Rapid detection method for heavy metal pollution in sewage based on multispectral imaging

Abstract

The application relates to the technical field of spectral detection, in particular to a sewage heavy metal pollution rapid detection method based on multispectral imaging, which comprises the following steps: obtaining high-spectral data of each river section of a river surface, obtaining high-spectral data sequences of each pixel point, calculating the deviation degree of the characteristic wave peak of each pixel point in the high-spectral data of each river section through the wave peak of the high-spectral data sequence of each pixel point, and then obtaining a spectral abnormality coefficient; analyzing the high-spectral data sequence of each pixel point at each sampling time, calculating the disturbed mutation difference of each pixel point in the high-spectral data of each river section and an environmental disturbance coefficient, so as to obtain the spectral disturbance degree of each pixel point in the high-spectral data of each river section, and then matching the high-spectral data of various water pollution metal ions. The application can improve the detection precision of heavy metal pollution of water bodies.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of spectral detection, in particular to a method for rapid detection of heavy metal pollution in sewage based on multi-spectral imaging. BACKGROUND

[0002] Due to the difference between heavy metal pollution and other types of pollution, it has the characteristics of concealment, long-term and irreversibility, etc. The toxicity is large, which can directly enter the atmosphere, water and soil to cause direct pollution of various environmental elements, and can also migrate in the atmosphere, water and soil to cause indirect pollution of various environmental elements, leading to accumulation in the body and difficult to degrade, if absorbed by aquatic plants into the food chain, not only will cause damage to the natural ecology, but also will bring serious threat to human health.

[0003] In the prior art, relevant data is collected by intelligent sensors, and atomic absorption spectrometry, atomic fluorescence spectrometry, inductively coupled plasma method, ultraviolet-visible spectrophotometry, high performance liquid chromatography, electrochemical analysis method and biological detection method are used. Among them, atomic absorption spectrometry is one of the most mature and widely used methods for detecting heavy metals in sewage due to its high detection sensitivity, fast analysis speed, small interference when measuring high concentration elements, stable signal, etc. However, in the actual detection process, the sources of sewage are different, and the degree of pollution, the types and contents of heavy metals are greatly different, so when atomic absorption spectrometry is used to monitor heavy metals in sewage after the data is collected by the spectral intelligent sensor, the collected spectrum is seriously interfered, resulting in a decrease in detection accuracy and an inability to accurately assess the heavy metal pollution of the sewage. SUMMARY

[0004] In order to solve the above technical problems, the present application provides a method for rapid detection of heavy metal pollution in sewage based on multi-spectral imaging to solve the existing problems.

[0005] The method for rapid detection of heavy metal pollution in sewage based on multi-spectral imaging of the present application adopts the following technical scheme:

[0006] One embodiment of the present application provides a method for rapid detection of heavy metal pollution in sewage based on multi-spectral imaging, comprising the following steps:

[0007] Obtain hyperspectral data of each river section of the river surface;

[0008] The hyperspectral data sequence of each pixel point is obtained through the hyperspectral data of each pixel point at the sampling time, the characteristic wave peak of each pixel point in the river section hyperspectral data is obtained according to the extraction of the wave peak of the hyperspectral data sequence of each pixel point, the deviation degree of the characteristic wave peak of each pixel point in the river section hyperspectral data is obtained by analyzing the absorption rate difference and the wavelength difference of the characteristic wave peak of different pixel points in the river section hyperspectral data, and then the spectral abnormality coefficient of each pixel point in the river section hyperspectral data is obtained in combination with the distance of the hyperspectral data sequence of each pixel point at different sampling times;

[0009] The peak value in the hyperspectral data sequence of each pixel point at each sampling time is detected, the mutation time of each pixel point is extracted, and the disturbed mutation difference of each pixel point in the river section hyperspectral data is obtained in combination with the average level of the wave peak value in the hyperspectral data sequence, and then the environmental disturbance coefficient of each pixel point in the river section hyperspectral data is obtained in combination with the number of wave peak points in the hyperspectral data sequence of the pixel point at each sampling time.

[0010] The spectral disturbance degree of each pixel point in the river section hyperspectral data is obtained through the spectral abnormality coefficient and the environmental disturbance coefficient of each pixel point in the river section hyperspectral data, so as to extract the hyperspectral data to be matched, and the heavy metal pollution of sewage is detected by matching with the standard hyperspectral data of various water pollution metal ions.

[0011] Preferably, the method for obtaining the hyperspectral data sequence of each pixel point is as follows:

[0012] The hyperspectral data of each pixel point at each sampling time is arranged in ascending order of wavelength to form the hyperspectral data sequence of each pixel point at each sampling time.

[0013] Preferably, the extraction of the characteristic wave peak of each pixel point in the river section hyperspectral data comprises:

[0014] The wave peaks in the hyperspectral data sequence of all pixel points of the river section are extracted, the threshold value of the wave peak value corresponding to all pixel points of the river section is segmented to obtain a segmentation threshold value, and the wave peak with a peak value greater than or equal to the segmentation threshold value is taken as the characteristic wave peak of each pixel point in the river section.

[0015] Preferably, the calculation method of the deviation degree of the characteristic wave peak of each pixel point in the river section hyperspectral data is as follows:

[0016]

[0017] In the formula, E i is the deviation degree of the characteristic wave peak of the i th pixel point in the single river section hyperspectral data, B i,p ,B u,p are the absorption rates of the p th characteristic wave peak of the i th and u th pixel points in the single river section hyperspectral data, respectively, and Di,p D u,p respectively, are the corresponding waveband lengths of the pth characteristic wave peak in the ith and u th pixel points in the single river reach hyperspectral data, U is the total number of pixel points in the single river reach hyperspectral data, and P is the total number of characteristic wave peaks in the hyperspectral data of the ith pixel point.

[0018] Preferably, the method for calculating the spectral anomaly coefficient of each pixel point in the hyperspectral data of each river reach is as follows:

[0019]

[0020] In the formula, A i is the spectral anomaly coefficient of the ith pixel point in the single river reach hyperspectral data, E i is the characteristic wave peak deviation degree of the ith pixel point in the single river reach hyperspectral data, C i,j,j+1 is the dtw distance of the hyperspectral data sequence of the ith pixel point at the jth and j+1th sampling time, and J is the total number of sampling times.

[0021] Preferably, the method for extracting the mutation time of each pixel point comprises:

[0022] The peak values of the corresponding wave peaks of each pixel point in each river reach are sorted according to the sampling time sequence to obtain the spectral peak value sequence of each pixel point.

[0023] The mutation point of the spectral peak value sequence of the pixel point is detected, and the sampling time corresponding to the mutation point is taken as the mutation time of each pixel point.

[0024] Preferably, the method for calculating the disturbed mutation difference of each pixel point in the hyperspectral data of each river reach is as follows:

[0025]

[0026] In the formula, h r,i is the disturbed mutation difference of the ith pixel point in the rth river reach hyperspectral data, h r,i,m , h w,i,m are the mth mutation times of the ith pixel point in the rth and wth river reaches, respectively, M is the total number of mutation times of the ith pixel point in the rth river reach, q r,i , q w,i are the sum values of the absolute difference values of the hyperspectral data sequence peak value mean of each mutation time and the previous time of the ith pixel point in the rth and wth river reaches, respectively.

[0027] Preferably, the method for calculating the environmental disturbance coefficient of each pixel point in the hyperspectral data of each river reach is as follows:

[0028] F r,i =G r,i ×Hr,i ;

[0029] In the formula, F r,i is the environmental disturbance coefficient of the ith pixel point in the hyperspectral data of the rth river section, G r,i is the average value of the total number of peak points in the hyperspectral data sequence at each sampling time of the ith pixel point in the hyperspectral data of the rth river section, H r,i is the sum of the disturbance mutation difference of the ith pixel point in the hyperspectral data of the rth river section and the remaining river sections.

[0030] Preferably, the calculation method of the spectral disturbance degree of each pixel point in the hyperspectral data of each river section is as follows:

[0031] Z r,i = norm (A r,i * F r,i );

[0032] In the formula, Z r,i is the spectral disturbance degree of the ith pixel point in the hyperspectral data of the rth river section, norm(·) represents a normalization function, A r,i is the spectral anomaly coefficient of the ith pixel point in the hyperspectral data of the rth river section, F r,i is the environmental disturbance coefficient of the ith pixel point in the hyperspectral data of the rth river section.

[0033] Preferably, the extraction process of the hyperspectral data to be matched includes:

[0034] The average value of the spectral disturbance degree of each pixel point at all sampling times is calculated, and the threshold value obtained through cross-validation is used to determine the hyperspectral data corresponding to the pixel points less than the threshold value as the hyperspectral data to be matched.

[0035] The present application has at least the following beneficial effects:

[0036] The present application analyzes the change difference characteristics of the hyperspectral data of each detection river section in the target river to be detected, and the disturbance characteristics of the hyperspectral data of each river section under the interference of the natural environment, judges the accuracy of the hyperspectral data collection of each river section, further eliminates the hyperspectral data with interference, thereby ensuring the accuracy of the water body hyperspectral data, and finally combines the standard heavy metal hyperspectral data to detect the water body heavy metal pollution. The present application solves the problem that when the traditional spectral detection method is used to detect the water body heavy metal pollution, the water body changes are complex, the hyperspectral data collection is easily disturbed and interfered, and the detection result is inaccurate. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the accompanying drawings required to be used in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0038] Figure 1 The step flow chart of the method for rapid detection of heavy metal pollution in sewage provided by the present application based on multi-spectral imaging is provided.

[0039] Figure 2 The step flow chart of the method for obtaining spectral disturbance provided by the present application is provided. DETAILED DESCRIPTION

[0040] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined purposes, the specific embodiments, structures, features and effects of the method for rapid detection of heavy metal pollution in sewage based on multi-spectral imaging according to the present application are described in detail as follows in combination with the 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.

[0041] Unless otherwise defined, such as the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed or inherent to such article or device. Without more limitation, the element limited by the statement "including one" does not exclude the presence of another identical element in the article or device including the element. In addition, the term "and / or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as understood by those skilled in the art of the technology to which the present application belongs.

[0042] The specific scheme of the method for rapid detection of heavy metal pollution in sewage based on multi-spectral imaging provided by the present application is specifically described below in combination with the drawings.

[0043] The method for rapid detection of heavy metal pollution in sewage based on multi-spectral imaging provided by one embodiment of the present application, specifically, please refer to Figure 1 , including the following steps:

[0044] Step 1: Obtain hyperspectral data of river surface.

[0045] With the river area needing to be detected for heavy metal pollution in sewage as an example, multiple unmanned aerial vehicles are used to carry intelligent sensors, and high-spectral image data is acquired along the target river needing to be monitored, and the river section is sampled every preset length of time. Preferably, in the embodiment, the intelligent sensor is a hyperspectral camera, the preset time is 0.5s, and the preset length is 100.

[0046] Meanwhile, the image captured by the selected hyperspectral camera covers the river within the preset range, and the river range in the subsequent process is collectively referred to as a river section. Each unmanned aerial vehicle stays in a single river section of the river and captures N data. In addition, the ranges between river sections do not overlap, and the implementer can set them according to the actual size of the river, the resolution of the hyperspectral camera, and other actual conditions. Preferably, in the embodiment, the preset range is 5x5m.

[0047] Step two: Obtain the high-spectral data sequence of each pixel point through the high-spectral data of each pixel point at the sampling time, obtain the characteristic wave peak of each pixel point in the high-spectral data of each river section according to the extracted wave peak of the high-spectral data sequence of each pixel point, obtain the deviation degree of the characteristic wave peak of each pixel point in the high-spectral data of each river section by analyzing the absorption rate difference and the wave band length difference of the characteristic wave peaks of different pixel points in the high-spectral data of each river section, and then obtain the spectral abnormality coefficient of each pixel point in the high-spectral data of each river section in combination with the distance of the high-spectral data sequence of each pixel point at different sampling times.

[0048] Under normal circumstances, for the same river, when it is polluted by heavy metals, the heavy metal pollution in the water body will gradually spread as the water body flows, and finally the river section closer to the pollution source will have a higher heavy metal concentration, and the river section farther away from the pollution source will have a lower heavy metal concentration. However, for the same river section, the overall heavy metal pollution is balanced, the pollution degree of each part of the river section is approximately the same, and the change degree of the water body is weak in a short period of time, and the overall change of the high-spectral data is small. When the water body high-spectral data collection is disturbed, such as light change affecting water body reflection, shielding caused by weather change or random noise, the water body high-spectral data at the same position changes dramatically within the sampling period, and the disturbance degree of each part of the river section is different, the overall high-spectral data changes chaotically, and the high-spectral data changes of each part will have certain differences. Specifically, the high-spectral data of a single pixel point collected in a single river section has a large difference in the high-spectral data collected at each time within the sampling period, and the high-spectral data wave peaks of different pixel points will shift, and the peak values will have a large difference.

[0049] Therefore, for the hyperspectral data collected in a single river section, the hyperspectral data collected by a single pixel point at a single sampling time is arranged in ascending order of wavelength length to construct a hyperspectral data sequence of a single pixel point, further, the wave peaks in all the hyperspectral data sequences of all the pixel points in the river section are obtained, and all the wave peak values are taken as inputs to obtain a segmentation threshold of the wave peak value by using the Otsu threshold method, and when a single wave peak value is greater than or equal to the segmentation threshold, the wave peak is a representative wave peak. Through the above method, all the representative wave peaks in the hyperspectral data of each pixel point can be calculated.

[0050] Based on the above analysis, in order to represent the spectral anomaly degree of a single pixel point in the hyperspectral data of a single river section, the representative wave peak deviation degree of each pixel point in the hyperspectral data of a single river section is obtained according to the absorption rate difference and the corresponding wavelength difference of the representative wave peaks of different pixel points in the hyperspectral data of a single river section:

[0051]

[0052] In the formula, E i is the representative wave peak deviation degree of the i th pixel point in the hyperspectral data of a single river section, B i,p is the absorption rate of the p th representative wave peak in the i th pixel point in the hyperspectral data of a single river section, B u,p is the absorption rate of the p th representative wave peak in the u th pixel point in the hyperspectral data of a single river section, D i,p is the corresponding wavelength length of the p th representative wave peak in the i th pixel point in the hyperspectral data of a single river section, D u,p is the corresponding wavelength length of the p th representative wave peak in the u th pixel point in the hyperspectral data of a single river section, U is the total number of pixel points in the hyperspectral data of a single river section, and P is the total number of representative wave peaks in the hyperspectral data of the i th pixel point.

[0053] Further, according to the representative wave peak deviation degree of each pixel point in the hyperspectral data of each river section, the spectral anomaly coefficient of each pixel point in the hyperspectral data of each river section is obtained by combining the distances of the hyperspectral data sequences of each pixel point at different sampling times:

[0054]

[0055] In the formula, A i is the spectral anomaly coefficient of the i th pixel point in the hyperspectral data of a single river section, E i is the representative wave peak deviation degree of the i th pixel point in the hyperspectral data of a single river section, C i,j,j+1 is the dtw distance of the hyperspectral data sequence of the i th pixel point at the j th and the j+1 th sampling time, and J is the total number of sampling times.

[0056] When the variation range of the overall data of a single pixel point in a single river section hyperspectral data within a sampling time period is larger, and the deviation degree of the hyperspectral data of the pixel point from the rest of the pixel points is larger, it indicates that the spectral data contained in the hyperspectral data point is more likely to be interfered, the abnormality degree is higher, and it should not be used to monitor the heavy metal pollution of the water body.

[0057] Step three: detecting the mutation of the peak value in the hyperspectral data sequence of each pixel point at each sampling time, extracting the mutation time of each pixel point, and combining the average level of the peak value in the hyperspectral data sequence to obtain the disturbed mutation difference of each pixel point in the hyperspectral data of each river section, and then combining the number of peak points in the hyperspectral data sequence of the pixel point at each sampling time to obtain the environmental disturbance coefficient of each pixel point in the hyperspectral data of each river section.

[0058] Due to the natural environment of the river channel, there may be certain interference or interference during shooting, such as high or low ambient light during shooting of hyperspectral data by a drone, or certain cloud weather interference, etc., and these interference conditions have strong overall interference on the river channel, and the interference degree is similar, which will cause the noise or interference in the collected hyperspectral data to be similar, and the disturbed condition in each river section is also similar, and then by calculating the change trend of the hyperspectral data and ignoring the natural environment interference, the calculated spectral data change degree may be low, which may misjudge that the collected hyperspectral data is not disturbed, and finally cause deviation in monitoring of water body heavy metal pollution, so further analysis is required.

[0059] Specifically, the natural environmental interference in the river channel usually has a large interference range, and when it occurs, it usually interferes with the entire river channel, and the time and degree of interference are similar. Specifically, when the river channel is disturbed by the natural environment, the hyperspectral data collected in each river section will have similar spectral data changes, and the time of occurrence is close. In addition, if the river channel itself has natural environmental interference factors, there will be more noise in the hyperspectral data collected in each river section, thereby increasing the confusion degree of the overall spectral data and increasing the peak density.

[0060] Therefore, taking a single pixel point in a single river section as an example, the peak points and their peak values in the hyperspectral data sequence at each sampling time are obtained, and the spectral peak value sequence of the single pixel point is obtained by sorting the spectral peak value sequence in the order of sampling time. The mutation points in the spectral peak value sequence are obtained by using the mutation point detection algorithm, and the sampling time corresponding to the mutation point is taken as the mutation time of the pixel point.

[0061] Based on the above analysis, in order to express the situation that the hyperspectral data of a single pixel point in a single river section is disturbed by the river environment, according to the mutation time of each pixel point and the average level of the peak value of the hyperspectral data sequence, the disturbed mutation difference of each pixel point in the hyperspectral data of each river section is obtained:

[0062]

[0063] In the formula, h r,i is the disturbed mutation difference of the i-th pixel point in the r-th river section hyperspectral data, h r,i,m is the m-th mutation time of the i-th pixel point in the r-th river section, h w,i,m is the m-th mutation time of the i-th pixel point in the w-th river section, M is the total number of mutation times of the i-th pixel point in the r-th river section, q r,i is the sum of the absolute difference values of the mutation time of the i-th pixel point in the r-th river section and the average value of the peak value of the hyperspectral data sequence at the previous time, q w,i is the sum of the absolute difference values of each mutation time of the i-th pixel point in the w-th river section and the average value of the peak value of the hyperspectral data sequence at the previous time.

[0064] Further, by summing the disturbed mutation difference of each pixel point in the hyperspectral data of each river section and the average value of the total number of peak points in the hyperspectral data sequence at each sampling time, the environmental disturbance coefficient of each pixel point in the hyperspectral data of each river section is obtained:

[0065] F r,i =G r,i ×H r,i ;

[0066] In the formula, F r,i is the environmental disturbance coefficient of the i-th pixel point in the r-th river section hyperspectral data, G r,i is the average value of the total number of peak points in the hyperspectral data sequence at each sampling time of the i-th pixel point in the r-th river section hyperspectral data, H r,i is the sum of the disturbed mutation difference of the i-th pixel point in the hyperspectral data of the r-th river section and the remaining river sections.

[0067] Wherein, when the mutation time of the i-th pixel point in the hyperspectral data collected in a single river section and the same position in the hyperspectral data of the remaining river sections is closer, the amplitude of the mutation of the hyperspectral data is closer, and the total number of peaks in the collected hyperspectral data is more, it is more likely that the river section itself has certain natural environmental factor disturbance or natural environmental disturbance occurs in the process of collecting the hyperspectral data, at this time the collected hyperspectral data is more unsuitable for application in the detection process of water body heavy metal pollution.

[0068] Step four: obtaining the spectral disturbance degree of each pixel point in the hyperspectral data of each river section by the spectral anomaly coefficient and the environmental disturbance coefficient of each pixel point in the hyperspectral data of each river section, so as to extract the hyperspectral data to be matched, and detect the heavy metal pollution in sewage by matching with the standard hyperspectral data of various water pollution metal ions.

[0069] In order to represent the disturbance situation of the hyperspectral data, the spectral disturbance degree of each pixel point in the hyperspectral data of each river section is obtained by the spectral anomaly coefficient and the environmental disturbance coefficient of each pixel point in the hyperspectral data of each river section:

[0070] Z r,i = norm(A r,i × F r,i );

[0071] In the formula, Z r,i is the spectral disturbance degree of the i-th pixel point in the r-th river section hyperspectral data, norm(·) represents a normalization function, A r,i is the spectral anomaly coefficient of the i-th pixel point in the r-th river section hyperspectral data, and F r,i is the environmental disturbance coefficient of the i-th pixel point in the r-th river section hyperspectral data.

[0072] Wherein, when the spectral anomaly coefficient of the i-th pixel point in a single river section is smaller, and the environmental disturbance coefficient is smaller, it means that the hyperspectral data contained in the pixel point is more likely to have less noise and interference, and the water body hyperspectral data characteristics represented by the pixel point are more accurate, and more suitable for detecting water body heavy metal pollution by using the hyperspectral data of the pixel point. The step flow chart of the method for obtaining the spectral disturbance degree provided in the embodiments of the present application is shown in Figure 2 .

[0073] The spectral disturbance degree of each pixel point in each river section at each sampling time can be calculated by the above-mentioned manner. For a single pixel point in a single river, the mean value of the spectral disturbance degree of the single pixel point at each sampling time is calculated as an evaluation of the real degree of the spectral data of the pixel point. Further, the mean value of the spectral disturbance degree corresponding to each pixel point is used as an input, and a threshold value of the mean value of the spectral disturbance degree is output by using the cross-validation method. When the mean value of the spectral disturbance degree of a single pixel point is greater than or equal to the threshold value, it means that the hyperspectral data in the pixel point is more likely to have more interference components, and is less suitable as the hyperspectral data for detecting water body heavy metal pollution. On the contrary, it is considered that the accuracy of the hyperspectral data in the pixel point is higher, and is more suitable as the hyperspectral data to be matched for detecting water body heavy metal pollution. Therefore, the hyperspectral data corresponding to the pixel points less than the threshold value is used as the hyperspectral data to be matched.

[0074] Therefore, by the above manner, accurate hyperspectral data in each river section in the river channel that can be used for heavy metal pollution detection of the water body can be screened out. Further, the standard hyperspectral data of various water pollution metal ions in the database is acquired by the heavy metal pollution detection system, and combined with the screened hyperspectral data to be matched, a matching score of the hyperspectral data to be matched and the standard hyperspectral data of various water pollution metal ions is output by using the data matching method. When the matching score is greater than or equal to a preset score threshold Y, it is considered that the water body contains the corresponding metal ion. Finally, the heavy metal pollution detection system outputs the result of the heavy metal contained in the current detection water body.

[0075] It is to be understood that a reference to "one embodiment" or "some embodiments" or "one implementation" or "some implementations" etc. in this specification means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment or implementation of the application. The appearances of the phrases "in one embodiment" or "in some embodiments" or "in other embodiments" or "in other implementations" etc. in various places in the specification are not necessarily all referring to the same embodiment, unless otherwise specified specifically. The terms "comprising", "including", "containing", and "having" etc. are meant not to be limiting and are to be interpreted as "including but not limited to", unless otherwise specifically noted.

[0076] It is to be noted that the sequence of the above-mentioned embodiments is merely for description, and does not represent the advantages or disadvantages of the embodiments. In addition, the above-mentioned embodiments are described in the specification. 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-task processing and parallel processing are possible or advantageous. At the same time, the size of the serial number of each step in the embodiments does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments in the specification.

[0077] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A rapid detection method for heavy metal pollution in wastewater based on multispectral imaging, characterized in that, Includes the following steps: Acquire hyperspectral data of each section of the river surface; By using the hyperspectral data of each pixel at the sampling time, the hyperspectral data sequence of each pixel is obtained. Based on the peaks of the extracted hyperspectral data sequence of each pixel, the characteristic peaks of each pixel in the hyperspectral data of each river segment are obtained. By analyzing the differences in absorptivity and wavelength of the characteristic peaks of different pixels in the hyperspectral data of each river segment, the degree of deviation of the characteristic peaks of each pixel in the hyperspectral data of each river segment is obtained. Then, combined with the distance of each pixel in the hyperspectral data sequence at different sampling times, the spectral anomaly coefficient of each pixel in the hyperspectral data of each river segment is obtained. Abrupt changes are detected in the peak values ​​of the hyperspectral data sequence at each sampling time for each pixel. The abrupt change time of each pixel is extracted, and combined with the average level of the peak values ​​in the hyperspectral data sequence, the differences in the disturbance abrupt changes of each pixel in the hyperspectral data of each river segment are obtained. Then, combined with the number of peak points in the hyperspectral data sequence at each sampling time, the environmental disturbance coefficient of each pixel in the hyperspectral data of each river segment is obtained. By using the spectral anomaly coefficient and environmental disturbance coefficient of each pixel in the hyperspectral data of each river section, the spectral disturbance degree of each pixel in the hyperspectral data of each river section is obtained, so as to extract the hyperspectral data to be matched. By matching with the hyperspectral data of various standard water pollutant metal ions, the heavy metal pollution in sewage is detected.

2. The rapid detection method for heavy metal pollution in wastewater based on multispectral imaging as described in claim 1, characterized in that, The method for obtaining the hyperspectral data sequence of each pixel is as follows: the hyperspectral data of each pixel at each sampling time are arranged in ascending order of wavelength to form the hyperspectral data sequence of each pixel at each sampling time.

3. The rapid detection method for heavy metal pollution in wastewater based on multispectral imaging as described in claim 1, characterized in that, The step of extracting the characteristic peaks of each pixel in the hyperspectral data of each river segment includes: extracting the peaks in the hyperspectral data sequence of all pixels in each river segment, performing threshold segmentation on the peak values ​​corresponding to all pixels in the river segment to obtain a segmentation threshold, and taking the peak values ​​that are greater than or equal to the segmentation threshold as the characteristic peaks of each pixel in each river segment.

4. The rapid detection method for heavy metal pollution in wastewater based on multispectral imaging as described in claim 1, characterized in that, The method for calculating the peak deviation of each pixel in the hyperspectral data of each river segment is as follows: In the formula, E i B represents the degree of peak deviation for the i-th pixel within the hyperspectral data of a single river segment. i,p B u,p D represents the absorbance of the p-th characteristic peak within the i-th and u-th pixels of a single river segment's hyperspectral data, respectively. i,p D u,p , respectively, are the corresponding band lengths of the p-th characterizing peak in the i-th and u-th pixels of a single river segment hyperspectral data, where U is the total number of pixels in the hyperspectral data of a single river segment, and P is the total number of characterizing peaks in the hyperspectral data of the i-th pixel.

5. The rapid detection method for heavy metal pollution in wastewater based on multispectral imaging as described in claim 1, characterized in that, The method for calculating the spectral anomaly coefficient of each pixel in the hyperspectral data of each river segment is as follows: In the formula, A i E represents the spectral anomaly coefficient of the i-th pixel within the hyperspectral data of a single river segment. i C represents the degree of peak deviation for the i-th pixel within the hyperspectral data of a single river segment. i,j,j+1 Let dtw be the distance between the i-th pixel and the j+1-th sampling time in the hyperspectral data sequence, where J is the total number of sampling times.

6. The rapid detection method for heavy metal pollution in wastewater based on multispectral imaging as described in claim 3, characterized in that, The extraction of the abrupt change moments of each pixel includes: The peak values ​​corresponding to each pixel in each river section are sorted according to the sampling time sequence to obtain the spectral peak sequence of each pixel. Abrupt changes are detected in the spectral peak sequence of pixels, and the sampling time corresponding to the abrupt change is taken as the abrupt change time of each pixel.

7. The rapid detection method for heavy metal pollution in wastewater based on multispectral imaging as described in claim 1, characterized in that, The method for calculating the differences in perturbation and abrupt changes of each pixel in the hyperspectral data of each river segment is as follows: In the formula, h r,i h represents the perturbation-induced abrupt change difference of the i-th pixel in the hyperspectral data of the r-th river segment. r,i,m ,h w,i,m Let q be the m-th mutation time of the i-th pixel in the r-th and w-th river segments, respectively, where M is the total number of mutation times of the i-th pixel in the r-th river segment. r,i ,q w,i These are the sums of the absolute differences between the abrupt change times of the i-th pixel in the r-th and w-th river segments and the mean of the peak values ​​of the hyperspectral data sequence at the previous time.

8. The rapid detection method for heavy metal pollution in wastewater based on multispectral imaging as described in claim 1, characterized in that, The method for calculating the environmental disturbance coefficient of each pixel in the hyperspectral data of each river segment is as follows: F r,i =G r,i ×H r,i ; In the formula, F r,i Let G be the environmental disturbance coefficient of the i-th pixel in the hyperspectral data of the r-th river segment. r,i H represents the average number of peaks in the hyperspectral data sequence for the i-th pixel in the r-th river segment at each sampling time. r,i This is the sum of the differences in the perturbation and abrupt changes of the i-th pixel in the hyperspectral data of the r-th river segment and the other river segments.

9. The rapid detection method for heavy metal pollution in wastewater based on multispectral imaging as described in claim 1, characterized in that, The method for calculating the spectral perturbation of each pixel in the hyperspectral data of each river segment is as follows: Z r,i =norm(A r,i ×F r,i ); In the formula, Z r,i Let A be the spectral perturbation of the i-th pixel within the hyperspectral data of the r-th river segment, and norm(·) represent the normalization function. r,i F is the spectral anomaly coefficient of the i-th pixel within the hyperspectral data of the r-th river segment. r,i Let be the environmental disturbance coefficient of the i-th pixel in the hyperspectral data of the r-th river segment.

10. The rapid detection method for heavy metal pollution in wastewater based on multispectral imaging as described in claim 1, characterized in that, The process of extracting the hyperspectral data to be matched includes: calculating the mean of the spectral perturbation of each pixel at all sampling times, obtaining a threshold through cross-validation, and using the hyperspectral data corresponding to pixels with values ​​less than the threshold as the hyperspectral data to be matched.

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