阅读说明：本技术 含有基线漂移和高频噪声的光电传感信号重构方法及系统 (Photoelectric sensing signal reconstruction method and system containing baseline drift and high-frequency noise ) 是由 刘欣 刘建翔 李绍鹏 薛莹 赵志鹏 李杨 许兆霞 殷艳华 于 2020-12-04 设计创作，主要内容包括：本发明公开了一种含有基线漂移和高频噪声的光电传感信号重构方法及系统,包括：对光电传感信号进行EEMD分解后得到IMF分量；对存在基线漂移的IMF分量,根据其阶数选择自适应滤波器以获取基线漂移估计值,对剩余的IMF分量获取高频噪声估计值；对光电传感信号滤除前述两类估计值后,得到重构信号。在原始光电传感信号中加入高斯噪声后进行EEMD分解,根据IMF分量阶数设计自适应低通滤波器,对含有基线漂移的IMF分量进行处理,对剩余的IMF分量处理高频噪声,得到无漂移、无噪声的重构信号,实现准确的极早期预警。(The invention discloses a photoelectric sensing signal reconstruction method and a system containing baseline drift and high-frequency noise, which comprises the following steps: EEMD decomposition is carried out on the photoelectric sensing signal to obtain an IMF component; selecting an adaptive filter according to the order of the IMF component with the baseline drift to obtain a baseline drift estimated value, and obtaining a high-frequency noise estimated value for the rest IMF component; and filtering the two types of estimated values of the photoelectric sensing signal to obtain a reconstructed signal. The method comprises the steps of adding Gaussian noise into an original photoelectric sensing signal, then carrying out EEMD decomposition, designing a self-adaptive low-pass filter according to the IMF component order, processing the IMF component containing baseline drift, processing high-frequency noise on the rest IMF component, obtaining a drift-free and noise-free reconstructed signal, and realizing accurate extremely early warning.)

1. A photoelectric sensing signal reconstruction method containing baseline drift and high-frequency noise is characterized by comprising the following steps:

EEMD decomposition is carried out on the photoelectric sensing signal to obtain an IMF component;

selecting an adaptive filter according to the order of the IMF component with the baseline drift to obtain a baseline drift estimated value, and obtaining a high-frequency noise estimated value for the rest IMF component;

and filtering the two types of estimated values of the photoelectric sensing signal to obtain a reconstructed signal.

2. The method of claim 1, wherein the adaptive filter has a cut-off frequency that decreases with decreasing order of the IMF component.

3. The method of claim 1, wherein the output of the adaptive filter is a convolution of the adaptive filter and the IMF component.

4. The method of claim 3, wherein the cutoff order of the IMF component is determined based on the adaptive filter output standard deviation and a baseline shift threshold coefficient.

5. The method as claimed in claim 1, wherein the baseline wander and high frequency noise are used to reconstruct the photoelectric sensor signal, wherein the baseline wander is estimated by summing all IMF components with baseline wander via the output of the adaptive filter.

6. The method of claim 1, wherein the high frequency noise signal estimate is derived from a product of a high frequency noise threshold coefficient and the remaining IMF component.

7. The method as claimed in claim 1, wherein the EEMD decomposition is performed after Gaussian noise is added to the photoelectric sensing signal.

8. An optoelectronic sensing signal reconstruction system including baseline wander and high frequency noise, comprising:

the decomposition module is used for carrying out EEMD decomposition on the photoelectric sensing signal to obtain an IMF component;

the calculation module is used for selecting the self-adaptive filter according to the order of the IMF component with the baseline drift to obtain a baseline drift estimation value and obtaining a high-frequency noise estimation value for the rest IMF component;

and the reconstruction module is used for filtering the two types of estimation values of the photoelectric sensing signal to obtain a reconstructed signal.

9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.

10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.

Technical Field

The invention relates to the technical field of signal processing, in particular to a photoelectric sensing signal reconstruction method and system containing baseline drift and high-frequency noise.

Background

The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.

Important small space facilities such as a data center cabinet are unique and professional in function, and once a fire disaster occurs, serious information interruption and economic and social influence can be caused if the fire disaster is not put out for help in time, so that a fire disaster detection early warning system capable of early warning in the fire disaster germination stage is urgently needed in the places. The research on the inhalation type smoke detection technology is carried out at home and abroad, and related products are formed, but the method is basically blank in the aspect of 'very early stage' detection and early warning of fire disasters.

The "very early stage" of fire refers to the stage from overheating the material beyond its thermal decomposition point to oxidation combustion and starting to produce soot, and no large amount of smoke particles are produced; therefore, a trace amount of soot particles needs to be detected, and the converted electric signal is very weak and is very easy to be doped in noise and cannot be judged.

Ideally, the voltage signal of the photoelectric receiver should be a constant value when there is no smoke particle, but in practical applications, it is found that the voltage value will slowly drift with low frequency along with temperature change, light diffraction, slow change of interference, etc., and if the drift is too large, a false alarm will be caused, so a proper algorithm is needed to filter out the baseline drift.

In addition, the principle of the silicon photodiode as a photoconductive device is that when infrared incident light irradiates on a PN junction, light energy is absorbed and then converted into electric energy, and in the process of transmitting the incident light to the silicon photodiode, random noise is inevitably accompanied, mainly including device internal noise and environmental noise; the environmental noise is noise occurring in the process of receiving signals by the photodetector, interference sources of the environmental noise are from outside of the device, such as incidence of stray light, electromagnetic interference, turbulence of a light path transmission medium, and the like, and the noise can be improved by methods of shielding, blocking stray light, selecting an optical filter, and the like, but in practical application, the interference cannot be completely eliminated even if a cassette is applied; the internal noise mainly includes shot noise and thermal noise, which are independent of frequency and belong to white noise, and all the noises constitute the background noise of the original signal.

Therefore, the output signal of the photoelectric detector is not an undoped pure signal but a signal with low-frequency drift and white noise, the low-frequency drift can cause false alarm, the white noise can reduce the detection precision of the system, and the early warning in the 'very early' stage of a fire disaster can be interfered, so that the problems of large baseline drift amplitude and large background noise exist in the 'very early' smoke detection of the data center cabinet.

Disclosure of Invention

In order to solve the problems, the invention provides a photoelectric sensing signal reconstruction method and a system containing baseline drift and high-frequency noise.

In order to achieve the purpose, the invention adopts the following technical scheme:

in a first aspect, the present invention provides a method for reconstructing an optoelectronic sensing signal containing baseline drift and high-frequency noise, including:

EEMD decomposition is carried out on the photoelectric sensing signal to obtain an IMF component;

selecting an adaptive filter according to the order of the IMF component with the baseline drift to obtain a baseline drift estimated value, and obtaining a high-frequency noise estimated value for the rest IMF component;

and filtering the two types of estimated values of the photoelectric sensing signal to obtain a reconstructed signal.

In a second aspect, the present invention provides a photoelectric sensing signal reconstruction system containing baseline drift and high-frequency noise, comprising:

the decomposition module is used for carrying out EEMD decomposition on the photoelectric sensing signal to obtain an IMF component;

the calculation module is used for selecting the self-adaptive filter according to the order of the IMF component with the baseline drift to obtain a baseline drift estimation value and obtaining a high-frequency noise estimation value for the rest IMF component;

and the reconstruction module is used for filtering the two types of estimation values of the photoelectric sensing signal to obtain a reconstructed signal.

In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.

In a fourth aspect, the present invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.

Compared with the prior art, the invention has the beneficial effects that:

the invention provides an improved EEMD (ensemble empirical mode decomposition) decomposition method, which is a commonly used method for processing signal noise, is applied to the aspects of image analysis processing, electrocardiosignal ECG (electrocardiogram) denoising and the like at present, is applied to the aspect of smoke detection, and is characterized in that Gaussian noise is added into an original signal and then decomposed, a group of adaptive low-pass filters are introduced, the adaptive low-pass filters are designed according to the IMF component order, the IMF component containing baseline drift is processed in sequence, the influence of high-frequency noise is processed on the residual IMF component, the aims of filtering the baseline drift, reducing background noise and improving the signal to noise ratio are fulfilled, and accurate early warning is realized.

Advantages of additional aspects of the invention 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 invention.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.

Fig. 1 is a flowchart of a photoelectric sensing signal reconstruction method including baseline wander and high-frequency noise according to embodiment 1 of the present invention;

FIGS. 2(a) -2(n) are schematic illustrations of EEMD decomposition results provided in example 1 of the present invention;

fig. 3(a) -3(b) are schematic diagrams comparing the original signal and the decomposed signal provided in example 1 of the present invention.

The specific implementation mode is as follows:

the invention is further described with reference to the following figures and examples.

It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.

Example 1

As shown in fig. 1, the present embodiment provides a method for reconstructing an optoelectronic sensing signal containing baseline drift and high-frequency noise, including:

s1: EEMD decomposition is carried out on the photoelectric sensing signal to obtain an IMF component;

s2: selecting an adaptive filter according to the order of the IMF component with the baseline drift to obtain a baseline drift estimated value, and obtaining a high-frequency noise estimated value for the rest IMF component;

s3: and filtering the two types of estimated values of the photoelectric sensing signal to obtain a reconstructed signal.

In this embodiment, in order to calculate the signal-to-noise ratio conveniently and accurately, gaussian noise is added to the original photoelectric sensing signal, and EEMD decomposition is performed on the photoelectric sensing signal to decompose the photoelectric sensing signal into an N-level IMF component and a residual component.

In this embodiment, according to the characteristics of baseline drift and high-frequency white noise of the photoelectric sensing signal, that is, the baseline drift is distributed in the last several orders of IMF components, the noise signal is mainly distributed in the first several orders of IMF components, and the deletion of the last several orders of IMF components may cause distortion of the reconstructed signal, therefore, the embodiment provides the EEMD adaptive filtering, and introduces a set of adaptive low-pass filters to sequentially process the last several orders of IMF components to obtain the baseline drift signal estimation value, so as to filter the baseline drift signal.

In this embodiment, in step S2, for the IMF component, a low-pass filter adaptive to the IMF order is selected to process the baseline wander signal, and the processed IMF components of each order are reconstructed to obtain a baseline wander estimation value and a high-frequency noise estimation value; the method specifically comprises the following steps:

white noise is added into an original photoelectric sensing signal to obtain a signal X (T), EEMD decomposition processing is carried out, and the signal X (T) is decomposed into a plurality of IMF components which are set as x_k(t)：

Where N is the number of IMF components;

for M-order IMF component with baseline drift, pass through low-pass filter h_k(t) mixing x_k(t) is processed from high order to low order, the low pass filter h_kThe output of (t) is:

y_k(t)＝h_k(t)*x_k(t)

wherein, denotes convolution, h_k(t) is a low pass filter.

As the IMF order is reduced, each IMF component contains fewer slow oscillation components and more signal components; therefore, a set of cut-off frequencies ω is designed_kThe adaptive low-pass filter which decreases with the decrease of the IMF order is specifically as follows: starting first with the last order IMF component, i.e. x_N(t) the adaptive filter cut-off frequency of the component is ω_N(t), therefore, the cut-off frequency of the kth filter is:

ω_k-1＝ω_k*α

where α is a number between 0.1 and 0.99, and k is N, …,2, 1.

The baseline amplitude is gradually reduced as the order is reduced according to the characteristics of the components of each order of the IMF, and therefore, the filter output y containing the low-frequency component is extracted from each IMF component_k(t) calculating y_kStandard deviation of (std) (y)_k) And the evaluation coefficient P_KAs a stopping criterion, to determine the value of M:

where k is 1,2, … n, flip represents the inversion function, and a threshold coefficient epsilon for baseline drift is set, ranging from 0-0.1, if P is_KIf the cutoff order is less than epsilon, the cutoff order M is equal to N + 1-k;

in the present embodiment, ω is set_N10, α 0.9, ε 0.01, and the sum of the M +1 to N order IMF filters was taken as the reconstructed baseline estimate.

Setting a threshold coefficient of the high-frequency noise to be delta by adopting a threshold function method, and multiplying the delta by IMF components which represent the high-frequency noise in the residual IMF components to obtain a high-frequency noise signal estimation, so that the sum of the baseline drift and the estimation value of the high-frequency noise is as follows:

and finally, subtracting the sum of the baseline drift and the high-frequency noise estimation value from the original photoelectric sensing signal to obtain a drift-free and noise-free reconstructed signal.

The embodiment simulates the smoke state of the 'very early stage' in the actual fire process, preheats the equipment after starting up for enough time to ensure that the equipment is fully stable, and continuously obtains 6000 data points in the normal operating environment; meanwhile, in order to verify the effectiveness of the algorithm, the signal to noise ratio is calculated, and in the process of acquiring 6000 photoelectric signal data, the smoke state of the 'very early' stage in the actual fire process is simulated by adding the same amount of smoke particles for three times through a standard smoke box. The processing results of the EEMD are shown in fig. 2(a) -2(n), and the pair of the original signal and the processed signal is shown in fig. 3(a) -3 (b).

Calculating the relation between an original signal and a decomposition quantity capable of reflecting the low-frequency drift trend by adopting a correlation coefficient, wherein the correlation coefficient represents the quantity of linear correlation degree between variables, and the absolute value of the correlation coefficient is generally more than 0.8, so that A and B have strong correlation; a weak correlation was considered between 0.3 and 0.8; no correlation was considered below 0.3.

In order to calculate the closeness degree of the high-frequency component and the baseline drift trend of the original signal and remove the interference of the effective smoke signal, firstly, manually removing the effective smoke signal data from the original data, only leaving the background data of the smoke-free signal, and calculating the relationship between the low-frequency drift component of the improved EEMD method and the original signal by using MATLAB, wherein the result is as follows:

r_EEMD＝0.9925

the signal-to-noise ratio method is adopted for comparison, the signal-to-noise ratio reflects the relation between signals and noise energy, and generally, the method with high signal-to-noise ratio and low absolute average error is better than the method with low signal-to-noise ratio and high absolute average error; the formula of the signal-to-noise ratio and the absolute average error is:

SNR＝10lg{∑x(t)²/∑[x(t)-x(t)']²}

the signal-to-noise ratio obtained by using MATLAB to calculate and processing data by using an EEMD method is as follows:

SNR_EEMD＝22.3986

in the embodiment, an improved EEMD method is adopted to decompose an original signal accounting for a noise signal, the signal is decomposed into 12-stage IMF components, and the baseline drift distribution is mainly in the last-stage IMF component and is very similar to the low-frequency drift trend of the original signal; selecting a proper self-adaptive low-pass filter to process the last-stage IMF, and taking the result output by the filter as baseline drift estimation; the high-frequency noise signals are mainly distributed in the IMF components of the previous stages, and proper threshold values are selected to process the IMF components of the previous stages to obtain estimated pure noise signals; finally, a baseline drift estimated value and a pure noise signal estimated value are subtracted from the signal to obtain a drift-free and noise-free reconstructed signal, so that the aims of filtering the baseline drift, reducing the background noise and improving the signal to noise ratio are better fulfilled.

Example 2

The embodiment provides a photoelectric sensing signal reconstruction system containing baseline drift and high-frequency noise, which comprises:

the decomposition module is used for carrying out EEMD decomposition on the photoelectric sensing signal to obtain an IMF component;

the calculation module is used for selecting the self-adaptive filter according to the order of the IMF component with the baseline drift to obtain a baseline drift estimation value and obtaining a high-frequency noise estimation value for the rest IMF component;

and the reconstruction module is used for filtering the two types of estimation values of the photoelectric sensing signal to obtain a reconstructed signal.

It should be noted that the above modules correspond to steps S1 to S3 in embodiment 1, and the above modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.

In further embodiments, there is also provided:

an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment 1. For brevity, no further description is provided herein.

It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.

A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.

The method in embodiment 1 may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.

Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.