Method for making multi-parameter monitoring wells for groundwater environment in multiple micro-layer groups of a plot

Abstract

The present invention relates to the technical field of engineering monitoring facilities, and in particular to a method for producing multi-parameter monitoring wells for groundwater environments in multiple micro-layer groups of a plot. According to the foundation production parameters, a specific foundation of the multi-parameter monitoring well is selected and drilled, and based on the permeability coefficient of different lithologic strata for groundwater distribution, the hydrological characteristic gradient of each lithologic stratum in the two-dimensional Fourier transform image is drilled to calculate the impact trend distribution of groundwater and design the production material and construction parameters of the main well pipe; a multi-dimensional simulated concentration data set is obtained, and based on the thermal kernel density index, the multi-dimensional simulated concentration data set is densely connected in units on the basis of the color domain to generate an enrichment hierarchy map, and multiple micro-layer monitoring groups inside the main well pipe are arranged according to the enrichment hierarchy map. The present invention can accurately design the foundation production parameters, production materials, construction parameters, and micro-layer groups of different monitoring depth layers of the groundwater environment multi-parameter monitoring well, thereby improving the diversity expression and accuracy of the groundwater monitoring results.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering monitoring facilities, and in particular to a method for fabricating multi-parameter monitoring wells for groundwater environment in multiple micro-layer groups of a plot. Background Art

[0002] With the acceleration of urbanization and the continuous increase in industrial activity, groundwater resources are receiving increasing attention. Environmental monitoring of groundwater is particularly important in contaminated sites, such as industrial sites, chemical parks, and abandoned factories. Groundwater pollution is highly concealed, widespread, and difficult to remediate, necessitating the use of scientific and effective monitoring methods to track and assess it in real time.

[0003] Currently, commonly used groundwater monitoring wells primarily monitor single layers or layers, using filters and seals at varying depths to sample and acquire data from different aquifers. These wells have considerable application value in geological conditions where a single aquifer or interlayer hydraulic connections are weak. However, in actual field investigations, complex stratigraphic structures, densely distributed microstrata, and significant hydrogeological variations are often encountered. Traditional stratified monitoring wells operate at large scales, making it difficult to distinguish between microstrata, resulting in monitoring results that fail to reflect micro-level pollution migration patterns. Furthermore, traditional monitoring wells struggle to accurately select their foundations based on the random distribution of groundwater flow, making it difficult for multi-parameter monitoring wells to obtain high-value groundwater samples. Furthermore, the design and planning of different microstrata based on the concentration of groundwater pollutants is difficult, resulting in monitoring results that fail to reflect micro-level pollution migration patterns. Furthermore, the design and planning of traditional multi-parameter monitoring wells and the analysis of groundwater environments often require extensive human effort, significantly increasing human and material costs. Summary of the Invention

[0004] The present invention overcomes the deficiencies of the prior art and provides a method for fabricating multi-parameter monitoring wells for groundwater environment in multiple micro-layer groups of a plot.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A first aspect of the present invention provides a method for fabricating a multi-parameter monitoring well for a plurality of micro-layer groups of a plot of land, comprising the following steps:

[0007] S102: obtaining real-time hydrological values and a flow convergence network of groundwater in the target monitoring area, performing Hamiltonian dynamics distribution state calculation on the position variables of the flow convergence network based on the momentum variables of the real-time hydrological values, and obtaining a random state chain of groundwater distribution;

[0008] S104: Factor calculation is performed to determine the types of coupled pollution components associated with the individual multi-parameter monitoring indicators, so that the coupled pollution components visit the attraction source points with attraction bias gradients set by the random state chain to analyze the direction of the pollutants, output the visit results, and determine the foundation parameters of the multi-parameter monitoring well based on the visit results;

[0009] S106: Select and drill a specific foundation for a multi-parameter monitoring well based on the foundation fabrication parameters. Drill the hydrological characteristic gradient of each lithologic layer in the two-dimensional Fourier transform image based on the permeability coefficient of each lithologic layer to groundwater distribution, calculate the impact trend distribution of groundwater, and design the fabrication material and construction parameters of the main well pipe.

[0010] S108: Construct a color domain foundation, simulate groundwater monitoring of the main well pipe using the migration simulation field of coupled pollution component types in groundwater, obtain a multidimensional simulated concentration data set, densely connect the multidimensional simulated concentration data set based on the color domain foundation based on the thermal kernel density index, generate an enrichment hierarchy map, and arrange multiple micro-layer monitoring groups inside the main well pipe according to the enrichment hierarchy map.

[0011] More specifically, the step S102 includes the following steps:

[0012] Obtaining real-time hydrological values and flow convergence network of groundwater in the target monitoring area within a preset time period, extracting several flow convergence nodes of the flow convergence network and the distribution pattern of each flow convergence node;

[0013] Based on the real-time hydrological numerical preset momentum variables of different specifications, each flow convergence node is defined as a position variable, and each momentum variable is directed to each position variable one by one to generate a real-time dynamic distribution matrix of the change of the flow convergence position guided by the hydrological momentum;

[0014] The Hamiltonian dynamics law is introduced, and the Hamiltonian potential energy function and Hamiltonian kinetic energy function of the flow distribution are set based on the Hamiltonian dynamics law. The Hamiltonian equation of groundwater dynamic distribution is constructed based on the Hamiltonian potential energy function and Hamiltonian kinetic energy function.

[0015] The real-time dynamic distribution matrix is simulated and solved by the groundwater dynamic distribution Hamiltonian equation and a compressed time step is preset. The state of the hydrological momentum-flow convergence position in the matrix is integrated and updated during the simulation and solution process using the compressed time step integral as a discrete basis to obtain the updated distribution state probability and the new Hamiltonian energy;

[0016] Obtaining the original Hamiltonian energy before the real-time power distribution matrix simulation, calculating the difference between the new Hamiltonian energy and the original Hamiltonian energy to obtain the Hamiltonian energy deviation, and constructing the state inclusion probability based on the Hamiltonian energy deviation;

[0017] If the updated distribution state probability is greater than the state inclusion probability, the state of the hydrological momentum-flow convergence position corresponding to the updated distribution state probability is accepted; if the updated distribution state probability is less than the state inclusion probability, the state before the update is retained;

[0018] Repeat the above steps of state updating and inclusion judgment until each momentum variable is included, and obtain the random state chain of groundwater distribution.

[0019] More specifically, the step S104 includes the following steps:

[0020] Obtain the target monitoring area and monitoring log of the groundwater environment, extract the types of pollutants covered by the groundwater environment in the target monitoring area within a preset time period through the monitoring log, and obtain individual multi-parameter monitoring indicators of the groundwater environment according to monitoring requirements;

[0021] The factor extraction method is introduced to extract one or more types of pollutants to obtain several pollution characteristic factors. Based on the weak correlation of individual multi-parameter detection indicators, each pollution characteristic factor is orthogonally rotated one by one to generate the load matrix of each pollution characteristic factor.

[0022] Based on the load matrix, the load score of each multi-parameter detection indicator individual on each pollution characteristic factor is calculated to obtain the factor coupling score of each pollution characteristic factor associated with the multi-parameter monitoring indicator individual. Only one or more pollution component types corresponding to the pollution characteristic factors with factor coupling scores greater than the preset factor coupling scores are extracted and calibrated as coupled pollution component types;

[0023] Obtain groundwater pollution knowledge graph based on the big data platform, identify one or more types of coupled pollution components through the groundwater pollution knowledge graph, and output the current solubility index of groundwater for each type of coupled pollution component;

[0024] The chain nodes and chain-locked edges of each chain node on the random state chain of groundwater distribution are extracted, one or more attraction source points of the chain nodes are defined, and the type of coupled pollution components is defined as wandering particles. The attraction function of the attraction source point is constructed based on the current dissolution index.

[0025] Create an attraction bias gradient based on the attraction function, and start from the current node to make the current wandering particle perform a random step based on the attraction bias gradient to move to the next adjacent attraction source with higher attraction. After each random step is completed, the position of the current wandering particle is updated and the visit result is output;

[0026] Based on the visit results, a visit path structure diagram of the coupled pollution components is drawn, and the foundation preparation parameters of the multi-parameter monitoring well are determined based on the visit path structure diagram.

[0027] More specifically, the step S106 includes the following steps:

[0028] Based on the foundation production parameters, multi-parameter monitoring wells are drilled and photographed in the target monitoring area to obtain geological profile image data of the target monitoring area;

[0029] The Scharr operator is introduced to calculate the characteristics of different lithologic strata on the geological profile image data to obtain the hydrological characteristic gradient of each lithologic stratum and the gradient amplitude of each hydrological characteristic gradient;

[0030] Based on the hydrological characteristic gradient and random state chain, the permeability coefficients of different lithologic strata for groundwater distribution are retrieved in the big data network. Based on the permeability coefficient, a preliminary one-dimensional fast Fourier transform is performed on each row of pixels of each hydrological characteristic gradient at the gradient amplitude to obtain the intermediate gradient value of the frequency component.

[0031] After the initial one-dimensional transformation, a second one-dimensional fast Fourier transform is performed on each column pixel of each hydrological characteristic gradient according to the intermediate gradient value of the frequency component, and the final two-dimensional spectrum of the lithologic formation gradient permeability is output;

[0032] The energy spectrum of the groundwater infiltration in each lithologic stratum under the condition of random state chain distribution is calculated by terminating the two-dimensional spectrum diagram, the radial radius frequency is preset, and the energy spectrum is averaged using the radial radius frequency as the averaging criterion to obtain the gradient energy spectrum curve of groundwater infiltration in the target monitoring area;

[0033] The historical hydraulic gradient of the groundwater environment under the condition of a random state chain is obtained, and Darcy's law is introduced to construct the Darcy water flow velocity model. The gradient energy spectrum curve and the historical hydraulic gradient are linearly calculated through the Darcy water flow velocity model to obtain the current groundwater flow velocity. The current groundwater flow velocity is retrieved to obtain the impact trend distribution of the groundwater, and the manufacturing material and construction parameters of the main well pipe are designed according to the impact trend distribution.

[0034] More specifically, the step S108 includes the following steps:

[0035] Obtain historical pollution migration monitoring parameters of coupled pollution components in the groundwater environment, interpolate the particle distribution function based on the historical pollution migration monitoring parameters to the discretized particle grid to perform collision solution and propagation update of migrating particles, and obtain the migration simulation field of coupled pollution components in the groundwater;

[0036] Constructing a three-dimensional simulation model of the main well pipe, injecting the migration simulation field into the three-dimensional simulation model for simulation, and recording the concentration of the types of coupled pollution components inside the main well pipe during the simulation process to obtain a multi-dimensional simulation concentration data set;

[0037] Constructing a vertical retention dimension domain of a multidimensional simulated concentration data set, and dividing the vertical retention dimension domain into M sub-dimensional interval units based on the dimension of the simulation time series step;

[0038] Based on the big data network, we obtain a thermal density system and a thermal color gamut reference table for visualizing the scale of pollution. We use the thermal density system to identify and evaluate each simulated concentration value in the simulated concentration data set, and obtain the thermal kernel density index for each simulated concentration value.

[0039] Calculate the ratio between the number of simulated concentration values whose thermal kernel density index is greater than a preset index and the number of simulated concentration values whose thermal kernel density index is less than a preset index in each sub-dimensional interval unit; if the ratio is higher than the preset ratio, mark the sub-dimensional interval unit as a dense unit;

[0040] According to the thermal color gamut reference table, K color gamut foundations are rooted. Starting from the color gamut foundation, one-dimensional dense units in the adjacent dimension are connected layer by layer in the vertical retention dimension field to generate two-dimensional dense units. The adjacent two-dimensional connection generates three-dimensional units, and so on, until no higher-dimensional dense units can be generated. The vertical retention thermal hierarchy of the coupled pollution components located inside the main well pipe under groundwater environmental conditions is obtained;

[0041] The thermal layers with thermal values greater than the preset thermal values in the vertical retention thermal layers are marked as enriched thermal layers, and an enrichment layer map is obtained. Multiple microlayer monitoring groups are arranged inside the main well pipe according to the enrichment layer map.

[0042] More specifically, the method of obtaining historical pollution migration monitoring parameters of coupled pollution components in a groundwater environment, interpolating a particle distribution function based on the historical pollution migration monitoring parameters on a discretized particle grid to perform collision solution and propagation update of migrating particles, and obtaining a migration simulation field of coupled pollution components in groundwater specifically includes the following steps:

[0043] Through monitoring logs, historical pollution migration monitoring parameters and historical water flow monitoring parameters of coupled pollution components in the random state chain conditions of groundwater within a preset time period are obtained, and a geological profile spatial model is simultaneously constructed;

[0044] The coupled pollution components are defined as migrating particles, the historical water flow characteristics of the groundwater environment under the random state chain condition are determined according to the historical water flow monitoring parameters, and the geological profile spatial model is discretized into a number of particle grids based on the historical water flow characteristics;

[0045] The fluid Boltzmann equation is introduced. Based on the historical pollution migration monitoring parameters, the particle distribution function of each migrating particle is preset as the step size changes over a preset time period. The particle distribution function is solved by interpolating the Boltzmann equation within each particle grid point to generate a series of particle migration collision terms.

[0046] A trade-off algorithm is introduced to calculate the docking weight between each particle grid point and its neighboring particle grid points during the collision interpolation solution process based on a series of particle migration collision terms. The discrete collision vector between each particle grid point and its neighboring particle grid points is determined based on the collision weight.

[0047] Each migrating particle is propagated to the adjacent particle grid along the discrete collision vector, and the above collision solution and propagation steps are repeated to continuously update the particle distribution function of the migrating particles until all migrating particles are updated, and multiple new seedling particle distribution functions are obtained;

[0048] A migration simulation field of coupled pollution component types in a groundwater environment is established based on the multiple seedling particle distribution functions.

[0049] The second aspect of the present invention provides a groundwater environment multi-parameter monitoring well production system for multiple micro-layer groups of a plot, the groundwater environment multi-parameter monitoring well production system includes a memory and a processor, the memory stores a groundwater environment multi-parameter monitoring well production method program for multiple micro-layer groups of a plot, when the groundwater environment multi-parameter monitoring well production method program is executed by the processor, any one of the steps of the groundwater environment multi-parameter monitoring well production method is implemented.

[0050] The present invention solves the technical defects existing in the background technology, and the beneficial technical effects of the present invention are:

[0051] The real-time hydrological values and flow convergence network of groundwater in the target monitoring area are obtained, and the distribution state of the position variable of the flow convergence network is calculated by Hamiltonian dynamics based on the momentum variable of the real-time hydrological values to obtain the random state chain of groundwater distribution; the factor calculation is associated with the type of coupled pollution components of the multi-parameter monitoring indicator individuals, so that the coupled pollution components are allowed to visit the attraction source point with attraction bias gradient set in the random state chain to analyze the direction of pollutants, output the visit results, and determine the foundation making parameters of the multi-parameter monitoring well according to the visit results; select and drill the multi-parameter monitoring well according to the foundation making parameters. The specific foundation of the well logging is to drill the hydrological characteristic gradient of each lithologic stratum in the two-dimensional Fourier transform image based on the permeability coefficient of different lithologic strata to groundwater distribution, and then calculate the impact trend distribution of groundwater to design the main well pipe manufacturing material and construction parameters; construct a color domain foundation, and use the coupled contaminant component types in the groundwater migration simulation field to simulate groundwater monitoring of the main well pipe, obtain a multi-dimensional simulated concentration data set, and based on the thermal kernel density index, densely connect the multi-dimensional simulated concentration data set on the color domain foundation to generate an enrichment hierarchy map. According to the enrichment hierarchy map, multiple micro-layer monitoring groups inside the main well pipe are arranged. The present invention can accurately design the foundation manufacturing parameters, manufacturing materials, and construction parameters of the groundwater environment multi-parameter monitoring well through multi-dimensional calculations such as groundwater distribution, the direction of pollutants with water flow, and the permeability of lithologic strata. At the same time, multiple micro-layer groups at different monitoring depths can be reasonably set according to the enrichment status of pollutants in the monitoring well, achieving efficient multi-parameter monitoring performance and improving the diversity and accuracy of groundwater monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, they can also obtain drawings of other embodiments based on these drawings.

[0053] Figure 1 A first method flow chart of a method for making multi-parameter monitoring wells for groundwater environment in multiple micro-layer groups of a plot is shown;

[0054] Figure 2 A second method flow chart of a method for making multi-parameter monitoring wells for groundwater environment in multiple micro-layer groups of a plot is shown;

[0055] Figure 3 The system framework diagram of the groundwater environment multi-parameter monitoring well production system for multiple micro-layer groups in a plot is shown. DETAILED DESCRIPTION

[0056] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0058] The first aspect of the present invention provides a method for making multi-parameter monitoring wells for groundwater environment in multiple micro-layer groups of a plot, such as Figure 1 As shown, the following steps are included:

[0059] S102: obtaining real-time hydrological values and a flow convergence network of groundwater in the target monitoring area, performing Hamiltonian dynamics distribution state calculation on the position variables of the flow convergence network based on the momentum variables of the real-time hydrological values, and obtaining a random state chain of groundwater distribution;

[0060] S104: Factor calculation is performed to determine the types of coupled pollution components associated with the individual multi-parameter monitoring indicators, so that the coupled pollution components visit the attraction source points with attraction bias gradients set by the random state chain to analyze the direction of the pollutants, output the visit results, and determine the foundation parameters of the multi-parameter monitoring well based on the visit results;

[0061] S106: Select and drill a specific foundation for a multi-parameter monitoring well based on the foundation fabrication parameters. Drill the hydrological characteristic gradient of each lithologic layer in the two-dimensional Fourier transform image based on the permeability coefficient of each lithologic layer to groundwater distribution, calculate the impact trend distribution of groundwater, and design the fabrication material and construction parameters of the main well pipe.

[0062] S108: Construct a color domain foundation, simulate groundwater monitoring of the main well pipe using the migration simulation field of coupled pollution component types in groundwater, obtain a multidimensional simulated concentration data set, densely connect the multidimensional simulated concentration data set based on the color domain foundation based on the thermal kernel density index, generate an enrichment hierarchy map, and arrange multiple micro-layer monitoring groups inside the main well pipe according to the enrichment hierarchy map.

[0063] More specifically, the step S102 includes the following steps:

[0064] Obtaining real-time hydrological values and flow convergence network of groundwater in the target monitoring area within a preset time period, extracting several flow convergence nodes of the flow convergence network and the distribution pattern of each flow convergence node;

[0065] Based on the real-time hydrological numerical preset momentum variables of different specifications, each flow convergence node is defined as a position variable, and each momentum variable is directed to each position variable one by one to generate a real-time dynamic distribution matrix of the change of the flow convergence position guided by the hydrological momentum;

[0066] The Hamiltonian dynamics law is introduced, and the Hamiltonian potential energy function and Hamiltonian kinetic energy function of the flow distribution are set based on the Hamiltonian dynamics law. The Hamiltonian equation of groundwater dynamic distribution is constructed based on the Hamiltonian potential energy function and Hamiltonian kinetic energy function.

[0067] The real-time dynamic distribution matrix is simulated and solved by the groundwater dynamic distribution Hamiltonian equation and a compressed time step is preset. The state of the hydrological momentum-flow convergence position in the matrix is integrated and updated during the simulation and solution process using the compressed time step integral as a discrete basis to obtain the updated distribution state probability and the new Hamiltonian energy;

[0068] Obtaining the original Hamiltonian energy before the real-time power distribution matrix simulation, calculating the difference between the new Hamiltonian energy and the original Hamiltonian energy to obtain the Hamiltonian energy deviation, and constructing the state inclusion probability based on the Hamiltonian energy deviation;

[0069] If the updated distribution state probability is greater than the state inclusion probability, the state of the hydrological momentum-flow convergence position corresponding to the updated distribution state probability is accepted; if the updated distribution state probability is less than the state inclusion probability, the state before the update is retained;

[0070] Repeat the above steps of state updating and inclusion judgment until each momentum variable is included, and obtain the random state chain of groundwater distribution.

[0071] It should be noted that real-time hydrological values include real-time water flow velocity, real-time water flow rate, and real-time water flow direction. Existing multi-parameter monitoring wells for groundwater environments are usually made by simple manual water conservancy calculations. Due to the random fluidity of groundwater, there are large errors in the site selection of multi-parameter monitoring wells within the target monitoring area, resulting in the inability of traditional multi-parameter monitoring wells to accurately control the sample collection of the groundwater environment, making it difficult for the monitored groundwater environment parameters to have reliable and effective research and reference value. Therefore, it is particularly important to understand the status and trend of groundwater distribution before designing and making multi-parameter monitoring wells. Therefore, this method first introduces a momentum variable about the momentary hydrological flow change of groundwater to each target position distribution sample that gradually converges under the continuous flow of groundwater. This is based on the principle that groundwater flow will guide the change of the flow convergence position, thereby constructing a joint distribution real-time dynamic distribution matrix so that the movement of the state can be characterized in space using dynamic methods. Momentum imparts inertia to groundwater flow trajectories, avoiding the limitations of traditional random state descriptions of low-density regions that cannot be crossed, without changing the distribution of water flow convergence, significantly improving the accuracy of the random state representation of groundwater distribution. A Hamiltonian equation for groundwater dynamic distribution is then constructed using the Hamiltonian potential energy function and Hamiltonian kinetic energy function for flow distribution, as defined by the Hamiltonian law of dynamics. The Hamiltonian equation is then used to discretize and integrate the hydrological momentum-flow convergence position in the real-time dynamic distribution matrix, accurately exploring the state trajectory of the position variables affected by the sliding of momentum variables along equipotential surfaces in space. The discretized integral update ensures long-range dependence in the generation of random dynamic proposal points, preventing stagnation of local random states in the joint distribution and ensuring the temporal and coherent evolution of the random state during groundwater flow convergence distribution.

[0072] It should be noted that the Hamiltonian energy deviation of the new Hamiltonian energy compared to the original Hamiltonian energy is the change fluctuation of the Hamiltonian energy. If the updated distribution state probability is greater than the state inclusion probability, it means that the simulation deviation brought by the state of the hydrological momentum-flow convergence position under the updated distribution state probability is small, so the update of the state is accepted. Otherwise, it means that the simulation deviation is large, and its state stable distribution has deviated from the groundwater distribution of the original flow convergence. Compared with this state, the original state is more consistent with the flow convergence state of the groundwater distribution, so the state before the update is retained. This step can make the randomness of the state evolution approach the real trajectory as stably as possible, ensuring that the random state of the groundwater distribution has a higher degree of credibility. Through this method, the state of random flow convergence of real-time groundwater as the hydrological value changes can be visualized, so that the subsequent multi-parameter monitoring well production has a sampling-oriented foundation point site selection and construction basis, ensuring that the constructed multi-parameter monitoring well can closely rely on the distribution state changes of the groundwater environment to obtain more accurate monitoring water quality expectations, and significantly improve the monitoring performance of the multi-parameter monitoring well for the groundwater environment.

[0073] More specifically, the step S104 includes the following steps:

[0074] Obtain the target monitoring area and monitoring log of the groundwater environment, extract the types of pollutants covered by the groundwater environment in the target monitoring area within a preset time period through the monitoring log, and obtain individual multi-parameter monitoring indicators of the groundwater environment according to monitoring requirements;

[0075] The factor extraction method is introduced to extract one or more types of pollutants to obtain several pollution characteristic factors. Based on the weak correlation of individual multi-parameter detection indicators, each pollution characteristic factor is orthogonally rotated one by one to generate the load matrix of each pollution characteristic factor.

[0076] Based on the load matrix, the load score of each multi-parameter detection indicator individual on each pollution characteristic factor is calculated to obtain the factor coupling score of each pollution characteristic factor associated with the multi-parameter monitoring indicator individual. Only one or more pollution component types corresponding to the pollution characteristic factors with factor coupling scores greater than the preset factor coupling scores are extracted and calibrated as coupled pollution component types;

[0077] Obtain groundwater pollution knowledge graph based on the big data platform, identify one or more types of coupled pollution components through the groundwater pollution knowledge graph, and output the current solubility index of groundwater for each type of coupled pollution component;

[0078] The chain nodes and chain-locked edges of each chain node on the random state chain of groundwater distribution are extracted, one or more attraction source points of the chain nodes are defined, and the type of coupled pollution components is defined as wandering particles. The attraction function of the attraction source point is constructed based on the current dissolution index.

[0079] Create an attraction bias gradient based on the attraction function, and start from the current node to make the current wandering particle perform a random step based on the attraction bias gradient to move to the next adjacent attraction source with higher attraction. After each random step is completed, the position of the current wandering particle is updated and the visit result is output;

[0080] Based on the visit results, a visit path structure diagram of the coupled pollution components is drawn, and the foundation preparation parameters of the multi-parameter monitoring well are determined based on the visit path structure diagram.

[0081] It should be noted that the multi-parameter monitoring indicators include groundwater pH, conductivity, redox potential, dissolved oxygen, heavy metals, and organic matter. Due to the complexity of the groundwater system and transportation environment, groundwater is contaminated with a variety of different pollutants. These pollutants will continue to undergo random distribution changes as the groundwater flows. Pollutants have a high value for groundwater environmental research. Therefore, multi-parameter monitoring wells are constructed at locations where groundwater pollutants are more frequently randomly distributed so that complete monitoring samples can be collected at all times. Previously, due to the large number of pollutants rich in groundwater, not every pollutant has an inevitable correlation and research significance with the monitoring of multi-parameter groundwater environment. If all pollutant types are randomly distributed and calculated, it will consume a lot of manpower and material computing costs, reducing the efficiency of multi-parameter monitoring well construction. Therefore, only the pollutant types that are highly correlated with the multi-parameter monitoring indicators are randomly distributed and calculated. To this end, this method extracts pollution characteristic factors for various pollutant types. The pollution characteristic factor is a general expression that quantitatively describes the characteristics of the pollutant's impact on the environment. It can be a toxicity factor, an environmental persistence factor, a bioaccumulation factor, or a migration factor. Then, relying on the weak correlation of the multi-parameter detection indicator individuals, each pollution characteristic factor is orthogonally rotated one by one, so that each factor is associated with a main individual, and then the conceptual association between the multi-parameter detection indicator individual and each pollution characteristic factor is more clearly represented, generating a simpler and non-overlapping factor loading structure, improving the interpretability of the factor structure and the efficiency of the association interpretation. Subsequently, based on the loading matrix, a factor coupling score is calculated for each multi-parameter monitoring indicator individual on each pollution characteristic factor to evaluate the respective correlation. If the factor coupling score is greater than the preset factor coupling score, it means that the pollution characteristic factor has a high correlation with a certain multi-parameter monitoring indicator individual, and this type of pollution component is a pollutant that needs close and high attention to its distribution trend. In this way, the pollution distribution subject based on multi-parameter monitoring can be accurately screened, the purpose of groundwater distribution monitoring can be clarified, and the site selection accuracy of multi-parameter monitoring wells can be improved.

[0082] It should be noted that the current solubility index is the value of the groundwater's solubility and attachment capacity for coupled pollutant types, and is an important performance basis for the random migration and distribution of coupled pollutant types with groundwater. The chain nodes of the random state chain define the convergence nodes of the random distribution of groundwater, and the chain locking edges between the chain nodes express the trend of random convergence of groundwater. Since the water flow distribution and trend of some convergence nodes are faster, there may be certain solubility and adsorption for some coupled pollutants with poor current solubility index, thereby causing the pollutant types to produce randomly distributed branches. Therefore, this method uses the random state chain to provide a basic environmental structure for the movement of each coupled pollutant type in the real-time random distribution state of groundwater; defines the attraction source point according to the chain node, provides directionality for particle movement, and uses the current solubility index. The attraction function of the attracting source points for the coupled pollutant species is constructed, determining the strength of each attracting source point's attraction for dissolution and attachment of wandering particles. The attraction bias gradient guides the particles toward a designated target during their wandering process. Starting from the current node, the wandering particle performs a random step based on the attraction bias gradient to move to the next adjacent attracting source point with higher attraction, forming a non-uniform distribution. This makes the wandering particle more inclined to move toward nodes with stronger attraction, better simulating the mobility or community affiliation within the random state distribution graph structure and improving the wandering focus of the coupled pollutant species under the random state distribution of the groundwater. The final output is the distribution path of the coupled pollutant species under the groundwater random state chain. Based on this path, locations with higher concentrations of coupled pollutant species are selected as the foundation sites for multi-parameter monitoring wells. This method can provide a more valuable and accurate basis for the pollutant distribution path for the foundation parameters of multi-parameter monitoring wells, making the site selection of multi-parameter monitoring wells more stable and accurate within the target monitoring area, and improving the effectiveness of groundwater environmental monitoring.

[0083] More specifically, the step S106 includes the following steps:

[0084] Based on the foundation production parameters, multi-parameter monitoring wells are drilled and photographed in the target monitoring area to obtain geological profile image data of the target monitoring area;

[0085] The Scharr operator is introduced to calculate the characteristics of different lithologic strata on the geological profile image data to obtain the hydrological characteristic gradient of each lithologic stratum and the gradient amplitude of each hydrological characteristic gradient;

[0086] Based on the hydrological characteristic gradient and random state chain, the permeability coefficients of different lithologic strata for groundwater distribution are retrieved in the big data network. Based on the permeability coefficient, a preliminary one-dimensional fast Fourier transform is performed on each row of pixels of each hydrological characteristic gradient at the gradient amplitude to obtain the intermediate gradient value of the frequency component.

[0087] After the initial one-dimensional transformation, a second one-dimensional fast Fourier transform is performed on each column pixel of each hydrological characteristic gradient according to the intermediate gradient value of the frequency component, and the final two-dimensional spectrum of the lithologic formation gradient permeability is output;

[0088] The energy spectrum of the groundwater infiltration in each lithologic stratum under the condition of random state chain distribution is calculated by terminating the two-dimensional spectrum diagram, the radial radius frequency is preset, and the energy spectrum is averaged using the radial radius frequency as the averaging criterion to obtain the gradient energy spectrum curve of groundwater infiltration in the target monitoring area;

[0089] The historical hydraulic gradient of the groundwater environment under the condition of a random state chain is obtained, and Darcy's law is introduced to construct the Darcy water flow velocity model. The gradient energy spectrum curve and the historical hydraulic gradient are linearly calculated through the Darcy water flow velocity model to obtain the current groundwater flow velocity. The current groundwater flow velocity is retrieved to obtain the impact trend distribution of the groundwater, and the manufacturing material and construction parameters of the main well pipe are designed according to the impact trend distribution.

[0090] It should be noted that at certain times, groundwater flow and velocity are high, resulting in a large impact potential energy for the groundwater environment. If the material selection and construction parameters of the main well pipe of a multi-parameter monitoring well are not reliable, it may easily lead to cracks and collapse in the multi-parameter monitoring well under the impact of groundwater with large impact forces, making it difficult to achieve long-term groundwater environmental monitoring. Therefore, a reasonable main well pipe design determines whether the multi-parameter monitoring well can withstand the impact of the groundwater environment. To address this, this method drills the foundation according to the foundation construction parameters and captures geological profile image data. Different lithologic strata have different permeability to groundwater, and the impact trend of groundwater under these permeability differences will increase or decrease. Therefore, the impact force and trend of groundwater will vary when it reaches different rock layers. Therefore, this method first uses the Scharr operator to extract the hydrological characteristic gradient and corresponding gradient amplitude of each lithologic stratum in the geological profile image data, and simultaneously obtains the permeability coefficient of different lithologic strata to groundwater distribution. Among them, the Scharr operator is a technology and means for extracting image characteristic gradients. Subsequently, based on the permeability coefficient, a preliminary one-dimensional fast Fourier transform is performed on each row of pixels of each hydrological characteristic gradient in the gradient amplitude, so as to accurately identify the permeability frequency component of each lithologic stratum in the horizontal direction in the image, and a secondary one-dimensional fast Fourier transform is continued on each column of pixels of each hydrological characteristic gradient, which can accurately identify the permeability frequency component of each lithologic stratum in the vertical direction. Averaging the energy spectrum with the radial radius frequency as the averaging criterion can visualize the permeability trend of the rock stratum distribution. The final averaged gradient energy spectrum curve is the embodiment of the permeability gradient, which realizes the clarification of the permeability performance of each rock stratum pattern of the geological profile to groundwater only according to the image characteristics, replacing the traditional manual field sampling of rock stratum samples to study their permeability to groundwater one by one to measure the groundwater impact of the tedious steps, saving a lot of manual labor and material cost output, and improving the calculation accuracy of the influence of rock strata on the groundwater impact trend.

[0091] It should be noted that the hydraulic gradient describes the rate of change of hydraulic head with distance and reflects the driving force of water flow. The larger the gradient, the faster the water flow. In Darcy's law, the hydraulic gradient is used to calculate flow velocity and is closely related to the permeability of the rock formation. Therefore, this method uses Darcy's law to construct a Darcy water flow velocity model. This combines the permeability of each lithologic stratum in the geological profile with the groundwater hydraulic gradient to reveal the current groundwater flow velocity. Furthermore, the groundwater impact trend distribution is obtained based on the current flow velocity and used to design the material and construction parameters of the main well pipe. This method can calculate the groundwater permeability based on the hydrological characteristic gradient of the lithologic strata in the image, thereby clarifying the impact of the rock formation on groundwater impact at the current multi-parameter monitoring well foundation location. This provides an accurate basis for calculating the impact trend distribution under subsequent groundwater flow velocity. It can also rationally design the material and construction parameters of the multi-parameter monitoring well, improve the robustness and stability of the multi-parameter monitoring well in withstanding groundwater impact during long-term monitoring of the groundwater environment, and avoid collapse and impact damage to the multi-parameter monitoring well.

[0092] More specifically, the step S108 includes the following steps:

[0093] Obtain historical pollution migration monitoring parameters of coupled pollution components in the groundwater environment, interpolate the particle distribution function based on the historical pollution migration monitoring parameters to the discretized particle grid to perform collision solution and propagation update of migrating particles, and obtain the migration simulation field of coupled pollution components in the groundwater;

[0094] Constructing a three-dimensional simulation model of the main well pipe, injecting the migration simulation field into the three-dimensional simulation model for simulation, and recording the concentration of the types of coupled pollution components inside the main well pipe during the simulation process to obtain a multi-dimensional simulation concentration data set;

[0095] Constructing a vertical retention dimension domain of a multidimensional simulated concentration data set, and dividing the vertical retention dimension domain into M sub-dimensional interval units based on the dimension of the simulation time series step;

[0096] Based on the big data network, we obtain a thermal density system and a thermal color gamut reference table for visualizing the scale of pollution. We use the thermal density system to identify and evaluate each simulated concentration value in the simulated concentration data set, and obtain the thermal kernel density index for each simulated concentration value.

[0097] Calculate the ratio between the number of simulated concentration values whose thermal kernel density index is greater than a preset index and the number of simulated concentration values whose thermal kernel density index is less than a preset index in each sub-dimensional interval unit; if the ratio is higher than the preset ratio, mark the sub-dimensional interval unit as a dense unit;

[0098] According to the thermal color gamut reference table, K color gamut foundations are rooted. Starting from the color gamut foundation, one-dimensional dense units in the adjacent dimension are connected layer by layer in the vertical retention dimension field to generate two-dimensional dense units. The adjacent two-dimensional connection generates three-dimensional units, and so on, until no higher-dimensional dense units can be generated. The vertical retention thermal hierarchy of the coupled pollution components located inside the main well pipe under groundwater environmental conditions is obtained;

[0099] The thermal layers with thermal values greater than the preset thermal values in the vertical retention thermal layers are marked as enriched thermal layers, and an enrichment layer map is obtained. Multiple microlayer monitoring groups are arranged inside the main well pipe according to the enrichment layer map.

[0100] It should be noted that multi-parameter monitoring wells are typically vertical water collection and monitoring facilities. As the water level within them rises significantly, certain coupled contaminants will vertically distribute and accumulate at different depths as the water level rises. Because these coupled contaminants can provide high groundwater research value, multiple microlayer groups are necessary to collect these coupled contaminants enriched at different depths. To address this, this method first uses historical pollution migration monitoring parameters of coupled contaminant species in groundwater environments to solve collisions and update propagation of migrating particles. This constructs a realistic simulation field that can maximize the restoration of the vertical migration and enrichment of coupled contaminant species within the groundwater environment. This serves as a basis for subsequent simulations within multi-parameter monitoring wells, improving the consistency and practical relevance of the microlayer group settings within the multi-parameter monitoring wells. The model is used to simulate the simulated concentration of the vertical retention of coupled pollution components inside the main well pipe. Since the simulated concentration exists in a multi-dimensional data form, in order to improve the accuracy of concentration quantification, this method divides the vertical retention dimension of the multi-dimensional simulated concentration data set into M non-overlapping sub-dimensional interval units based on the dimension of the simulation time step. For example, in two-dimensional space, if the x-axis is divided into m intervals and the y-axis is divided into n intervals, then the entire two-dimensional space will be divided into m×n dimensional interval units. This can more conveniently calculate the thermal kernel density measurement and assignment of the thermal distribution density of each simulated concentration data point in each dimensional interval unit, avoiding one-by-one operations on massive simulated data points, greatly reducing the complexity of thermal density processing of concentration data, and thereby improving the accuracy of the thermal kernel density index definition of each simulated concentration value in the multi-dimensional data space.

[0101] It should be noted that if the ratio is higher than the preset ratio, it indicates that the number of simulated concentration values with high thermal kernel density indices within the sub-dimensional interval unit is greater than the number of simulated concentration values with low thermal kernel density indices. This indicates that the global thermal kernel density within the sub-dimensional interval unit is generally high and the thermal trend is densely distributed. Therefore, this sub-dimensional interval is a concentration area with a relatively high concentration of coupled pollutant components, and is therefore calibrated as a dense unit. When the thermal kernel density index reaches the interval, it will display a unique thermal color. This thermal color can quickly identify vertical strata where coupled pollutants are frequently enriched. These strata are excellent locations for setting microlayer groups. This method, starting from the color domain root, connects dense cells of different dimensions in the vertical retention dimension layer by layer, and then extrapolates this information. By merging one-dimensional dense cells layer by layer to generate high-dimensional dense cells, this method can reveal the dense distribution structure of these hidden concentration data in high-dimensional space and reveal the high-dimensional clustering pattern of simulated concentration data. Furthermore, integrating high-dimensional dense cells into clusters ensures high density and similarity among simulated concentration data points within each cluster, improving the coherence and saturation of thermal color expression. The resulting vertical retention thermal strata represent the vertical retention concentration density of coupled contaminants within the multi-parameter monitoring well. The thermal strata with thermal values greater than the preset thermal value represent the strata with the highest concentration of coupled contaminants. This method can express the enrichment concentration of coupled contaminants within the main well pipe in the form of thermal density, thereby determining the location of microlayer groups based on thermal anomalies and improving the sampling accuracy of high-value groundwater samples collected from different microlayer groups.

[0102] More specifically, the historical pollution migration monitoring parameters of the coupled pollution component types in the groundwater environment are obtained, and the particle distribution function based on the historical pollution migration monitoring parameters is interpolated on the discretized particle grid to perform collision solution and propagation update of the migrating particles, so as to obtain the migration simulation field of the coupled pollution component types in the groundwater, such as Figure 2 As shown, the specific steps include:

[0103] S202: Obtain historical pollution migration monitoring parameters and historical water flow monitoring parameters of coupled pollution component types under the random state chain condition of groundwater within a preset time period through monitoring logs, and simultaneously construct a geological profile spatial model;

[0104] S204: defining the type of coupled pollution components as migrating particles, determining historical water flow characteristics of the groundwater environment under a random state chain condition based on historical water flow monitoring parameters, and discretizing the geological profile spatial model into a plurality of particle grid points based on the historical water flow characteristics;

[0105] S206: Introducing the fluid Boltzmann equation, presetting the particle distribution function of each migrating particle as the step size changes over a preset time period based on historical pollution migration monitoring parameters, interpolating the Boltzmann equation within each particle grid point to solve the particle distribution function and generate a series of particle migration collision terms;

[0106] S208: introducing a trade-off algorithm to calculate the docking weight between each particle grid point and the adjacent particle grid points based on a series of particle migration collision terms during the collision interpolation solution process, and determining the discrete collision vector between each particle grid point and the adjacent particle grid points according to the collision weight;

[0107] S210: propagating each migrating particle to a neighboring particle grid along the discrete collision vector, repeating the above collision solution and propagation steps to continuously update the particle distribution function of the migrating particles until all migrating particles are updated, thereby obtaining multiple seedling particle distribution functions;

[0108] S212: Establishing a migration simulation field of coupled pollution component types in the groundwater environment based on the plurality of new seedling particle distribution functions.

[0109] It should be noted that to construct a simulation field for the migration of coupled contaminant species within a groundwater environment, this method constructs a groundwater migration and transport scenario, namely a spatial model of a geological profile, and then defines the coupled contaminant species as migrating particles. Because the migration of coupled contaminant species depends on groundwater flow variations, historical flow characteristics of groundwater flow variations over historical periods are used to determine a flow characteristic. Based on this flow characteristic, the spatial model of the geological profile is discretized, allowing for efficient and accurate determination of particle propagation trajectories and velocity sets, ensuring local updates and realistic distribution of particle migration calculations. The particle distribution function can express the distribution of initial macroscopic quantities such as the density and velocity of coupled contaminants. The Boltzmann equation is then interpolated to solve the particle distribution function at each particle grid point. This essentially performs a fluid collision calculation of the migration and distribution of coupled contaminants in groundwater. A series of particle migration collision terms represent the collisional interactions between particles at the grid point, reflecting the linear motion of coupled contaminants as they migrate and distribute with groundwater under the constraints of fluid dynamics. Therefore, the docking weight between each particle grid point and its neighbors is calculated during the collision interpolation process based on these particle migration collision terms. This docking weight represents the priority of each particle in colliding with its neighbors toward a local equilibrium state, revealing the migration direction and trend of the coupled contaminants as they flow through the particle grid. Finally, the newly generated seedling particle distribution function, generated through propagation, provides an animated visualization of the random collisional migration of coupled contaminants under groundwater flow. This method enables the construction of a realistic simulation field that maximizes the vertical migration and enrichment of coupled contaminant species within the groundwater environment, improving the accuracy of subsequent coupled contaminant simulation and vertical enrichment thermal analysis within the main well, and ensuring the reliability and rationality of monitoring of different microlayer groups.

[0110] The second aspect of the present invention provides a system for making multi-parameter monitoring wells for groundwater environment in multiple micro-layer groups of a plot, such as Figure 3 As shown, the groundwater environment multi-parameter monitoring well production system includes a memory 31 and a processor 32. The memory 31 stores a groundwater environment multi-parameter monitoring well production method program for multiple micro-layer groups of a plot. When the groundwater environment multi-parameter monitoring well production method program is executed by the processor 32, any one of the steps of the groundwater environment multi-parameter monitoring well production method is implemented.

[0111] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for making multi-parameter monitoring wells for groundwater environment in multiple micro-layer groups of a plot, characterized by: The following steps are involved: S102: obtaining real-time hydrological values and a flow convergence network of groundwater in the target monitoring area, performing Hamiltonian dynamics distribution state calculation on the position variables of the flow convergence network based on the momentum variables of the real-time hydrological values, and obtaining a random state chain of groundwater distribution; S104: Factor calculation is performed to determine the types of coupled pollution components associated with the individual multi-parameter monitoring indicators, so that the coupled pollution components visit the attraction source points with attraction bias gradients set by the random state chain to analyze the direction of the pollutants, output the visit results, and determine the foundation parameters of the multi-parameter monitoring well based on the visit results; S106: Select and drill a specific foundation for a multi-parameter monitoring well based on the foundation fabrication parameters. Drill the hydrological characteristic gradient of each lithologic layer in the two-dimensional Fourier transform image based on the permeability coefficient of each lithologic layer to groundwater distribution, calculate the impact trend distribution of groundwater, and design the fabrication material and construction parameters of the main well pipe. S108: Construct a color domain foundation, simulate groundwater monitoring of the main well pipe using the migration simulation field of coupled pollution component types in groundwater, obtain a multidimensional simulated concentration data set, densely connect the multidimensional simulated concentration data set based on the color domain foundation based on the thermal kernel density index, generate an enrichment hierarchy map, and arrange multiple micro-layer monitoring groups inside the main well pipe according to the enrichment hierarchy map.

2. The method for making multi-parameter monitoring wells for groundwater environment of multiple micro-layer groups of a plot according to claim 1 is characterized in that: The step S102 specifically includes the following steps: Obtaining real-time hydrological values and flow convergence network of groundwater in the target monitoring area within a preset time period, extracting several flow convergence nodes of the flow convergence network and the distribution pattern of each flow convergence node; Based on the real-time hydrological numerical preset momentum variables of different specifications, each flow convergence node is defined as a position variable, and each momentum variable is directed to each position variable one by one to generate a real-time dynamic distribution matrix of the change of the flow convergence position guided by the hydrological momentum; The Hamiltonian dynamics law is introduced, and the Hamiltonian potential energy function and Hamiltonian kinetic energy function of the flow distribution are set based on the Hamiltonian dynamics law. The Hamiltonian equation of groundwater dynamic distribution is constructed based on the Hamiltonian potential energy function and Hamiltonian kinetic energy function. The real-time dynamic distribution matrix is simulated and solved by the groundwater dynamic distribution Hamiltonian equation and a compressed time step is preset. The state of the hydrological momentum-flow convergence position in the matrix is integrated and updated during the simulation and solution process using the compressed time step integral as a discrete basis to obtain the updated distribution state probability and the new Hamiltonian energy; Obtaining the original Hamiltonian energy before the real-time power distribution matrix simulation, calculating the difference between the new Hamiltonian energy and the original Hamiltonian energy to obtain the Hamiltonian energy deviation, and constructing the state inclusion probability based on the Hamiltonian energy deviation; If the updated distribution state probability is greater than the state inclusion probability, the state of the hydrological momentum-flow convergence position corresponding to the updated distribution state probability is accepted; if the updated distribution state probability is less than the state inclusion probability, the state before the update is retained; Repeat the above steps of state updating and inclusion judgment until each momentum variable is included, and obtain the random state chain of groundwater distribution.

3. The method for making multi-parameter monitoring wells for groundwater environment of multiple micro-layer groups of a plot of land according to claim 1 is characterized in that: The step S104 specifically includes the following steps: Obtain the target monitoring area and monitoring log of the groundwater environment, extract the types of pollutants covered by the groundwater environment in the target monitoring area within a preset time period through the monitoring log, and obtain individual multi-parameter monitoring indicators of the groundwater environment according to monitoring requirements; The factor extraction method is introduced to extract one or more types of pollutants to obtain several pollution characteristic factors. Based on the weak correlation of individual multi-parameter detection indicators, each pollution characteristic factor is orthogonally rotated one by one to generate the load matrix of each pollution characteristic factor. Based on the load matrix, the load score of each multi-parameter detection indicator individual on each pollution characteristic factor is calculated to obtain the factor coupling score of each pollution characteristic factor associated with the multi-parameter monitoring indicator individual. Only one or more pollution component types corresponding to the pollution characteristic factors with factor coupling scores greater than the preset factor coupling scores are extracted and calibrated as coupled pollution component types; Obtain groundwater pollution knowledge graph based on the big data platform, identify one or more types of coupled pollution components through the groundwater pollution knowledge graph, and output the current solubility index of groundwater for each type of coupled pollution component; The chain nodes and chain-locked edges of each chain node on the random state chain of groundwater distribution are extracted, one or more attraction source points of the chain nodes are defined, and the type of coupled pollution components is defined as wandering particles. The attraction function of the attraction source point is constructed based on the current dissolution index. Create an attraction bias gradient based on the attraction function, and start from the current node to make the current wandering particle perform a random step based on the attraction bias gradient to move to the next adjacent attraction source with higher attraction. After each random step is completed, the position of the current wandering particle is updated and the visit result is output; Based on the visit results, a visit path structure diagram of the coupled pollution components is drawn, and the foundation preparation parameters of the multi-parameter monitoring well are determined based on the visit path structure diagram.

4. The method for making multi-parameter monitoring wells for groundwater environment of multiple micro-layer groups in a plot of land according to claim 1, characterized in that: The step S106 specifically includes the following steps: Based on the foundation production parameters, multi-parameter monitoring wells are drilled and photographed in the target monitoring area to obtain geological profile image data of the target monitoring area; The Scharr operator is introduced to calculate the characteristics of different lithologic strata on the geological profile image data to obtain the hydrological characteristic gradient of each lithologic stratum and the gradient amplitude of each hydrological characteristic gradient; Based on the hydrological characteristic gradient and random state chain, the permeability coefficients of different lithologic strata for groundwater distribution are retrieved in the big data network. Based on the permeability coefficient, a preliminary one-dimensional fast Fourier transform is performed on each row of pixels of each hydrological characteristic gradient at the gradient amplitude to obtain the intermediate gradient value of the frequency component. After the initial one-dimensional transformation, a second one-dimensional fast Fourier transform is performed on each column pixel of each hydrological characteristic gradient according to the intermediate gradient value of the frequency component, and the final two-dimensional spectrum of the lithologic formation gradient permeability is output; The energy spectrum of the groundwater infiltration in each lithologic stratum under the condition of random state chain distribution is calculated by terminating the two-dimensional spectrum diagram, the radial radius frequency is preset, and the energy spectrum is averaged using the radial radius frequency as the averaging criterion to obtain the gradient energy spectrum curve of groundwater infiltration in the target monitoring area; The historical hydraulic gradient of the groundwater environment under the condition of a random state chain is obtained, and Darcy's law is introduced to construct the Darcy water flow velocity model. The gradient energy spectrum curve and the historical hydraulic gradient are linearly calculated through the Darcy water flow velocity model to obtain the current groundwater flow velocity. The current groundwater flow velocity is retrieved to obtain the impact trend distribution of the groundwater, and the manufacturing material and construction parameters of the main well pipe are designed according to the impact trend distribution.

5. The method for making multi-parameter monitoring wells for groundwater environment of multiple micro-layer groups in a plot of land according to claim 1 is characterized in that: The step S108 specifically includes the following steps: Obtain historical pollution migration monitoring parameters of coupled pollution components in the groundwater environment, interpolate the particle distribution function based on the historical pollution migration monitoring parameters to the discretized particle grid to perform collision solution and propagation update of migrating particles, and obtain the migration simulation field of coupled pollution components in the groundwater; Constructing a three-dimensional simulation model of the main well pipe, injecting the migration simulation field into the three-dimensional simulation model for simulation, and recording the concentration of the types of coupled pollution components inside the main well pipe during the simulation process to obtain a multi-dimensional simulation concentration data set; Constructing a vertical retention dimension domain of a multidimensional simulated concentration data set, and dividing the vertical retention dimension domain into M sub-dimensional interval units based on the dimension of the simulation time series step; Based on the big data network, we obtain a thermal density system and a thermal color gamut reference table for visualizing the scale of pollution. We use the thermal density system to identify and evaluate each simulated concentration value in the simulated concentration data set, and obtain the thermal kernel density index for each simulated concentration value. Calculate the ratio between the number of simulated concentration values whose thermal kernel density index is greater than a preset index and the number of simulated concentration values whose thermal kernel density index is less than a preset index in each sub-dimensional interval unit; if the ratio is higher than the preset ratio, mark the sub-dimensional interval unit as a dense unit; According to the thermal color gamut reference table, K color gamut foundations are rooted. Starting from the color gamut foundation, one-dimensional dense units in the adjacent dimension are connected layer by layer in the vertical retention dimension field to generate two-dimensional dense units. The adjacent two-dimensional connection generates three-dimensional units, and so on, until no higher-dimensional dense units can be generated. The vertical retention thermal hierarchy of the coupled pollution components located inside the main well pipe under groundwater environmental conditions is obtained; The thermal layers with thermal values greater than the preset thermal values in the vertical retention thermal layers are marked as enriched thermal layers, and an enrichment layer map is obtained. Multiple microlayer monitoring groups are arranged inside the main well pipe according to the enrichment layer map.

6. The method for making multi-parameter monitoring wells for groundwater environment of multiple micro-layer groups of a plot of land according to claim 5, characterized in that: The method of obtaining historical pollution migration monitoring parameters of coupled pollution components in a groundwater environment, interpolating a particle distribution function based on the historical pollution migration monitoring parameters on a discretized particle grid to perform collision solution and propagation update of migrating particles, and obtaining a migration simulation field of coupled pollution components in groundwater specifically includes the following steps: Through monitoring logs, historical pollution migration monitoring parameters and historical water flow monitoring parameters of coupled pollution components in the random state chain conditions of groundwater within a preset time period are obtained, and a geological profile spatial model is simultaneously constructed; The coupled pollution components are defined as migrating particles, the historical water flow characteristics of the groundwater environment under the random state chain condition are determined according to the historical water flow monitoring parameters, and the geological profile spatial model is discretized into a number of particle grids based on the historical water flow characteristics; The fluid Boltzmann equation is introduced. Based on the historical pollution migration monitoring parameters, the particle distribution function of each migrating particle is preset as the step size changes over a preset time period. The particle distribution function is solved by interpolating the Boltzmann equation within each particle grid point to generate a series of particle migration collision terms. A trade-off algorithm is introduced to calculate the docking weight between each particle grid point and its neighboring particle grid points during the collision interpolation solution process based on a series of particle migration collision terms. The discrete collision vector between each particle grid point and its neighboring particle grid points is determined based on the collision weight. Each migrating particle is propagated to the adjacent particle grid along the discrete collision vector, and the above collision solution and propagation steps are repeated to continuously update the particle distribution function of the migrating particles until all migrating particles are updated, and multiple new seedling particle distribution functions are obtained; A migration simulation field of coupled pollution component types in a groundwater environment is established based on the multiple seedling particle distribution functions.

7. A system for making multi-parameter monitoring wells for groundwater environment in multiple micro-layer groups of a plot, characterized by: The groundwater environment multi-parameter monitoring well production system includes a memory and a processor, and the memory stores a groundwater environment multi-parameter monitoring well production method program for multiple micro-layer groups of a plot. When the groundwater environment multi-parameter monitoring well production method program is executed by the processor, the groundwater environment multi-parameter monitoring well production method steps as described in any one of claims 1-6 are implemented.

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