed by acceleration, and integration of the speed by acceleration in time is obtained.It is as follows:
V [t] is the speed of t, and A represents acceleration, and u [t] is that have process caused by acceleration transducer measurement error
Noise.
(2.2.2) seeks speed by GPS, and speed can derivation be obtained in time by GPS displacements, as follows：
V [t]=X [t]+v [t] formula (2)
X [t] is the estimate of the speed obtained by GPS, and v [t] is that have the GPS measurement errors to cause observation noise.
(2.3) speed is asked by data fusion, two estimations of speed are carried out using data fusion correlation technique
Merge to obtain optimal estimate.
Step 3, feature extraction and assessment, including：
(3.1) data prediction, main standardization and normalization including data, denoising.Signal weight is taken for denoising
Actual signal, is considered as x ∈ R by the method builtn(n represents the length of signal), it is assumed that signal is disturbed by additional noise v, xcor
=x+v, wherein be the primary signal uploaded by sensor, it is simple herein to assume that noise v is to be considered as unknown, smaller and rapid change
Change with xcorMachine variable.Goal is in known xcorIn the case of, obtain the estimate of x
Signal reconstruction[42]The problem of following standard can be turned to by form
Minimize
WhereinIt is to need optimised target, xcorIt is the parameter of optimization problem.Function phi：Rn→ R is a convergence letter
Number, it is smooth object function here, and its value can be withThe measurement of degree of roughness.Optimization problem is attempted to look for close to original
Signal (xcor), while again smooth (It is smaller)
(3.2) space-time consistency data sample is generated, for the sensor that each needs is calibrated, by other sensors
Data generate data sample consistent on space-time as feature, the test of sensor correspondence high precision instrument as mark.
(3.3) for every group of data sample, the importance of feature is estimated with related method.Including linear special
Levy appraisal procedure and nonlinear characteristic appraisal procedure.
(3.2.1) linear character is assessed, and the importance assessment of linear character tries to achieve Monomial coefficient using least square
It is estimated with Pearson's coefficient.
(3.2.2) nonlinear characteristic is assessed, and the importance assessment of nonlinear characteristic is carried out using the method for categorised decision tree
Assessment.
Content described in this specification embodiment is only enumerating to the way of realization of inventive concept, protection of the invention
Scope is not construed as being only limitted to the concrete form that embodiment is stated, protection scope of the present invention is also and in art technology
Personnel according to present inventive concept it is conceivable that equivalent technologies mean.
Claims (3)
1. a kind of extraction of sensing data alignment features and appraisal procedure, it is characterised in that following steps：
Step 1, cross-beta platform is built, including：
(1.1) selection equipment, it is necessary to the corresponding high precision measuring instrument of sensor array and each of which that is calibrated and its
The sensor array of his correlation；
(1.2) deployed with devices, it is main to include static deployment and Dynamical Deployment；
(1.2.1) static deployment, in static scene lower sensor array, together with high-precision reference instrument and other sensings
Device is disposed in a static environment；
(1.2.1) Dynamical Deployment, for being integrated in mobile context lower sensor array and together with other sensors, together with
Corresponding high-precision reference instrument is deployed on mobile vehicle；
(1.3) data acquisition, the data that sensor and reference instrument are gathered are passed back high in the clouds by corresponding communication module；
Velocity characteristic estimation under step 2, mobile context, including：
(2.1) integrated motion sensor, including accelerometer module and GPS module；
(2.2) equation of accelerometer and GPS module on estimating speed is set up respectively；
(2.2.1) seeks speed by acceleration, and integration of the speed by acceleration in time is obtained；
(2.2.2) seeks speed by GPS, and speed can derivation be obtained in time by GPS displacements；
(2.3) speed is asked by data fusion, the estimation using data fusion method to two speed is merged to obtain most
Excellent estimate；
Step 3, feature extraction and assessment, including：
(3.1) data prediction, main standardization and normalization including data, denoising；
(3.2) space-time consistency data sample is generated, for the sensor that each needs is calibrated, by the data of other sensors
Used as feature, the test of sensor correspondence high precision instrument generates data sample consistent on space-time as mark；
(3.3) for every group of data sample, the importance of feature is estimated with related method.Commented including linear character
Estimate method and nonlinear characteristic appraisal procedure；
(3.2.1) linear character is assessed, and the importance assessment of linear character tries to achieve Monomial coefficient and skin using least square
Ademilson coefficient is estimated；
(3.2.2) nonlinear characteristic is assessed, and the importance assessment of nonlinear characteristic is estimated using the method for categorised decision tree.
2. the extraction and assessment of a kind of sensing data alignment features based on mobile sensor network according to claim 1
Method, it is characterised in that in the speed estimation method described in the step (2.3), is by merging acceleration and GPS module
Measured value is come the method that minimizes speed estimation error.
3. the extraction and assessment of a kind of sensing data alignment features based on mobile sensor network according to claim 1
Method, it is characterised in that in the feature evaluation method described in the step (3.3), is to include linear character and nonlinear characteristic
Appraisal procedure.
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| CN109429194A (en) * | 2017-08-17 | 2019-03-05 | 浙江大学 | Reference mode location determining method and device in mobile awareness network |
| CN109631973A (en) * | 2018-11-30 | 2019-04-16 | 苏州数言信息技术有限公司 | A kind of automatic calibrating method and system of sensor |
| CN109900309A (en) * | 2019-03-08 | 2019-06-18 | 重庆邮电大学 | A kind of sensing data blind correction method based on admixture spatial model |
| CN111114463A (en) * | 2018-10-30 | 2020-05-08 | 百度在线网络技术（北京）有限公司 | Method and device for acquiring blind area noise |
| CN113391040A (en) * | 2021-07-12 | 2021-09-14 | 北京清环宜境技术有限公司 | Data artificial intelligence automatic calibration method for atmospheric micro-station |
| CN118013465A (en) * | 2024-04-09 | 2024-05-10 | 微网优联科技(成都)有限公司 | Non-motor vehicle identification method and system based on multi-sensor cooperation |
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| CN118013465A (en) * | 2024-04-09 | 2024-05-10 | 微网优联科技(成都)有限公司 | Non-motor vehicle identification method and system based on multi-sensor cooperation |
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