A method and system for analyzing migration rules of cadmium pollution based on data analysis

Abstract

The application discloses a kind of based on data analysis's cadmium pollution migration rule analysis method and system, comprising: constructing cadmium pollution migration instance dataset, carries out cadmium pollution migration rule analysis, obtains first analysis information;According to cadmium pollution migration instance dataset, migration influence factor analysis is carried out, and the influence degree of each influence factor is evaluated, obtains second analysis information;First analysis information and second analysis information are combined to analyze the cadmium pollution migration rule under different electric field, and construct migration rule analysis model;Regional soil information is acquired, and according to regional soil information, target regional soil is analyzed, and soil condition analysis information is obtained;According to the soil condition analysis information, formulate initial repair scheme, analyze the cadmium pollution migration rule and repair benefit of initial repair scheme, and carry out repair scheme optimization.Cadmium pollution is analyzed by migration rule, so that the cadmium pollution migration rule under different electric field is understood, repair scheme is optimized, and repair effect is improved.

Description

Technical Field

[0001] This invention relates to the field of cadmium pollution migration pattern analysis technology, and in particular to a method and system for analyzing cadmium pollution migration patterns based on data analysis. Background Technology

[0002] Traditional analyses of cadmium pollution migration patterns largely rely on laboratory analysis. While this method provides relatively accurate data, it has several limitations. Laboratory analyses are time-consuming and cannot reflect changes in pollution conditions in real time. Furthermore, traditional methods often only provide localized, static pollution information, lacking a deep understanding of the dynamic migration and diffusion processes of pollutants.

[0003] Electrostatic remediation (ESR), an emerging soil remediation method, removes heavy metal ions from the soil by applying an electric field, causing them to migrate. This technology offers advantages such as high remediation efficiency and ease of operation, providing a new approach for the treatment of cadmium pollution in soil. This study analyzes the migration patterns of cadmium pollution under different electric fields and environments to optimize operating curves and parameters, thereby improving the effectiveness of soil remediation. Summary of the Invention

[0004] This invention overcomes the shortcomings of the prior art and provides a data analysis-based method and system for analyzing the migration patterns of cadmium pollution, with the main objective of improving soil remediation effectiveness.

[0005] To achieve the above objectives, the first aspect of this invention provides a data analysis-based method for analyzing the migration patterns of cadmium pollution, comprising:

[0006] A dataset of cadmium pollution migration instances was constructed, and the migration patterns of cadmium pollution were analyzed to obtain the first analytical information.

[0007] Based on the aforementioned cadmium pollution migration instance dataset, migration influencing factors were analyzed, and the degree of influence of each influencing factor was assessed to obtain the second analysis information;

[0008] Combining the information from the first and second analyses, we analyzed the migration patterns of cadmium pollution under different electric fields and constructed a migration pattern analysis model.

[0009] Obtain regional soil information, and perform soil condition analysis on the target area soil based on the regional soil information to obtain soil condition analysis information;

[0010] Based on the soil condition analysis information, an initial remediation plan was developed, the migration pattern of cadmium pollution and the remediation benefits of the initial remediation plan were analyzed, and the remediation plan was optimized.

[0011] In this solution, the construction of a cadmium pollution migration instance dataset and the analysis of cadmium pollution migration patterns specifically involve:

[0012] Based on big data retrieval, cadmium pollution migration instances under different environments and electric fields are obtained to form a cadmium pollution migration instance dataset. Cadmium pollution migration characteristics under each electric field state are extracted to obtain the first feature information.

[0013] Based on the first feature information, a migration pattern analysis is performed, and a corresponding migration change trend map is constructed based on the migration characteristics under different electric fields.

[0014] The migration trend diagram was constructed to calculate the migration amount and rate of cadmium pollution in different time periods under different electric fields. As an analytical indicator, the migration trend of cadmium pollution under different electric fields was analyzed to obtain migration trend analysis information.

[0015] Based on the migration trend analysis information, abnormal migration detection is performed. The migration changes of cadmium pollution in each time period are compared with preset thresholds to analyze abnormal change trends and obtain abnormal migration detection information.

[0016] Based on the abnormal migration detection information, the corresponding abnormal time period is extracted, and the corresponding electric field parameters are extracted and marked through the first feature information to obtain abnormal migration analysis information;

[0017] The first analysis information is formed by combining the migration trend analysis information and the abnormal migration analysis information.

[0018] In this solution, the step of analyzing migration influencing factors based on the cadmium pollution migration instance dataset and assessing the degree of influence of each factor specifically involves:

[0019] Obtain a dataset of cadmium pollution migration instances, and extract environmental features under each electric field based on the dataset to obtain second feature information;

[0020] First analytical information is obtained, and migration influencing factor analysis is performed in combination with the second feature information. The Mahalanobis distance between the second feature information and the first feature information is calculated based on the Mahalanobis distance algorithm and used as the influencing factor analysis index.

[0021] The calculated Mahalanobis distance is compared with a preset threshold. Based on the comparison results, the correlation between each environmental characteristic and the migration pattern of cadmium pollution under the corresponding electric field is analyzed to obtain correlation analysis information.

[0022] Based on the correlation analysis information, the factors influencing the migration pattern of cadmium pollution are analyzed, and the ratio of the characteristics corresponding to each influencing factor to the total characteristics is calculated in combination with the second feature information, and a probability matrix is ​​constructed.

[0023] The entropy weight method is introduced to assess the degree of influence. The information entropy of each influencing factor is calculated based on the constructed probability matrix, and the difference coefficient is calculated through the information entropy of each influencing factor.

[0024] The weights of each influencing factor are calculated based on the calculated difference coefficients. The influence of each influencing factor on the migration pattern is analyzed based on the calculated weights to obtain information on the degree of influence analysis.

[0025] The second set of analytical information is formed by combining the correlation analysis information and the influence degree analysis information.

[0026] In this scheme, the key feature is that the step of combining the first and second analytical information to analyze the migration patterns of cadmium pollution under different electric fields and constructing a migration pattern analysis model specifically involves:

[0027] Obtain first and second analytical information, analyze the migration pattern of cadmium pollution under different electric fields based on the first and second analytical information, and construct a migration pattern knowledge graph.

[0028] A migration pattern analysis model was constructed based on a migration pattern knowledge graph and a dataset of cadmium pollution migration instances, and a Markov algorithm was introduced for model construction.

[0029] A state space is constructed based on the migration pattern knowledge graph. Based on the migration patterns under different environments and electric fields, the migration states of cadmium pollution are defined, and each migration state represents an independent migration pattern.

[0030] The state transition probability is calculated based on the cadmium pollution migration instance dataset. The probability of each state in the state space transitioning to the next state is calculated by analyzing historical data in the cadmium pollution migration instance dataset.

[0031] A transition probability matrix is ​​constructed based on the state transition probabilities. The constructed transition probability matrix is ​​then corrected based on expert analysis. An initial state probability distribution is set, and a migration pattern analysis model is constructed by combining the state space and the transition probability matrix.

[0032] A training dataset was constructed based on a migration pattern knowledge graph and a cadmium pollution migration instance dataset. The migration pattern analysis model was then subjected to deep learning and training to obtain a migration pattern analysis model that met the expectations.

[0033] In this solution, the key feature is that the step of acquiring regional soil information and performing soil condition analysis on the target area based on the regional soil information specifically includes:

[0034] Soil samples were taken from the target area to obtain soil samples at different locations and depths. Soil indicators in each sample were analyzed based on soil testing technology to obtain regional soil information.

[0035] Several soil condition assessment indicators were obtained based on big data retrieval, and the Pearson correlation coefficient between each assessment indicator and the corresponding soil condition was calculated as the correlation analysis indicator.

[0036] The calculated correlation analysis indicators are compared with preset thresholds, and the corresponding evaluation indicators are selected based on the judgment results to obtain evaluation indicator information.

[0037] Based on the expert knowledge method, the influence scores of each evaluation indicator are obtained according to the evaluation indicator information. The intuitionistic fuzzy analysis method is introduced to assign weights to each evaluation indicator and to construct an intuitionistic fuzzy matrix based on the influence scores.

[0038] The intuitive fuzzy entropy of each evaluation index is calculated based on the intuitive fuzzy matrix. The intuitive fuzzy number of each evaluation index is calculated through the intuitive fuzzy entropy. The weights of each evaluation index are then assigned to obtain the weight information of the evaluation index.

[0039] A soil condition analysis model is constructed based on the weight information of the evaluation indicators. The soil information of the region is input into the soil condition analysis model for analysis to obtain soil condition analysis information.

[0040] Based on the soil information of the region, the sampling location of each sampling point is extracted, and the target area is divided into multiple sub-regions. The soil condition characteristics of each sub-region are extracted through the soil condition analysis information to obtain the soil condition characteristic information of the sub-region.

[0041] A preset soil condition level classification threshold is set, and the soil condition feature information of the sub-region is judged against the soil condition level classification threshold. The level is classified according to the judgment result, and adjacent sub-regions of the same level are merged to obtain regional soil classification information.

[0042] In this scheme, the key feature is that the initial remediation plan is formulated based on the soil condition analysis information, the migration pattern and remediation benefits of the initial remediation plan are analyzed, and the remediation plan is optimized, specifically as follows:

[0043] Obtain soil condition analysis information and regional soil classification information, and retrieve remediation scheme examples for various soil conditions based on big data retrieval to form a remediation scheme example dataset;

[0044] The similarity between the soil condition analysis information, regional soil division information and the remediation scheme instance dataset is calculated and compared with a preset threshold. An initial remediation scheme is then formulated based on the judgment result.

[0045] The soil condition analysis information and the initial remediation plan are input into the migration pattern analysis model for analysis. The migration pattern of cadmium pollution under each remediation plan is analyzed and predicted to obtain migration pattern prediction information.

[0046] Based on the migration pattern prediction information, the repair time of the corresponding initial repair plan is calculated, and the cost of each repair plan is calculated. Repair benefit analysis is then performed to obtain repair benefit analysis information.

[0047] Based on the repair benefit analysis information, determine whether the corresponding repair plan can be adopted. If it cannot be adopted, optimize the repair plan.

[0048] By extracting all remediation schemes under similar soil conditions from the remediation scheme instance dataset using the soil condition analysis information and regional soil division information, and performing remediation benefit analysis, the first remediation benefit analysis information is obtained.

[0049] Preset optimization goals and screening rules, and combine the first repair benefit analysis information to screen corresponding solutions and obtain candidate optimization solutions;

[0050] Feature extraction is performed on candidate optimization schemes, and the differences between them and the initial remediation schemes are analyzed. Based on the differences, optimization schemes are formulated, and migration patterns and remediation benefits are analyzed. Based on the analysis results, the optimal optimization scheme is selected for soil remediation.

[0051] A second aspect of the present invention provides a data analysis-based system for analyzing the migration patterns of cadmium pollution. The system includes a memory and a processor. The memory contains a program for analyzing the migration patterns of cadmium pollution based on data analysis. When executed by the processor, the program for analyzing the migration patterns of cadmium pollution based on data analysis performs the following steps:

[0052] A dataset of cadmium pollution migration instances was constructed, and the migration patterns of cadmium pollution were analyzed to obtain the first analytical information.

[0053] Based on the aforementioned cadmium pollution migration instance dataset, migration influencing factors were analyzed, and the degree of influence of each influencing factor was assessed to obtain the second analysis information;

[0054] Combining the information from the first and second analyses, we analyzed the migration patterns of cadmium pollution under different electric fields and constructed a migration pattern analysis model.

[0055] Obtain regional soil information, and perform soil condition analysis on the target area soil based on the regional soil information to obtain soil condition analysis information;

[0056] Based on the soil condition analysis information, an initial remediation plan was developed, the migration pattern of cadmium pollution and the remediation benefits of the initial remediation plan were analyzed, and the remediation plan was optimized.

[0057] This invention discloses a method and system for analyzing cadmium pollution migration patterns based on data analysis. The method includes: constructing a dataset of cadmium pollution migration examples and performing cadmium pollution migration pattern analysis to obtain first analysis information; analyzing migration influencing factors based on the cadmium pollution migration example dataset and assessing the influence of each factor to obtain second analysis information; combining the first and second analysis information to analyze cadmium pollution migration patterns under different electric fields and constructing a migration pattern analysis model; acquiring regional soil information and performing soil condition analysis on the target area soil based on the regional soil information to obtain soil condition analysis information; formulating an initial remediation plan based on the soil condition analysis information, analyzing the cadmium pollution migration patterns and remediation benefits of the initial remediation plan, and optimizing the remediation plan. By analyzing the migration patterns of cadmium pollution, the migration patterns of cadmium pollution under different electric fields can be understood, remediation plans can be optimized, and remediation effects can be improved. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments or examples of the present invention, the drawings used in the embodiments or examples will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained according to these drawings without creative effort.

[0059] Figure 1 A flowchart illustrating a data analysis-based method for analyzing the migration patterns of cadmium pollution, as provided in an embodiment of the present invention;

[0060] Figure 2 This is a flowchart of an optimized repair scheme provided in an embodiment of the present invention;

[0061] Figure 3 A block diagram of a cadmium pollution migration pattern analysis system based on data analysis is provided in one embodiment of the present invention.

[0062] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0063] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0064] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0065] Figure 1 A flowchart illustrating a data analysis-based method for analyzing the migration patterns of cadmium pollution, as provided in an embodiment of the present invention;

[0066] like Figure 1 As shown, this invention provides a flowchart of a data analysis-based method for analyzing the migration patterns of cadmium pollution, including:

[0067] S102, Construct a dataset of cadmium pollution migration instances, analyze the migration patterns of cadmium pollution, and obtain the first analytical information;

[0068] S104, Based on the cadmium pollution migration instance dataset, analyze the migration influencing factors and assess the degree of influence of each influencing factor to obtain the second analysis information;

[0069] S106, Combining the first and second analysis information, we analyzed the migration pattern of cadmium pollution under different electric fields and constructed a migration pattern analysis model;

[0070] S108, Obtain regional soil information, and perform soil condition analysis on the target area soil based on the regional soil information to obtain soil condition analysis information;

[0071] S110, Based on the soil condition analysis information, formulate an initial remediation plan, analyze the cadmium pollution migration pattern and remediation benefits of the initial remediation plan, and optimize the remediation plan.

[0072] It should be noted that this invention provides a data analysis-based method for analyzing the migration patterns of cadmium pollution. By analyzing historical data under different environmental conditions and electric fields, it reveals the impact and patterns of different electric field intensities on cadmium pollution migration in various soil environments. Based on the analysis results, a migration pattern analysis model is constructed. Adaptive solutions are developed according to the soil environment of the target area, and the migration patterns and remediation benefits of each solution are analyzed to determine whether optimization is necessary. This improves the remediation effect of electrodynamic remediation of cadmium pollution.

[0073] Furthermore, in a preferred embodiment of the present invention, the step of constructing a dataset of cadmium pollution migration instances and analyzing the migration patterns of cadmium pollution specifically includes:

[0074] Based on big data retrieval, cadmium pollution migration instances under different environments and electric fields are obtained to form a cadmium pollution migration instance dataset. Cadmium pollution migration characteristics under each electric field state are extracted to obtain the first feature information.

[0075] Based on the first feature information, a migration pattern analysis is performed, and a corresponding migration change trend map is constructed based on the migration characteristics under different electric fields.

[0076] The migration trend diagram was constructed to calculate the migration amount and rate of cadmium pollution in different time periods under different electric fields. As an analytical indicator, the migration trend of cadmium pollution under different electric fields was analyzed to obtain migration trend analysis information.

[0077] Based on the migration trend analysis information, abnormal migration detection is performed. The migration changes of cadmium pollution in each time period are compared with preset thresholds to analyze abnormal change trends and obtain abnormal migration detection information.

[0078] Based on the abnormal migration detection information, the corresponding abnormal time period is extracted, and the corresponding electric field parameters are extracted and marked through the first feature information to obtain abnormal migration analysis information;

[0079] The first analysis information is formed by combining the migration trend analysis information and the abnormal migration analysis information.

[0080] It should be noted that a cadmium pollution migration instance dataset was constructed by retrieving cadmium pollution migration instances under different environmental and electric field conditions based on big data retrieval. The different environments refer to different soil environments, and the different electric fields refer to different electric field strengths and layouts used in electrokinetic remediation of cadmium pollution. Feature extraction was performed on the cadmium pollution migration instance dataset to extract corresponding migration features and construct migration trend maps. The migration amount and rate of cadmium pollution under different electric fields over different time periods were calculated to analyze the migration patterns of cadmium pollution. Next, anomaly migration detection was performed. In electrokinetic remediation of cadmium pollution, different electric field layouts are used under different environments, and different electric field strengths are set according to requirements to improve the corresponding remediation effect and rate. The electric field strength is related to cadmium pollution migration; changes in electric field strength can not only accelerate cadmium pollution migration but also slow it down due to excessively high electric fields, reducing the remediation effect and efficiency. Anomaly migration detection can identify cadmium pollution migration anomalies under various environments, thereby identifying corresponding abnormal electric field parameters and providing a basis for subsequent analysis.

[0081] Furthermore, in a preferred embodiment of the present invention, the step of analyzing migration influencing factors based on the cadmium pollution migration instance dataset and assessing the degree of influence of each influencing factor specifically includes:

[0082] Obtain a dataset of cadmium pollution migration instances, and extract environmental features under each electric field based on the dataset to obtain second feature information;

[0083] First analytical information is obtained, and migration influencing factor analysis is performed in combination with the second feature information. The Mahalanobis distance between the second feature information and the first feature information is calculated based on the Mahalanobis distance algorithm and used as the influencing factor analysis index.

[0084] The calculated Mahalanobis distance is compared with a preset threshold. Based on the comparison results, the correlation between each environmental characteristic and the migration pattern of cadmium pollution under the corresponding electric field is analyzed to obtain correlation analysis information.

[0085] Based on the correlation analysis information, the factors influencing the migration pattern of cadmium pollution are analyzed, and the ratio of the characteristics corresponding to each influencing factor to the total characteristics is calculated in combination with the second feature information, and a probability matrix is ​​constructed.

[0086] The entropy weight method is introduced to assess the degree of influence. The information entropy of each influencing factor is calculated based on the constructed probability matrix, and the difference coefficient is calculated through the information entropy of each influencing factor.

[0087] The weights of each influencing factor are calculated based on the calculated difference coefficients. The influence of each influencing factor on the migration pattern is analyzed based on the calculated weights to obtain information on the degree of influence analysis.

[0088] The second set of analytical information is formed by combining the correlation analysis information and the influence degree analysis information.

[0089] It should be noted that in the migration pattern analysis, cadmium pollution migration is influenced not only by the electric field but also by different environmental factors. First, a dataset of cadmium pollution migration instances is obtained, from which environmental characteristics under each electric field are extracted. These characteristics include soil type, humidity, pH value, etc., constituting the second feature information. This feature information provides the basic data for analyzing the influencing factors of cadmium pollution migration. Next, the first analysis information, namely the overall trend and abnormal migration of cadmium pollution, is compared and analyzed with the second feature information. The Mahalanobis distance algorithm is used to calculate the Mahalanobis distance between the second and first feature information, serving as a key indicator for judging the importance of influencing factors. The calculated distance is compared with a preset threshold to analyze the correlation between each environmental feature and the cadmium pollution migration pattern under the corresponding electric field, revealing which environmental factors are closely related to the cadmium pollution migration pattern. Then, the proportion of each influencing factor's corresponding feature to the total features is calculated, and a probability matrix is ​​constructed. This matrix visually demonstrates the relative importance of each factor in influencing cadmium pollution migration. The entropy weight method is introduced to assess the degree of influence of each influencing factor. Based on the constructed probability matrix, the information entropy and difference coefficient of each influencing factor are calculated. These difference coefficients reflect the variability of various factors influencing the migration of cadmium pollution. Finally, based on the calculated difference coefficients, the weights of each influencing factor are determined, and the magnitude of the weights reflects the degree of influence of each factor on the migration pattern.

[0090] Furthermore, in a preferred embodiment of the present invention, the step of combining the first analytical information and the second analytical information to analyze the migration patterns of cadmium pollution under different electric fields and constructing a migration pattern analysis model specifically includes:

[0091] Obtain first and second analytical information, analyze the migration pattern of cadmium pollution under different electric fields based on the first and second analytical information, and construct a migration pattern knowledge graph.

[0092] A migration pattern analysis model was constructed based on a migration pattern knowledge graph and a dataset of cadmium pollution migration instances, and a Markov algorithm was introduced for model construction.

[0093] A state space is constructed based on the migration pattern knowledge graph. Based on the migration patterns under different environments and electric fields, the migration states of cadmium pollution are defined, and each migration state represents an independent migration pattern.

[0094] The state transition probability is calculated based on the cadmium pollution migration instance dataset. The probability of each state in the state space transitioning to the next state is calculated by analyzing historical data in the cadmium pollution migration instance dataset.

[0095] A transition probability matrix is ​​constructed based on the state transition probabilities. The constructed transition probability matrix is ​​then corrected based on expert analysis. An initial state probability distribution is set, and a migration pattern analysis model is constructed by combining the state space and the transition probability matrix.

[0096] A training dataset was constructed based on a migration pattern knowledge graph and a cadmium pollution migration instance dataset. The migration pattern analysis model was then subjected to deep learning and training to obtain a migration pattern analysis model that met the expectations.

[0097] It should be noted that, firstly, based on the first and second analysis information, the overall trend of cadmium pollution migration, abnormal migration situations, and key factors influencing migration are understood, and migration pattern analysis is conducted. A migration pattern knowledge graph is constructed based on the first and second analysis information, demonstrating the migration patterns of cadmium pollution under different electric fields and the relationships and degrees of influence between these influencing factors. Next, using the migration pattern knowledge graph and a dataset of cadmium pollution migration examples, a migration pattern analysis model is constructed. A Markov algorithm is introduced, which can predict future migration trends based on the current state, thereby analyzing the migration patterns of cadmium pollution. A state space is constructed based on the migration pattern knowledge graph. In this state space, each state represents the migration pattern of cadmium pollution under a specific environment and electric field. These states are defined to analyze the migration of cadmium pollution more systematically. States in the migration pattern analysis can represent different migration situations, locations, or stages, etc. For example, states can represent different directions, velocities, etc. The state space should be comprehensive and non-overlapping, ensuring that all possible migration situations are covered by these states, and each state represents a unique situation. Then, using a cadmium pollution migration instance dataset, state transition probabilities are calculated, reflecting the likelihood of cadmium pollution transitioning from one migration state to another under specific environmental and electric field conditions. A transition probability matrix is ​​constructed based on the calculated state transition probabilities. Expert analysis is used to correct the matrix, ensuring its accuracy. Simultaneously, an initial state probability distribution is defined, describing the state distribution of the stochastic process at the initial time step, i.e., the probability of each state being occupied at the initial time step. This distribution is a crucial starting point for stochastic process analysis, determining the basis for subsequent state transitions and evolution. The transition probability matrix is ​​a key component, where each element represents the probability of transitioning from one state to another. The sum of probabilities in each row equals 1, ensuring a transition to a new state at every time step. Furthermore, the matrix elements are non-negative to avoid negative probabilities. Finally, combining the state space, the transition probability matrix, and the initial state probability distribution, a migration law analysis model is constructed. A training dataset is built using a migration law knowledge graph and the cadmium pollution migration instance dataset. The model is then trained using deep learning, and validation and test sets are constructed to test and validate the model, resulting in a model that meets expectations. This study reveals the migration patterns of cadmium pollution under different electric fields, providing strong technical support for soil remediation and environmental protection.

[0098] Furthermore, in a preferred embodiment of the present invention, the step of acquiring regional soil information and performing soil condition analysis on the target area based on the regional soil information specifically includes:

[0099] Soil samples were taken from the target area to obtain soil samples at different locations and depths. Soil indicators in each sample were analyzed based on soil testing technology to obtain regional soil information.

[0100] Several soil condition assessment indicators were obtained based on big data retrieval, and the Pearson correlation coefficient between each assessment indicator and the corresponding soil condition was calculated as the correlation analysis indicator.

[0101] The calculated correlation analysis indicators are compared with preset thresholds, and the corresponding evaluation indicators are selected based on the judgment results to obtain evaluation indicator information.

[0102] Based on the expert knowledge method, the influence scores of each evaluation indicator are obtained according to the evaluation indicator information. The intuitionistic fuzzy analysis method is introduced to assign weights to each evaluation indicator and to construct an intuitionistic fuzzy matrix based on the influence scores.

[0103] The intuitive fuzzy entropy of each evaluation index is calculated based on the intuitive fuzzy matrix. The intuitive fuzzy number of each evaluation index is calculated through the intuitive fuzzy entropy. The weights of each evaluation index are then assigned to obtain the weight information of the evaluation index.

[0104] A soil condition analysis model is constructed based on the weight information of the evaluation indicators. The soil information of the region is input into the soil condition analysis model for analysis to obtain soil condition analysis information.

[0105] Based on the soil information of the region, the sampling location of each sampling point is extracted, and the target area is divided into multiple sub-regions. The soil condition characteristics of each sub-region are extracted through the soil condition analysis information to obtain the soil condition characteristic information of the sub-region.

[0106] A preset soil condition level classification threshold is set, and the soil condition feature information of the sub-region is judged against the soil condition level classification threshold. The level is classified according to the judgment result, and adjacent sub-regions of the same level are merged to obtain regional soil classification information.

[0107] It is important to note that before performing electroremediation on the target area, a soil condition analysis is necessary to determine the cadmium pollution status and soil condition. Sampling of the target area and analysis of soil samples using soil testing technology yielded detailed information on various soil indicators, such as organic matter content, pH value, and heavy metal content. Next, several soil condition assessment indicators were obtained through big data retrieval. The Pearson correlation coefficients between these indicators and their corresponding soil conditions were calculated to quantify the correlation between the indicators and soil conditions. These coefficients were then compared with preset thresholds to identify those indicators highly correlated with soil conditions, ensuring the accuracy and effectiveness of subsequent analyses. Expert knowledge methods and intuitionistic fuzzy analysis were introduced to assign weights to each assessment indicator, resulting in weight information. Finally, the target area was divided into multiple sub-regions based on the sampling point locations. Soil condition characteristics of each sub-region were extracted using soil condition analysis information, yielding sub-region soil condition feature information. Finally, a preset threshold for soil condition classification was established. The soil condition characteristics of each sub-region were compared with this threshold to classify the sub-regions into different levels. Adjacent sub-regions of the same level were merged, resulting in the final regional soil classification information. This provides a foundation for subsequent remediation plan development and optimization.

[0108] Furthermore, in a preferred embodiment of the present invention, the step of formulating an initial remediation plan based on the soil condition analysis information, analyzing the cadmium pollution migration pattern and remediation benefits of the initial remediation plan, and optimizing the remediation plan specifically includes:

[0109] Obtain soil condition analysis information and regional soil classification information, and retrieve remediation scheme examples for various soil conditions based on big data retrieval to form a remediation scheme example dataset;

[0110] The similarity between the soil condition analysis information, regional soil division information and the remediation scheme instance dataset is calculated and compared with a preset threshold. An initial remediation scheme is then formulated based on the judgment result.

[0111] The soil condition analysis information and the initial remediation plan are input into the migration pattern analysis model for analysis. The migration pattern of cadmium pollution under each remediation plan is analyzed and predicted to obtain migration pattern prediction information.

[0112] Based on the migration pattern prediction information, the repair time of the corresponding initial repair plan is calculated, and the cost of each repair plan is calculated. Repair benefit analysis is then performed to obtain repair benefit analysis information.

[0113] Based on the repair benefit analysis information, determine whether the corresponding repair plan can be adopted. If it cannot be adopted, optimize the repair plan.

[0114] By extracting all remediation schemes under similar soil conditions from the remediation scheme instance dataset using the soil condition analysis information and regional soil division information, and performing remediation benefit analysis, the first remediation benefit analysis information is obtained.

[0115] Preset optimization goals and screening rules, and combine the first repair benefit analysis information to screen corresponding solutions and obtain candidate optimization solutions;

[0116] Feature extraction is performed on candidate optimization schemes, and the differences between them and the initial remediation schemes are analyzed. Based on the differences, optimization schemes are formulated, and migration patterns and remediation benefits are analyzed. Based on the analysis results, the optimal optimization scheme is selected for soil remediation.

[0117] It should be noted that, firstly, big data retrieval technology was used to collect remediation scheme examples for various soil conditions, constructing a remediation scheme example dataset. The similarity between the soil condition analysis information, regional soil classification information, and the remediation scheme example dataset was calculated. By comparing their similarities and judging against preset thresholds, remediation schemes matching the current soil conditions were initially screened, forming initial remediation schemes. Then, for the initial remediation schemes, migration analysis models were used to analyze and predict the migration patterns of each initial remediation scheme. The soil condition analysis information and the initial remediation schemes were input into the migration pattern analysis model to analyze and predict the migration patterns of cadmium pollution under each remediation scheme. Further calculations of the remediation time and cost for the corresponding initial remediation schemes were performed, resulting in a remediation benefit analysis. This provides important information for judging the feasibility of the remediation schemes. The judgment criteria were set according to requirements, such as the need for good remediation effect or short remediation time. If the corresponding initial remediation scheme cannot be adopted, the remediation scheme needs to be optimized. In this stage, the soil condition analysis information and regional soil classification information were again used to extract all remediation schemes under similar soil conditions from the remediation scheme example dataset, and a remediation benefit analysis was performed, obtaining the first remediation benefit analysis information. To select the optimal candidate optimization scheme, optimization objectives and selection rules are preset, such as optimization objectives related to remediation time and cost. Based on the information from the initial remediation benefit analysis, qualified candidate optimization schemes are selected. Subsequently, feature extraction is performed on the candidate optimization schemes, and their differences from the initial remediation scheme are analyzed in depth. Based on these differences, specific optimization schemes are formulated, such as replacing the electric field strength features used in the initial scheme with those of the electric field strength difference features. Migration law analysis and remediation benefit analysis are then performed again to determine the feasibility of the optimization schemes. Finally, the optimal optimization scheme is selected for soil remediation, thereby improving the remediation effect and efficiency.

[0118] Figure 2This is a flowchart of an optimized repair scheme provided in an embodiment of the present invention;

[0119] like Figure 2 As shown, the present invention provides an optimized flowchart of the repair scheme, including:

[0120] S202, Sampling of soil in the target area to obtain soil samples at different locations and depths, and analysis of soil indicators in each sample based on soil testing technology;

[0121] S204. Input the regional soil information into the soil condition analysis model for analysis, and divide the target area according to the soil condition analysis results.

[0122] S206. Similarity calculation is performed between soil condition analysis information, regional soil classification information and remediation scheme instance dataset to formulate an initial remediation scheme and conduct remediation benefit analysis.

[0123] S208. Based on the repair benefit analysis information, determine whether the corresponding repair plan can be adopted. If it cannot be adopted, optimize the repair plan.

[0124] S210: Extract all repair scheme instances under similar environments, analyze the differences between the original repair scheme and the initial repair scheme, optimize the scheme based on the differences, analyze the repair benefits of the optimized scheme, and select the optimal optimized scheme based on the analysis results.

[0125] It should be noted that during the formulation and optimization of remediation plans, the environmental characteristics of the target area are fixed. These fixed characteristics allow for the acquisition of many remediation plans under similar environments. An initial remediation plan is then selected, and this plan requires a remediation benefit analysis before it can be used. If the initial plan is unusable, optimization is necessary. This involves selecting all remediation plan instances under the given environmental characteristics and extracting and analyzing the corresponding difference features. These difference features represent electric field strength, gradient, electric field layout, etc. The initial plan is then optimized using these difference features to obtain candidate plans. Next, plan optimization considers not only the feasibility of the plan but also actual needs, such as achieving good remediation results and minimizing time. A remediation benefit analysis is performed on the optimized plan to select the one that meets both feasibility and requirements, thereby improving the remediation effect.

[0126] Furthermore, the aforementioned data analysis-based method for analyzing cadmium pollution migration patterns also includes the following steps:

[0127] Obtain soil monitoring information of the target area, input the soil monitoring information of the target area into the soil condition analysis model for analysis, and obtain the first soil condition analysis information;

[0128] The target area is divided based on the first soil condition analysis information to obtain regional division information;

[0129] The first soil condition analysis information and regional division information are input into the migration pattern analysis model for analysis. The migration pattern of cadmium pollution in each region is analyzed to obtain the cadmium pollution migration pattern analysis information.

[0130] Based on the cadmium pollution migration pattern analysis information, the migration direction and trend of cadmium pollution in each region are extracted, and the migration change analysis of cadmium pollution is carried out. Regions with the same migration direction and trend and adjacent regions are connected to draw a cadmium pollution migration path map.

[0131] The pollution source is traced based on the cadmium pollution migration path map, and the pollution source is determined based on the cadmium pollution migration path to obtain pollution source tracing analysis information.

[0132] Geographic information of the target area is obtained based on the pollution source tracing analysis information. The attribute characteristics of each building in the target area are extracted, attribute analysis is performed on each building, and the buildings are screened based on the attribute analysis results. Buildings that meet the criteria for pollution generation are analyzed to obtain the screening result information.

[0133] An early warning will be issued based on the screening results, prompting an investigation into abnormal emissions.

[0134] Figure 3 A cadmium pollution migration pattern analysis system 3 based on data analysis is provided in one embodiment of the present invention. The system includes: a memory 31 and a processor 32. The memory 31 contains a cadmium pollution migration pattern analysis method program based on data analysis. When the processor 32 executes the cadmium pollution migration pattern analysis method program based on data analysis, it performs the following steps:

[0135] A dataset of cadmium pollution migration instances was constructed, and the migration patterns of cadmium pollution were analyzed to obtain the first analytical information.

[0136] Based on the aforementioned cadmium pollution migration instance dataset, migration influencing factors were analyzed, and the degree of influence of each influencing factor was assessed to obtain the second analysis information;

[0137] Combining the information from the first and second analyses, we analyzed the migration patterns of cadmium pollution under different electric fields and constructed a migration pattern analysis model.

[0138] Obtain regional soil information, and perform soil condition analysis on the target area soil based on the regional soil information to obtain soil condition analysis information;

[0139] Based on the soil condition analysis information, an initial remediation plan was developed, the migration pattern of cadmium pollution and the remediation benefits of the initial remediation plan were analyzed, and the remediation plan was optimized.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0141] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0142] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0143] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0145] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those 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 determined by the scope of the claims.

Claims

1. A method for analyzing the migration rule of cadmium pollution based on data analysis, characterized in that, include: A dataset of cadmium pollution migration instances was constructed, and the migration patterns of cadmium pollution were analyzed to obtain the first analytical information. Based on the aforementioned cadmium pollution migration instance dataset, migration influencing factors were analyzed, and the degree of influence of each influencing factor was assessed to obtain the second analysis information; Combining the information from the first and second analyses, we analyzed the migration patterns of cadmium pollution under different electric fields and constructed a migration pattern analysis model. Obtain regional soil information, and perform soil condition analysis on the target area soil based on the regional soil information to obtain soil condition analysis information; Based on the soil condition analysis information, an initial remediation plan was formulated, the migration pattern of cadmium pollution and the remediation benefits of the initial remediation plan were analyzed, and the remediation plan was optimized. The step of acquiring regional soil information and performing soil condition analysis on the target area based on the regional soil information specifically includes: Soil samples were taken from the target area to obtain soil samples at different locations and depths. Soil indicators in each sample were analyzed based on soil testing technology to obtain regional soil information. Several soil condition assessment indicators were obtained based on big data retrieval, and the Pearson correlation coefficient between each assessment indicator and the corresponding soil condition was calculated as the correlation analysis indicator. The calculated correlation analysis indicators are compared with preset thresholds, and the corresponding evaluation indicators are selected based on the judgment results to obtain evaluation indicator information. Based on the expert knowledge method, the influence scores of each evaluation indicator are obtained according to the evaluation indicator information. The intuitionistic fuzzy analysis method is introduced to assign weights to each evaluation indicator and to construct an intuitionistic fuzzy matrix based on the influence scores. The intuitive fuzzy entropy of each evaluation index is calculated based on the intuitive fuzzy matrix. The intuitive fuzzy number of each evaluation index is calculated through the intuitive fuzzy entropy. The weights of each evaluation index are then assigned to obtain the weight information of the evaluation index. A soil condition analysis model is constructed based on the weight information of the evaluation indicators. The soil information of the region is input into the soil condition analysis model for analysis to obtain soil condition analysis information. Based on the soil information of the region, the sampling location of each sampling point is extracted, and the target area is divided into multiple sub-regions. The soil condition characteristics of each sub-region are extracted through the soil condition analysis information to obtain the soil condition characteristic information of the sub-region. A preset soil condition level classification threshold is set, and the soil condition feature information of the sub-region is judged against the soil condition level classification threshold. The level is classified according to the judgment result, and adjacent sub-regions of the same level are merged to obtain regional soil classification information. The process of formulating an initial remediation plan based on the soil condition analysis information, analyzing the cadmium pollution migration patterns and remediation benefits of the initial remediation plan, and optimizing the remediation plan specifically includes: Obtain soil condition analysis information and regional soil classification information, and retrieve remediation scheme examples for various soil conditions based on big data retrieval to form a remediation scheme example dataset; The similarity between the soil condition analysis information, regional soil division information and the remediation scheme instance dataset is calculated and compared with a preset threshold. An initial remediation scheme is then formulated based on the judgment result. The soil condition analysis information and the initial remediation plan are input into the migration pattern analysis model for analysis. The migration pattern of cadmium pollution under each remediation plan is analyzed and predicted to obtain migration pattern prediction information. Based on the migration pattern prediction information, the repair time of the corresponding initial repair plan is calculated, and the cost of each repair plan is calculated. Repair benefit analysis is then performed to obtain repair benefit analysis information. Based on the repair benefit analysis information, determine whether the corresponding repair plan can be adopted. If it cannot be adopted, optimize the repair plan. By extracting all remediation schemes under similar soil conditions from the remediation scheme instance dataset using the soil condition analysis information and regional soil division information, and performing remediation benefit analysis, the first remediation benefit analysis information is obtained. Preset optimization goals and screening rules, and combine the first repair benefit analysis information to screen corresponding solutions and obtain candidate optimization solutions; Feature extraction is performed on candidate optimization schemes, and the differences between them and the initial remediation schemes are analyzed. Based on the differences, optimization schemes are formulated, and migration patterns and remediation benefits are analyzed. Based on the analysis results, the optimal optimization scheme is selected for soil remediation. 2.The method of claim 1, wherein, The construction of the cadmium pollution migration instance dataset and the analysis of cadmium pollution migration patterns specifically include: Based on big data retrieval, cadmium pollution migration instances under different environments and electric fields are obtained to form a cadmium pollution migration instance dataset. Cadmium pollution migration characteristics under each electric field state are extracted to obtain the first feature information. Based on the first feature information, a migration pattern analysis is performed, and a corresponding migration change trend map is constructed based on the migration characteristics under different electric fields. The migration trend diagram was constructed to calculate the migration amount and rate of cadmium pollution in different time periods under different electric fields. As an analytical indicator, the migration trend of cadmium pollution under different electric fields was analyzed to obtain migration trend analysis information. Based on the migration trend analysis information, abnormal migration detection is performed. The migration changes of cadmium pollution in each time period are compared with preset thresholds to analyze abnormal change trends and obtain abnormal migration detection information. Based on the abnormal migration detection information, the corresponding abnormal time period is extracted, and the corresponding electric field parameters are extracted and marked through the first feature information to obtain abnormal migration analysis information; The first analysis information is formed by combining the migration trend analysis information and the abnormal migration analysis information. 3.The method of claim 1, wherein, The analysis of migration influencing factors based on the cadmium pollution migration instance dataset, and the assessment of the influence of each factor, specifically includes: Obtain a dataset of cadmium pollution migration instances, and extract environmental features under each electric field based on the dataset to obtain second feature information; First analytical information is obtained, and migration influencing factor analysis is performed in combination with the second feature information. The Mahalanobis distance between the second feature information and the first feature information is calculated based on the Mahalanobis distance algorithm and used as the influencing factor analysis index. The calculated Mahalanobis distance is compared with a preset threshold. Based on the comparison results, the correlation between each environmental characteristic and the migration pattern of cadmium pollution under the corresponding electric field is analyzed to obtain correlation analysis information. Based on the correlation analysis information, the factors influencing the migration pattern of cadmium pollution are analyzed, and the ratio of the characteristics corresponding to each influencing factor to the total characteristics is calculated in combination with the second feature information, and a probability matrix is ​​constructed. The entropy weight method is introduced to assess the degree of influence. The information entropy of each influencing factor is calculated based on the constructed probability matrix, and the difference coefficient is calculated through the information entropy of each influencing factor. The weights of each influencing factor are calculated based on the calculated difference coefficients. The influence of each influencing factor on the migration pattern is analyzed based on the calculated weights to obtain information on the degree of influence analysis. The second set of analytical information is formed by combining the correlation analysis information and the influence degree analysis information.

4. The method for analyzing the migration rule of cadmium pollution based on data analysis according to claim 1, characterized in that, The analysis of cadmium pollution migration patterns under different electric fields, combining the first and second analytical information, and the construction of a migration pattern analysis model, specifically includes: Obtain first and second analytical information, analyze the migration pattern of cadmium pollution under different electric fields based on the first and second analytical information, and construct a migration pattern knowledge graph. A migration pattern analysis model was constructed based on a migration pattern knowledge graph and a dataset of cadmium pollution migration instances, and a Markov algorithm was introduced for model construction. A state space is constructed based on the migration pattern knowledge graph. Based on the migration patterns under different environments and electric fields, the migration states of cadmium pollution are defined, and each migration state represents an independent migration pattern. The state transition probability is calculated based on the cadmium pollution migration instance dataset. The probability of each state in the state space transitioning to the next state is calculated by analyzing historical data in the cadmium pollution migration instance dataset. A transition probability matrix is ​​constructed based on the state transition probabilities. The constructed transition probability matrix is ​​then corrected based on expert analysis. An initial state probability distribution is set, and a migration pattern analysis model is constructed by combining the state space and the transition probability matrix. A training dataset was constructed based on a migration pattern knowledge graph and a cadmium pollution migration instance dataset. The migration pattern analysis model was then subjected to deep learning and training to obtain a migration pattern analysis model that met the expectations.

5. A data analysis-based cadmium pollution migration rule analysis system, characterized in that, The system includes: a memory and a processor. The memory contains a program for analyzing the migration patterns of cadmium pollution based on data analysis. When the processor executes the program for analyzing the migration patterns of cadmium pollution based on data analysis, it performs the following steps: A dataset of cadmium pollution migration instances was constructed, and the migration patterns of cadmium pollution were analyzed to obtain the first analytical information. Based on the aforementioned cadmium pollution migration instance dataset, migration influencing factors were analyzed, and the degree of influence of each influencing factor was assessed to obtain the second analysis information; Combining the information from the first and second analyses, we analyzed the migration patterns of cadmium pollution under different electric fields and constructed a migration pattern analysis model. Obtain regional soil information, and perform soil condition analysis on the target area soil based on the regional soil information to obtain soil condition analysis information; Based on the soil condition analysis information, an initial remediation plan was formulated, the migration pattern of cadmium pollution and the remediation benefits of the initial remediation plan were analyzed, and the remediation plan was optimized. The step of acquiring regional soil information and performing soil condition analysis on the target area based on the regional soil information specifically includes: Soil samples were taken from the target area to obtain soil samples at different locations and depths. Soil indicators in each sample were analyzed based on soil testing technology to obtain regional soil information. Several soil condition assessment indicators were obtained based on big data retrieval, and the Pearson correlation coefficient between each assessment indicator and the corresponding soil condition was calculated as the correlation analysis indicator. The calculated correlation analysis indicators are compared with preset thresholds, and the corresponding evaluation indicators are selected based on the judgment results to obtain evaluation indicator information. Based on the expert knowledge method, the influence scores of each evaluation indicator are obtained according to the evaluation indicator information. The intuitionistic fuzzy analysis method is introduced to assign weights to each evaluation indicator and to construct an intuitionistic fuzzy matrix based on the influence scores. The intuitive fuzzy entropy of each evaluation index is calculated based on the intuitive fuzzy matrix. The intuitive fuzzy number of each evaluation index is calculated through the intuitive fuzzy entropy. The weights of each evaluation index are then assigned to obtain the weight information of the evaluation index. A soil condition analysis model is constructed based on the weight information of the evaluation indicators. The soil information of the region is input into the soil condition analysis model for analysis to obtain soil condition analysis information. Based on the soil information of the region, the sampling location of each sampling point is extracted, and the target area is divided into multiple sub-regions. The soil condition characteristics of each sub-region are extracted through the soil condition analysis information to obtain the soil condition characteristic information of the sub-region. A preset soil condition level classification threshold is set, and the soil condition feature information of the sub-region is judged against the soil condition level classification threshold. The level is classified according to the judgment result, and adjacent sub-regions of the same level are merged to obtain regional soil classification information. The process of formulating an initial remediation plan based on the soil condition analysis information, analyzing the cadmium pollution migration patterns and remediation benefits of the initial remediation plan, and optimizing the remediation plan specifically includes: Obtain soil condition analysis information and regional soil classification information, and retrieve remediation scheme examples for various soil conditions based on big data retrieval to form a remediation scheme example dataset; The similarity between the soil condition analysis information, regional soil division information and the remediation scheme instance dataset is calculated and compared with a preset threshold. An initial remediation scheme is then formulated based on the judgment result. The soil condition analysis information and the initial remediation plan are input into the migration pattern analysis model for analysis. The migration pattern of cadmium pollution under each remediation plan is analyzed and predicted to obtain migration pattern prediction information. Based on the migration pattern prediction information, the repair time of the corresponding initial repair plan is calculated, and the cost of each repair plan is calculated. Repair benefit analysis is then performed to obtain repair benefit analysis information. Based on the repair benefit analysis information, determine whether the corresponding repair plan can be adopted. If it cannot be adopted, optimize the repair plan. By extracting all remediation schemes under similar soil conditions from the remediation scheme instance dataset using the soil condition analysis information and regional soil division information, and performing remediation benefit analysis, the first remediation benefit analysis information is obtained. Preset optimization goals and screening rules, and combine the first repair benefit analysis information to screen corresponding solutions and obtain candidate optimization solutions; Feature extraction is performed on candidate optimization schemes, and the differences between them and the initial remediation schemes are analyzed. Based on the differences, optimization schemes are formulated, and migration patterns and remediation benefits are analyzed. Based on the analysis results, the optimal optimization scheme is selected for soil remediation.

6. The cadmium pollution migration rule analysis system based on data analysis according to claim 5, characterized in that, The construction of the cadmium pollution migration instance dataset and the analysis of cadmium pollution migration patterns specifically include: Based on big data retrieval, cadmium pollution migration instances under different environments and electric fields are obtained to form a cadmium pollution migration instance dataset. Cadmium pollution migration characteristics under each electric field state are extracted to obtain the first feature information. Based on the first feature information, a migration pattern analysis is performed, and a corresponding migration change trend map is constructed based on the migration characteristics under different electric fields. The migration trend diagram was constructed to calculate the migration amount and rate of cadmium pollution in different time periods under different electric fields. As an analytical indicator, the migration trend of cadmium pollution under different electric fields was analyzed to obtain migration trend analysis information. Based on the migration trend analysis information, abnormal migration detection is performed. The migration changes of cadmium pollution in each time period are compared with preset thresholds to analyze abnormal change trends and obtain abnormal migration detection information. Based on the abnormal migration detection information, the corresponding abnormal time period is extracted, and the corresponding electric field parameters are extracted and marked through the first feature information to obtain abnormal migration analysis information; The first analysis information is formed by combining the migration trend analysis information and the abnormal migration analysis information.

7. The cadmium pollution migration pattern analysis system based on data analysis according to claim 5, characterized in that, The analysis of migration influencing factors based on the cadmium pollution migration instance dataset, and the assessment of the influence of each factor, specifically includes: Obtain a dataset of cadmium pollution migration instances, and extract environmental features under each electric field based on the dataset to obtain second feature information; First analytical information is obtained, and migration influencing factor analysis is performed in combination with the second feature information. The Mahalanobis distance between the second feature information and the first feature information is calculated based on the Mahalanobis distance algorithm and used as the influencing factor analysis index. The calculated Mahalanobis distance is compared with a preset threshold. Based on the comparison results, the correlation between each environmental characteristic and the migration pattern of cadmium pollution under the corresponding electric field is analyzed to obtain correlation analysis information. Based on the correlation analysis information, the factors influencing the migration pattern of cadmium pollution are analyzed, and the ratio of the characteristics corresponding to each influencing factor to the total characteristics is calculated in combination with the second feature information, and a probability matrix is ​​constructed. The entropy weight method is introduced to assess the degree of influence. The information entropy of each influencing factor is calculated based on the constructed probability matrix, and the difference coefficient is calculated through the information entropy of each influencing factor. The weights of each influencing factor are calculated based on the calculated difference coefficients. The influence of each influencing factor on the migration pattern is analyzed based on the calculated weights to obtain information on the degree of influence analysis. The second set of analytical information is formed by combining the correlation analysis information and the influence degree analysis information.

8. The cadmium pollution migration pattern analysis system based on data analysis according to claim 5, characterized in that, The analysis of cadmium pollution migration patterns under different electric fields, combining the first and second analytical information, and the construction of a migration pattern analysis model, specifically includes: Obtain first and second analytical information, analyze the migration pattern of cadmium pollution under different electric fields based on the first and second analytical information, and construct a migration pattern knowledge graph. A migration pattern analysis model was constructed based on a migration pattern knowledge graph and a dataset of cadmium pollution migration instances, and a Markov algorithm was introduced for model construction. A state space is constructed based on the migration pattern knowledge graph. Based on the migration patterns under different environments and electric fields, the migration states of cadmium pollution are defined, and each migration state represents an independent migration pattern. The state transition probability is calculated based on the cadmium pollution migration instance dataset. The probability of each state in the state space transitioning to the next state is calculated by analyzing historical data in the cadmium pollution migration instance dataset. A transition probability matrix is ​​constructed based on the state transition probabilities. The constructed transition probability matrix is ​​then corrected based on expert analysis. An initial state probability distribution is set, and a migration pattern analysis model is constructed by combining the state space and the transition probability matrix. A training dataset was constructed based on a migration pattern knowledge graph and a cadmium pollution migration instance dataset. The migration pattern analysis model was then subjected to deep learning and training to obtain a migration pattern analysis model that met the expectations.

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