An environmental monitoring method and system
By combining intelligent spectral sensors and soil particle size information, spectral feature values of soil samples are extracted, and confidence feature spectral lines are constructed. This solves the detection accuracy problem caused by the heterogeneity of soil samples and achieves higher accuracy in detecting pollutant content.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-30
- Publication Date
- 2026-03-13
Smart Images
Figure CN120028270B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of environmental monitoring technology, and more specifically, to an environmental monitoring method and system. Background Technology
[0002] Environmental monitoring is a technology that utilizes advanced sensors, instruments, and data analysis methods to monitor and analyze various physical, chemical, and biological parameters in the natural or industrial environment in real time. Its purpose is to identify and assess the impacts of environmental pollution, ecological changes, and human activities on the environment, ensuring that environmental quality meets safety and health standards. Commonly used methods in environmental monitoring include air quality monitoring, water quality analysis, noise monitoring, soil pollution detection, and radiation monitoring. These methods are widely applied in industrial emissions monitoring, environmental governance, agriculture, urban construction, and public health. Environmental monitoring not only provides policymakers with scientific evidence to help governments and businesses achieve their environmental management goals, but also provides effective technical support for public health and the protection of the ecological environment.
[0003] Soil pollution testing involves using a series of technical means and detection methods to analyze the types, concentrations, and distribution of pollutants in soil, in order to assess the level of soil pollution and provide a scientific basis for environmental remediation and pollution control. Soil pollution problems typically stem from industrial waste, excessive use of agricultural fertilizers and pesticides, and the dumping of domestic sewage and garbage, which can lead to the accumulation of pollutants such as heavy metals, organic pollutants, and radioactive substances, posing a serious threat to ecosystems and human health. Therefore, timely and accurate detection of soil pollution, especially the content of heavy metals and harmful chemicals, is a crucial aspect of environmental protection. Current soil pollution detection processes typically involve collecting spectral data from soil samples using a spectrometer, analyzing this data to obtain characteristic spectral lines, and then using machine algorithms to establish a model relating the spectral data to pollutant content in the target area. However, this method often suffers from overlapping and distortion of the characteristic spectral lines due to the heterogeneity among the collected soil samples (i.e., soil samples from the target area contain various different components and structures, resulting in different absorption or emission characteristics in the spectrum). This reduces the accuracy of pollutant content detection. Therefore, avoiding the influence of soil sample heterogeneity on characteristic spectral lines to improve the accuracy of pollutant content detection has become a challenge for the industry. Summary of the Invention
[0004] This application provides an environmental testing method and system that can avoid the influence of soil sample heterogeneity on characteristic spectral lines, thereby improving the accuracy of pollutant content detection.
[0005] In a first aspect, this application provides an environmental testing method, comprising the following steps:
[0006] Multiple soil samples are obtained from the target environment, and the spectral information of each soil sample is automatically collected through a smart spectral sensor;
[0007] The background spectral reference value is extracted from the spectral information of each soil sample, and then the absorption spectrum of the pollutants in each soil sample is determined based on the reference spectral value and the spectral information.
[0008] Principal component analysis was performed on the absorption spectra of pollutants in all soil samples to obtain the spectral characteristic values of each soil sample on the principal components. Based on the spectral characteristic values, the spectral fluctuation of the soil in the target environment at each pollution level was determined.
[0009] The particle size information of each soil sample is collected using an intelligent acoustic particle size sensor, and the interaction characteristics between the absorption spectrum and soil particle size of each soil sample are determined based on the particle size information of each soil sample.
[0010] Confidence characteristic spectral lines of pollutants are determined based on all spectral fluctuations and all interaction characteristics, and then the content of pollutants in the target environment is detected by means of the confidence characteristic spectral lines.
[0011] In some embodiments, extracting reference spectral values of the background spectrum from the spectral information of each soil sample specifically includes:
[0012] Select one soil sample as the selected soil sample;
[0013] Determine multiple smoothed spectral values from the spectral information of a selected soil sample;
[0014] Reference spectral values for the background spectrum are extracted from the spectral information of a selected soil sample using all smoothed spectral values.
[0015] Continue to extract the reference spectral values of the background spectrum from the spectral information of the remaining soil samples.
[0016] In some embodiments, determining the absorption spectrum of pollutants in each soil sample based on the reference spectral value and the spectral information specifically includes:
[0017] Select one soil sample as the selected soil sample;
[0018] Multiple absorption spectral values of pollutants in a selected soil sample are determined by using the reference spectral values of the selected soil sample and the spectral information of the selected soil sample.
[0019] Continue to determine multiple absorption spectral values of pollutants in the remaining soil samples;
[0020] The absorption spectrum of the pollutant in each soil sample is constructed from all the absorption spectral values.
[0021] In some embodiments, determining the spectral fluctuation of soil in the target environment at each pollution level based on various spectral characteristic values specifically includes:
[0022] The sample proximity between each pair of soil samples is determined based on various spectral characteristic values;
[0023] All soil samples were classified into different pollution levels based on their similarity to the samples.
[0024] The spectral fluctuation of soil in the target environment at each pollution level was determined based on soil samples corresponding to each pollution level.
[0025] In some embodiments, determining the interaction characteristics between the absorption spectrum and soil particle size of each soil sample based on the particle size information of each soil sample specifically includes:
[0026] Select one soil sample as the selected soil sample;
[0027] Obtain the spectral peaks in the absorption spectrum of the selected soil sample;
[0028] The characteristic particle size of a selected soil sample is determined by the particle size information of the selected soil sample;
[0029] The interaction characteristics between the absorption spectrum and soil particle size of the selected soil sample are determined by the spectral peaks and the characteristic particle size.
[0030] Further investigation was conducted to determine the interaction characteristics between the absorption spectra and soil particle size of the remaining soil samples.
[0031] In some embodiments, determining the confidence characteristic spectral lines of a pollutant based on all spectral fluctuations and all interaction features specifically includes:
[0032] Select a pollution level as the selected pollution level;
[0033] Based on all spectral fluctuations corresponding to a selected pollution level, determine multiple characteristic wavelengths of the pollutant at that pollution level;
[0034] By performing confidence adjustment on all characteristic wavelengths using all interactive features, multiple confidence characteristic wavelengths are obtained at the selected pollution level.
[0035] Continue to determine multiple confidence characteristic wavelengths at the remaining pollution level;
[0036] Confidence characteristic spectral lines of pollutants are generated using all confidence characteristic wavelengths.
[0037] In some embodiments, multiple soil samples are obtained by randomly collecting soil from different sampling points within the target environment using a random sampling method.
[0038] Secondly, this application provides an environmental monitoring system, comprising:
[0039] The acquisition module is used to acquire multiple soil samples from the target environment and automatically acquire the spectral information of each soil sample through an intelligent spectral sensor.
[0040] The processing module is used to extract the reference spectral value of the background spectrum from the spectral information of each soil sample, and then determine the absorption spectrum of the pollutants in each soil sample based on the reference spectral value and the spectral information.
[0041] The processing module 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 components, and determine the spectral fluctuation of the soil in the target environment at each pollution level based on the spectral characteristic values.
[0042] The processing module is also used to control the intelligent acoustic particle size sensor to collect particle size information of each soil sample, and to determine the interaction characteristics between the absorption spectrum and soil particle size of each soil sample based on the particle size information of each soil sample.
[0043] The execution module is used to determine the confidence feature spectral lines of pollutants based on all spectral fluctuations and all interaction features, and then detect the content of pollutants in the target environment through the confidence feature spectral lines.
[0044] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described environmental detection method.
[0045] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described environmental detection method.
[0046] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:
[0047] The environmental monitoring method and system provided in this application involves acquiring multiple soil samples from a target environment and automatically collecting the spectral information of each soil sample using an intelligent spectral sensor. A reference spectral value for the background spectrum is extracted from the spectral information of each soil sample, and the absorption spectrum of pollutants in each soil sample is determined based on the reference spectral value and the spectral information. Principal component analysis is performed on the absorption spectra of pollutants in all soil samples to obtain the spectral characteristic values of each soil sample on the principal components. Based on these spectral characteristic values, the spectral fluctuation of the soil in the target environment at each pollution level is determined. A smart acoustic particle size sensor is used to collect the particle size information of each soil sample, and the interaction characteristics between the absorption spectrum and soil particle size of each soil sample are determined based on the particle size information. Confidence characteristic spectral lines of pollutants are determined based on all spectral fluctuations and all interaction characteristics, and the content of pollutants in the target environment is detected using these confidence characteristic spectral lines.
[0048] Therefore, this application firstly employs an intelligent spectral sensor to automatically collect the spectral information of soil samples. This avoids spectral deviations caused by improper operation or different collection angles during automated collection, improving data accuracy. Subsequently, by extracting reference spectral values of the background spectrum from the spectral information of each soil sample, this application effectively removes interfering factors and obtains the absorption spectrum of pollutants, providing more accurate benchmark data for subsequent analysis and reducing spectral overlap and distortion. Then, after performing 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 components, representative features are effectively extracted from complex spectral data, reducing overlap and distortion caused by sample heterogeneity. Based on the principal component analysis results, the spectral fluctuation amount under each pollution level in the target environment is determined, and key spectral features (characteristic spectral lines) under different pollution levels are identified. Finally, in the process of identifying key spectral features, soil particle size information is combined to determine the confidence characteristic spectral lines of pollutants, thereby detecting the content of pollutants in the target environment through the confidence characteristic spectral lines. In summary, this scheme can avoid the influence of soil sample heterogeneity on characteristic spectral lines, thereby improving the accuracy of pollutant content detection. Attached Figure Description
[0049] Figure 1 This is a schematic flowchart of an environmental detection method according to some embodiments of this application;
[0050] Figure 2 This is a schematic flowchart illustrating the determination of spectral fluctuations according to some embodiments of this application;
[0051] Figure 3 This is a schematic flowchart illustrating the process of constructing a pollutant content prediction model according to some embodiments of this application;
[0052] Figure 4 This is a schematic diagram of the structure of an environmental monitoring system according to some embodiments of this application;
[0053] Figure 5 This is an internal structural diagram of a computer device that implements an environmental detection method according to some embodiments of this application. Detailed Implementation
[0054] To better understand the technical solutions in this embodiment, the technical solutions in this embodiment will be described in detail below with reference to the accompanying drawings and specific implementation methods.
[0055] refer to Figure 1 The figure is a schematic flowchart of an environmental detection method according to some embodiments of this application. The environmental detection method 100 mainly includes the following steps:
[0056] In step 101, multiple soil samples are obtained from the target environment, and the spectral information of each soil sample is automatically collected by a smart spectral sensor.
[0057] Preferably, multiple soil samples can be obtained by randomly collecting soil from different sampling points within the target environment using a random sampling method. Each soil sample is then pre-processed (dried, crushed, and sieved). Each soil sample is placed within the measurement area of an intelligent spectral sensor. Subsequently, light of different wavelengths is emitted by a spectrometer, and the intelligent spectral sensor collects the spectral values of each soil sample at each wavelength. Finally, all the measured spectral values are used as the spectral information of the corresponding soil sample, thereby obtaining the spectral information of each soil sample.
[0058] It should be noted that the wavelength range of the light emitted by the spectrometer in this 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) sensor, used to collect the reflected light intensity of the soil sample and convert the measured reflected light intensity into an electrical signal.
[0059] In step 102, 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 pollutants in each soil sample is determined based on the reference spectral value and the spectral information.
[0060] In some embodiments, extracting the reference spectral values of the background spectrum from the spectral information of each soil sample can be achieved by the following steps:
[0061] Select one soil sample as the selected soil sample;
[0062] Determine multiple smoothed spectral values from the spectral information of a selected soil sample;
[0063] Reference spectral values for the background spectrum are extracted from the spectral information of a selected soil sample using all smoothed spectral values.
[0064] Continue to extract the reference spectral values of the background spectrum from the spectral information of the remaining soil samples.
[0065] It should be noted that the smoothed spectral values mentioned in this application refer to the spectral values obtained after removing high-frequency noise and abrupt changes from the spectral information of the soil sample. By extracting the smoothed spectral values, the spectral information of the wavelength regions in the soil sample without absorption peaks can be obtained. This part of the spectral data (smoothed spectral values) is used as the basis for determining the background spectrum (i.e., the reference spectral value). In specific implementation, the determination of multiple smoothed spectral values in the spectral information of the selected soil sample can be achieved in the following way: First, obtain all wavelength ranges without absorption peaks in the spectral information of the selected soil sample. Then, use all the corresponding spectral values in all wavelength ranges as multiple smoothed spectral values in the spectral information of the selected soil sample.
[0066] It should be noted that the reference spectral value mentioned in this 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 under the influence of no pollutants. In specific implementation, the reference spectral value of the background spectrum can be extracted from the spectral information of the selected soil sample by using all smoothed spectral values. That is, 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 are not limited here.
[0067] In some embodiments, determining the absorption spectrum of pollutants in each soil sample based on the reference spectral value and the spectral information can be achieved using the following steps:
[0068] Select one soil sample as the selected soil sample;
[0069] Multiple absorption spectral values of pollutants in a selected soil sample are determined by using the reference spectral values of the selected soil sample and the spectral information of the selected soil sample.
[0070] Continue to determine multiple absorption spectral values of pollutants in the remaining soil samples;
[0071] The absorption spectrum of the pollutant in each soil sample is constructed from all the absorption spectral values.
[0072] In specific implementation, determining multiple absorption spectral values of pollutants in a selected soil sample by using a reference spectral value and the spectral information of the selected soil sample can be achieved in the following way: First, obtain a wavelength from the spectral information of the selected soil sample and determine the spectral value corresponding to that wavelength. Then, take the difference between the spectral value and the reference spectral value as the absorption spectral value of the pollutant in the selected soil sample at that wavelength. Repeat the above steps to determine the absorption spectral values of the pollutant in the selected soil sample at the remaining wavelengths, thereby obtaining multiple absorption spectral values of the pollutant in the selected soil sample. Alternatively, as a preferred embodiment, the absorption spectrum of the pollutant in each soil sample can be constructed by using all the absorption spectral values. That is, first, obtain the wavelength corresponding to each absorption spectral value. Then, sort all the absorption spectral values according to the size of the corresponding wavelength from smallest to largest and use the resulting set as the absorption spectrum of the pollutant in the corresponding soil sample, thereby obtaining the absorption spectrum of the pollutant in each soil sample.
[0073] It should be noted that the absorption spectral value mentioned in this application refers to the intensity of light absorbed by the soil sample at a specific wavelength. The absorption spectral value can characterize the ability of pollutants in the soil sample to absorb spectral signals. The larger the absorption spectral value, the stronger the ability of pollutants in the soil sample to absorb spectral signals; the smaller the absorption spectral value, the weaker the ability of pollutants in the soil sample to absorb spectral signals. In addition, the absorption spectrum is a collection of absorption spectral values at different wavelengths, and each absorption spectral value corresponds to a wavelength.
[0074] In step 103, principal component analysis is performed on the absorption spectra of pollutants in all soil samples to obtain the spectral characteristic values of each soil sample on the principal components. Based on the spectral characteristic values, the spectral fluctuation of the soil in the target environment at each pollution level is determined.
[0075] In practice, principal component analysis is performed on the absorption spectra of pollutants in all soil samples to obtain the spectral characteristic values of each soil sample on the principal components. This can be achieved as follows: First, a wavelength of the pollutant is selected as the chosen wavelength. Multiple absorption spectral values of the chosen wavelength are obtained from the absorption spectra of all soil samples. Using a normalization algorithm in existing technology, all absorption spectral values are mapped between (0, 1), thus obtaining the mapped values of each absorption spectral value between (0, 1) at the chosen wavelength. The mapping values of each absorption spectral value between (0, 1) at the remaining wavelengths are then determined. Finally, all soil samples are sorted according to the sampling order, and all wavelengths are sorted according to their wavelength values. Following the sequence, a spectral data matrix of all soil samples is constructed, with the sort number of the soil sample as the column and the sort number of the wavelength value as the row. Then, using the spectral data matrix as input, a principal component analysis algorithm in the prior art is used to determine the covariance matrix of the spectral data matrix. Subsequently, the covariance matrix is decomposed into eigenvalues to obtain multiple eigenvalues and eigenvectors corresponding to each eigenvalue. Preferably, the eigenvectors corresponding to the three largest eigenvalues can be selected as principal components, and the absorption spectrum of each soil sample is projected onto the principal components to obtain the spectral eigenvalues of each soil sample on the principal components. In other embodiments, other methods can also be used to determine the spectral characteristics, which are not limited here.
[0076] It should be noted that the spectral feature values mentioned in this application are the projection values of each soil sample in the principal component direction obtained by principal component analysis. The spectral feature values can reflect the amount of information of the soil sample in the principal component direction. The larger the spectral feature value, the greater the amount of information of the soil sample in the principal component direction. The smaller the spectral feature value, the less the amount of information of the soil sample in the principal component direction.
[0077] In some embodiments, reference Figure 2 As shown in the figure, this is a schematic flowchart illustrating the determination of spectral fluctuations in some embodiments of this application. The determination of spectral fluctuations in soil at each pollution level in the target environment based on various spectral characteristic values can be achieved through the following steps:
[0078] First, in 1031, the sample proximity between every two soil samples is determined based on each spectral feature value;
[0079] Then, in 1032, all soil samples were classified into different pollution levels based on the similarity of all samples;
[0080] Finally, in 1033, the spectral fluctuation of soil in the target environment at each pollution level is determined based on soil samples corresponding to each pollution level.
[0081] In specific implementation, the sample proximity between two soil samples can be determined based on the spectral feature values of each soil sample on the principal components in the following way: First, determine the difference between the spectral feature values of each two soil samples on the principal components. Then, use the result 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 features of each two soil samples on each principal component can be determined. Then, the square root of the sum of the results corresponding to all principal components can be used as the sample proximity between the corresponding two soil samples, thereby obtaining the sample proximity between two soil samples. In other embodiments, other methods can also be used to determine this, which are not limited here.
[0082] It should be noted that the sample proximity described in this application is used to represent the degree of closeness between two soil samples in the principal component space. The greater the sample proximity, the less close the two soil samples are in the principal component space, and the less close the sample proximity, the greater the closeness between the two soil samples in the principal component space.
[0083] In specific implementation, classifying all soil samples into different pollution levels based on sample proximity can be achieved in the following way: First, all sample proximity is used as a similarity index between corresponding two soil samples. Then, the K-means clustering algorithm in the prior art is used to divide all soil samples into sample clusters. Finally, each sample cluster is used as a pollution level, that is, the soil samples in each sample cluster uniquely correspond to a pollution level, thereby classifying all soil samples into different pollution levels. Other methods can also be used in other embodiments, which are not limited here.
[0084] In practice, the determination of the spectral fluctuation of soil in the target environment at each pollution level can be achieved by the following method: First, select a pollution level. Then, obtain all soil samples corresponding to that pollution level. Next, select a wavelength as the selected wavelength and determine the variance of the spectral values of each soil sample at the selected wavelength. Finally, use the obtained result as the spectral fluctuation of that pollution level at the selected wavelength. Continue to determine the spectral fluctuation of that pollution level at the remaining wavelengths. Repeat the above steps to determine the spectral fluctuation of soil in the target environment at the remaining pollution levels.
[0085] It should be noted that the spectral fluctuation mentioned in this application refers to the fluctuation of the spectral values of all soil samples at a specific wavelength under the pollution level. The spectral fluctuation can characterize the consistency of the absorption spectral characteristics of pollutants in all soil samples under the pollution level. The larger the spectral fluctuation, the lower the consistency of the absorption spectral characteristics of pollutants in all soil samples under the specific pollution level. The smaller the spectral fluctuation, the higher the consistency of the absorption spectral characteristics of pollutants in all soil samples under the specific pollution level.
[0086] In step 104, a smart acoustic particle size sensor is used to collect particle size information of each soil sample, and the interaction characteristics between the absorption spectrum and soil particle size of each soil sample are determined based on the particle size information of each soil sample.
[0087] In specific implementation, after emitting rated sound waves in each direction to each soil sample, the propagation speed and scattering characteristics of the sound waves in the soil sample are collected by the intelligent sound wave particle size sensor to determine the soil particle size in each direction, 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, a high-frequency sound wave (10kHz-1MHz) is set as the rated sound wave of this application; when the particles of the soil sample are large, a low-frequency sound wave (1kHz-50kHz) is set as the rated sound wave of this application. Other methods can be used in other embodiments, which are not limited here.
[0088] In some embodiments, determining the interaction characteristics between the absorption spectrum and soil particle size of each soil sample based on the particle size information of each soil sample can be achieved by the following steps:
[0089] Select one soil sample as the selected soil sample;
[0090] Obtain the spectral peaks in the absorption spectrum of the selected soil sample;
[0091] The characteristic particle size of a selected soil sample is determined by the particle size information of the selected soil sample;
[0092] The interaction characteristics between the absorption spectrum and soil particle size of the selected soil sample are determined by the spectral peaks and the characteristic particle size.
[0093] Further investigation was conducted to determine the interaction characteristics between the absorption spectra and soil particle size of the remaining soil samples.
[0094] Preferably, the maximum absorption spectral value in the absorption spectrum of the selected soil sample can be obtained as the spectral peak. In other embodiments, other methods can be used to obtain the peak value, which are not limited here. Therefore, in specific implementation, the characteristic particle size of the selected soil sample can be determined by the following method: first, the soil particle size in each direction in the particle size information of the selected soil sample is obtained; then, the average value of all soil particle sizes is used as the characteristic particle size of the selected soil sample. In other embodiments, other methods can be used to determine the characteristic particle size, which will not be elaborated here. In addition, as a preferred embodiment, the interaction characteristics between the absorption spectrum and soil particle size of the selected soil sample can be determined by the following method: the ratio of the spectral peak value to the characteristic particle size is used as the interaction characteristics between the absorption spectrum and soil particle size of the selected soil sample.
[0095] It should be noted that the characteristic granularity mentioned in this application is a quantitative value of the size of soil particles in the soil sample. The characteristic granularity can reflect the overall particle size distribution of soil particles in the soil sample. The larger the characteristic granularity, the larger the average size of soil particles in the soil sample, and the coarser the particle size. The smaller the characteristic granularity, 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 index describing the relationship between spectral absorption characteristics and soil particle size. The interaction feature can reflect the degree of influence of soil particle size on the absorption spectrum. The larger the interaction feature, the larger the spectral peak at a specific soil particle size. The smaller the interaction feature, the smaller the spectral peak at a specific soil particle size.
[0096] In step 105, confidence feature spectral lines of pollutants are determined based on all spectral fluctuations and all interaction features, and then the content of pollutants in the target environment is detected by the confidence feature spectral lines.
[0097] In some embodiments, determining the confidence characteristic spectral lines of a pollutant based on all spectral fluctuations and all interaction features can be achieved using the following steps:
[0098] Select a pollution level as the selected pollution level;
[0099] Based on all spectral fluctuations corresponding to a selected pollution level, determine multiple characteristic wavelengths of the pollutant at that pollution level;
[0100] By performing confidence adjustment on all characteristic wavelengths using all interactive features, multiple confidence characteristic wavelengths are obtained at the selected pollution level.
[0101] Continue to determine multiple confidence characteristic wavelengths at the remaining pollution level;
[0102] Confidence characteristic spectral lines of pollutants are generated using all confidence characteristic wavelengths.
[0103] In practice, determining multiple characteristic wavelengths of a pollutant at a given pollution level based on all spectral fluctuations corresponding to that pollution level can be achieved in the following way: First, a fluctuation threshold is set. 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 taken 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.
[0104] It should be noted that the fluctuation threshold mentioned in this application can be set based on the historical pollutant content in the target area. The higher the historical pollutant content in the target area, the more significant the spectral changes will be. Therefore, a larger fluctuation threshold can be set to detect more obvious fluctuations. Conversely, a smaller fluctuation threshold can be set. In addition, the characteristic wavelength mentioned in this 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.
[0105] In specific implementation, confidence adjustment of all feature wavelengths based on all interaction features to obtain multiple confidence feature wavelengths under a selected pollution level can be achieved in the following way: First, obtain multiple soil samples corresponding to the selected pollution level, select a feature wavelength, then set an adjustment coefficient, multiply the adjustment coefficient by the mean of the interaction features of each soil sample, and add 1. Subsequently, multiply the result by the feature wavelength as the confidence adjustment result (confidence feature wavelength) of that feature wavelength, and continue to determine the confidence adjustment results of the remaining feature wavelengths, thereby obtaining multiple confidence feature wavelengths under the selected pollution level; as a preferred embodiment, through... The generation of confidence feature spectral lines for pollutants using all confidence feature wavelengths can be achieved as follows: First, select a confidence feature wavelength, determine the mean spectral value of each soil sample at that confidence feature wavelength, and use the result as the standard spectral value corresponding to that confidence feature wavelength. Repeat the above steps to determine the standard spectral values corresponding to the remaining confidence feature wavelengths. Finally, use the confidence feature wavelength as the abscissa and the standard spectral value as the ordinate to obtain a coordinate system, and use the curve formed by all confidence feature wavelengths on the coordinate axis as the confidence feature spectral line of the pollutant. In other embodiments, other methods can also be used, which are not limited here.
[0106] It should be noted that the confidence feature wavelength mentioned in this application is the wavelength after weighted correction through interactive features based on the feature wavelength. In addition, the adjustment coefficient mentioned in this 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. Conversely, a smaller adjustment coefficient is set. Finally, the confidence feature spectral line is a curve composed of the confidence feature wavelength and the standard spectral value. The confidence feature spectral line can reflect the absorption characteristics of pollutants under different pollution levels.
[0107] In some embodiments, the detection of pollutant content in a target environment using the confidence feature spectral lines can be achieved through the following steps:
[0108] A pollutant content prediction model for the target environment is constructed using the confidence feature spectral lines.
[0109] The pollutant content prediction model is used to detect the pollutant content in the target environment.
[0110] For specific implementation, refer to Figure 3 As shown in the figure, this is a schematic diagram of the process of constructing a pollutant content prediction model in some embodiments of this application. The pollutant content prediction model of the target environment can be constructed by the confidence feature spectral line in the following way: First, all soil samples are divided into training set and test set (for example, 80% are set as training set and 20% are set as test set). Then, the confidence feature spectral line 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 model is trained by the supervised learning algorithm in the prior art (such as logistic regression, support vector machine, random forest, neural network, etc.). 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 this application. Other methods can be used to determine the model in other embodiments, which are not limited here.
[0111] It should be noted that the pollutant content prediction model is used to detect the pollutant content in the target environment. Specifically, after collecting the test sample in the target environment in real time, the spectral information of the test sample is collected by the intelligent spectral sensor. Then, the spectral information of the test sample is input into the pollutant content prediction model, and the output of the pollutant content early warning model is used as the pollutant content in the test sample.
[0112] Furthermore, in another aspect of this application, in some embodiments, this application provides an environmental monitoring system, with reference to... Figure 4The figure is a schematic diagram of the structure of an environmental monitoring system according to some embodiments of this application. The environmental monitoring system 200 includes: a data acquisition module 201, a processing module 202, and an execution module 203, which are described below:
[0113] The acquisition module 201 in this application is mainly used to acquire multiple soil samples from the target environment and automatically acquire the spectral information of each soil sample through an intelligent spectral sensor.
[0114] Processing module 202, in this application, is mainly used to extract the reference spectral value of the background spectrum from the spectral information of each soil sample, and then determine the absorption spectrum of pollutants in each soil sample based on the reference spectral value and the spectral information.
[0115] In addition, the processing module 202 in this 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 components, and determine the spectral fluctuation of the soil in the target environment at each pollution level based on the spectral characteristic values.
[0116] In addition, the processing module 202 in this application is also used to control the intelligent acoustic particle size sensor to collect particle size information of each soil sample, and to determine the interaction characteristics between the absorption spectrum and soil particle size of each soil sample based on the particle size information of each soil sample.
[0117] The execution module 203 in this application is mainly used to determine the confidence feature spectral lines of pollutants based on all spectral fluctuations and all interaction features, and then detect the content of pollutants in the target environment through the confidence feature spectral lines.
[0118] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described environmental detection method.
[0119] In some embodiments, reference Figure 5 This figure is an internal structural diagram of a computer device implementing an environmental detection method according to some embodiments of this application. The environmental detection method in the above embodiments can be implemented through... Figure 5 The computer device shown is used to implement this, 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.
[0120] The processor 301 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices for controlling the execution of the environmental detection method in this application.
[0121] The communication bus 302 is used to transmit information between the aforementioned components.
[0122] Memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 303 may exist independently and be connected to processor 301 via communication bus 302. Memory 303 may also be integrated with processor 301.
[0123] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the environmental detection method can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.
[0124] Communication interface 304 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.
[0125] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).
[0126] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device may be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.
[0127] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described environmental detection method.
[0128] In summary, the environmental monitoring method and system disclosed in this application involves acquiring multiple soil samples from the target environment and automatically collecting the spectral information of each soil sample using an intelligent spectral sensor. A reference spectral value for the background spectrum is extracted from the spectral information of each soil sample, and the absorption spectrum of pollutants in each soil sample is determined based on the reference spectral value and the spectral information. Principal component analysis is performed on the absorption spectra of pollutants in all soil samples to obtain the spectral characteristic values of each soil sample on the principal components. Based on these spectral characteristic values, the spectral fluctuation of the soil in the target environment at each pollution level is determined. A smart acoustic particle size sensor is used to collect the particle size information of each soil sample, and the interaction characteristics between the absorption spectrum and soil particle size of each soil sample are determined based on the particle size information. Confidence characteristic spectral lines of pollutants are determined based on all spectral fluctuations and all interaction characteristics, and the content of pollutants in the target environment is detected using these confidence characteristic spectral lines.
[0129] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.
[0130] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.
Claims
1. An environmental detection method, characterized in that, Includes the following steps: Multiple soil samples are obtained from the target environment, and the spectral information of each soil sample is automatically collected through a smart spectral sensor; The background spectral reference value is extracted from the spectral information of each soil sample, and then the absorption spectrum of the pollutants in each soil sample is determined based on the reference spectral value and the spectral information. Principal component analysis was performed on the absorption spectra of pollutants in all soil samples to obtain the spectral characteristic values of each soil sample on the principal components. Based on the spectral characteristic values, the spectral fluctuation of the soil in the target environment at each pollution level was determined. The particle size information of each soil sample is collected using an intelligent acoustic particle size sensor, and the interaction characteristics between the absorption spectrum and soil particle size of each soil sample are determined based on the particle size information of each soil sample. Confidence feature spectral lines of pollutants are determined based on all spectral fluctuations and all interaction characteristics, and then the content of pollutants in the target environment is detected through the confidence feature spectral lines. Specifically, determining the confidence characteristic spectral lines of pollutants based on all spectral fluctuations and all interaction characteristics includes: Select a pollution level as the selected pollution level; Based on all spectral fluctuations corresponding to a selected pollution level, determine multiple characteristic wavelengths of the pollutant at that pollution level; By performing confidence adjustment on all characteristic wavelengths using all interactive features, multiple confidence characteristic wavelengths are obtained at the selected pollution level. Continue to determine multiple confidence characteristic wavelengths at the remaining pollution level; Confidence characteristic spectral lines of pollutants are generated using all confidence characteristic wavelengths.
2. The method as described in claim 1, characterized in that, The specific steps for extracting the reference spectral values for the background spectrum from the spectral information of each soil sample include: Select one soil sample as the selected soil sample; Determine multiple smoothed spectral values from the spectral information of a selected soil sample; Reference spectral values for the background spectrum are extracted from the spectral information of a selected soil sample using all smoothed spectral values. Continue to extract the reference spectral values of the background spectrum from the spectral information of the remaining soil samples.
3. The method as described in claim 1, characterized in that, Determining the absorption spectrum of pollutants in each soil sample based on the reference spectral values and the spectral information specifically includes: Select one soil sample as the selected soil sample; Multiple absorption spectral values of pollutants in a selected soil sample are determined by using the reference spectral values of the selected soil sample and the spectral information of the selected soil sample. Continue to determine multiple absorption spectral values of pollutants in the remaining soil samples; The absorption spectrum of the pollutant in each soil sample is constructed from all the absorption spectral values.
4. The method as described in claim 1, characterized in that, Determining the spectral fluctuation of soil in the target environment at each pollution level based on various spectral characteristic values specifically includes: The sample proximity between each pair of soil samples is determined based on various spectral characteristic values; All soil samples were classified into different pollution levels based on their similarity to the samples. The spectral fluctuation of soil in the target environment at each pollution level was determined based on soil samples corresponding to each pollution level.
5. The method as described in claim 1, characterized in that, Determining the interaction characteristics between the absorption spectrum and soil particle size of each soil sample based on its particle size information specifically includes: Select one soil sample as the selected soil sample; Obtain the spectral peaks in the absorption spectrum of the selected soil sample; The characteristic particle size of a selected soil sample is determined by the particle size information of the selected soil sample; The interaction characteristics between the absorption spectrum and soil particle size of the selected soil sample are determined by the spectral peaks and the characteristic particle size. Further investigation was conducted to determine the interaction characteristics between the absorption spectra and soil particle size of the remaining soil samples.
6. The method as described in claim 1, characterized in that, Multiple soil samples were obtained by randomly collecting soil from different sampling points within the target environment using a random sampling method.
7. An environmental monitoring system, wherein environmental monitoring is performed using the method described in any one of claims 1 to 6, characterized in that, The system includes: The acquisition module is used to acquire multiple soil samples from the target environment and automatically acquire the spectral information of each soil sample through an intelligent spectral sensor. The processing module is used to extract the reference spectral value of the background spectrum from the spectral information of each soil sample, and then determine the absorption spectrum of the pollutants in each soil sample based on the reference spectral value and the spectral information. The processing module 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 components, and determine the spectral fluctuation of the soil in the target environment at each pollution level based on the spectral characteristic values. The processing module is also used to control the intelligent acoustic particle size sensor to collect particle size information of each soil sample, and to determine the interaction characteristics between the absorption spectrum and soil particle size of each soil sample based on the particle size information of each soil sample. The execution module is used to determine the confidence feature spectral lines of pollutants based on all spectral fluctuations and all interaction features, and then detect the content of pollutants in the target environment through the confidence feature spectral lines.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the environmental detection method according to any one of claims 1 to 6.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the environmental detection method as described in any one of claims 1 to 6.
Citation Information
Patent Citations
System for detecting soil contamination degree
CN108344691A
PROCEDE D'ANALYSE ET DE TRAITEMENT DE TERRE EXCAVEE
FR3074296A1