Environment detection method and system

Abstract

The invention provides an environment detection method and system. The method comprises the following steps: detecting spectral information of each soil sample through an intelligent spectral sensor; extracting a reference spectral value from the spectral information of each soil sample, and determining an absorption spectrum of the pollutants according to the reference spectral value and the spectral information; performing principal component analysis on the absorption spectrum to obtain a spectral characteristic value of each soil sample in principal component, and determining a spectral fluctuation quantity based on each spectral characteristic value; acquiring granularity information of each soil sample by using an intelligent sound wave granularity sensor, and determining interaction characteristics of each soil sample according to the granularity information of each soil sample; and determining a confidence characteristic spectral line according to all the spectrum fluctuation quantities and all the interaction characteristics, and further detecting the content of the pollutants in the target environment through the confidence characteristic spectral line. By adopting the scheme provided by the invention, the influence of heterogeneity of the soil sample on the characteristic spectral line can be avoided, so that the content detection precision of pollutants is improved.

Description

Technical Field

[0001] The present application relates to the field of environmental detection technology, and more specifically, to an environmental detection method and system. Background Art

[0002] Environmental testing is a technology that uses various advanced sensors, instruments and equipment, and data analysis methods to monitor and analyze various physical, chemical and biological parameters in natural or industrial environments in real time. Its purpose is to identify and evaluate the impact of environmental pollution, ecological changes and human activities on the environment, and ensure that environmental quality meets safety and health standards. In the field of environmental testing, commonly used testing methods include air quality monitoring, water quality analysis, noise monitoring, soil pollution detection, radiation monitoring, etc., which are widely used in industrial emission monitoring, environmental governance, agriculture, urban construction, public health and other fields. Environmental testing not only provides a scientific basis for policy makers and helps governments and enterprises achieve environmental management goals, but also provides effective technical support for the health of the public and the protection of the ecological environment.

[0003] Soil pollution detection is to analyze the types, concentrations and distribution of pollutants in the soil through a series of technical means and detection methods to assess the pollution level of the soil and provide a scientific basis for environmental remediation and pollution control. The emergence of soil pollution problems usually stems from excessive use of industrial waste, agricultural fertilizers and pesticides, domestic sewage and garbage dumping, etc., which may lead to the accumulation of pollutants such as heavy metals, organic pollutants and radioactive substances, posing a serious threat to the ecosystem and human health. Therefore, timely and accurate detection of soil pollution, especially the content of heavy metals and harmful chemicals, is an important part of environmental protection. In the existing soil pollution detection process, the spectral data of soil samples is usually collected by a spectrometer, and the characteristic spectrum lines of the soil samples are obtained by analyzing the spectral data. Then, a relationship model between the spectral data of the soil samples and the pollutant content is established by combining the characteristic spectrum lines with a machine algorithm to realize the detection of pollutant content in the target area. However, this method often causes overlapping distortion of the characteristic spectrum lines obtained by analysis due to the heterogeneity between the collected soil samples (that is, the soil samples collected in the target area contain a variety of different components and structures, thereby showing different absorption or emission characteristics in the spectrum), thereby reducing the accuracy of pollutant content detection. Therefore, how to avoid the influence of the heterogeneity of soil samples on the characteristic spectrum lines to improve the accuracy of pollutant content detection has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides an environmental detection method and system, which can avoid the influence of the heterogeneity of soil samples on characteristic spectra, so as to improve the detection accuracy of pollutant content.

[0005] In a first aspect, the present application provides an environment detection method, comprising the following steps: Obtain multiple soil samples from the target environment, and automatically collect the spectral information of each soil sample through an intelligent spectral sensor; Extracting a reference spectrum value of a background spectrum from the spectrum information of each soil sample, and then determining the absorption spectrum of the pollutants in each soil sample based on the reference spectrum value and the spectrum information; Perform principal component analysis on the absorption spectra of pollutants in all soil samples to obtain the spectral characteristic values ​​of each soil sample on the principal component, and determine the spectral fluctuation amount of the soil in the target environment at each pollution level based on each spectral characteristic value; Using an intelligent acoustic wave particle size sensor to collect particle size information of each soil sample, and determining the interaction characteristics between the absorption spectrum of each soil sample and the soil particle size according to the particle size information of each soil sample; The confidence characteristic spectrum of the pollutant is determined based on all the spectral fluctuation quantities and all the interactive features, and then the content of the pollutant in the target environment is detected through the confidence characteristic spectrum.

[0006] In some embodiments, extracting a reference spectrum value of a background spectrum from the spectrum information of each soil sample specifically includes: selecting a soil sample as a selected soil sample; determining a plurality of smoothed spectral values ​​in the spectral information of the selected soil sample; A reference spectrum value of the background spectrum is extracted from the spectrum information of the selected soil sample through all smoothed spectrum values; Continue to extract reference spectral values ​​of the background spectrum from the spectral information of the remaining soil samples.

[0007] In some embodiments, determining the absorption spectrum of the pollutants in each soil sample according to the reference spectrum value and the spectrum information specifically includes: A soil sample is selected as a selected soil sample; determining a plurality of absorption spectrum values ​​of pollutants in the selected soil sample by using a reference spectrum value of the selected soil sample and spectrum information of the selected soil sample; continuing to determine multiple absorption spectral values ​​of the contaminants in the remaining soil samples; The absorption spectrum of the pollutants in each soil sample is constructed by all the absorption spectrum values.

[0008] In some embodiments, determining the spectral fluctuation amount of the soil in the target environment at each pollution level based on each spectral characteristic value specifically includes: Determine the sample proximity between every two soil samples based on each spectral feature value; All soil samples were divided into different pollution levels by all sample proximity; The spectral fluctuation amount of the soil in the target environment at each pollution level is determined based on the soil samples corresponding to each pollution level.

[0009] In some embodiments, determining the interaction characteristics between the absorption spectrum of each soil sample and the soil particle size according to the particle size information of each soil sample specifically includes: A soil sample is selected as a selected soil sample; obtaining spectral peaks in the absorption spectrum of the selected soil sample; determining a characteristic particle size of the selected soil sample through particle size information of the selected soil sample; Determine the interaction characteristics between the absorption spectrum of the selected soil sample and the soil particle size through the spectrum peak and the characteristic particle size; Continue to determine the interaction characteristics between the absorption spectra of the remaining soil samples and the soil particle size.

[0010] In some embodiments, determining the confidence characteristic spectral line of the pollutant based on all spectral fluctuation quantities and all interactive features specifically includes: Select a pollution level as the selected pollution level; Determine multiple characteristic wavelengths of pollutants at the selected pollution level based on all spectral fluctuations corresponding to the pollution level; Confidence adjustment is performed on all characteristic wavelengths through all interactive features to obtain multiple confident characteristic wavelengths under the selected pollution level; Continue to determine multiple confident characteristic wavelengths at the remaining pollution level; Generate the contaminant's confident characteristic spectrum through all the confident characteristic wavelengths.

[0011] In some embodiments, multiple soil samples are obtained by randomly collecting soil from different sampling points in the target environment through random sampling.

[0012] In a second aspect, the present application provides an environment detection system, comprising: A collection module is used to obtain multiple soil samples from the target environment and automatically collect the spectral information of each soil sample through an intelligent spectral sensor; A processing module, used to extract a reference spectrum value of a background spectrum from the spectrum information of each soil sample, and then determine the absorption spectrum of the pollutants in each soil sample according to the reference spectrum value and the spectrum information; The processing module is further used to perform principal component analysis on the absorption spectra of pollutants in all soil samples to obtain spectral characteristic values ​​of each soil sample on the principal component, and determine the spectral fluctuation amount of the soil in the target environment at each pollution level based on each spectral characteristic value; The processing module is also used to control the intelligent acoustic wave particle size sensor to collect the particle size information of each soil sample, and determine the interaction characteristics between the absorption spectrum of each soil sample and the soil particle size according to the particle size information of each soil sample; The execution module is used to determine the confidence characteristic spectrum line of the pollutant based on all the spectral fluctuation quantities and all the interactive characteristics, and then detect the content of the pollutant in the target environment through the confidence characteristic spectrum line.

[0013] In a third aspect, the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned environment detection method when executing the computer program.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned environment detection method are implemented.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the environmental detection method and system provided by the present application, multiple soil samples are obtained from the target environment, and the spectral information of each soil sample is automatically collected by an intelligent spectral sensor; a reference spectral value of the background spectrum is extracted from the spectral information of each soil sample, and then the absorption spectrum of the pollutant in each soil sample is determined based on the reference spectral value and the spectral information; the absorption spectra of the pollutants in all soil samples are subjected to principal component analysis to obtain the spectral characteristic values ​​of each soil sample on the principal component, and the spectral fluctuation amount of the soil in the target environment at each pollution level is determined based on each spectral characteristic value; the particle size information of each soil sample is collected using an intelligent acoustic wave particle size sensor, and the interaction characteristics between the absorption spectrum of each soil sample and the soil particle size are determined according to the particle size information of each soil sample; the confidence characteristic spectral line of the pollutant is determined based on all the spectral fluctuation amounts and all the interaction characteristics, and then the content of the pollutant in the target environment is detected through the confidence characteristic spectral line.

[0016] It can be seen that in the present application, firstly, an intelligent spectral sensor is used to automatically collect the spectral information of the soil sample, so that in the process of automatic collection, the spectral deviation caused by improper operation or different collection angles is avoided, and the data accuracy is improved. Subsequently, the present application can effectively remove interference factors by extracting the reference spectral value of the background spectrum from the spectral information of each soil sample, and obtain the absorption spectrum of the pollutant, which provides more accurate benchmark data for subsequent analysis, thereby reducing spectral overlap and distortion. Then, after the absorption spectrum of the pollutants in all soil samples is subjected to principal component analysis to obtain the spectral characteristic values ​​of each soil sample on the principal component, representative features are effectively extracted from the complex spectral data, and the overlapping distortion problem caused by sample heterogeneity is reduced. Then, based on the results of the principal component analysis, the spectral fluctuation amount at each pollution level in the target environment is determined, and the key spectral features (characteristic spectral lines) at different pollution levels are identified. Finally, in the process of identifying the key spectral features, the confidence characteristic spectral lines of the pollutants are determined in combination with the soil particle size information, so that the content of the pollutants in the target environment is detected through the confidence characteristic spectral lines. In summary, the scheme can avoid the influence of the heterogeneity of the soil samples on the characteristic spectral lines to improve the detection accuracy of the content of the pollutants. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of an environment detection method according to some embodiments of the present application; Figure 2 is a schematic diagram of a process for determining spectral fluctuation amount according to some embodiments of the present application; Figure 3 It is a schematic diagram of a process for constructing a pollutant content prediction model according to some embodiments of the present application; Figure 4 is a schematic diagram of the structure of an environment detection system according to some embodiments of the present application; Figure 5 It is an internal structure diagram of a computer device for implementing an environment detection method according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solution in this embodiment, the technical solution in this embodiment will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is a flow chart of an environment detection method according to some embodiments of the present application. The environment detection method 100 mainly includes the following steps: In step 101, a plurality of soil samples are obtained from a target environment, and spectral information of each soil sample is automatically collected by an intelligent spectral sensor.

[0020] Preferably, a plurality of soil samples can be obtained by randomly collecting soil at different sampling points in the target environment through a random sampling method, and each soil sample is pre-processed (dried, crushed and sieved), and each soil sample is placed in the measurement area of ​​the intelligent spectral sensor. Subsequently, light of different wavelengths is emitted by a spectrometer, and the spectral value of each soil sample at each wavelength is collected by the intelligent spectral sensor. Finally, all measured spectral values ​​are used as the spectral information of the corresponding soil sample, thereby obtaining the spectral information of each soil sample.

[0021] It should be noted that the wavelength range of the light emitted by the spectrometer in the present application is the mid-infrared region (2500nm, 250000nm). In addition, the intelligent spectral sensor includes a photodiode, a charge-coupled device CCD or a complementary metal oxide semiconductor CMOS, which is used to collect the reflected light intensity of the soil sample and convert the measured reflected light intensity into an electrical signal.

[0022] In step 102, a reference spectrum value of a background spectrum is extracted from the spectrum information of each soil sample, and then the absorption spectrum of the pollutants in each soil sample is determined based on the reference spectrum value and the spectrum information.

[0023] In some embodiments, extracting the reference spectrum value of the background spectrum from the spectrum information of each soil sample can be achieved by using the following steps: selecting a soil sample as a selected soil sample; determining a plurality of smoothed spectral values ​​in the spectral information of the selected soil sample; A reference spectrum value of the background spectrum is extracted from the spectrum information of the selected soil sample through all smoothed spectrum values; Continue to extract reference spectral values ​​of the background spectrum from the spectral information of the remaining soil samples.

[0024] It should be noted that the smoothed spectral value described in the present application refers to the spectral value obtained after removing high-frequency noise and mutations from the spectral information of the soil sample. By extracting the smoothed spectral value, the spectral information of the wavelength region without absorption peak in the soil sample can be obtained. This part of the spectral data (smoothed spectral value) is used as the basis for determining the background spectrum (i.e., the reference spectral value); in specific implementation, determining multiple smoothed spectral values ​​in the spectral information of the selected soil sample can be achieved in the following manner, namely: first, obtaining all wavelength ranges without absorption peaks in the spectral information of the selected soil sample, and then using all spectral values ​​corresponding to all wavelength ranges as multiple smoothed spectral values ​​in the spectral information of the selected soil sample.

[0025] It should be noted that the reference spectral value described in the present application is a baseline value of the background spectrum extracted from the spectral information of the soil sample. The reference spectral value can reflect the basic spectral characteristics of the soil sample without the influence of pollutants. In specific implementation, the reference spectral value of the background spectrum is extracted from the spectral information of the selected soil sample through all smoothed spectral values. The following method can be used, namely: the mathematical expectation of all smoothed spectral values ​​is used as the reference spectral value of the background spectrum. In other embodiments, other methods can also be used to determine it, which is not limited here.

[0026] In some embodiments, determining the absorption spectrum of the pollutants in each soil sample according to the reference spectrum value and the spectrum information can be achieved by using the following steps: selecting a soil sample as a selected soil sample; determining a plurality of absorption spectrum values ​​of pollutants in the selected soil sample by using a reference spectrum value of the selected soil sample and spectrum information of the selected soil sample; continuing to determine multiple absorption spectral values ​​of the contaminants in the remaining soil samples; The absorption spectrum of the pollutants in each soil sample is constructed by all the absorption spectrum values.

[0027] In specific implementation, determining multiple absorption spectrum values ​​of pollutants in the selected soil sample by selecting the reference spectrum value of the soil sample and the spectrum information of the selected soil sample can be achieved in the following manner, namely: first, obtaining a wavelength in the spectrum information of the selected soil sample, determining the spectrum value corresponding to the wavelength, and then taking the difference between the spectrum value and the reference spectrum value as the absorption spectrum value of the pollutant in the selected soil sample at the wavelength, repeating the above steps to determine the absorption spectrum values ​​of the pollutants in the selected soil sample at the remaining wavelengths, thereby obtaining multiple absorption spectrum values ​​of the pollutants in the selected soil sample; in addition, as a preferred embodiment, the absorption spectrum of the pollutants in each soil sample is constructed by all absorption spectrum values, namely: first, obtaining the wavelength corresponding to each absorption spectrum value, and then sorting all the absorption spectrum values ​​from small to large according to the size of the corresponding wavelength as a set formed as the absorption spectrum of the pollutants in the corresponding soil sample, thereby obtaining the absorption spectrum of the pollutants in each soil sample.

[0028] It should be noted that the absorption spectrum value described in the present application refers to the intensity of light absorption by the soil sample at a specific wavelength. The absorption spectrum value can be used to characterize the absorption capacity of the pollutants in the soil sample to the spectral signal. The larger the absorption spectrum value, the stronger the absorption capacity of the pollutants in the soil sample to the spectral signal, and the smaller the absorption spectrum value, the weaker the absorption capacity of the pollutants in the soil sample to the spectral signal. In addition, the absorption spectrum is a collection of absorption spectrum values ​​at different wavelengths, and each absorption spectrum value corresponds to a wavelength.

[0029] In step 103, principal component analysis is performed on the absorption spectra of pollutants in all soil samples to obtain spectral characteristic values ​​of each soil sample on the principal component, and the spectral fluctuation amount of the soil in the target environment at each pollution level is determined based on each spectral characteristic value.

[0030] In specific implementation, principal component analysis is performed on the absorption spectra of pollutants in all soil samples, and the spectral characteristic values ​​of each soil sample on the principal component can be obtained in the following manner, namely: first, a wavelength of the pollutant is selected as the selected wavelength, and multiple absorption spectrum values ​​of the selected wavelength are obtained from the absorption spectra of all soil samples. The normalization algorithm in the prior art is used to map all absorption spectrum values ​​between (0, 1), so as to obtain the mapping values ​​of each absorption spectrum value between (0, 1) at the selected wavelength, and then the mapping values ​​of each absorption spectrum value between (0, 1) at the remaining wavelengths are determined. Then, all soil samples are sorted in the sampling order, and all wavelengths are sorted according to the size of the wavelength value. After sorting, the spectral data matrix of all soil samples is constructed with the sorting number of soil samples as columns and the sorting number of wavelength values ​​as rows. Subsequently, the spectral data matrix is ​​used as input and the principal component analysis algorithm in the prior art is used to determine the covariance matrix of the spectral data matrix, and then the covariance matrix is ​​subjected to eigenvalue decomposition to obtain multiple eigenvalues ​​and eigenvectors corresponding to each eigenvalue. Preferably, the eigenvectors corresponding to the first three largest eigenvalues ​​can be selected as principal components, and then the absorption spectra of each soil sample are projected onto the principal component to obtain the spectral eigenvalues ​​of each soil sample on the principal component. In other embodiments, other methods can be used for determination, which are not limited here.

[0031] It should be noted that the spectral characteristic value described in the present application is the projection value of each soil sample in the principal component direction obtained by principal component analysis, and the spectral characteristic value can be used to reflect the amount of information of the soil sample in the principal component direction. The larger the spectral characteristic value, the greater the amount of information reflected by the spectral characteristic value of the soil sample in the principal component direction, and the smaller the spectral characteristic value, the smaller the amount of information reflected by the spectral characteristic value of the soil sample in the principal component direction.

[0032] In some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of the process of determining the spectral fluctuation amount shown in some embodiments of the present application. The spectral fluctuation amount of the soil in the target environment at each pollution level based on each spectral characteristic value can be determined by the following steps: First, in 1031, the sample proximity between every two soil samples is determined based on each spectral characteristic value; Then, in 1032, all soil samples are classified into different pollution levels by all sample proximity; Finally, in 1033, the spectral fluctuation amount of the soil in the target environment at each pollution level is determined based on the soil samples corresponding to each pollution level.

[0033] In specific implementation, the sample proximity between every two soil samples based on the spectral characteristic values ​​of each soil sample on the principal component can be determined in the following manner, namely: first, the difference between the spectral characteristic values ​​of every two soil samples on the principal component is determined, and then the obtained result is used as the sample proximity between the corresponding two soil samples. In some preferred embodiments, when multiple principal components are selected, the square of the difference between the spectral characteristics of every two soil samples on each principal component can be determined, and then the square root of the sum of the results corresponding to all the principal components is used as the sample proximity between the corresponding two soil samples, thereby obtaining the sample proximity between every two soil samples. In other embodiments, other methods can be used to determine, which are not limited here.

[0034] It should be noted that the sample proximity described in this application is used to indicate the degree of proximity between two soil samples in the principal component space. The larger the sample proximity, the smaller the degree of proximity between the two soil samples in the principal component space, and the smaller the sample proximity, the greater the degree of proximity between the two soil samples in the principal component space.

[0035] In specific implementation, all soil samples can be divided into different pollution levels by all sample proximity, which can be achieved in the following way, namely: first, all sample proximity is used as the similarity index between the corresponding two soil samples, then, the K-means clustering algorithm in the prior art is used to divide all soil samples into pairs of sample clusters, and finally, each sample cluster is regarded as a pollution level, that is, the soil sample in each sample cluster uniquely corresponds to a pollution level, so that all soil samples are divided into different pollution levels. In other embodiments, other methods can also be used for implementation, which are not limited here.

[0036] In specific implementation, the spectral fluctuation amount of the soil in the target environment at each pollution level can be determined based on the soil samples corresponding to each pollution level in the following manner, namely: first, a pollution level is selected, then all soil samples corresponding to the pollution level are obtained, and then a wavelength is selected as the selected wavelength, and the variance of the spectral values ​​of each soil sample at the selected wavelength is determined. Finally, the obtained result is used as the spectral fluctuation amount of the pollution level at the selected wavelength, and the spectral fluctuation amount of the pollution level at the remaining wavelengths is continued to be determined. The above steps are repeated to determine the spectral fluctuation amount of the soil in the target environment at the remaining pollution levels.

[0037] It should be noted that the spectral fluctuation amount mentioned in the present application refers to the fluctuation amount of the spectral values ​​of all soil samples at the said pollution level at a specific wavelength. The spectral fluctuation amount can be used to characterize the consistency of the absorption spectral characteristics of pollutants in all soil samples at the said pollution level. The larger the spectral fluctuation amount, the lower the consistency of the absorption spectral characteristics of pollutants in all soil samples at the specific pollution level. The smaller the spectral fluctuation amount, the higher the consistency of the absorption spectral characteristics of pollutants in all soil samples at the specific pollution level.

[0038] In step 104, the particle size information of each soil sample is collected using an intelligent acoustic wave particle size sensor, and the interaction characteristics between the absorption spectrum of each soil sample and the soil particle size are determined according to the particle size information of each soil sample.

[0039] In specific implementation, after emitting rated sound waves in various directions to each soil sample, the propagation speed and scattering characteristics of the sound waves in the soil sample can be collected by the intelligent acoustic wave particle size sensor to determine the soil particle size in various directions in the soil sample, thereby obtaining the particle size information of each soil sample. As a preferred embodiment, the rated sound wave can be set according to the particle size of the soil sample. For example, when the particles of the soil sample are small, high-frequency sound waves (10kHz-1MHz) are set as the rated sound waves of the present application; when the particles of the soil sample are large, low-frequency sound waves (1kHz-50kHz) are set as the rated sound waves of the present application. In other embodiments, other methods can be used for setting, which are not limited here.

[0040] In some embodiments, determining the interaction characteristics between the absorption spectrum of each soil sample and the soil particle size according to the particle size information of each soil sample can be achieved by using the following steps: selecting a soil sample as a selected soil sample; obtaining spectral peaks in the absorption spectrum of the selected soil sample; determining a characteristic particle size of the selected soil sample through particle size information of the selected soil sample; Determine the interaction characteristics between the absorption spectrum of the selected soil sample and the soil particle size through the spectrum peak and the characteristic particle size; Continue to determine the interaction characteristics between the absorption spectra of the remaining soil samples and the soil particle size.

[0041] Preferably, the maximum absorption spectrum value can be obtained in the absorption spectrum of the selected soil sample as the spectrum peak value. In other embodiments, other methods can be used to obtain it, which is not limited here. Therefore, in specific implementation, determining the characteristic particle size of the selected soil sample through the particle size information of the selected soil sample can be achieved in the following manner, namely: first, obtaining the soil particle size in each direction in the particle size information of the selected soil sample, and then taking the mean of all soil particle sizes as the characteristic particle size of the selected soil sample. In other embodiments, other methods can be used to determine it, which is not repeated here. In addition, as a preferred embodiment, determining the interaction feature between the absorption spectrum of the selected soil sample and the soil particle size through the spectrum peak value and the characteristic particle size can be achieved in the following manner, namely: taking the ratio of the spectrum peak value to the characteristic particle size as the interaction feature between the absorption spectrum of the selected soil sample and the soil particle size.

[0042] It should be noted that the characteristic particle size described in the present application is a quantitative value of the size of soil particles in the soil sample, and the characteristic particle size can be used to reflect the overall particle size distribution of soil particles in the soil sample. The larger the characteristic particle size, the larger the average size of soil particles in the soil sample and the coarser the particle size. The smaller the characteristic particle size, the smaller the average size of soil particles in the soil sample and the finer the particle size. In addition, the interaction feature is a quantitative indicator that describes the relationship between spectral absorption characteristics and soil particle size. The interaction feature can be used to reflect the degree of influence of soil particle size on the absorption spectrum. The larger the interaction feature, the larger the spectral peak value at a specific soil particle size. The smaller the interaction feature, the smaller the spectral peak value at a specific soil particle size.

[0043] In step 105, the confidence characteristic spectrum of the pollutant is determined based on all the spectral fluctuation quantities and all the interactive features, and then the content of the pollutant in the target environment is detected through the confidence characteristic spectrum.

[0044] In some embodiments, determining the confidence characteristic spectral line of the pollutant based on all spectral fluctuation quantities and all interactive features can be achieved by using the following steps: Select a pollution level as the selected pollution level; Determine multiple characteristic wavelengths of pollutants at the selected pollution level based on all spectral fluctuations corresponding to the pollution level; Confidence adjustment is performed on all characteristic wavelengths through all interactive features to obtain multiple confident characteristic wavelengths under the selected pollution level; Continue to determine multiple confident characteristic wavelengths at the remaining pollution level; Generate the contaminant's confident characteristic spectrum through all the confident characteristic wavelengths.

[0045] In specific implementation, the following method can be used to determine multiple characteristic wavelengths of pollutants at the selected pollution level based on all spectral fluctuations corresponding to the pollution level, namely: first, set a fluctuation threshold, among all spectral fluctuations corresponding to the selected pollution level, when the spectral fluctuation is greater than or equal to the fluctuation threshold, the corresponding wavelength is used as the characteristic wavelength of the pollutant at the selected pollution level; when the spectral fluctuation is less than the fluctuation threshold, no processing is performed, thereby obtaining multiple characteristic wavelengths of the pollutant at the selected pollution level.

[0046] It should be noted that the fluctuation threshold described in the present application can be set according to the historical pollutant content in the target area. The higher the historical pollutant content in the target area, the more significant spectral changes will occur. Therefore, a larger fluctuation threshold can be set to detect more obvious fluctuations. Conversely, a smaller fluctuation threshold is set. In addition, the characteristic wavelength described in the present application refers to the wavelength at which the spectral value changes most significantly under the selected pollution level, that is, the wavelength that is most sensitive to the pollutant content.

[0047] In specific implementation, confidence adjustment is performed on all characteristic wavelengths through all interactive features to obtain multiple confident characteristic wavelengths under the selected pollution level. This can be achieved in the following manner, namely: first, multiple soil samples corresponding to the selected pollution level are obtained, a characteristic wavelength is selected, and then an adjustment coefficient is set. The adjustment coefficient is multiplied by the mean of the interactive features of each soil sample and then added to 1. Subsequently, the result obtained by multiplying the characteristic wavelength is used as the confidence adjustment result of the characteristic wavelength (confident characteristic wavelength), and the confidence adjustment results of the remaining characteristic wavelengths are continuously determined, thereby obtaining multiple confident characteristic wavelengths under the selected pollution level. As a preferred embodiment, through Generating the confidence characteristic spectrum line of the pollutant through all the confidence characteristic wavelengths can be achieved in the following manner, namely: first, select a confidence characteristic wavelength, determine the mean of the spectral values ​​of each soil sample at the confidence characteristic wavelength, and use the obtained result as the standard spectral value corresponding to the confidence characteristic wavelength, repeat the above steps, and determine the standard spectral values ​​corresponding to the remaining confidence characteristic wavelengths. Finally, a coordinate system is obtained by using the confidence characteristic wavelength as the horizontal coordinate and the standard spectral value as the vertical coordinate, and the curve formed by all the confidence characteristic wavelengths on the coordinate axis is used as the confidence characteristic spectrum line of the pollutant. In other embodiments, other methods can also be used for implementation, which are not limited here.

[0048] It should be noted that the confidence characteristic wavelength described in the present application is a wavelength that is weighted and corrected through interactive characteristics based on the characteristic wavelength. In addition, the adjustment coefficient described in the present application can be set according to the degree of difference between soil samples in the target area. When the degree of difference between soil samples in the target area is high, a larger adjustment coefficient is set to enhance the adaptability of the characteristics between the soil samples and avoid large errors between different soil samples. Otherwise, a smaller adjustment coefficient is set. Finally, the confidence characteristic spectrum line is a curve composed of the confidence characteristic wavelength and the standard spectrum value, and the absorption characteristics of pollutants under different pollution levels can be reflected through the confidence characteristic spectrum line.

[0049] In some embodiments, the detection of the content of pollutants in the target environment by using the confidence characteristic spectrum line can be achieved by the following steps: Constructing a pollutant content prediction model of the target environment through the confidence characteristic spectrum line; The pollutant content in the target environment is detected based on the pollutant content prediction model.

[0050] For specific implementation, refer to Figure 3 As shown, this figure is a schematic diagram of the process of constructing a pollutant content prediction model shown in some embodiments of the present application. The pollutant content prediction model of the target environment constructed by the confidence characteristic spectrum can be implemented in the following manner, namely: first, all soil samples are divided into a training set and a test set (for example, 80% are set as a training set, and 20% are set as a test set). Then, the confidence characteristic spectrum and the pollution level corresponding to all soil samples in the training set are used as input, and the pollutant content is used as output. The supervised learning algorithm in the prior art (such as logistic regression, support vector machine, random forest, neural network, etc.) is used to train the model, and the model is verified by the pollution level corresponding to all soil samples in the test set until the training is completed. Finally, the trained model is used as the pollutant content prediction model in the present application. In other embodiments, other methods can also be used for determination, which is not limited here.

[0051] It should be noted that the pollutant content in the target environment is detected based on the pollutant content prediction model, that is: after collecting the test samples in the target environment in real time, the spectral information of the test samples is obtained by collecting the intelligent spectral sensor, and then the spectral information of the test samples is input into the pollutant content prediction model, and the output result of the pollutant content early warning model is used as the pollutant content in the test sample.

[0052] In addition, in another aspect of the present application, in some embodiments, the present application provides an environment detection system, referring to Figure 4, which is a schematic diagram of the structure of an environment detection system according to some embodiments of the present application, the environment detection system 200 includes: a collection module 201, a processing module 202 and an execution module 203, which are described as follows: The acquisition module 201 in the present application is mainly used to obtain multiple soil samples from the target environment, and automatically collect the spectral information of each soil sample through the intelligent spectral sensor; Processing module 202, in the present application, the processing module 202 is mainly used to extract a reference spectrum value of a background spectrum from the spectrum information of each soil sample, and then determine the absorption spectrum of the pollutants in each soil sample according to the reference spectrum value and the spectrum information; In addition, the processing module 202 in the present application is also used to perform principal component analysis on the absorption spectra of pollutants in all soil samples, obtain the spectral characteristic values ​​of each soil sample on the principal component, and determine the spectral fluctuation amount of the soil in the target environment at each pollution level based on each spectral characteristic value; In addition, the processing module 202 in the present application is also used to control the intelligent acoustic wave particle size sensor to collect the particle size information of each soil sample, and determine the interaction characteristics between the absorption spectrum of each soil sample and the soil particle size according to the particle size information of each soil sample; Execution module 203, in the present application, the execution module 203 is mainly used to determine the confidence characteristic spectrum line of the pollutant based on all spectral fluctuation quantities and all interactive features, and then detect the content of the pollutant in the target environment through the confidence characteristic spectrum line.

[0053] In addition, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned environment detection method.

[0054] In some embodiments, reference Figure 5 , which is an internal structure diagram of a computer device for implementing an environment detection method according to some embodiments of the present application. The environment detection method in the above embodiment can be Figure 5 The computer device 300 shown in the figure is implemented, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304.

[0055] The processor 301 may be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC) or one or more processors for controlling the execution of the environment detection method in the present application.

[0056] The communication bus 302 is used to transmit information between the above components.

[0057] The memory 303 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0058] The memory 303 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The environment detection method in the above embodiment can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0059] The communication interface 304 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0060] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0061] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0062] In addition, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned environment detection method is implemented.

[0063] In summary, in the environmental detection method and system disclosed in the embodiments of the present application, multiple soil samples are obtained from the target environment, and the spectral information of each soil sample is automatically collected by an intelligent spectral sensor; a reference spectral value of the background spectrum is extracted from the spectral information of each soil sample, and then the absorption spectrum of the pollutant in each soil sample is determined based on the reference spectral value and the spectral information; the absorption spectra of the pollutants in all soil samples are subjected to principal component analysis to obtain the spectral characteristic values ​​of each soil sample on the principal component, and the spectral fluctuation amount of the soil in the target environment at each pollution level is determined based on each spectral characteristic value; the particle size information of each soil sample is collected using an intelligent acoustic wave particle size sensor, and the interaction characteristics between the absorption spectrum of each soil sample and the soil particle size are determined according to the particle size information of each soil sample; the confidence characteristic spectral line of the pollutant is determined based on all the spectral fluctuation amounts and all the interaction characteristics, and then the content of the pollutant in the target environment is detected through the confidence characteristic spectral line.

[0064] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0065] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An environmental detection method, characterized in that: The steps include: Obtain multiple soil samples from the target environment and automatically collect spectral information of each soil sample through an intelligent spectral sensor; Extracting a reference spectrum value of a background spectrum from the spectrum information of each soil sample, and then determining the absorption spectrum of the pollutants in each soil sample based on the reference spectrum value and the spectrum information; Perform principal component analysis on the absorption spectra of pollutants in all soil samples to obtain the spectral characteristic values ​​of each soil sample on the principal component, and determine the spectral fluctuation amount of the soil in the target environment at each pollution level based on each spectral characteristic value; Using an intelligent acoustic wave particle size sensor to collect particle size information of each soil sample, and determining the interaction characteristics between the absorption spectrum of each soil sample and the soil particle size according to the particle size information of each soil sample; The confidence characteristic spectrum of the pollutant is determined based on all the spectral fluctuation quantities and all the interactive features, and then the content of the pollutant in the target environment is detected through the confidence characteristic spectrum.

2. The method according to claim 1, characterized in that The reference spectral values ​​of the background spectrum extracted from the spectral information of each soil sample specifically include: selecting a soil sample as a selected soil sample; determining a plurality of smoothed spectral values ​​in the spectral information of the selected soil sample; A reference spectrum value of the background spectrum is extracted from the spectrum information of the selected soil sample through all smoothed spectrum values; Continue to extract reference spectral values ​​of the background spectrum from the spectral information of the remaining soil samples.

3. The method according to claim 1, characterized in that Determining the absorption spectrum of the pollutants in each soil sample according to the reference spectrum value and the spectrum information specifically includes: selecting a soil sample as a selected soil sample; determining a plurality of absorption spectrum values ​​of pollutants in the selected soil sample by using a reference spectrum value of the selected soil sample and spectrum information of the selected soil sample; continuing to determine multiple absorption spectral values ​​of the contaminants in the remaining soil samples; The absorption spectrum of the pollutants in each soil sample is constructed by all the absorption spectrum values.

4. The method according to claim 1, characterized in that Determining the spectral fluctuation of the soil in the target environment at each pollution level based on each spectral characteristic value specifically includes: Determine the sample proximity between every two soil samples based on each spectral feature value; All soil samples were divided into different pollution levels by all sample proximity; The spectral fluctuation amount of the soil in the target environment at each pollution level is determined based on the soil samples corresponding to each pollution level.

5. The method according to claim 1, characterized in that Determining the interaction characteristics between the absorption spectrum of each soil sample and the soil particle size according to the particle size information of each soil sample specifically includes: selecting a soil sample as a selected soil sample; obtaining spectral peaks in the absorption spectrum of the selected soil sample; determining a characteristic particle size of the selected soil sample through particle size information of the selected soil sample; Determine the interaction characteristics between the absorption spectrum of the selected soil sample and the soil particle size through the spectrum peak and the characteristic particle size; Continue to determine the interaction characteristics between the absorption spectra of the remaining soil samples and the soil particle size.

6. The method according to claim 1, characterized in that The confidence characteristic spectral lines of pollutants are determined based on all spectral fluctuations and all interactive features, including: Select a pollution level as the selected pollution level; Determine multiple characteristic wavelengths of pollutants at the selected pollution level based on all spectral fluctuations corresponding to the pollution level; Confidence adjustment is performed on all characteristic wavelengths through all interactive features to obtain multiple confident characteristic wavelengths under the selected pollution level; Continue to determine multiple confident characteristic wavelengths at the remaining pollution level; Generate the contaminant's confident characteristic spectral lines through all the confident characteristic wavelengths.

7. The method according to claim 1, characterized in that Multiple soil samples were obtained by randomly collecting soil from different sampling points in the target environment through random sampling method.

8. An environmental detection system, characterized in that: include: A collection module is used to obtain multiple soil samples from the target environment and automatically collect the spectral information of each soil sample through an intelligent spectral sensor; A processing module, used to extract a reference spectrum value of a background spectrum from the spectrum information of each soil sample, and then determine the absorption spectrum of the pollutants in each soil sample according to the reference spectrum value and the spectrum information; The processing module is further used to perform principal component analysis on the absorption spectra of pollutants in all soil samples to obtain spectral characteristic values ​​of each soil sample on the principal component, and determine the spectral fluctuation amount of the soil in the target environment at each pollution level based on each spectral characteristic value; The processing module is also used to control the intelligent acoustic wave particle size sensor to collect the particle size information of each soil sample, and determine the interaction characteristics between the absorption spectrum of each soil sample and the soil particle size according to the particle size information of each soil sample; The execution module is used to determine the confidence characteristic spectrum line of the pollutant based on all the spectral fluctuation quantities and all the interactive characteristics, and then detect the content of the pollutant in the target environment through the confidence characteristic spectrum line.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the environment detection method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the environment detection method according to any one of claims 1 to 7 are implemented.

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