Soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction

Abstract

The invention discloses a soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction, and relates to the technical field of soil pollution assessment, and the system comprises a spectrum periodic scanning module, a primary pollution early warning module, a simulation model construction module and a secondary pollution early warning module. Heavy metal pollution of soil is deeply evaluated in a hyperspectral feature extraction mode, primary pollution early warning is started in time after abnormity is evaluated, a simulation model construction module and a secondary pollution early warning module are arranged, various parameters of the soil are analyzed through hyperspectral features, and then a soil change simulation model is constructed. According to the method, the subsequent heavy metal pollution change of the soil is comprehensively and stereoscopically analyzed, secondary pollution early warning is started in time after abnormity is analyzed, and the heavy metal pollution of the soil is accurately evaluated from different angles through secondary different pollution early warning mechanisms.

Description

Technical Field

[0001] The present invention relates to the technical field of soil pollution assessment, and more specifically, to a soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction. Background Art

[0002] With the acceleration of industrialization and urbanization, the problem of soil heavy metal pollution is becoming increasingly serious, posing a serious threat to human health, ecological environment and agricultural production. Traditional soil heavy metal pollution assessment methods mainly rely on laboratory analysis. Although this method is accurate, it is time-consuming, costly, and difficult to achieve large-scale, real-time monitoring. Therefore, it is particularly important to develop an efficient, accurate, and real-time soil heavy metal pollution assessment and early warning system.

[0003] In recent years, hyperspectral remote sensing technology has attracted much attention due to its ability to obtain detailed spectral information of surface materials. Hyperspectral data contains rich information on material composition and physical and chemical properties. Through specific data processing and analysis methods, it can achieve rapid and accurate assessment of soil heavy metal pollution. However, most of the current soil heavy metal pollution assessment methods based on hyperspectral characteristics remain in the initial exploration stage, with problems such as low assessment accuracy and imperfect early warning mechanism.

[0004] In order to overcome the shortcomings of the prior art, the present invention proposes a soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction.

[0006] To achieve the above object, the present invention provides the following technical solutions: Soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction, including spectral periodic scanning module, primary pollution early warning module, simulation model building module, and secondary pollution early warning module; The spectral periodic scanning module is used to set the spectral scanning period, and whenever the period node of the spectral scanning period is reached, the target soil area is scanned by a hyperspectral imager to obtain the hyperspectral image data of the target soil area; The primary pollution warning module obtains a primary assessment index of heavy metal pollution based on the hyperspectral image data, and determines whether to initiate a primary pollution warning based on a comparison result of the primary assessment index of heavy metal pollution with a primary assessment threshold index of heavy metal pollution; The simulation model building module builds a soil change simulation model after the primary pollution warning is initiated; The secondary pollution warning module controls the soil change simulation model to simulate the soil changes in a spectral scanning cycle, and then obtains the heavy metal pollution secondary assessment index. Based on the comparison result of the heavy metal pollution secondary assessment index and the heavy metal pollution secondary assessment threshold index, it is determined whether to start the secondary pollution warning.

[0007] Furthermore, a primary assessment index of heavy metal pollution is obtained based on the hyperspectral image data, specifically: feature extraction is performed on the hyperspectral image data to obtain hyperspectral features, a primary assessment model for heavy metal pollution is obtained, the hyperspectral features are used as input data of the primary assessment model for heavy metal pollution, and the primary assessment model for heavy metal pollution outputs the primary assessment index for heavy metal pollution.

[0008] Furthermore, after the primary pollution warning is initiated, a soil change simulation model is constructed, specifically: a soil parameter analysis model of various soil parameters is obtained, the hyperspectral features are input into the soil parameter analysis model of various soil parameters in sequence, and then various soil parameters are obtained, and a soil change simulation model is constructed based on the various soil parameters.

[0009] Furthermore, the soil change simulation model simulates the soil change of a spectral scanning cycle, and then obtains the secondary assessment index of heavy metal pollution, specifically: c pollution index nodes with the same time interval are set in the spectral scanning cycle, and in the process of simulating soil changes, whenever a pollution index node is reached, the primary assessment index of heavy metal pollution and the comprehensive assessment index of soil parameters of the pollution index node are obtained, and then the heavy metal pollution change index of the pollution index node is obtained, and all heavy metal pollution change indices are sorted in the order of the pollution index nodes, and the previous heavy metal pollution change index adjacent to the sorting is compared with the next heavy metal pollution change index. When the previous heavy metal pollution change index is less than the next heavy metal pollution change index, the number of pollution aggravation changes is increased by one, and the number of pollution aggravation changes is marked as Xtgh. The two adjacent heavy metal pollution change indices after sorting are summed to obtain the continuous index of heavy metal pollution change, and the continuous index of heavy metal pollution change is set. When the continuous index of heavy metal pollution change is greater than or equal to the continuous index of heavy metal pollution change, the continuous number of pollution changes is increased by one, and the continuous number of pollution changes is marked as Kawt. The formula is used. The secondary assessment index of heavy metal pollution BNk is obtained, where qa is the coefficient of pollution aggravation change and qb is the continuous coefficient of pollution change.

[0010] Furthermore, the heavy metal pollution change index of the pollution index node is obtained by the following steps: obtaining the heavy metal pollution primary assessment index Mbd and the soil parameter comprehensive assessment index Seg of the pollution index node, using the formula The heavy metal pollution change index BTws of the pollution index node is obtained, where d1 is the first exponential coefficient, d2 is the second exponential coefficient, and d3 is the third exponential coefficient.

[0011] Furthermore, the soil parameter comprehensive evaluation index of the pollution index node is obtained through the following steps: when reaching a pollution index node, various soil parameters of the target soil area entity are obtained, and the representation promotion value Bph of various soil parameters of the target soil area entity is obtained, p=1, 2, …, P, p represents the type of soil parameter, P is the total number of soil parameter types, and the representation promotion coefficient is set to Rh, h=1, 2, 3, …, h, R1<R2<R3<…<Rh, and the mean value of the representation promotion gap Fdz is obtained. The formula is used The soil parameter comprehensive evaluation index Seg of the pollution index node is obtained.

[0012] Furthermore, the representation promotion gap mean Fdz is obtained through the following steps: all the representation promotion values ​​are matched pairwise into a representation promotion group, the difference between the two representation promotion values ​​in the representation promotion group is calculated and the absolute value is taken to obtain the representation promotion gap value of the representation promotion group, the representation promotion gap values ​​of all the representation promotion groups are summed and the average is taken to obtain the representation promotion gap mean Fdz.

[0013] Furthermore, the characterization promotion values ​​of various soil parameters of the target soil area entity are obtained, specifically: the characterization promotion models corresponding to various soil parameters are obtained, various soil parameters are input into the corresponding characterization promotion models respectively, and then the characterization promotion values ​​of various soil parameters are outputted.

[0014] Compared with the prior art, the present invention has the following beneficial effects: A spectral periodic scanning module and a primary pollution warning module are set up to conduct an in-depth assessment of the heavy metal pollution in the soil through the extraction of hyperspectral features, and the primary pollution warning is promptly initiated after the assessment is abnormal. A simulation model construction module and a secondary pollution warning module are set up to analyze various parameters of the soil through hyperspectral features, and then build a soil change simulation model, conduct a comprehensive and three-dimensional analysis of the subsequent heavy metal pollution changes in the soil, and promptly initiate a secondary pollution warning after analyzing the abnormalities. Through different secondary pollution warning mechanisms, the heavy metal pollution in the soil is accurately assessed from different angles, providing a longer time window for decision-making support for soil pollution prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is the system module diagram of the soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction; Figure 2 This is the system operation flow chart of the soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction. DETAILED DESCRIPTION

[0016] Reference Figure 1-Figure 2 The soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction includes a spectral periodic scanning module, a primary pollution early warning module, a simulation model building module, and a secondary pollution early warning module.

[0017] Spectral periodic scanning module: set the spectral scanning cycle (when the system is running, the spectral scanning cycle is infinitely looped). Whenever the period node of the spectral scanning cycle is reached, the target soil area is scanned by the hyperspectral imager to obtain the hyperspectral image data of the target soil area.

[0018] Primary pollution warning module: extract features from hyperspectral image data to obtain hyperspectral features, obtain a primary assessment model for heavy metal pollution, use the hyperspectral features as input data for the primary assessment model for heavy metal pollution, output the primary assessment index for heavy metal pollution, set the primary assessment threshold index for heavy metal pollution (the primary assessment threshold index for heavy metal pollution is a system preset value), and start a primary pollution warning when the primary assessment index for heavy metal pollution is greater than or equal to the primary assessment threshold index for heavy metal pollution; and do nothing when the primary assessment index for heavy metal pollution is less than the primary assessment threshold index for heavy metal pollution.

[0019] The primary assessment model for heavy metal pollution is constructed as follows: multiple hyperspectral features are collected to construct a deep learning model, and the hyperspectral features are used as training data for the deep learning model. A primary assessment index for heavy metal pollution is assigned to each training data. The index range of the primary assessment index for heavy metal pollution is (0.1~2.0). The closer the primary assessment index of heavy metal pollution is to 2.0, the more serious the heavy metal pollution is assessed to be; the closer the primary assessment index of heavy metal pollution is to 0.1, the less serious the heavy metal pollution is assessed to be. The training data is divided into training set, validation set, and test set according to the set ratio of 3:2:1, and the training set, validation set, and test set are trained. After the training is completed, a primary assessment model for heavy metal pollution is constructed.

[0020] Simulation model construction module: When the primary pollution warning is initiated, the soil parameter analysis model of various soil parameters is obtained (soil parameters include soil acid-base parameters, soil composition parameters, heavy metal distribution parameters, etc.), and the hyperspectral features are input into the soil parameter analysis model of various soil parameters in turn, and then various soil parameters are obtained, and a soil change simulation model is constructed based on various soil parameters.

[0021] Each soil parameter corresponds to an analysis method, therefore, each soil parameter corresponds to a soil parameter analysis model. In a specific embodiment of the present invention, a method for constructing a soil parameter analysis model corresponding to soil acid-base parameters is disclosed: multiple hyperspectral features are collected, a deep learning model is constructed, and the hyperspectral features are used as training data for the deep learning model. A soil acid-base parameter is assigned to each training data, and the training data is divided into a training set, a validation set, and a test set according to a set ratio of 4:1:1. The training set, validation set, and test set are trained. After the training is completed, a soil parameter analysis model of the soil acid-base parameters is constructed.

[0022] If it is a soil parameter analytical model of soil component parameters, a soil component parameter is assigned to each training data.

[0023] Secondary pollution warning module: The soil change simulation model simulates the soil changes in a spectral scanning cycle, and then obtains the secondary assessment index of heavy metal pollution, sets the secondary assessment threshold index of heavy metal pollution (the secondary assessment threshold index of heavy metal pollution is the system preset value), and when the secondary assessment index of heavy metal pollution is greater than or equal to the secondary assessment threshold index of heavy metal pollution, the secondary pollution warning is activated; when the secondary assessment index of heavy metal pollution is less than the secondary assessment threshold index of heavy metal pollution, no action is taken.

[0024] The soil change simulation model simulates the soil changes in a spectral scanning cycle, and then obtains the secondary assessment index of heavy metal pollution. Specifically, c pollution index nodes with the same time interval are set in the spectral scanning cycle. During the simulated soil change process, whenever a pollution index node is reached, the primary assessment index of heavy metal pollution and the comprehensive assessment index of soil parameters of the pollution index node are obtained, and then the heavy metal pollution change index of the pollution index node is obtained. All heavy metal pollution change indices are sorted in the order of the pollution index nodes, and the adjacent previous heavy metal pollution change index after sorting is compared with the next heavy metal pollution change index. The current heavy metal pollution change index is greater than or equal to the next heavy metal pollution change index. When the pollution change index is greater than or equal to the pollution change index, no processing is performed. When the previous heavy metal pollution change index is less than the next heavy metal pollution change index, the pollution aggravation change times are increased by one, and the pollution aggravation change times are marked as Xtgh. The two adjacent heavy metal pollution change indexes after sorting are summed to obtain the heavy metal pollution change continuous index. The heavy metal pollution change continuous index is set (the heavy metal pollution change continuous index is the system preset value). When the heavy metal pollution change continuous index is greater than or equal to the heavy metal pollution change continuous index, the pollution change continuous times are increased by one. When the heavy metal pollution change continuous index is less than the heavy metal pollution change continuous index, no processing is performed, and the pollution change continuous times are marked as Kawt. The formula is used. The secondary assessment index of heavy metal pollution BNk is obtained, where qa is the pollution aggravation coefficient, qb is the pollution change continuity coefficient, the value of qa is 0.98, and the value of qb is 0.92.

[0025] The heavy metal pollution change index of the pollution index node is obtained by the following steps: obtain the heavy metal pollution primary assessment index Mbd and the soil parameter comprehensive assessment index Seg of the pollution index node, and use the formula The heavy metal pollution change index BTws of the pollution index node is obtained, where d1 is the first exponential coefficient, d2 is the second exponential coefficient, and d3 is the third exponential coefficient. The value of d1 is 0.81, the value of d2 is 1.39, and the value of d3 is 0.68.

[0026] The soil parameter comprehensive evaluation index of the pollution index node is obtained through the following steps: when reaching a pollution index node, various soil parameters of the target soil area entity are obtained, and the characterization promotion value Bph of various soil parameters of the target soil area entity is obtained, p=1, 2, ..., P, p represents the type of soil parameter, P is the total number of soil parameter types, and the characterization promotion coefficient is set to Rh, h=1, 2, 3, ..., h, R1<R2<R3<...<Rh, each characterization promotion coefficient Rh corresponds to a characterization promotion value B within a range. ph, the range of the representation promotion value Bph includes (0, Bp1], (Bp1, Bp2], …, (Bph-1, Bph], when Bph∈(0, Bp1], the representation promotion coefficient is R1, all the representation promotion values ​​are matched into a representation promotion group, the difference between the two representation promotion values ​​in the representation promotion group is calculated and the absolute value is taken to obtain the representation promotion gap value of the representation promotion group, the representation promotion gap values ​​of all the representation promotion groups are summed and averaged to obtain the representation promotion gap mean Fdz, and the formula is used The soil parameter comprehensive evaluation index Seg of the pollution index node is obtained.

[0027] The characterization promotion values ​​of various soil parameters of the target soil area entity are obtained, specifically: the characterization promotion models corresponding to various soil parameters are obtained, various soil parameters are input into the corresponding characterization promotion models respectively, and then the characterization promotion values ​​of various soil parameters are outputted.

[0028] Each soil parameter corresponds to a characterization promotion model, and the difference between different soil parameter analysis models lies only in the difference in training data. In a specific embodiment of the present invention, a construction method of a characterization promotion model corresponding to soil acid-base parameters is disclosed: multiple soil acid-base parameters are collected to construct a deep learning model, and the soil acid-base parameters are used as training data for the deep learning model. A characterization promotion value is assigned to each training data, and the range of the characterization promotion value is (1.1~3.0). The closer the characterization promotion value is to 3.0, the more the soil acid-base parameter promotes the development of heavy metal pollution, and the closer the characterization promotion value is to 1.1, the more the soil acid-base parameter inhibits the development of heavy metal pollution. The training data is divided into a training set, a validation set, and a test set according to a set ratio of 5:2:1, and the training set, validation set, and test set are trained. After the training is completed, a characterization promotion model of soil acid-base parameters is constructed.

[0029] If the purpose is to construct a characterization promotion model for soil composition parameters, multiple soil composition parameters are collected. The closer the characterization promotion value is to 3.0, the more the soil composition parameter promotes the development of heavy metal pollution. The closer the characterization promotion value is to 1.1, the more the soil composition parameter inhibits the development of heavy metal pollution.

[0030] A soil change simulation model is constructed based on various soil parameters. Specifically, dedicated soil simulation software is selected, a target soil area entity is created in the simulation software, and corresponding parameters are added to the target soil area entity based on various soil parameters, such as soil acid-base parameters, soil composition parameters, heavy metal distribution parameters, etc., to construct a soil change simulation model. The soil change simulation model can simulate subsequent changes in the target soil area.

[0031] A spectral periodic scanning module and a primary pollution warning module are set up to conduct an in-depth assessment of the heavy metal pollution in the soil through the extraction of hyperspectral features, and the primary pollution warning is promptly initiated after the assessment is abnormal. A simulation model construction module and a secondary pollution warning module are set up to analyze various parameters of the soil through hyperspectral features, and then build a soil change simulation model, conduct a comprehensive and three-dimensional analysis of the subsequent heavy metal pollution changes in the soil, and promptly initiate a secondary pollution warning after analyzing the abnormalities. Through different secondary pollution warning mechanisms, the heavy metal pollution in the soil is accurately assessed from different angles, providing a longer time window for decision-making support for soil pollution prevention and control.

[0032] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0033] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0034] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0035] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0036] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0037] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0038] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0039] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. Soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction, characterized in that: It includes spectrum periodic scanning module, primary pollution warning module, simulation model building module and secondary pollution warning module; The spectral periodic scanning module is used to set the spectral scanning period, and whenever the period node of the spectral scanning period is reached, the target soil area is scanned by a hyperspectral imager to obtain the hyperspectral image data of the target soil area; The primary pollution warning module obtains a primary assessment index of heavy metal pollution based on the hyperspectral image data, and determines whether to initiate a primary pollution warning based on a comparison result of the primary assessment index of heavy metal pollution with a primary assessment threshold index of heavy metal pollution; The simulation model building module builds a soil change simulation model after the primary pollution warning is initiated; The secondary pollution warning module controls the soil change simulation model to simulate the soil changes in a spectral scanning cycle, and then obtains the heavy metal pollution secondary assessment index. Based on the comparison result of the heavy metal pollution secondary assessment index and the heavy metal pollution secondary assessment threshold index, it is determined whether to start the secondary pollution warning.

2. The soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction according to claim 1 is characterized in that: A primary assessment index of heavy metal pollution is obtained based on hyperspectral image data, specifically: feature extraction is performed on the hyperspectral image data to obtain hyperspectral features, a primary assessment model for heavy metal pollution is obtained, the hyperspectral features are used as input data of the primary assessment model for heavy metal pollution, and the primary assessment model for heavy metal pollution outputs the primary assessment index for heavy metal pollution.

3. The soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction according to claim 1 is characterized in that: After the primary pollution warning is initiated, a soil change simulation model is constructed, specifically: a soil parameter analysis model of various soil parameters is obtained, the hyperspectral features are input into the soil parameter analysis model of various soil parameters in turn, and then various soil parameters are obtained, and a soil change simulation model is constructed based on various soil parameters.

4. The soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction according to claim 1 is characterized in that: The soil change simulation model simulates the soil change of a spectral scanning cycle, and then obtains the secondary assessment index of heavy metal pollution. Specifically, c pollution index nodes with the same time interval are set in the spectral scanning cycle. In the process of simulating soil changes, whenever a pollution index node is reached, the primary assessment index of heavy metal pollution and the comprehensive assessment index of soil parameters of the pollution index node are obtained, and then the heavy metal pollution change index of the pollution index node is obtained. All heavy metal pollution change indexes are sorted in the order of the pollution index nodes, and the previous heavy metal pollution change index adjacent to the sorting is compared with the next heavy metal pollution change index. When the previous heavy metal pollution change index is less than the next heavy metal pollution change index, the number of pollution aggravation changes is increased by one, and the number of pollution aggravation changes is marked as Xtgh. The two adjacent heavy metal pollution change indexes after sorting are summed to obtain the continuous index of heavy metal pollution change, and the continuous index of heavy metal pollution change is set. When the continuous index of heavy metal pollution change is greater than or equal to the continuous index of heavy metal pollution change, the continuous number of pollution changes is increased by one, and the continuous number of pollution changes is marked as Kawt. The formula is used. The secondary assessment index of heavy metal pollution BNk is obtained, where qa is the coefficient of pollution aggravation change and qb is the continuous coefficient of pollution change.

5. The soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction according to claim 4 is characterized in that: The heavy metal pollution change index of the pollution index node is obtained by the following steps: obtain the heavy metal pollution primary assessment index Mbd and the soil parameter comprehensive assessment index Seg of the pollution index node, and use the formula The heavy metal pollution change index BTws of the pollution index node is obtained, where d1 is the first exponential coefficient, d2 is the second exponential coefficient, and d3 is the third exponential coefficient.

6. The soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction according to claim 4 is characterized in that: The soil parameter comprehensive evaluation index of the pollution index node is obtained through the following steps: when reaching a pollution index node, various soil parameters of the target soil area entity are obtained, and the representation promotion value Bph of various soil parameters of the target soil area entity is obtained, p=1, 2, ..., P, p represents the type of soil parameter, P is the total number of soil parameter types, and the representation promotion coefficient is set to Rh, h=1, 2, 3, ..., h, R1<R2<R3<...<Rh, and the mean value of the representation promotion gap Fdz is obtained. The formula is used The soil parameter comprehensive evaluation index Seg of the pollution index node is obtained.

7. The soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction according to claim 6 is characterized in that: The representation promotion gap mean Fdz is obtained through the following steps: all the representation promotion values ​​are matched in pairs into a representation promotion group, the difference between the two representation promotion values ​​in the representation promotion group is calculated and the absolute value is taken to obtain the representation promotion gap value of the representation promotion group, the representation promotion gap values ​​of all the representation promotion groups are summed up and the average is taken to obtain the representation promotion gap mean Fdz.

8. The soil heavy metal pollution assessment and early warning system based on hyperspectral feature extraction according to claim 6 is characterized in that: The characterization promotion values ​​of various soil parameters of the target soil area entity are obtained, specifically: the characterization promotion models corresponding to various soil parameters are obtained, various soil parameters are input into the corresponding characterization promotion models respectively, and then the characterization promotion values ​​of various soil parameters are outputted.

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