CN113361596A - Sensor data augmentation method, system and storage medium - Google Patents

Sensor data augmentation method, system and storage medium Download PDF

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CN113361596A
CN113361596A CN202110623634.2A CN202110623634A CN113361596A CN 113361596 A CN113361596 A CN 113361596A CN 202110623634 A CN202110623634 A CN 202110623634A CN 113361596 A CN113361596 A CN 113361596A
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drift
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matrix
sensing
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CN113361596B (en
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饶元
王文
江朝晖
朱军
张武
高宁
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Anhui Agricultural University AHAU
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Abstract

The invention discloses a sensor data augmentation method, a sensor data augmentation system and a storage medium, and belongs to the technical field of sensor data processing. The method includes the steps that a random cutting method is adopted for data without drift to achieve augmentation of data samples without drift, a plurality of function models which accord with drift characteristics of a sensor are selected as trend items to construct a non-stable random walk process containing the trend items to simulate the data drift process of the sensor, drift amount simulation is achieved by setting a drift probability threshold of the sensor and determining a maximum drift range according to data characteristics, and the augmented data samples without drift are augmented to achieve augmentation. According to the method, the time and space characteristics of the drift data of the sensor are considered comprehensively by fusing the data of each adjacent sensor in the sensing field, so that the correctness of the drift amount simulation and the diversity of the characteristics of the augmented data are ensured, and the problem of poor generalization capability of the model caused by the shortage of training samples in the sensor drift calibration model is solved.

Description

Sensor data augmentation method, system and storage medium
Technical Field
The invention relates to the technical field of sensor data processing, in particular to a sensor data amplification method, a system, a storage medium and a sensor data drift calibration method.
Background
Drift amount of sensor data generated along with time in the agricultural internet of things is a key restriction factor influencing data acquisition quality of a sensor, intelligent regulation and control of internet of things equipment and effective analysis of data cannot be guaranteed due to the fact that the sensor drift phenomenon exists, independent unloading and recalibration of the sensor are often difficult to achieve due to the fact that the sensor is usually deployed in a large scale and for a long time, and therefore calibration of the sensor is more and more important under the condition that no drift real signal cannot be obtained.
At present, the inventors have attracted attention to calibration of sensors based on deep learning. The Convolutional Neural Network (CNN) is a subset of deep learning, has been applied to other fields, and achieves good results. Research on the application of CNN to the time series field is also growing. For example, the electroencephalogram signal is denoised, the method can establish accurate mapping from the noise signal to the electroencephalogram signal, real-time denoising is realized, and the efficiency and quality of the electroencephalogram signal denoising can be effectively improved. However, this method is limited to denoising in a single time series data and is not suitable for drift calibration of multiple sensors.
Furthermore, Bao et al, 2018, in Structural Health Monitoring,18(2),401-421, convert time series signals into image vectors, segment-draw them in grayscale images, and input training data sets consisting of randomly selected and manually labeled image vectors into a deep neural network or a set of deep neural networks, which are trained by a stacked auto-encoder and a greedy hierarchical training technique, to more accurately detect time series data, including multi-modal anomalies including data drift, but this method can only detect and classify anomalous patterns in time series data, and does not involve calibration of sensor drift. Tian et al propose a novel deep learning model in IEEE Access,8,121385 and 121397 in 2020, and combine unsupervised technology and supervised technology to realize drift compensation of an electronic nose, but the method is complex and does not involve training set construction method research, and the applicable scenario is limited.
Therefore, the existing method cannot effectively meet the sensor drift calibration requirement based on the analysis. The deep learning has the capability of automatically learning features from a large amount of data, and the deep learning method has great application prospect when being applied to the field of sensor drift calibration. Therefore, deep learning is adopted to automatically extract drift characteristics in the sensing data, and it is very meaningful work to realize drift calibration of the sensor. However, training of deep learning models such as neural networks requires a large number of data samples, and how to construct sufficient drift-containing data samples and iteratively input the appropriate size of the training samples is an urgent problem to be solved by the person skilled in the art; on the other hand, how to ensure the accuracy of extracting the drift features and the accuracy of calibration is also a difficult problem.
Disclosure of Invention
1. Problems to be solved
Aiming at the problems that a large number of data samples are needed for training deep learning models such as a neural network and the like in the prior art, and how to construct sufficient drift-containing data samples and how to iteratively input proper sizes of training samples, the invention provides a sensor data augmentation method, which adopts a random cutting method to augment the drift-free data samples, selects a plurality of function models which accord with the drift characteristics of a sensor as trend items to construct a non-stationary random walk process containing the trend items for simulating the drift process of the sensor data, determines the maximum drift range according to the data characteristics by setting the drift probability threshold of the sensor, simulates the drift characteristics of the data to realize the drift simulation, adopts a mode of adding the drift amount to the augmented drift-free data samples to achieve the augmentation of the drift-containing data samples, and integrates the data of adjacent sensors in a sensing field to comprehensively consider the time and space characteristics of the drift data of the sensor, the method ensures the correctness of drift amount simulation and the diversity of the characteristics of the augmented data, and solves the problem of weak model generalization capability caused by insufficient training samples in the sensor drift calibration method based on deep learning. A squeezing-exciting (SE) module is embedded in a Residual block of a Residual network (ResNet), and a drift calibration method based on the squeezing-exciting Residual network (SE-ResNet) is constructed to realize the calibration of the drift data of the sensor, thereby effectively improving the efficiency and the quality of the drift calibration of the sensor.
2. Technical scheme
In order to solve the above problems, the present invention adopts the following technical solutions.
A first aspect of the invention provides a method of augmenting sensor data, the method comprising:
and SA: deploying the calibrated sensor, and constructing a target sensing field;
SB: acquiring first sensing data of calibrated sensors in a target sensing field, cutting the first sensing data into a plurality of data matrixes by using a random cutting method, selecting partial row data in the data matrixes from the single cut data matrixes according to the positions of the sensors corresponding to all rows in the matrixes in the sensing field to construct data matrixes of adjacent sensors, and determining first augmented data;
SC: and according to the data matrix cut by the first sensing data, performing drift process simulation on the sensor data by using a non-stationary random walk process containing a trend item to obtain second sensing data, selecting a drift amount matrix with the same position and size as the first augmented data from the second sensing data, and determining the drift amount matrix as the second augmented data.
In some embodiments, the first sensing data is determined according to the continuous L sampling points collected by the N calibrated sensors in the target sensing field, and is recorded as an N × L drift-free data matrix X, where N matrix row data in the drift-free data matrix X respectively correspond to data collected by the N sensors in the sensing field;
randomly cutting a plurality of data moments with the size of NxL from the drift-free data matrix XMarking any data matrix obtained by cutting as XlSaid data matrix XlNumber of columns of said drift-free data matrix X, from a single clipped data matrix XlFrom the data matrix X in dependence on the position in the sensing field of the sensor corresponding to each matrix rowlN rows of data are selected to construct an nxl-sized data matrix X of the adjacent sensorsbAnd recording as first augmented data; specifically, it can be expressed as:
Figure BDA0003100231450000031
Figure BDA0003100231450000032
wherein
Figure BDA0003100231450000033
The initial cutting position of the sampling point is defined as l, the column number of a cutting data matrix is defined as the number of continuous sampling points of the sensor corresponding to each matrix row; n denotes data of 1 st to nth rows of the matrix,
Figure BDA0003100231450000034
to express taking the matrix
Figure BDA0003100231450000035
To
Figure BDA0003100231450000036
Column data; n is a secondary cut data matrix XlThe selected number of rows.
Method for constructing drift amount simulation matrix D with size of Nxl by adopting non-stationary random walk process containing trend itemlAccording to from XlSelecting n rows of data to construct XbMethod from DlConstructing a drift amount matrix D with the size of nxl from n rows of data with the same row positionbAs second augmented data; specifically, it can be expressed as:
Figure BDA0003100231450000037
Figure BDA0003100231450000038
where d is the drift amount of each sensor at different times.
In some embodiments, the number n of the proximity sensors is selected by:
adaptively determining the size of the number n of neighboring sensors according to the data augmentation application scenario, from the clipped data matrix XlRandomly selecting a matrix row as a reference, sequentially selecting n-1 adjacent sensors from small to large according to Euclidean distances between other sensors in a sensing field and the reference sensors corresponding to the matrix row, and finally selecting a data matrix X corresponding to the n sensorslThe matrix row data in (1) is the n row data to be selected.
In some embodiments, the length l of the clipped data matrix is determined by the sensor data acquisition time interval and the characteristic period of the data distribution by:
Figure BDA0003100231450000039
wherein, T is a characteristic period of regular distribution of sensor data stream, Δ T is a time interval of data acquisition by the sensor, λ is a positive rational number, and a suitable positive integer is generally selected according to the required training sample size.
In some embodiments, a non-stationary random walk process with trend terms is used to construct a sensor drift amount simulation matrix D of size NxLlAccording to from XlSelecting n rows of data to construct XbMethod from DlConstructing the n rows of data with the same row position as a drift amount matrix D with the size of nxlbAnd recording as second augmentation data;
setting a sensor drift probability threshold, and performing drift process simulation on sensor data by adopting a non-stationary random walk process containing a trend item, wherein the simulated sensor data can be expressed as follows:
Figure BDA0003100231450000041
wherein, yi,tFor the drift-containing data, x, of the sensor i at time t after simulationi,tFor drift-free data of sensor i at time t, di,tFor the drift amount generated by the sensor i at the moment t through simulation, rand (0,1) is a random floating point number between 0 and 1, alpha is a floating point number and alpha epsilon (0,1) is a probability threshold value of the drift of the sensor when rand (0,1)>When alpha is reached, the sensor i does not drift, and the generated drift amount is zero; otherwise, drift occurs;
when drift amount simulation is carried out on the data of each sensor, one of a linear function, an exponential function, a square root function and a sine function is selected in a self-adaptive mode to serve as a trend item in the non-stationary random walk process containing the trend item, and the trend item is used for generating a corresponding drift trend; the drift amount simulation specific mode is as follows:
in the linear drift trend, the drift amount of the sensor i at the time t can be represented as:
Figure BDA0003100231450000042
in the exponential drift trend, the drift amount of the sensor i at the time t can be represented as:
Figure BDA0003100231450000043
in the square root drift trend, the drift amount of the sensor i at the time t can be represented as:
Figure BDA0003100231450000044
in the sinusoidal drift trend, the drift amount of the sensor i at the time t can be represented as:
Figure BDA0003100231450000045
wherein e is the maximum drift amount in each trend term, ui,tFor the random walk data amount, r is an angular velocity parameter.
In some embodiments, the method for selecting the drift amount simulation parameters of each trend item is as follows:
the value of the maximum drift e in each trend item is determined by the collected data characteristics and the column number of the cutting data matrix, and is positively correlated with the column number of the data matrix, specifically
Figure BDA0003100231450000046
Wherein s is a standard deviation of the normalized data in the characteristic period T, and Δ T is a sensor data sampling interval. Amount of random walk data ui,t～iid(0,σ2) Wherein
Figure BDA0003100231450000047
The angular velocity parameter r is used to adjust the sinusoidal period, and
Figure BDA0003100231450000048
a second aspect of the invention provides a method of calibrating drift of a sensor, the method comprising:
and acquiring sensor data to be calibrated, inputting the sensor data to be calibrated into the trained extrusion-excitation residual error network model, and outputting corresponding drift calibration data.
Wherein the squeeze-excitation residual error network model is obtained by training a plurality of groups of data samples; the plurality of sets of data samples are data sets constructed from first augmented data and second augmented data.
In some embodiments, the step of constructing the data set from the first augmented data and the second augmented data comprises:
the first augmentationData as drift-free data samples XbTaking the second augmentation data as a drift amount simulation data sample Db；
Performing matrix addition on the drift-free data sample and the drift amount simulation data sample to obtain a drift-containing data sample Yb(ii) a Using a plurality of drift-free data samples XbComposing a drift-free data set XMForming a drift-containing data set Y by using a plurality of drift-containing data samplesM(ii) a Will contain the drift data set YMAnd drift-free data set XMAs a data set for training the squeeze-excitation residual network model. A third aspect of the present invention provides a sensor data augmentation system, comprising:
the perception field construction module is used for deploying the calibrated sensor and constructing a target perception field;
the non-drift data sample construction module is used for collecting first sensing data of a calibrated sensor in a target sensing field, cutting the first sensing data into a plurality of data matrixes by using a random cutting method, and selecting partial rows in the data matrixes to construct adjacent sensor data matrixes as first augmented data according to the positions of the sensors corresponding to the rows in the matrixes in the sensing field from the single cut data matrix, wherein the first augmented data are non-drift data samples;
and the drift amount simulation data sample construction module is used for carrying out drift process simulation on the sensor data by utilizing a non-stationary random walk process containing a trend item according to a data matrix cut by the first sensing data to obtain second sensing data, selecting a drift amount matrix with the same position and size as the first augmented data from the second sensing data, and determining the drift amount matrix as second augmented data, wherein the second augmented data is a drift amount simulation data sample.
A fourth aspect of the invention provides a readable storage medium, the storage medium storing a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method described above.
3. Advantageous effects
Compared with the prior art, the invention has the obvious technical advantages that:
(1) according to the data amplification method, the sensor data drifting process is established by adopting the non-stationary random walk process containing the trend item, the amplification operation of the sensor data is realized by utilizing the method of randomly cutting and selecting the data of the adjacent sensor, and the characteristic diversity of the amplification data is increased; the data augmentation method can fuse time and space characteristics of sensor drift data, and solves the problem of poor model generalization capability caused by insufficient training samples in a sensor drift calibration model.
(2) According to the data augmentation method, a sensor drifting process is established by adopting a non-stationary random walk process containing a trend item, a probability threshold value of sensor drifting is set according to uncertainty of sensor drifting, and meanwhile, the range of the maximum drifting amount of the trend item is set according to characteristics of sensing data to control the drifting amplitude of the sensing data, so that controllability is improved; 4 types of function models conforming to the drift characteristics are used as the trend item to simulate the drift characteristics of the sensor, and each drift quantity simulation parameter is selected based on the data characteristics, so that the applicability of the drift data sample construction method in different application scenes and different data types is ensured.
(3) According to the data augmentation method provided by the invention, the data of each adjacent sensor in the sensing field is fused in the same training sample, and the size of the cutting data matrix is determined as the iterative input value of the sensor drift calibration network model according to the sampling interval and the data characteristic period of the sensor data, so that the problem of data characteristic loss caused by iterative input of the data sample can be avoided, the input size of the neural network can be ensured to be moderate, and the operation cost is effectively reduced.
(4) The invention provides a sensor data drift calibration method, which adopts an extrusion-excitation residual error network to construct a sensor drift calibration model, wherein an extrusion-excitation residual error module contained in the sensor drift calibration model can extract time and space characteristics of data to realize calibration of sensor drift data by utilizing the correlation of adjacent sensor data; the data augmentation method can fuse the time and space characteristics of the sensor drift data and provide data guarantee for training the deep neural network.
(5) The sensor data drift calibration method provided by the invention can provide guarantee for accurately extracting the drift characteristics in the data of a plurality of sensors, and ensures the reliability of simultaneously extracting the data characteristics of the plurality of sensors in the sensing field and compensating the drift. By adopting the squeeze-excitation residual error network, the correlation of the data of the adjacent sensors can be effectively utilized, the network depth is increased, and the drift calibration quality of the data of the sensors is effectively improved.
(6) The data augmentation method provided by the invention has strong expandability, the data of a plurality of sensors in a sensing field and the data of a single sensor in different time periods can be used as basic data required by data augmentation, and the basic data is used for acquiring a required data set by the data augmentation method.
Drawings
Fig. 1 is a flowchart of a data augmentation method according to an embodiment of the present invention;
fig. 2 is a block diagram of a data augmentation system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a data set expansion method provided by the present invention;
FIG. 4 is a flow chart of data set construction provided by an embodiment of the present invention;
FIG. 5 is a flow chart of a method for calibrating sensor drift provided by the present invention;
FIG. 6 is a schematic diagram of a SE-ResNet residual module provided by the present invention;
FIG. 7 is a histogram of SE-ResNet and ResNet drift calibration errors for different drift trends provided by an embodiment of the present invention;
Detailed Description
Hereinafter, embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be understood that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the embodiments described herein.
Example 1
As shown in fig. 1, the embodiment of the present application discloses a sensor data augmentation method, which includes the following general processes:
and SA: and deploying the calibrated sensors to construct a target perception field.
Specifically, in soil environment information monitoring, calibrated sensors are deployed to construct a target sensing field, in this example, the sensors arranged in the target sensing field are soil temperature and humidity sensors, the number of the sensors is 20, the sampling interval of the sensors is 5 minutes, the characteristic period of sensing data flow regular distribution is 24 hours, and soil temperature data of 90 days, namely, 2 months, 1 day to 4 months and 30 days in 2020 and after the sensors are calibrated, is taken as initial acquisition data of the sensors. The periodic characteristics refer to that the distribution rule of the sensing data changes periodically with time.
This data can be considered drift-free by collecting initial sensing data of calibrated sensors within the sensing field, and this data is used as a basis for drift data samples and calibration data required for building a sensor drift calibration network model.
SB: the method comprises the steps of collecting first sensing data of calibrated sensors in a target sensing field, cutting the first sensing data into a plurality of data matrixes by using a random cutting method, selecting partial row data in the data matrixes from the single cut data matrixes according to the positions of the sensors corresponding to all rows in the matrixes in the sensing field to construct data matrixes of adjacent sensors, and determining first augmented data.
Specifically, the method comprises the following steps: SB 1: modeling the first perception data as a drift-free data matrix.
And selecting the soil temperature data of 90 days in total from 1 day 2 month to 30 days 4 months in 2020, which is acquired by the calibrated soil temperature sensor from the target sensing field as first sensing data. The number N of the sensors is 20, the sampling interval of the sensors is 5 minutes, the characteristic period is 24 hours, and the sensors are recorded as a drift-free data matrix X with the size of 20 multiplied by 25920, wherein 20 matrix row data in the drift-free data matrix X respectively correspond to data collected by 20 sensors in a sensing field;
SB 2: cutting the drift-free data matrix X into a plurality of data matrices, wherein the cut data matrix XlIs smaller than the columns of the drift-free data matrix XAnd (4) the number L.
Specifically, in order to avoid the loss of data characteristics, a drift-free data matrix needs to be cut into a data matrix with a smaller size according to data acquisition characteristics in a specific monitoring scene, and a data matrix with the size of nxl is randomly cut from the drift-free data matrix X and is recorded as Xl。
SB 3: and selecting a part of row data in the data matrix from the single cut data matrix in the cut data matrix according to the positions of the sensors corresponding to the rows in the matrix in the sensing field to construct a data matrix adjacent to the sensors so as to determine first augmented data.
From the data matrix XlN rows of data are selected to construct an nxl-sized data matrix X of the adjacent sensorsbAnd recording as first augmentation data, the specific mode is as follows:
Figure BDA0003100231450000071
Figure BDA0003100231450000072
wherein
Figure BDA0003100231450000073
The initial cutting position of the sampling point is represented by l, which is the column number of the cutting data matrix, namely the number of continuous sampling points of the sensor corresponding to each matrix row. N denotes data of 1 st to nth rows of the matrix,
Figure BDA0003100231450000074
to express taking the matrix
Figure BDA0003100231450000075
To
Figure BDA0003100231450000076
Column data. n is a secondary cut data matrix XlThe selected number of rows.
As a variation, the method for selecting the number n of sensors in this example is:
determining the size of the number n of adjacent sensors according to the neural network receptive field, and obtaining the data matrix X from the cut data matrixlRandomly selecting a matrix row as a reference, sequentially selecting n-1 adjacent sensors from small to large according to Euclidean distances between other sensors in a sensing field and the reference sensors corresponding to the matrix row, and finally selecting a data matrix X corresponding to the n sensorslThe matrix row data in (1) is the n row data to be selected. In this example n is chosen to be 10, where n is determined by the neural network receptive field. It should be noted that the size of each data sample is determined by selecting the number of sensors, the neural network receptive field is determined by the size and step size of the convolution kernel, and the convolution kernel slides on the data sample to obtain the features.
The column number l of the cut data matrix is determined by the time interval of data acquisition of the sensor and the characteristic period of data distribution, and the specific calculation mode is as follows:
Figure BDA0003100231450000081
wherein, T is a characteristic period of regular distribution of sensor data stream, Δ T is a time interval of data acquisition by the sensor, λ is a positive rational number, and a suitable positive integer is generally selected according to the required training sample size.
In this example, λ select 8 is calculated by equation (3) to obtain 2304 as column number l of data matrix, and data matrix X is then obtainedlCan be represented as a two-dimensional matrix with a shape of 20 × 2304, and the drift-free data sample X with a shape of 10 × 2304 is constructed by extracting the neighboring 10 sensor data according to equation (2)b。
SC: method for constructing drift amount simulation matrix D with size of Nxl by adopting non-stationary random walk process containing trend itemlAccording to from XlN matrix rows are selected to construct XbFrom D tolN matrix row data are selected to construct a drift amount matrix D with the size of nxlbAs second augmented data, wherein the second sensing data is a drift amount simulation matrix; second augmentationThe data is a drift amount simulation data sample, and can be specifically expressed as:
Figure BDA0003100231450000082
Figure BDA0003100231450000083
where d is the drift amount of each sensor at different times.
Specifically, when a sensor drift process is constructed, drift occurring among different sensors is generally independent, a sensor drift probability threshold is set in the example, a non-stationary random walk process containing a trend item is adopted to simulate the drift process of sensor data, and the simulated sensor data can be represented as follows:
Figure BDA0003100231450000084
wherein, yi,tFor the drift-containing data, x, of the sensor i at time t after simulationi,tFor drift-free data of sensor i at time t, di,tThe amount of drift generated at time t is simulated for sensor i. rand (0,1) is a random floating point number between 0 and 1, alpha is a floating point number and alpha belongs to (0,1) is a probability threshold value of the sensor drifting when rand (0,1)>When alpha is reached, the sensor i does not drift, and the generated drift amount is zero; otherwise, drift occurs;
and when the drift amount simulation is carried out on the data of each sensor, one of a linear function, an exponential function, a square root function and a sine function is selected in a self-adaptive mode to serve as a trend item in the non-stationary random walk process containing the trend item, so that the corresponding drift trend is generated. The drift amount simulation specific mode is as follows:
in the linear drift trend, the drift amount of the sensor i at the time t can be represented as:
Figure BDA0003100231450000091
in the exponential drift trend, the drift amount of the sensor i at the time t can be represented as:
Figure BDA0003100231450000092
in the square root drift trend, the drift amount of the sensor i at the time t can be represented as:
Figure BDA0003100231450000093
in the sinusoidal drift trend, the drift amount of the sensor i at the time t can be represented as:
Figure BDA0003100231450000094
wherein e is the maximum drift amount in each trend term, ui,tFor random walk data amount, riIs an angular velocity parameter.
In some embodiments, the method for selecting the drift amount simulation parameters of each trend item is as follows:
the value of the maximum drift e in each trend item is determined by the collected data characteristics and the column number of the cutting data matrix, and is positively correlated with the column number of the data matrix, specifically
Figure BDA0003100231450000095
Wherein s is a standard deviation of the normalized data in the characteristic period T, and Δ T is a sensor data sampling interval. Amount of random walk data ui,t～iid(0,σ2) Wherein
Figure BDA0003100231450000096
The angular velocity parameter r is used to adjust the sinusoidal period, and
Figure BDA0003100231450000097
in this example, in order to ensure the sufficiency of the drift samples, the threshold value of the probability of drift occurring in each sensor in the same time period in which the number of sampling points is l is set to 0.5, i.e., the probability of drift occurring in 10 sensors of the same data matrix is 50%. The 4-class drift amount simulation mode can adjust various drift occurrence probabilities according to application scenarios, and in this example, 4-class data sets are respectively constructed by adopting the 4-class sensor drift trends. Calculating the maximum drift e-U (2.97, 5.94) and the random walk data U in each trend itemi,t～iid(0,σ2) Middle sigma-U (0.03, 0.06); angular velocity parameters r to U (2, 4).
In one possible embodiment, the first augmented data and the second augmented data are constructed into a data set as follows:
SD: will not have drift data sample XbSimulation data sample D with drift amountbPerforming matrix addition to obtain drift-containing data samples Yb。
Specifically, there will be no drift data samples XbAnd drift amount simulation data sample DbThe drift-containing data samples obtained by performing the matrix addition should be:
Yb＝Xb+Db (11)
and SE: selecting multiple drift data samples XbAnd a plurality of drift-containing data samples YbSeparately forming drift-free data sets XMAnd a drift-containing data set YM(ii) a Will have no drift data set XMData set Y containing driftMAnd respectively dividing one part of the training set into a network training set and the other part of the training set into a network test set, wherein the network training set and the network test set are not crossed.
Specifically, a measurement matrix of 20 × 25920 is obtained from the sensing field, 8000 data matrices X of size 20 × 2304 are randomly clipped according to equation (1)lSelecting 10 proximity sensors to obtain 8000 drift-free data samples X with the size of 10X 2304 according to the formula (2)bAs a drift-free data set, 8000 drift-containing data samples Y of size 10X 2304 were likewise obtained in this mannerbAs a drift-containing data set YM. In summary, in the sensor data sample construction process, the training sample size input in each iteration is determined according to the characteristic period and the sampling interval of data in the data stream of the internet of things, the drift process of the sensor is simulated by adopting the non-stationary random walk process containing the trend item, large-scale high-quality basic data are provided for the drift calibration of the sensor, the problem of insufficient training samples when the sensor is calibrated by adopting a deep learning method can be solved, and the data acquisition quality is guaranteed.
Example 2
As shown in fig. 5, on the basis of embodiment 1, the present embodiment discloses a sensor drift calibration method, which includes the following general flow:
acquiring sensor data to be calibrated, inputting the sensor data to be calibrated into a trained extrusion-excitation residual error network model, and outputting corresponding drift calibration data;
wherein the squeeze-excitation residual error network model is obtained by training a plurality of groups of data samples; the plurality of sets of data samples are data sets constructed from first augmented data and second augmented data.
Specifically, referring to step SF, the squeeze-excitation residual network model training step is as follows: an extrusion-excitation module is added into a one-dimensional convolution residual error network to fully utilize data correlation between adjacent sensors to realize sensor drift calibration, and as shown in fig. 6, the method specifically comprises the following steps:
SF 1: the 1 one-dimensional convolutional layer connected with the network input is used as the input of the residual error module, and the 1 one-dimensional convolutional layer connected with the output of the residual error module is used for adjusting the output result to be the same as the input size. The input of the residual error module is connected with 3 one-dimensional convolutional layers, each one-dimensional convolutional layer comprises convolution operation, batch normalization and activation function mapping output, the output of the last one-dimensional convolutional layer comprises two network branches, one network branch is connected with the extrusion-excitation module, and the other network branch is multiplied by the output result of the extrusion-excitation module. The extrusion-excitation module mainly comprises a global pooling layer and two full-connection layers, wherein the activation function adopted by the output of the first full-connection layer is Relu, and the Sigmoid function adopted by the output of the second full-connection layer is taken as the activation function.
In the example, the network input sample is a data matrix with 10 rows, so that the number of convolution kernels of a 1 st convolution layer input into the sensor drift calibration network in a channel 10 mode is 32, the size of the convolution kernels is 1 × 5, the input of a residual error module is connected with 3 one-dimensional convolution layers, the number of convolution kernels of each one-dimensional convolution layer is 32, the size of the convolution kernels is 1 × 3, the number of channels of a squeezing-actuating module in an SE-ResNet model is reduced by half through the number of channels of a first full connection layer, and the number of channels is reduced to 32 through a next full connection layer after being activated by a Relu activation function; the last one-dimensional convolutional layer sets the number of channels to 10 to keep the same as the initial input size.
SF 2: respectively adopting 4 types of drift trends to construct 4 types of data sets containing drift and data sets without drift, and selecting data set Y containing driftM6400 samples with drift data, i.e. the first 80% as training set for neural network; the other 1600 data samples, namely the last 20%, are taken as a neural network test set, and no intersection exists between the network training set and the network test set.
SF 3: will contain the drift data set YMDrift-containing data samples Y in partitioned training setbIteratively inputting the data into a drift calibration network for training, and adopting corresponding drift-free data samples XbAs the true output value of the neural network, the loss of the neural network adopts the mean square error function to achieve the purpose of data drift compensation, and the loss of the designed calibration module is as follows:
Figure BDA0003100231450000111
wherein m is the number of training samples,
Figure BDA0003100231450000112
for the output calibration data, XiCorresponding drift-free data samples.
The Adam optimizer is adopted for iterative training in the example, the Size of Batch Size is set to be 200, the number of iterations is 5000, and the network learning rate is set to be 0.001, so that a trained squeeze-excitation residual network model is obtained.
SG: calibrating drift sensor data, inputting drift-containing data samples of the test set as data to be calibrated into the trained squeeze-excitation residual error network model, and outputting corresponding drift calibration data.
The convolutional neural network extracts time and space characteristics in data containing drift from historical sensing data through convolutional layer characteristic learning capacity, and drift calibration of the sensor is achieved. However, the perception data collected from a specific sensor network is limited, the data containing drift amount is more deficient, and the requirement of the neural network on training samples is difficult to guarantee.
This example employs, as the first sensing data, soil temperature sensing data for 90 days from 1 day 2 to 30 days 4 months 2020. The sample data augmentation method provided by the invention meets the sample data required by neural network training, and the sample data obtained by the method of the invention has no obvious difference from drift data in a real scene. The method has the advantages that the neural network model is trained by adopting sample data with different drifting trends, experimental results show that the method can effectively solve the problem of data drifting in data streams of the Internet of things, root mean square errors serve as evaluation standards, 20 total data sets are adopted and are respectively tested for 20 times, the average value of the experimental results is shown in figure 7, and the total drifting calibration error is only about 1/2 of the traditional calibration method based on the residual error neural network.
Example 3
As shown in fig. 2, the present embodiment provides a sensor data augmentation system, which includes:
a perception field construction module 20 for deploying the calibrated sensors to construct a target perception field; the perceptual field building block furtherComprises a data determination module for adaptively determining the number n of adjacent sensors according to the data augmentation application scene, and a data matrix X obtained by cuttinglRandomly selecting a matrix row as a reference, sequentially selecting n-1 adjacent sensors from small to large according to Euclidean distance between other sensors in a sensing field and the reference sensor corresponding to the matrix row, and finally selecting n sensor data in a data matrix XlThe corresponding n rows of data are the n rows of adjacent sensor data to be selected.
The drift-free data sample construction module 30 is configured to collect first sensing data of a calibrated sensor in a target sensing field, cut the first sensing data into a plurality of data matrices by using a random cutting method, select a part of rows of data in a single cut data matrix according to positions of sensors corresponding to rows in the matrix in the sensing field, construct a data matrix of an adjacent sensor, and determine first augmented data.
Specifically, modeling the first sensing data into a drift-free data matrix, wherein a plurality of matrix rows in the drift-free data matrix respectively correspond to data collected by a plurality of sensors in the sensing field; cutting the drift-free data matrix X into a plurality of data matrices, wherein the data matrices XlIs less than the number of columns L of the drift-free data matrix; selecting partial row data from a single cut data matrix according to the position of a sensor corresponding to each matrix row in a sensing field to construct an adjacent sensor data matrix, and determining first augmentation data, wherein the first augmentation data are drift-free data samples; the drift-free data sample construction module also comprises a data matrix column number cutting calculation module which is used for determining the column number l of a data matrix to be cut according to the time interval of data acquisition of the sensor and the characteristic period of data distribution, and the calculation mode is as follows:
Figure BDA0003100231450000121
and the drift amount simulation data sample construction module 40 is used for performing drift process simulation on the sensor data by utilizing a non-stationary random walk process containing a trend item according to the data matrix cut by the first sensing data to obtain second sensing data, selecting a drift amount matrix with the same position and size as the first augmented data from the second sensing data, and determining the drift amount matrix as second augmented data, wherein the second augmented data is a drift amount simulation data sample.
Example 4
The present embodiments provide an exemplary computer program product and computer-readable storage medium
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the decision-making method according to various embodiments of the present application described in the "exemplary methods" section of this specification above.
The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform steps in a decision method according to various embodiments of the present application described in the "exemplary methods" section above of this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.
The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.

Claims (9)

1. A method of sensor data augmentation, the method comprising:
and SA: deploying the calibrated sensor, and constructing a target sensing field;
SB: acquiring first sensing data of calibrated sensors in a target sensing field, cutting the first sensing data into a plurality of data matrixes by using a random cutting method, selecting partial row data in the data matrixes from the single cut data matrixes according to the positions of the sensors corresponding to all rows in the matrixes in the sensing field to construct data matrixes of adjacent sensors, and determining first augmented data;
SC: and according to the data matrix cut by the first sensing data, performing drift process simulation on the sensor data by using a non-stationary random walk process containing a trend item to obtain second sensing data, selecting a drift amount matrix with the same position and size as the first augmented data from the second sensing data, and determining the second augmented data.
2. The method of claim 1, further comprising:
determining first sensing data according to continuous L sampling points acquired by N calibrated sensors in a target sensing field, and recording the first sensing data as a non-drifting data matrix X with the size of NxL, wherein N matrix row data in the non-drifting data matrix X respectively correspond to data acquired by the N sensors in the sensing field;
randomly cutting a plurality of data matrixes with the size of NxL from the drift-free data matrix X, and recording any one of the cut data matrixes as XlSaid data matrix XlIs smaller than the number of columns L of the drift-free data matrix X, from a single clipped data matrix XlFrom the data matrix X in dependence on the position in the sensing field of the sensor corresponding to each matrix rowlN rows of data are selected to construct an nxl-sized data matrix X of the adjacent sensorsbAnd recording as first augmented data;
the concrete mode is as follows:
Figure FDA0003100231440000011
Figure FDA0003100231440000012
wherein
Figure FDA0003100231440000013
The initial cutting position of the sampling point is defined as l, the column number of a cutting data matrix is defined as the number of continuous sampling points of the sensor corresponding to each matrix row; n denotes data of 1 st to nth rows of the matrix,
Figure FDA0003100231440000014
to express taking the matrix
Figure FDA0003100231440000015
To
Figure FDA0003100231440000016
Column data; n is a secondary cut data matrix XlThe selected number of rows.
3. The sensor data augmentation method of claim 2, wherein the n-row data selection method comprises:
adaptively determining the size of the number n of neighboring sensors according to the data augmentation application scenario, from the clipped data matrix XlRandomly selecting a matrix row as a reference, sequentially selecting n-1 adjacent sensors from small to large according to Euclidean distances between other sensors in a sensing field and the reference sensors corresponding to the matrix row, and finally selecting a data matrix X corresponding to the n sensorslThe matrix row data in (1) is the n row data to be selected.
4. The method of claim 2, wherein the number of columns/of the clipped data matrix is determined by the characteristic period of the data distribution and the time interval of the data acquisition of the sensor, and is calculated by:
Figure FDA0003100231440000021
wherein, T is a characteristic period of regular distribution of sensor data stream, Δ T is a time interval of data acquisition by the sensor, λ is a positive rational number, and a suitable positive integer is generally selected according to the required training sample size.
5. The method according to claim 2, wherein the second sensing data is a drift amount simulation matrix D with size of NxL constructed by a non-stationary random walk process with trend termslAccording to from XlSelecting n rows of data to construct XbMethod from DlConstructing a drift amount matrix D with the size of nxl from n rows of data with the same row positionbAnd recording as second augmentation data;
setting a sensor drift probability threshold, and performing drift process simulation on sensor data by adopting a non-stationary random walk process containing a trend item, wherein the simulated sensor data can be expressed as follows:
Figure FDA0003100231440000022
wherein, yi,tFor the drift-containing data, x, of the sensor i at time t after simulationi,tFor drift-free data of sensor i at time t, di,tSimulating the drift amount generated at the time t for the sensor i; rand (0,1) is a random floating point number between 0 and 1, alpha is a floating point number and alpha belongs to (0,1) is a probability threshold value of the sensor drifting when rand (0,1)>When alpha is reached, the sensor i does not drift, and the generated drift amount is zero; otherwise, drift occurs;
and when the drift amount simulation is carried out on the data of each sensor, one of a linear function, an exponential function, a square root function and a sine function is selected in a self-adaptive mode to serve as a trend item in the non-stationary random walk process containing the trend item, so that the corresponding drift trend is generated.
6. A sensor drift calibration method is characterized by comprising the following steps:
acquiring sensor data to be calibrated, inputting the sensor data to be calibrated into a trained extrusion-excitation residual error network model, and outputting corresponding drift calibration data;
wherein the squeeze-excitation residual error network model is obtained by training a plurality of groups of data samples; the plurality of sets of data samples are data sets constructed from first augmented data and second augmented data.
7. The method of claim 6, wherein the calibration method comprises: the step of constructing a data set from the first augmented data and the second augmented data comprises:
taking the first augmented data as a drift-free data sample XbTaking the second augmentation data as a drift amount simulation data sample Db；
Sampling the drift-free data sample XbAnd drift amount simulation data sample DbPerforming matrix addition to obtain drift-containing data samples Yb(ii) a Using a plurality of drift-free data samples XbComposing a drift-free data set XM(ii) a Using a plurality of drift-containing data samples YbForming a drift-containing data set YM(ii) a Will contain the drift data set YMAnd drift-free data set XMAs a training data set for the squeeze-excitation residual network model.
8. A sensor data augmentation system, comprising:
the perception field construction module is used for deploying the calibrated sensor and constructing a target perception field;
the drift-free data sample construction module is used for collecting first sensing data of a calibrated sensor in a target sensing field, cutting the first sensing data into a plurality of data matrixes by using a random cutting method, selecting part of row data in the data matrixes from the single cut data matrixes according to the positions of the sensors corresponding to all rows in the matrixes in the sensing field to construct adjacent sensor data matrixes, and determining first augmented data, wherein the first augmented data are drift-free data samples;
and the drift amount simulation data sample construction module is used for carrying out drift process simulation on the sensor data by utilizing a non-stationary random walk process containing a trend item according to a data matrix cut by the first sensing data to obtain second sensing data, selecting a drift amount matrix with the same position and size as the first augmented data from the second sensing data, and determining the drift amount matrix as second augmented data, wherein the second augmented data is a drift amount simulation data sample.
9. A readable storage medium, characterized in that the storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to carry out the method according to any one of claims 1-7.
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