An intelligent monitoring method and system for soil pollutant VOCs

Abstract

The present invention discloses an intelligent monitoring method and system for soil pollutant VOCs, which relates to the technical field of soil pollution monitoring. It includes obtaining soil characteristic data and constructing a VOCs diffusion kinetics model, calculating using the VOCs diffusion kinetics model to obtain the pollutant concentration gradient; obtaining environmental data based on the pollutant concentration gradient and performing preprocessing to obtain the preprocessed environmental data; using the preprocessed environmental data to optimize the VOCs diffusion kinetics model and constructing a VOCs concentration distribution prediction model; performing concentration prediction based on the VOCs concentration distribution prediction model to obtain the predicted VOCs concentration, thereby realizing the intelligent monitoring of soil pollutant VOCs. The present invention realizes the adaptive adjustment of the sampling point spacing and optimizes the environmental data acquisition efficiency by introducing a three-level concentration gradient threshold judgment mechanism; and improves the accuracy and reliability of VOCs concentration prediction by establishing a prediction model considering the influence of multiple environmental factors.

Description

Technical Field

[0001] The present invention relates to the technical field of soil pollution monitoring, and in particular to an intelligent monitoring method and system for soil pollutant VOCs. Background Art

[0002] The pollution of soil volatile organic compounds (VOCs) has become a global environmental problem. It has the characteristics of persistence, bioaccumulation and potential toxicity, posing a major threat to the ecological environment and human health. With the acceleration of the industrialization process, the soil VOCs pollution monitoring technology has experienced a development process from traditional sampling analysis to in-situ monitoring and then to intelligent monitoring. At present, the soil VOCs monitoring mainly adopts technical means such as gas chromatography-mass spectrometry (GC-MS), portable gas chromatograph (PGC) and optical sensors. These methods have made remarkable progress in the qualitative and quantitative analysis of pollutants. At the same time, domestic and foreign scholars have conducted in-depth research on the migration and diffusion mechanism of soil volatile organic compounds (VOCs) in soil, and established a variety of mathematical models to describe the transport process of soil volatile organic compounds (VOCs) in heterogeneous porous media.

[0003] However, the existing soil VOCs monitoring methods still have the following deficiencies: First, the traditional sampling analysis method is time-consuming and costly, and it is difficult to achieve real-time dynamic monitoring of pollutants. Second, the existing monitoring systems generally ignore the influence of soil characteristic parameters on the diffusion process of soil volatile organic compounds (VOCs), resulting in a large deviation between the monitoring results and the actual situation. Third, most of the current diffusion models are established based on ideal condition assumptions, and do not fully consider the influence of soil porosity or moisture content factors on the migration and diffusion of soil volatile organic compounds (VOCs), resulting in limited prediction accuracy of the models. Finally, the existing monitoring systems lack the ability to adaptively adjust the sampling strategy according to the pollutant concentration gradient, resulting in waste of monitoring resources or the emergence of monitoring blind spots. The noise interference of environmental data and the uncertainty of model parameters also significantly affect the accuracy of monitoring results. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to provide a method that can realize the intelligent monitoring of soil VOCs pollutants by constructing a diffusion kinetic model based on soil characteristics, combining an adaptive sampling strategy and a prediction model coupled with multiple environmental factors, not only improving the monitoring accuracy and efficiency, but also having strong practicability and popularization value, and can provide important technical support for soil pollution control.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides an intelligent monitoring method for soil pollutant VOCs, which includes obtaining soil characteristic data and constructing a VOCs diffusion kinetics model, calculating using the VOCs diffusion kinetics model to obtain a pollutant concentration gradient; obtaining environmental data based on the pollutant concentration gradient and performing preprocessing to obtain preprocessed environmental data; using the preprocessed environmental data to optimize the VOCs diffusion kinetics model to construct a VOCs concentration distribution prediction model; performing concentration prediction based on the VOCs concentration distribution prediction model to obtain a VOCs predicted concentration, thereby realizing intelligent monitoring of soil pollutant VOCs.

[0008] As a preferred embodiment of the intelligent monitoring method for soil pollutant VOCs of the present invention, wherein: the obtaining of soil characteristic data refers to collecting soil samples and performing measurements to obtain the porosity distribution parameters and moisture content parameters of the soil samples.

[0009] As a preferred embodiment of the intelligent monitoring method for soil pollutant VOCs of the present invention, wherein: the construction of the VOCs diffusion kinetics model includes the following steps: analyzing the diffusion process of pollutants according to Fick's second law, and the diffusion process of pollutant concentration C(x,t) is expressed as follows:

[0010]

[0011] where C(x,t) is the pollutant concentration; D0 is the free diffusion coefficient of the pollutant in a pure medium; x is the spatial position; t is the time; by introducing a decay term -k·C(x,t) into the diffusion process of the pollutant concentration C(x,t), where k is the degradation rate constant, reflecting the loss ratio of the pollutant per unit time; optimizing the free diffusion coefficient D0 by combining the porosity distribution parameters and the moisture content parameters to obtain an optimized free diffusion coefficient, and the specific formula is as follows:

[0012] D = D0·φ·(1 - ω)

[0013] where D is the optimized free diffusion coefficient; φ is the porosity distribution parameter; ω is the moisture content parameter; improving the diffusion process of the pollutant concentration C(x,t) based on the optimized free diffusion coefficient and the decay term to construct a VOCs diffusion kinetics model.

[0014] As a preferred embodiment of the intelligent monitoring method for soil pollutant VOCs of the present invention, wherein: the calculation formula of the VOCs diffusion kinetics model is as follows:

[0015]

[0016] where is the pollutant concentration gradient; C(x,t) is the pollutant concentration; D0 is the free diffusion coefficient of the pollutant in the pure medium; x is the spatial position; t is the time; φ is the porosity distribution parameter; ω is the moisture content parameter; k is the degradation rate constant.

[0017] As a preferred embodiment of the intelligent monitoring method for soil pollutant VOCs of the present invention, wherein: the environmental data includes temperature data, humidity data and air pressure gradient data; obtaining environmental data based on the pollutant concentration gradient includes: based on the pollutant concentration gradient make a judgment. If the pollutant concentration gradient is less than or equal to the first threshold, it is determined as a low gradient area, the spacing of the sampling points is set to T1 and the environmental data is obtained; if the pollutant concentration gradient is greater than the first threshold and less than the second threshold, it is determined as a medium gradient area, the spacing of the sampling points is set to T2 and the environmental data is obtained; if the pollutant concentration gradient is greater than or equal to the second threshold, it is determined as a high gradient area, the spacing of the sampling points is set to T3 and the environmental data is obtained.

[0018] As a preferred embodiment of the intelligent monitoring method for soil pollutant VOCs of the present invention, wherein: constructing the VOCs concentration distribution prediction model includes the following steps: based on the VOCs diffusion kinetics model, combining the air pressure gradient data and the relationship between the soil properties and the free diffusion coefficient, using the air pressure gradient data to improve the optimized free diffusion coefficient to obtain the dynamic diffusion coefficient; optimizing the degradation rate constant k by using the temperature data and the humidity data to obtain the optimized degradation rate; optimizing the VOCs diffusion kinetics model based on the dynamic diffusion coefficient D' and the optimized degradation rate k' to construct the VOCs concentration distribution prediction model.

[0019] As a preferred embodiment of the intelligent monitoring method for soil pollutant VOCs of the present invention, wherein: the specific formula of the dynamic diffusion coefficient is as follows:

[0020] D' = D0·φ·(1 - ω)·(1 + γ·P(t))

[0021] wherein, D' is the dynamic diffusion coefficient; γ is the air pressure gradient correction coefficient; P(t) is the air pressure gradient data; the specific formula of the optimized degradation rate is as follows:

[0022] k' = k0·(1 + α·H(t) - β·T(t))

[0023] wherein, k' is the optimized degradation rate; k0 is the initial degradation rate constant; α and β are the influence coefficients of the humidity data and the temperature data respectively; H(t) is the humidity data; T(t) is the temperature data.

[0024] In a second aspect, to further solve the safety problems existing in soil pollution monitoring, an embodiment of the present invention provides an intelligent monitoring system for soil pollutant VOCs, which includes: a diffusion power module for obtaining the porosity distribution parameters and moisture content parameters of a soil sample and constructing a VOCs diffusion kinetics model to obtain a pollutant concentration gradient; an environmental data module for obtaining environmental data based on the pollutant concentration gradient and performing preprocessing to obtain preprocessed environmental data; a concentration distribution module for optimizing the VOCs diffusion kinetics model by using the preprocessed environmental data and constructing a VOCs concentration distribution prediction model; and a concentration prediction module for performing concentration prediction based on the VOCs concentration distribution prediction model to obtain a predicted VOCs concentration and performing visualization.

[0025] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the intelligent monitoring method for soil pollutant VOCs as described in the first aspect of the present invention is implemented.

[0026] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the intelligent monitoring method for soil pollutant VOCs as described in the first aspect of the present invention is implemented.

[0027] Advantages of the present invention: By obtaining the soil porosity and moisture content parameters, the present invention constructs a VOCs diffusion kinetics model considering soil characteristics, realizes an accurate description of the diffusion behavior of VOCs in heterogeneous soil, improves the characterization ability of the model for the migration and diffusion process of VOCs in the actual soil environment, and provides a reliable theoretical basis for subsequent monitoring; by introducing a three-level concentration gradient threshold judgment mechanism, the present invention realizes an adaptive adjustment of the sampling point spacing, adopts a dense sampling strategy in the high-concentration gradient area and a sparse sampling strategy in the low-gradient area, not only ensures the monitoring accuracy of the key area, but also avoids the waste of monitoring resources, optimizes the environmental data acquisition efficiency, and improves the economy of the monitoring system; by introducing a barometric gradient correction coefficient to dynamically optimize the diffusion coefficient and using temperature and humidity data to optimize the degradation rate constant, the present invention establishes a prediction model considering the influence of multiple environmental factors, and improves the accuracy and reliability of VOCs concentration prediction. Description of the Drawings

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings. Among them:

[0029] Figure 1 It is the overall flowchart of the intelligent monitoring method for soil pollutant VOCs in Embodiment 1.

[0030] Figure 2 It is the structural schematic diagram of the computer device in Embodiment 3. Detailed implementation manners

[0031] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific implementation manners of the present invention in conjunction with the accompanying drawings of the specification.

[0032] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0033] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0034] Embodiment 1

[0035] Refer to Figure 1 , which is the first embodiment of the present invention. This embodiment provides an intelligent monitoring method for soil pollutant VOCs.

[0036] The existing soil VOCs monitoring methods mainly have the following problems: First, the traditional sampling and analysis methods are time-consuming and costly, and it is difficult to achieve real-time dynamic monitoring of pollutants; Second, the existing monitoring systems generally ignore the influence of soil characteristic parameters on the diffusion process of soil volatile organic compounds (VOCs), resulting in a large deviation between the monitoring results and the actual situation; Third, most of the current diffusion models are based on ideal condition assumptions and do not fully consider the influence of soil porosity or moisture content on the migration and diffusion of soil volatile organic compounds (VOCs), which limits the prediction accuracy of the models; Finally, the existing monitoring systems lack the ability to adaptively adjust the sampling strategy according to the pollutant concentration gradient, resulting in waste of monitoring resources or the emergence of monitoring blind spots. The noise interference of environmental data and the uncertainty of model parameters also significantly affect the accuracy of monitoring results.

[0037] This application provides a solution that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to elaborate in detail on how to implement this intelligent monitoring method for soil pollutants VOCs.

[0038] Figure 1 The overall flowchart of the intelligent monitoring method for soil pollutants VOCs is shown, including:

[0039] S1: Obtain soil characteristic data and construct a VOCs diffusion kinetics model, and use the VOCs diffusion kinetics model for calculation to obtain the pollutant concentration gradient.

[0040] Preferably, obtaining soil characteristic data means collecting soil samples and conducting measurements to obtain the porosity distribution parameters and moisture content parameters of the soil samples.

[0041] Furthermore, constructing the VOCs diffusion kinetics model includes the following steps:

[0042] Analyze the diffusion process of pollutants according to Fick's second law. The diffusion process of pollutant concentration C(x, t) is expressed as follows:

[0043]

[0044] Where C(x, t) is the pollutant concentration; D0 is the free diffusion coefficient of the pollutant in the pure medium; x is the spatial position; t is the time.

[0045] In actual soil, VOCs will gradually reduce their concentration due to adsorption or chemical degradation. Therefore, by adding a decay term -k·C(x, t) to the diffusion process of pollutant concentration C(x, t), where k is the degradation rate constant, reflecting the loss ratio of pollutants per unit time.

[0046] Considering the influence of soil pores and moisture on the diffusion process, the free diffusion coefficient D0 is optimized by combining the porosity distribution parameter and the water content parameter to obtain the optimized free diffusion coefficient. The specific formula is as follows:

[0047] D = D0·φ·(1 - ω)

[0048] Where D is the optimized free diffusion coefficient; φ is the porosity distribution parameter; ω is the water content parameter.

[0049] Based on the optimized free diffusion coefficient and the attenuation term, the diffusion process of the pollutant concentration C(x, t) is improved to construct a VOCs diffusion kinetic model.

[0050] Specifically, the calculation formula of the VOCs diffusion kinetic model is as follows:

[0051]

[0052] Where, is the pollutant concentration gradient, that is, the output result of the VOCs diffusion kinetic model; C(x, t) is the pollutant concentration; D0 is the free diffusion coefficient of the pollutant in the pure medium; x is the spatial position; t is the time; φ is the porosity distribution parameter; ω is the water content parameter; k is the degradation rate constant.

[0053] Preferably, the present invention introduces an attenuation term on the basis of the traditional Fick's second law, considering the adsorption and chemical degradation factors of VOCs in the soil. This improvement makes the model more in line with the actual situation because the concentration of VOCs in the soil does indeed decay over time; at the same time, the model also considers the influence of soil porosity and water content on the diffusion process. By optimizing the free diffusion coefficient, the model is more suitable for the diffusion behavior in the actual soil environment. This multi-parameter coupling processing method significantly improves the description accuracy of the model for the actual VOCs diffusion process.

[0054] S2: Obtain environmental data based on the pollutant concentration gradient and perform preprocessing to obtain the preprocessed environmental data.

[0055] Preferably, the environmental data includes temperature data, humidity data, and air pressure gradient data.

[0056] Furthermore, obtaining environmental data based on the pollutant concentration gradient includes:

[0057] Based on the pollutant concentration gradient Make a judgment. If the pollutant concentration gradient is less than or equal to the first threshold, it is determined as a low-gradient area, and the spacing of the sampling points is set to T1 and the environmental data is obtained.

[0058] If the pollutant concentration gradient If it is greater than the first threshold and less than the second threshold, it is determined as the medium gradient region, and the spacing of the sampling points is set to T2 and the environmental data is obtained.

[0059] If the pollutant concentration gradient is greater than or equal to the second threshold, it is determined as the high gradient region, and the spacing of the sampling points is set to T3 and the environmental data is obtained.

[0060] It should be noted that for the various thresholds for judging the pollutant concentration gradient, first, based on the diffusion characteristics of VOCs in the soil, a large number of experimental data are obtained by sampling at typical polluted sites, and statistical analysis is carried out. Then, combined with the actual monitoring requirements, the concentration gradient is classified, and finally the optimal first threshold and second threshold are obtained after continuous adjustment. Secondly, for the setting of the sampling point spacing, it is necessary to conduct a comprehensive evaluation according to the monitoring accuracy and cost-benefit, calculate the monitoring data error and cost-benefit error under different sampling spacings, and then obtain the optimal sampling point spacing.

[0061] Specifically, the preprocessing refers to performing wavelet transform denoising processing on the temperature data, humidity data and barometric gradient data to obtain the preprocessed environmental data, thereby improving the quality and stability of the data and providing more accurate data for the subsequent prediction of pollutant concentration.

[0062] Preferably, according to the magnitude of the pollutant concentration gradient, the present invention proposes an adaptive sampling strategy with three levels of sampling spacing, which significantly improves the sampling efficiency while ensuring the data accuracy. Especially in the high gradient region, a relatively dense sampling spacing is adopted, while in the low gradient region, a larger sampling spacing is adopted, which not only ensures the monitoring accuracy of the key area but also avoids resource waste.

[0063] S3: Optimize the VOCs diffusion kinetics model by using the preprocessed environmental data to construct a VOCs concentration distribution prediction model.

[0064] Preferably, constructing the VOCs concentration distribution prediction model includes the following steps:

[0065] Based on the VOCs diffusion kinetics model, combined with the relationship between the barometric gradient data and the soil characteristics and the free diffusion coefficient, use the barometric gradient data to improve the optimized free diffusion coefficient to obtain the dynamic diffusion coefficient. The specific formula is as follows:

[0066] D' = D0·φ·(1 - ω)·(1 + γ·P(t))

[0067] Where, D' is the dynamic diffusion coefficient; γ is the barometric gradient correction coefficient; P(t) is the barometric gradient data, which reflects the influence of atmospheric pressure on the diffusion process.

[0068] To reflect the influence of temperature and humidity on the degradation rate, the degradation rate constant k is optimized using temperature data and humidity data to obtain the optimized degradation rate. The specific formula is as follows:

[0069] k' = k0·(1 + α·H(t) - β·T(t))

[0070] Where k' is the optimized degradation rate; k0 is the initial degradation rate constant; α and β are the influence coefficients of humidity data and temperature data respectively; H(t) is the humidity data, the higher the humidity, the stronger the degradation effect; T(t) is the temperature data, the higher the temperature, the lower the degradation rate of some VOCs.

[0071] Based on the dynamic diffusion coefficient D' and the optimized degradation rate k', the VOCs diffusion kinetic model is optimized to construct a VOCs concentration distribution prediction model. The specific formula is as follows:

[0072]

[0073] Where C pred (x,t) is the predicted concentration of pollutant VOCs; C0 is the initial concentration of pollutant VOCs; cosh is the hyperbolic cosine function, used to describe non-linear spatial diffusion.

[0074] Specifically, the calculation formula of the VOCs concentration distribution prediction model is as follows:

[0075]

[0076] Where C pred (x,t) is the predicted concentration of pollutant VOCs; C0 is the initial concentration of pollutant VOCs; cosh is the hyperbolic cosine function, used to describe non-linear spatial diffusion; k0 is the initial degradation rate constant; α and β are the influence coefficients of humidity data and temperature data respectively; H(t) is the humidity data, the higher the humidity, the stronger the degradation effect; T(t) is the temperature data, the higher the temperature, the lower the degradation rate of some VOCs; D0 is the free diffusion coefficient of the pollutant in the pure medium; φ is the porosity distribution parameter; ω is the moisture content parameter; γ is the air pressure gradient correction coefficient; P(t) is the air pressure gradient data, reflecting the influence of atmospheric pressure on the diffusion process.

[0077] Preferably, by introducing the air pressure gradient correction coefficient and the air pressure gradient data, the present invention takes into account the influence of atmospheric pressure on the diffusion process, and by introducing the temperature and humidity influence coefficients to optimize the degradation rate, a quantitative relationship between temperature, humidity and the VOCs degradation rate is established, making the final prediction result more in line with the actual diffusion law and improving the prediction accuracy of the model.

[0078] S4: Based on the VOCs concentration distribution prediction model, conduct concentration prediction to obtain the predicted VOCs concentration, and achieve intelligent monitoring of soil pollutant VOCs.

[0079] Specifically, conducting concentration prediction based on the VOCs concentration distribution prediction model means inputting the real-time collected environmental data into the VOCs concentration distribution prediction model for calculation, calculating the predicted VOCs concentration at each sampling point respectively, and visualizing the predicted VOCs concentration to draw the distribution map of VOCs pollutant concentration.

[0080] In summary, the present invention obtains the soil porosity and water content parameters, constructs the VOCs diffusion kinetics model considering soil characteristics, realizes the accurate description of the diffusion behavior of VOCs in heterogeneous soil, improves the characterization ability of the model for the migration and diffusion process of VOCs in the actual soil environment, and provides a reliable theoretical basis for subsequent monitoring; by introducing a three-level concentration gradient threshold judgment mechanism, it realizes the adaptive adjustment of the sampling point spacing, adopts a dense sampling strategy in the high-concentration gradient area and a sparse sampling strategy in the low-gradient area, which not only ensures the monitoring accuracy of the key area but also avoids the waste of monitoring resources, optimizes the environmental data collection efficiency, and improves the economy of the monitoring system; by introducing a barometric gradient correction coefficient to dynamically optimize the diffusion coefficient and using temperature and humidity data to optimize the degradation rate constant, a prediction model considering the influence of multiple environmental factors is established, which improves the accuracy and reliability of VOCs concentration prediction.

[0081] Embodiment 2 is an embodiment of the present invention, which provides a soil pollutant VOCs intelligent monitoring system, including: a diffusion dynamics module, used to obtain the porosity distribution parameters and water content parameters of soil samples and construct a VOCs diffusion kinetics model to obtain the pollutant concentration gradient; an environmental data module, used to obtain environmental data based on the pollutant concentration gradient and perform preprocessing to obtain the preprocessed environmental data; a concentration distribution module, used to optimize the VOCs diffusion kinetics model by using the preprocessed environmental data to construct a VOCs concentration distribution prediction model; a concentration prediction module, used to conduct concentration prediction based on the VOCs concentration distribution prediction model to obtain the predicted VOCs concentration and visualize it.

[0082] Embodiment 3 is an embodiment of the present invention, which is different from the previous embodiment in that:

[0083] Such as Figure 2As shown, if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0084] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a predefined sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0085] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as necessary, and then storing it in a computer memory.

[0086] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A soil pollutant VOCs intelligent monitoring method, characterized by: include: Acquire soil characteristic data and construct a VOCs diffusion kinetic model, and use the VOCs diffusion kinetic model to perform calculations to obtain a pollutant concentration gradient; Acquire environmental data based on the pollutant concentration gradient and perform preprocessing to obtain preprocessed environmental data; Utilizing the pre-processed environmental data to optimize the VOCs diffusion kinetics model, and constructing a VOCs concentration distribution prediction model; Based on the VOCs concentration distribution prediction model, concentration prediction is performed to obtain VOCs predicted concentration, thereby realizing intelligent monitoring of soil pollutant VOCs; The construction of the VOCs diffusion kinetics model comprises the following steps: According to Fick's second law, the diffusion process of pollutants is analyzed and the concentration of pollutants The diffusion process is expressed as follows: ； in, is the pollutant concentration; is the free diffusion coefficient of the pollutant in the pure medium; is the spatial position; For time; To the pollutant concentration An attenuation term is introduced into the diffusion process ,in is the degradation rate constant; Combining porosity distribution parameters and water content parameters to predict free diffusion coefficient Optimize and obtain the optimized free diffusion coefficient. The specific formula is as follows: ； in, is the optimized free diffusion coefficient; is the porosity distribution parameter; is the moisture content parameter; Pollutant concentration based on the optimized free diffusion coefficient and attenuation term Improve the diffusion process and build a VOCs diffusion kinetic model; The calculation formula of the VOCs diffusion kinetic model is as follows: ； in, is the pollutant concentration gradient; is the pollutant concentration; is the free diffusion coefficient of the pollutant in the pure medium; is the spatial position; For time; is the porosity distribution parameter; is the moisture content parameter; is the degradation rate constant.

2. The soil pollutant VOCs intelligent monitoring method according to claim 1, characterized in that: The obtaining of soil characteristic data refers to collecting soil samples and measuring them to obtain porosity distribution parameters and water content parameters of the soil samples.

3. The soil pollutant VOCs intelligent monitoring method according to claim 2, characterized in that: The environmental data includes temperature data, humidity data and air pressure gradient data; Acquiring environmental data based on the pollutant concentration gradient includes: Based on the pollutant concentration gradient To judge, if the pollutant concentration gradient If it is less than or equal to the first threshold, it is determined to be a low-gradient area, the spacing of the sampling points is set to T1 and the environmental data is obtained; If the pollutant concentration gradient If it is greater than the first threshold and less than the second threshold, it is determined to be a medium gradient area, the spacing of the sampling points is set to T2 and the environmental data is obtained; If the pollutant concentration gradient If it is greater than or equal to the second threshold, it is determined to be a high gradient area, the spacing of the sampling points is set to T3 and the environmental data is obtained.

4. The soil pollutant VOCs intelligent monitoring method according to claim 3, characterized in that: The construction of the VOCs concentration distribution prediction model comprises the following steps: Based on the VOCs diffusion kinetic model, combined with the pressure gradient data and the relationship between soil properties and the free diffusion coefficient, the optimized free diffusion coefficient is improved using the pressure gradient data to obtain the dynamic diffusion coefficient; The degradation rate constant was calculated by using temperature and humidity data. Perform optimization to obtain an optimized degradation rate; Based on the dynamic diffusion coefficient and optimized degradation rate The VOCs diffusion kinetics model was optimized and a VOCs concentration distribution prediction model was constructed.

5. The method for intelligent monitoring of soil pollutants VOCs according to claim 4, characterized in that: The specific formula of the dynamic diffusion coefficient is as follows: ； in, is the dynamic diffusion coefficient; is the pressure gradient correction factor; is the pressure gradient data; The specific formula of the optimized degradation rate is as follows: ； in, is the optimized degradation rate; is the initial degradation rate constant; and are the influence coefficients of humidity data and temperature data respectively; is humidity data; is the temperature data.

6. A soil pollutant VOCs intelligent monitoring system, based on the soil pollutant VOCs intelligent monitoring method according to any one of claims 1 to 5, characterized in that: include, Diffusion dynamics module, used to obtain the porosity distribution parameters and moisture content parameters of soil samples and build a VOCs diffusion dynamics model to obtain the pollutant concentration gradient; An environmental data module is used to obtain environmental data based on the pollutant concentration gradient and perform preprocessing to obtain preprocessed environmental data; The concentration distribution module is used to optimize the VOCs diffusion kinetics model using pre-processed environmental data and build a VOCs concentration distribution prediction model; The concentration prediction module is used to predict the concentration based on the VOCs concentration distribution prediction model, obtain the VOCs predicted concentration and visualize it.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent monitoring method for soil pollutants VOCs described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent monitoring method for soil pollutants VOCs described in any one of claims 1 to 5 are implemented.

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