A fully automatic soot and dust detection method and tester

Abstract

This application relates to the technical field of physical and chemical property detection, and specifically relates to a fully automatic smoke and dust detection method and tester. The method includes: obtaining the concentration, temperature, and relative humidity data of the inner wall of the flue and performing preprocessing; dividing the temperature sequence into multiple temperature subsequences, and obtaining the corresponding concentration subsequences and relative humidity subsequences of the corresponding orders; obtaining the noise content index of a single temperature subsequence; obtaining the filtering window size of the filter in a single temperature subsequence; obtaining the peaks and valleys of all concentration subsequences and relative humidity subsequences, and obtaining the in-peak elements of each peak; obtaining the sharpness index of each peak; obtaining the humidity-independent coefficient of a single concentration subsequence; obtaining the concentration correction factor of a single concentration subsequence; and correcting each concentration subsequence. This application can adaptively adjust the concentration correction factor according to different process parameters, thereby improving the detection accuracy of the smoke and dust concentration.

Description

Technical Field

[0001] This application relates to the technical field of physical and chemical property detection, and particularly to a full-automatic smoke and dust detection method and tester. Background Art

[0002] In order to effectively control and reduce industrial smoke and dust emissions and ensure air quality and public health, real-time monitoring of smoke and dust emissions is particularly important. Among existing non-contact smoke and dust detection technologies, the light scattering method is widely used in the field of smoke and dust detection due to its advantages such as continuous real-time detection, small size, low cost, and high sensitivity. However, when the light scattering method is used for smoke and dust detection, it is mainly affected by the relative humidity in the smoke and dust, resulting in errors. Therefore, it is necessary to correct the smoke and dust concentration results detected by the light scattering method.

[0003] Using a correction coefficient to correct the light scattering result is a common method to eliminate the bias of the light scattering system. However, in the prior art, when using the correction coefficient for correction, although the influence of relative humidity on light scattering is considered, only fixed parameters are used to calculate the concentration correction factor, and the influence of different relative humidities on the smoke and dust concentration is not considered to be inconsistent, resulting in a poor correction effect on light scattering and unable to accurately obtain the smoke and dust concentration data. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a full-automatic smoke and dust detection method and tester, and the specific technical solutions adopted are as follows:

[0005] In the first aspect, an embodiment of this application provides a full-automatic smoke and dust detection method, and the method includes the following steps:

[0006] Obtain the concentration sequence, temperature sequence, and relative humidity sequence of the inner wall of the flue within the same time, and perform normalization processing on each of them;

[0007] Divide the temperature sequence into multiple temperature subsequences, obtain the corresponding concentration subsequence and relative humidity subsequence of the corresponding ordinal position according to the ordinal position of a single temperature subsequence in the sequence; obtain the noise content index of a single temperature subsequence according to the autocorrelation degree and data dispersion degree of the single temperature subsequence; obtain the filtering window size of the filter for the single temperature subsequence according to the noise content index of the single temperature subsequence, and respectively filter the single temperature subsequence and its corresponding concentration subsequence and relative humidity subsequence according to the filtering window size;

[0008] Obtain the peaks and valleys of all concentration subsequences and relative humidity subsequences, and obtain the intra-peak elements of each peak; according to the number of intra-peak elements of each peak, the coefficient of variation of the intra-peak elements, and the difference in the coefficient of variation from other peaks in its corresponding subsequence, obtain the sharpness index of each peak, and compare it with a preset threshold to obtain abnormal peaks.

[0009] According to the difference in abnormal peaks between a single concentration subsequence and the relative humidity subsequence of the same ordinal position, and the ordinal distance between each concentration abnormal peak and its nearest neighbor humidity abnormal peak, obtain the humidity independence coefficient of a single concentration subsequence; according to the humidity independence coefficient of a single concentration subsequence and the element mean in the relative humidity subsequence of the same ordinal position, obtain the concentration correction factor of the corresponding concentration subsequence, and correct the corresponding concentration subsequence.

[0010] Preferably, the method for determining the noise content index is as follows: take a single temperature subsequence as the input of the autocorrelation function, and output a set of autocorrelation coefficient values; denote the mean of the autocorrelation coefficient values as the autocorrelation index of the single temperature subsequence; obtain the fitting line of the single temperature subsequence, obtain the shortest distance from each data point in the single temperature subsequence to the fitting line, and denote the degree of difference between all the shortest distances in the single temperature subsequence as the discrete index of the single temperature subsequence; denote the sum of the autocorrelation index and the discrete index of the single temperature subsequence as the noise content index of the single temperature subsequence.

[0011] Preferably, the calculation method for the filtering window size of the single temperature subsequence is as follows: , where represents the filtering window size of the filter in the z-th temperature subsequence, is the rounding function, is the exponential function with the natural constant e as the base, is the first preset threshold, is the second preset value, represents the noise content index of the z-th temperature subsequence.

[0012] Preferably, the intra-peak elements of each peak refer to all elements between the previous nearest neighbor valley value and the next nearest neighbor valley value of each peak. If there is no valley value in front of or behind the peak, the intra-peak elements of the peak refer to all elements between the peak and the valley value at the beginning or end of the sequence.

[0013] Preferably, the calculation method for the sharpness index is as follows: , where is the sharpness index of the i-th peak, is the coefficient of variation between all intra-peak elements of the i-th peak, is the mean of the absolute differences between the coefficient of variation of the \(i\)-th peak and the coefficients of variation of all peaks in the corresponding subsequence. is the number of elements within the \(i\)-th peak.

[0014] Preferably, the method for determining the humidity independence coefficient is as follows:

[0015] Denote the abnormal peaks in the concentration subsequence and the relative humidity subsequence as concentration abnormal peaks and humidity abnormal peaks respectively;

[0016] Denote the sequence composed of all concentration abnormal peaks in a single concentration subsequence as the concentration abnormal peak sequence, and denote the sequence composed of all humidity abnormal peaks in a single relative humidity subsequence as the humidity abnormal peak sequence;

[0017] Calculate the DTW distance between the concentration abnormal peak sequence of a single concentration subsequence and the humidity abnormal peak sequence of the relative humidity subsequence with the same ordinal position, and denote the DTW distance as the humidity asynchrony index of the single concentration subsequence;

[0018] Obtain the humidity response interval of a single concentration subsequence according to the ordinal distance between each concentration abnormal peak in the single concentration subsequence and its nearest neighbor humidity abnormal peak;

[0019] Denote the product of the humidity asynchrony index and the humidity response interval of a single concentration subsequence as the humidity independence coefficient of the single concentration subsequence.

[0020] Preferably, the method for determining the humidity response interval is as follows: in a single concentration subsequence and the relative humidity subsequence with the same ordinal position, denote the humidity abnormal peak that is the nearest neighbor to each concentration abnormal peak and has an ordinal position less than or equal to the ordinal position of the corresponding concentration abnormal peak as the pre-humidity peak of each concentration abnormal peak; denote the absolute difference between the ordinal position of each concentration abnormal peak and the ordinal position of its pre-humidity peak as the humidity response index of each concentration abnormal peak; denote the mean of the humidity response indices of all concentration abnormal peaks in a single concentration subsequence as the humidity response interval of the single concentration subsequence.

[0021] Preferably, the calculation method of the concentration correction factor is as follows: , where is the concentration correction factor of the \(z\)-th concentration subsequence; is the normalized humidity independence coefficient of the \(z\)-th concentration subsequence; is the mean of all elements in the \(z\)-th relative humidity subsequence.

[0022] Preferably, the process of correcting the corresponding concentration subsequence is as follows: use the ratio of each element in each concentration subsequence to the concentration correction factor of each concentration subsequence as the corrected concentration subsequences.

[0023] In a second aspect, an embodiment of the present application further provides a fully automatic soot detection tester, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned fully automatic soot detection methods are implemented.

[0024] The present application has at least the following beneficial effects:

[0025] Considering that when soot is emitted, electronic components will be affected by the outside world, resulting in errors in the collected data, the present application filters the collected data by constructing a noise content index to improve data accuracy, so as to provide data support for subsequent calculations. Then, according to the influence of relative humidity on soot concentration, a severity index is constructed to select significant data from the concentration subsequence. Next, according to the difference between the abnormal peaks of a single concentration subsequence and the relative humidity subsequence of the same ordinal position, and the ordinal distance between each concentration abnormal peak and the humidity abnormal peak of its nearest neighbor, a humidity independence coefficient is constructed to reflect the influence degree of relative humidity on concentration under the same process parameters. Finally, the concentration correction factor is optimized according to the humidity independence coefficient. Compared with the prior art, the present application can adaptively adjust the concentration correction factor for different process parameters, thereby improving the detection accuracy of soot concentration. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0027] Figure 1 It is a flowchart of the steps of a fully automatic soot detection method provided by an embodiment of the present application;

[0028] Figure 2 It is a flowchart of the construction of a concentration correction factor provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] In order to further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following describes in detail a fully automatic smoke and dust detection method and tester proposed according to this application, including its specific implementation manner, structure, features, and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0031] The following specifically describes the specific solutions of a fully automatic smoke and dust detection method and tester provided by this application in combination with the accompanying drawings.

[0032] Please refer to Figure 1 , which shows a step flowchart of a fully automatic smoke and dust detection method provided by an embodiment of this application. The method includes the following steps:

[0033] Step 1: Obtain the concentration sequence, temperature sequence, and relative humidity sequence of the inner wall of the flue within the same time, and perform normalization processing on each of them.

[0034] The scenario targeted by this embodiment is to detect the discharged smoke and dust in the factory flue. The photoelectric sensor, temperature sensor, and humidity sensor are respectively connected to the inner wall of the flue. When the smoke and dust are discharged, the laser beam will be scattered by the smoke and dust particles in the detected flue. The backscattered light is collected by the photoelectric sensor to obtain the concentration information of the smoke and dust. At the same time, the temperature and humidity data of the discharged smoke and dust are collected through the temperature and humidity sensors, and the relative humidity data at this moment is calculated based on the temperature and humidity data at the same moment. It should be noted that the relative humidity refers to the ratio of the actual content of water vapor in the current air to the maximum amount of water vapor that the air can hold at this temperature. The calculation of relative humidity is a well-known technology and will not be elaborated here. The acquisition interval of all data in this embodiment is 1 second, and the duration of each data acquisition is 30 minutes.

[0035] According to the time sequence of data acquisition, a concentration sequence, a temperature sequence, and a relative humidity sequence are respectively constructed. In order to eliminate the influence of the dimension between data, normalization processing is performed on all data. The normalization processing methods include but are not limited to Z-score standardization, maximum normalization, and maximum-minimum normalization.

[0036] Step 2: Divide the temperature sequence into multiple temperature subsequences, and obtain the corresponding concentration subsequence and relative humidity subsequence of the corresponding order according to the order of a single temperature subsequence in the sequence; obtain the noise content index of a single temperature subsequence according to the autocorrelation degree and data dispersion degree of the single temperature subsequence; obtain the filtering window size of the filter for the single temperature subsequence according to the noise content index of the single temperature subsequence, and filter the single temperature subsequence and its corresponding concentration subsequence and relative humidity subsequence according to the filtering window size respectively.

[0037] Under different process parameters, the soot concentration and temperature of industrial emissions will also change. Since the soot emitted industrially usually has a high temperature, and both the high temperature and the emission speed of the soot will affect electronic components, the data collected by the sensor will have error noise. In addition, since the emission temperatures of the soot under different process parameters are inconsistent, and the high temperature will also change the humidity in the soot, thereby affecting the moisture content in the soot and causing fluctuations in the relative humidity. Therefore, it is first necessary to divide the soot data under multiple different process parameters, and then filter the data to eliminate noise, so as to further accurately analyze the influence degree of relative humidity on the soot concentration.

[0038] Considering that when collecting data in the flue through an intelligent sensor, the flow rate and high temperature of the soot in the flue will cause random disturbances to the electronic components, which will cause large errors in the collected data. Therefore, it is first necessary to filter the collected soot concentration data, and then it is possible to accurately calculate the influence degree of relative humidity on the concentration. However, when filtering, if the filtering window is too large, it may oversmooth the data and weaken the data features; if the filtering window is too small, effective denoising cannot be achieved. Therefore, it is first necessary to set a suitable filtering window according to the degree of noise interference of the data.

[0039] Since the emission temperatures of the soot under different process parameters are inconsistent, and the temperature changes under the same process parameter are relatively stable, then under the state of switching between different process parameters, the soot temperature data will show periodic changes. Therefore, the emissions of the soot under different process parameters can be divided by the periodic changes of the soot temperature.

[0040] Divide the temperature sequence into multiple temperature subsequences through the Bernaola Galvan segmentation algorithm (referred to as the BG segmentation algorithm). The sequence segmentation algorithm includes but is not limited to the BG segmentation algorithm, the PELT sequence segmentation algorithm, etc.

[0041] Taking the z-th temperature subsequence as an example, according to the order range of the z-th temperature subsequence in the temperature sequence, extract the subsequences within the corresponding order range from the concentration sequence and the relative humidity sequence respectively, and denote them as the z-th concentration subsequence and the z-th relative humidity subsequence respectively.

[0042] Under the same process parameters, within a short period of time, due to relatively consistent process control, the industrial production process is stable. Therefore, various collected data should have strong autocorrelation within a short period of time, that is, there is a short-term dependence relationship in the data. Considering that the soot concentration is affected by the relative humidity, and the relative humidity is affected by the soot temperature, in this embodiment, the temperature data with less influence from environmental factors is used for analysis. By detecting the autocorrelation of the soot temperature in the short term, the noise impact of the emitted soot on electronic components is evaluated.

[0043] Take the z-th temperature subsequence as the input of the autocorrelation function, and output a series of autocorrelation coefficient values. Each coefficient corresponds to a different lag amount, and the difference between adjacent lag amounts is 1. In this embodiment, the range of lag amounts takes the empirical value [1, 30]. It should be noted that if the data length of the z-th temperature subsequence is less than 30, then the range of lag amounts is set to [1, N / 2], where N is the data length of the z-th temperature subsequence.

[0044] Denote the mean value of all autocorrelation coefficient values as the autocorrelation index of the z-th temperature subsequence; the autocorrelation index can reflect the time dependence relationship of the soot temperature during emission under the same process parameters within a short period of time; since noise does not have obvious autocorrelation, if the autocorrelation index is smaller, it can reflect that the autocorrelation of the collected soot temperature in the short term is worse, which is less in line with the true physical characteristics of soot emission, indicating that the collected temperature data is more affected by noise.

[0045] Furthermore, perform a linear fit on the z-th temperature subsequence, calculate the shortest distance from each data point in the z-th temperature subsequence to the fitted line, and then calculate the degree of difference between all the shortest distances. The degree of difference can be calculated by methods such as variance, standard deviation, and coefficient of variation.

[0046] Denote the degree of difference as the dispersion index of the z-th temperature subsequence. The dispersion index can reflect the degree of deviation of each temperature data from the overall temperature change trend. The larger the dispersion index, the more obvious the difference between the data and the overall trend, indicating that the z-th temperature subsequence is more affected by noise interference.

[0047] Furthermore, denote the filter window size of the moving average filter as , , where represents the filter window size of the filter in the z-th temperature subsequence, is the rounding function, is the exponential function with the natural constant e as the base, is the noise content index of the z-th temperature subsequence, representing the sum of the autocorrelation index and the dispersion index. is the first preset value, is the second preset value, and in this embodiment, 45 and 11 are taken respectively. It should be noted that in order to ensure that the core elements of the filter are located at the center position and the weights on both sides are symmetrically distributed, the size of the filtering window is processed. If the calculation result is odd, then is used as the size of the filtering window. If the calculation result is even, then -1 is used as the size of the filtering window. According to the obtained size of the filtering window, the z-th temperature subsequence is filtered by using a moving average filter, and with the same size of the filtering window, the z-th concentration subsequence and the z-th relative humidity subsequence are filtered by using a moving average filter to eliminate the noise effects generated by the soot high temperature and the emission flow rate on the electronic components in the same period.

[0048] So far, the elimination process of the noise in the collected data is realized, thus providing accurate data support for the subsequent calculation of the influence of relative humidity on concentration.

[0049] Step 3: Obtain the peaks and valleys of all concentration subsequences and relative humidity subsequences, and obtain the intra-peak elements of each peak; according to the number of intra-peak elements of each peak, the coefficient of variation of the intra-peak elements, and the difference in the coefficient of variation with other peaks in its subsequence, obtain the sharpness index of each peak, and compare it with a preset threshold to obtain abnormal peaks.

[0050] Furthermore, if the relative humidity in the soot is relatively high, it will cause the water vapor in the air to condense on the surface of the particulate matter, resulting in a higher detected soot concentration. Moreover, the higher the relative humidity, the more serious the concentration deviation. As a result, abnormal peaks with increased concentration appear, and there is a large difference between the abnormal peaks and the normal peaks. Therefore, it is first necessary to extract the abnormal peaks from the concentration data to further analyze the influence degree of relative humidity.

[0051] Taking the z-th filtered concentration subsequence as an example. All the peaks and valleys in the z-th concentration sequence are obtained through a peak-valley detection algorithm. The peak-valley detection algorithm is a well-known technology, and the specific process will not be elaborated here. Taking the i-th peak in the z-th concentration subsequence as an example for analysis. For the i-th peak, all the elements between its previous nearest neighbor valley and its next nearest neighbor valley are called the intra-peak elements of this peak. It should be noted that if the peak is located in the head or tail region of the sequence and there is no valley in front of or behind it, then all the elements between this peak and the valley at the head or tail of the sequence are recorded as the intra-peak elements of this peak.

[0052] Let . In the formula, is the sharpness index of the i-th peak; is the coefficient of variation between all the elements within the i-th peak; is the mean of the absolute differences between the coefficient of variation of the i-th peak and the coefficients of variation of all the peaks within the corresponding subsequence; is the number of elements within the i-th peak, reflecting the duration of the continuous change in the soot concentration within the range of the i-th peak. The calculation of the coefficient of variation is a well-known technique, and the specific process will not be elaborated here. The change index can reflect the intensity of the change in the soot concentration within the peak region.

[0053] can reflect the change rate of the soot concentration within the range of the i-th peak. If the change in the soot concentration within the range of the i-th peak is greater and the time required for the change is shorter, it indicates that the change rate of the soot concentration within the range of the i-th peak is greater. Average change difference can measure the significance of the change of the i-th peak relative to other peaks. The larger it is, the more significant the change in the soot within the range of the i-th peak is among all the peaks. Therefore, if the intensity index is larger, it can reflect that the degree of change in the soot within the range of the i-th peak is greater, and there are significant differences from the changes in the soot within the ranges of other peaks. However, since the soot emissions under the same process parameters are strictly regulated, if is larger, it can reflect that the possibility of abnormal changes in the soot within the range of the i-th peak due to the influence of humidity is greater.

[0054] Furthermore, calculate the intensity indices of each peak in the z-th concentration subsequence, and use all the intensity indices as the input of the Otsu threshold method, and the output of the Otsu threshold method is the intensity threshold. Mark all the peaks with intensity indices greater than or equal to the intensity threshold as the abnormal peaks of the z-th concentration subsequence.

[0055] Similarly, the abnormal peaks in all concentration subsequences can be obtained.

[0056] Similarly, the abnormal peaks in all relative humidity subsequences can be obtained.

[0057] So far, the abnormal peaks in all concentration subsequences and all relative humidity subsequences have been obtained.

[0058] Step Four: Obtain the humidity independence coefficient of a single concentration subsequence based on the differences between the abnormal peaks of the single concentration subsequence and the relative humidity subsequence with the same ordinal position, and the ordinal distance between each concentration abnormal peak and its nearest neighbor humidity abnormal peak; obtain the concentration correction factor of the single concentration subsequence based on the humidity independence coefficient of the single concentration subsequence and the element mean in the relative humidity subsequence with the same ordinal position, and correct the corresponding concentration subsequence.

[0059] After extracting the abnormal peaks in each concentration subsequence and each relative humidity subsequence, the influence degree of relative humidity on the soot concentration can be calculated to further optimize the concentration correction factor.

[0060] Denote the abnormal peak in the z-th concentration subsequence and the z-th relative humidity subsequence as the concentration abnormal peak and the humidity abnormal peak respectively. According to the positions of each concentration abnormal peak and humidity abnormal peak in the z-th concentration subsequence and the z-th relative humidity subsequence, construct the concentration peak position sequence and the humidity peak position sequence of the z-th concentration subsequence and the z-th relative humidity subsequence respectively.

[0061] Obtain the DTW distance between the concentration peak position sequence and the humidity peak position sequence, and denote it as the humidity asynchrony index of the z-th concentration subsequence. The calculation method of the DTW distance is a well-known technology and will not be elaborated here. The humidity asynchrony index can reflect whether the concentration data in the soot will change correspondingly after a drastic change in humidity; the smaller the humidity asynchrony index, the more consistent the change in soot concentration and relative humidity, and the greater the influence of relative humidity on the soot concentration.

[0062] Taking the u-th concentration abnormal peak as an example. Among all the humidity abnormal peaks, find the humidity abnormal peak that is the nearest neighbor to the u-th concentration abnormal peak and whose position is less than or equal to the position of the u-th concentration abnormal peak, and denote it as the pre-humidity peak of the u-th concentration abnormal peak. Denote the absolute difference between the position of the u-th concentration abnormal peak and the position of its pre-humidity peak as the humidity response index of the u-th concentration abnormal peak.

[0063] In the same way, obtain the humidity response indexes of all concentration abnormal peaks and calculate the mean value, and denote the mean value as the humidity response interval of the z-th concentration subsequence. The smaller the humidity response interval, the greater the influence of relative humidity on the soot concentration, and the faster the response speed of the soot concentration to relative humidity. It should be noted that if a certain concentration abnormal peak does not have a corresponding pre-humidity peak, this concentration abnormal peak does not participate in the calculation.

[0064] Denote the product of the humidity asynchrony index and the humidity response interval of the z-th concentration subsequence as the humidity irrelevance coefficient of the z-th concentration subsequence. The humidity irrelevance coefficient can reflect whether the soot concentration will change rapidly and synchronously when the relative humidity changes drastically. The smaller the humidity irrelevance coefficient, the greater the influence degree of relative humidity on the concentration.

[0065] Calculate the concentration correction factor of the z-th concentration subsequence according to the influence of the z-th relative humidity subsequence on the z-th concentration subsequence. The construction flow chart of the concentration correction factor is as Figure 2 shown. Specifically, let . In the formula, is the concentration correction factor for the z-th concentration subsequence; is the humidity-independent coefficient after normalization for the z-th concentration subsequence; is the mean value of all elements in the z-th relative humidity subsequence.

[0066] When the relative humidity in the soot is higher and the influence of relative humidity on the concentration is greater, a larger concentration correction factor is required to correct the collected soot concentration; while if the relative humidity in the soot is lower and the humidity influence is smaller, the concentration correction factor is close to 1 and there is no need to over-correct the soot concentration data.

[0067] Respectively divide each element in the z-th concentration subsequence by the concentration correction factor to obtain the corrected z-th concentration subsequence; in the same way, correct all concentration subsequences to obtain accurate emission soot concentration data.

[0068] Thus, the adaptive optimization of the concentration correction factor under various process parameters is realized, so as to accurately correct the soot concentration and improve the data detection accuracy of the emission soot concentration.

[0069] Based on the same inventive concept as the above method, an embodiment of the present application also provides a full-automatic soot detection tester, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above full-automatic soot detection methods.

[0070] It should be noted that: the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0071] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0072] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A fully automatic soot detection method, characterized in that, The method includes the following steps: Obtain the concentration sequence, temperature sequence, and relative humidity sequence of the inner wall of the flue gas duct within the same time, and perform normalization processing on each of them; Divide the temperature sequence into multiple temperature subsequences, and obtain the corresponding concentration subsequence and relative humidity subsequence of the corresponding ordinal position according to the ordinal position of a single temperature subsequence in the sequence; obtain the noise content index of a single temperature subsequence according to the autocorrelation degree and data dispersion degree of the single temperature subsequence; obtain the filtering window size of the filter for the single temperature subsequence according to the noise content index of the single temperature subsequence, and filter the single temperature subsequence and its corresponding concentration subsequence and relative humidity subsequence of the corresponding ordinal position respectively according to the filtering window size; Obtain the peaks and valleys of all concentration subsequences and relative humidity subsequences, and obtain the in-peak elements of each peak; obtain the sharpness index of each peak according to the number of in-peak elements of each peak, the coefficient of variation of the in-peak elements, and the difference in the coefficient of variation from other peaks in its subsequence, and compare it with a preset threshold to obtain abnormal peaks; Obtain the humidity independence coefficient of a single concentration subsequence according to the difference in abnormal peaks between the single concentration subsequence and the relative humidity subsequence of the same ordinal position, and the ordinal distance between each concentration abnormal peak and its nearest neighbor humidity abnormal peak; obtain the concentration correction factor of the single concentration subsequence according to the humidity independence coefficient of the single concentration subsequence and the element mean value in the relative humidity subsequence of the same ordinal position, and correct the corresponding concentration subsequence.

2. The fully automatic smoke and dust detection method according to claim 1, characterized in that The method for determining the noise content index is as follows: Take a single temperature subsequence as the input of the autocorrelation function, and output a set of autocorrelation coefficient values; record the mean value of the autocorrelation coefficient values as the autocorrelation index of the single temperature subsequence; obtain the fitting straight line of the single temperature subsequence, obtain the shortest distance from each data point in the single temperature subsequence to the fitting straight line, and record the difference degree between all the shortest distances in the single temperature subsequence as the dispersion index of the single temperature subsequence; record the sum of the autocorrelation index and the dispersion index of the single temperature subsequence as the noise content index of the single temperature subsequence.

3. The full-automatic smoke and dust detection method according to claim 1, characterized in that The calculation method for the filtering window size of the single temperature subsequence is as follows: , where represents the filtering window size of the filter for the z-th temperature subsequence, is the rounding function, is the exponential function with the natural constant e as the base, is the first preset value, is the second preset value, represents the noise content index of the z-th temperature subsequence.

4. The full-automatic soot detection method according to claim 1, characterized in that The in-peak elements of each peak refer to all elements between the previous nearest neighbor valley and the next nearest neighbor valley of each peak. If there is no valley in front of or behind the peak, the in-peak elements of the peak refer to all elements between the peak and the valley at the head or tail of the sequence.

5. The full-automatic soot detection method according to claim 1, characterized in that The calculation method of the severity index is as follows: , where is the severity index of the i-th peak, is the coefficient of variation between all intra-peak elements of the i-th peak, is the mean of the absolute difference between the coefficient of variation of the i-th peak and the coefficient of variation of all peaks in the subsequence where it is located, is the number of intra-peak elements of the i-th peak.

6. The full-automatic soot detection method according to claim 1, characterized in that, The method for determining the humidity independence coefficient is as follows: Record each abnormal peak in the concentration subsequence and the relative humidity subsequence as a concentration abnormal peak and a humidity abnormal peak respectively; Record the sequence composed of all concentration abnormal peaks in a single concentration subsequence as the concentration abnormal peak sequence, and record the sequence composed of all humidity abnormal peaks in a single relative humidity subsequence as the humidity abnormal peak sequence; Calculate the DTW distance between the concentration abnormal peak sequence of a single concentration subsequence and the humidity abnormal peak sequence of the relative humidity subsequence of the same ordinal position, and record the DTW distance as the humidity asynchrony index of the single concentration subsequence; Obtain the humidity response interval of a single concentration subsequence based on the ordinal distance between each concentration anomaly peak and its nearest neighbor humidity anomaly peak in the single concentration subsequence; Denote the product of the humidity asynchronous index of a single concentration subsequence and the humidity response interval as the humidity irrelevance coefficient of the single concentration subsequence.

7. The fully automatic soot detection method according to claim 6, wherein The method for determining the humidity response interval is as follows: In the single concentration subsequence and the relative humidity subsequence with the same ordinal number, the humidity anomaly peak that is the nearest neighbor to each concentration anomaly peak and has an ordinal number less than or equal to the ordinal number of the corresponding concentration anomaly peak is denoted as the pre-humidity peak of each concentration anomaly peak; Denote the absolute difference between the ordinal number of each concentration anomaly peak and the ordinal number of its pre-humidity peak as the humidity response index of each concentration anomaly peak; Denote the mean value of the humidity response indices of all concentration anomaly peaks in the single concentration subsequence as the humidity response interval of the single concentration subsequence.

8. The full-automatic soot detection method according to claim 1, wherein The calculation method of the concentration correction factor is as follows: , where is the concentration correction factor of the z-th concentration subsequence; is the humidity-independent coefficient after normalization of the z-th concentration subsequence; is the mean value of all elements in the z-th relative humidity subsequence.

9. The full-automatic soot detection method according to claim 1, characterized in that, The process of correcting the corresponding concentration subsequence is as follows: Take the ratio of each element in each concentration subsequence to the concentration correction factor of the corresponding concentration subsequence as the corrected concentration subsequences.

10. A fully automatic smoke and dust detection tester, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a fully automatic soot detection method according to any one of claims 1-9.

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