Shale gas pollutant integrated intelligent comprehensive management system and method, electronic equipment and storage medium

Abstract

The invention discloses an integrated intelligent comprehensive management system and method for shale gas pollutants, electronic equipment and a storage medium. A pollutant monitoring module comprises a gas component sensor array unit, a liquid pollutant parameter detection unit and a solid detection unit; the intelligent analysis module is mainly used for constructing a digital twin shale gas pollutant simulation model and constructing a pollutant migration prediction model; the pollutant treatment module is mainly used for taking treatment measures based on pollutant types; the model optimization module is mainly used for calculating a difference value between prediction data and actual data and optimizing a model; the central control module is mainly used for communicating with each module, processing parameter real-time optimization and energy efficiency balance regulation and control; the database module is mainly used for storing monitored pollutant data and digital twin model data. The method has the advantages that the pollutant migration condition in the shale gas exploitation process is accurately simulated through the digital twinborn simulation model and the pollutant migration prediction model, and a scientific basis is provided for pollutant management.

Description

Technical Field

[0001] The present invention relates to digital twins and model prediction, and in particular to an integrated intelligent comprehensive management system, method, electronic equipment and storage medium for shale gas pollutants. Background Art

[0002] With the rapid development of shale gas extraction, the accompanying pollutant emission problem is becoming increasingly serious, especially the emission and migration of gaseous, liquid and solid pollutants, which may have a significant impact on the surrounding environment and ecosystem. Therefore, a more intelligent and integrated pollutant management method is urgently needed to deal with the complex pollutant emission problems in the shale gas extraction process.

[0003] The current shale gas pollutant management methods on the market usually rely on manual monitoring, fixed equipment detection and post-processing methods, and identify the source and diffusion range of pollutants by collecting real-time data and conducting routine analysis. These traditional methods generally lack dynamic predictions of the pollutant migration process, and rely more on feedback from field data and monitoring equipment, usually mainly static and post-correction. Although it can control the pollution source to a certain extent, its disadvantage is that it lacks specificity and has a slow response speed, which easily misses the best time for governance. In addition, traditional methods focus more on a single pollutant form or a single pollution source, and cannot fully deal with the complex migration patterns and interactive effects of gaseous, liquid and solid pollutants in the shale gas extraction process. Therefore, these methods have problems such as poor prediction ability, delayed emergency response, and low treatment efficiency in practical applications. Summary of the invention

[0004] In order to improve the existing shale gas pollutant management methods, an integrated intelligent comprehensive management system and management method for shale gas pollutants are provided. The method has the ability of accurate prediction and dynamic adjustment, can effectively reduce the negative impact of pollutants on the environment, and improve the predictability and real-time nature of pollutant treatment, thereby realizing intelligent, accurate and efficient management of pollutants in the shale gas extraction process.

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

[0006] An integrated intelligent comprehensive management system for shale gas pollutants, comprising:

[0007] Pollutant monitoring module: The pollutant monitoring module mainly includes a gas component sensor array unit, a liquid pollutant parameter detection unit, and a solid detection unit;

[0008] Intelligent analysis module: The intelligent analysis module is mainly used to build a digital twin shale gas pollutant simulation model based on historical data, and to build a pollutant migration prediction model based on the simulation model;

[0009] Pollutant treatment module: The pollutant treatment module is mainly used to take targeted treatment measures based on the types of shale gas pollutants;

[0010] Model optimization module: The model optimization module is mainly used to calculate the difference between the predicted data and the actual data, continuously optimize the prediction model, and reduce errors;

[0011] Central control module: The central control module is mainly used to communicate with each module and realize real-time optimization of processing parameters and balanced regulation of energy efficiency through adaptive control algorithms;

[0012] Database module: The database module is mainly used to store monitored pollutant data and digital twin model data.

[0013] Preferably, the pollutant monitoring module specifically includes:

[0014] Gas component sensor array unit: The gas component sensor array unit detects gas components in key areas such as well sites, gas gathering stations, and processing facilities through high-precision sensors. Key gases, and cover the diffusion path, and upload the collected data to the central control module;

[0015] Liquid pollutant parameter detection unit: The liquid pollutant parameter detection unit obtains liquid pollutant samples through an automatic sampling pump, obtains liquid pollutant data after multi-parameter detection, and uploads it to the central control module;

[0016] Solid detection unit: The solid detection unit collects surface sedimentation solid samples, performs component analysis and automatic classification, and uploads the collected data to the central control module.

[0017] Preferably, the intelligent analysis module specifically includes:

[0018] The intelligent analysis module is electrically connected to the central control module, the database module, and the pollutant treatment module, obtains historical data and real-time data from the database module, and obtains execution instructions from the central control module;

[0019] A shale gas pollutant digital twin simulation module, which constructs a digital twin simulation model based on shale gas production data to simulate shale gas production conditions, and obtains pollutant distribution data based on the simulation model;

[0020] Pollutant migration prediction model module: This module predicts the future migration direction, path, and rate of pollutants based on the pollutant data obtained by the simulation model, and constructs a pollutant migration prediction model.

[0021] Preferably, constructing a digital twin simulation model based on shale gas production data to simulate shale gas production conditions, and obtaining pollutant distribution data based on the simulation model specifically includes:

[0022] Construct a three-dimensional spatial framework based on the geological data of the mining area and realize multi-physical field coupling;

[0023] Construct a temporal variation framework of shale gas production based on production data;

[0024] Construction of simulation model based on three-dimensional space framework and shale gas production time variation framework;

[0025] Based on the simulation data of the simulation model, parameter data of pollutants are obtained.

[0026] Preferably, the pollutant data obtained based on the simulation model is used to predict the future migration direction, path and rate of pollutants, and building a pollutant migration prediction model specifically includes:

[0027] Based on the acquired pollutant parameter data, the pollutants are classified according to their forms, and divided into gaseous pollutant data, liquid pollutant data, and solid pollutant data;

[0028] Based on the pollutant data in various forms, modeling and prediction models are constructed separately, including:

[0029] Gaseous pollutant migration prediction model: The model is constructed based on simulated gaseous pollutant data and gaseous pollutant data acquired in real time by the pollutant monitoring module;

[0030] Weights are assigned to simulated data and real-time data to obtain unified data;

[0031] The migration behavior of gaseous pollutants over time is obtained through calculation and simulation of the convection-diffusion equation, and the diffusion direction and diffusion rate of gaseous pollutants are obtained;

[0032] Liquid pollutant migration prediction model: The model is constructed based on simulated liquid pollutant data and liquid pollutant data acquired in real time by the pollutant monitoring module;

[0033] Weights are assigned to simulated data and real-time data to obtain unified data;

[0034] Based on the properties of liquid pollutants, they are divided into oily pollutants and watery pollutants, and prediction models are constructed for the two types of pollutants using multiphase flow equations.

[0035] Based on the two prediction models obtained, the two models are integrated through machine learning to obtain a unified liquid pollutant migration prediction model;

[0036] Obtain the migration direction, path and rate of liquid pollutants based on the liquid pollutant migration prediction model;

[0037] Solid pollutant migration prediction model: The model is constructed based on simulated solid pollutant data and solid pollutant data acquired in real time by the pollutant monitoring module;

[0038] Weights are assigned to simulated data and real-time data to obtain unified data;

[0039] Based on the mass of solid pollutants, the mass gradient is divided;

[0040] Based on the mass gradient and the unified data of solid pollutants, the motion equations of solid pollutants under different masses are constructed to obtain the migration direction, path and rate of solid pollutants;

[0041] Based on the pollutant migration prediction models under various forms, the pollutant migration prediction models are summarized and combined to obtain the pollutant migration prediction models.

[0042] Preferably, the pollutant treatment module specifically includes:

[0043] Based on the migration prediction data of pollutants in various forms obtained by the pollutant migration prediction model, the technical coupling strategy is implemented, including: linking the waste scrubber and the wastewater treatment system for gas-liquid coordinated treatment, deploying a permeable reaction wall at the front of the pollution plume, and controlling the solid-liquid interface;

[0044] Dynamic risk assessment is conducted based on the amount of pollutants, and response priorities are divided into:

[0045] Continuous monitoring and natural attenuation assessment will be conducted in low-risk areas, restoration plans will be initiated in medium-risk areas, and automatic emergency mechanisms will be implemented in high-risk areas.

[0046] Preferably, the model optimization module specifically includes:

[0047] Based on the pollutant migration data obtained in real time, the error between the pollutant migration data and the predicted data of the pollutant migration prediction model is calculated;

[0048] Based on the error value, if it exceeds the threshold, the acquired real-time prediction data will be substituted into the pollutant migration prediction model under the corresponding form to optimize the model parameters.

[0049] Furthermore, a shale gas pollutant integrated intelligent comprehensive management method includes:

[0050] Build a digital twin simulation model of shale gas production based on shale gas production data and real-time acquisition data;

[0051] Based on the shale gas extraction digital twin simulation model, obtain the migration simulation data and pollutant property data of the shale gas extraction process;

[0052] Based on the pollutant migration simulation data and pollutant property data, according to the form of pollutants, gaseous pollutant migration prediction model, liquid pollutant migration prediction model, and solid pollutant migration prediction model are constructed respectively;

[0053] Based on the pollutant migration prediction models under various forms, a unified pollutant migration prediction model is obtained by fusion;

[0054] Based on the pollutant migration prediction model, the migration of pollutants in various forms is predicted and simulated to obtain the migration direction, path and rate of pollutants;

[0055] Deploy treatment strategies in advance based on the migration of pollutants;

[0056] The prediction model is optimized based on the difference between the predicted data and the actual data.

[0057] Compared with the prior art, the advantages of the present invention are:

[0058] By combining the digital twin simulation model with the pollutant migration prediction model, the migration process of pollutants in different forms during shale gas extraction can be fully and accurately simulated and predicted. Through modeling based on extraction data and real-time collection data, the migration direction, path and rate of pollutants can be grasped in real time, providing a scientific basis for pollutant management. In particular, by constructing prediction models for gaseous, liquid and solid pollutants respectively, more targeted and efficient management strategies can be provided based on the characteristics of different pollutants. At the same time, this method also optimizes the model by the difference between the predicted data and the actual data to ensure that the prediction results are constantly accurate, thereby improving the feasibility and response capabilities of the management strategy. This ability of accurate prediction and dynamic adjustment can effectively reduce the negative impact of pollutants on the environment, improve the predictability and real-time nature of pollutant treatment, and thus achieve intelligent, accurate and efficient management of pollutants during shale gas extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic diagram of the system proposed by the present invention;

[0060] Figure 2 This is a schematic diagram of the pollutant monitoring module proposed by the present invention;

[0061] Figure 3 This is a schematic diagram of the intelligent analysis module proposed by the present invention;

[0062] Figure 4 The pollutant distribution data workflow diagram proposed by the present invention;

[0063] Figure 5 This is a structural diagram of the pollutant migration prediction model proposed by the present invention;

[0064] Figure 6 The pollutant treatment module workflow diagram proposed by the present invention;

[0065] Figure 7 This is the workflow diagram of the model optimization module proposed by the present invention;

[0066] Figure 8 A schematic diagram of the method proposed by the present invention;

[0067] Fig. 9 This is a schematic diagram of the electronic device in this solution;

[0068] Fig.10 This is a schematic diagram of the computer-readable storage medium structure in this solution. DETAILED DESCRIPTION

[0069] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0070] See also Figure 1 As shown, an integrated intelligent comprehensive management system for shale gas pollutants includes:

[0071] Pollutant monitoring module: The pollutant monitoring module mainly includes a gas component sensor array unit, a liquid pollutant parameter detection unit, and a solid detection unit;

[0072] Intelligent analysis module: The intelligent analysis module is mainly used to build a digital twin shale gas pollutant simulation model based on historical data, and to build a pollutant migration prediction model based on the simulation model;

[0073] Pollutant treatment module: The pollutant treatment module is mainly used to take targeted treatment measures based on the types of shale gas pollutants;

[0074] Model optimization module: The model optimization module is mainly used to calculate the difference between the predicted data and the actual data, continuously optimize the prediction model, and reduce errors;

[0075] Central control module: The central control module is mainly used to communicate with each module and realize real-time optimization of processing parameters and balanced regulation of energy efficiency through adaptive control algorithms;

[0076] Database module: The database module is mainly used to store monitored pollutant data and digital twin model data.

[0077] See also Figure 2 As shown, the pollutant monitoring module specifically includes:

[0078] Gas component sensor array unit: The gas component sensor array unit detects gas components in key areas such as well sites, gas gathering stations, and processing facilities through high-precision sensors. Key gases, and cover the diffusion path, and upload the collected data to the central control module;

[0079] Liquid pollutant parameter detection unit: The liquid pollutant parameter detection unit obtains liquid pollutant samples through an automatic sampling pump, obtains liquid pollutant data after multi-parameter detection, and uploads it to the central control module;

[0080] Solid detection unit: The solid detection unit collects surface sedimentation solid samples, performs component analysis and automatic classification, and uploads the collected data to the central control module.

[0081] Specifically, some gases may dissolve in liquids and change the composition of liquid samples, some gas components may react with solid pollutants and change the properties of solid samples, and pollutants in liquids may adhere to the solid surface, causing errors in the weight and component analysis of solid samples. Therefore, after the mixture sample is collected, separation processing is first performed to extract or separate the gas, liquid and solid separately, and then detect their respective pollutants independently.

[0082] See also Figure 3 As shown, the intelligent analysis module specifically includes:

[0083] The intelligent analysis module is electrically connected to the central control module, the database module, and the pollutant treatment module, obtains historical data and real-time data from the database module, and obtains execution instructions from the central control module;

[0084] A shale gas pollutant digital twin simulation module, which constructs a digital twin simulation model based on shale gas production data to simulate shale gas production conditions, and obtains pollutant distribution data based on the simulation model;

[0085] Pollutant migration prediction model module: This module predicts the future migration direction, path, and rate of pollutants based on the pollutant data obtained by the simulation model, and constructs a pollutant migration prediction model.

[0086] Specifically, by collecting shale gas mining data in real time and building a digital twin simulation model, it is possible to simulate in detail the generation and diffusion of pollutants such as gas emissions and liquid leakage during the mining process. By considering factors such as soil, hydrological conditions, and geological structure, the prediction model can simulate the migration process of pollutants in media such as groundwater and air, and reveal their possible diffusion trends.

[0087] See also Figure 4As shown in the figure, a digital twin simulation model is constructed based on shale gas production data to simulate shale gas production, and pollutant distribution data is obtained based on the simulation model, including:

[0088] Construct a three-dimensional spatial framework based on the geological data of the mining area and realize multi-physical field coupling;

[0089] Construct a temporal variation framework of shale gas production based on production data;

[0090] Construction of simulation model based on three-dimensional space framework and shale gas production time variation framework;

[0091] Based on the simulation data of the simulation model, parameter data of pollutants are obtained.

[0092] Specifically, the three-dimensional spatial framework takes into account important factors such as underground geological structure, rock characteristics and porosity, providing a basis for subsequent multi-physical field coupling. By coupling different physical fields (such as pressure field, temperature field, fluid field, etc.), the mutual influence of various physical phenomena in the mining process can be simulated more accurately, thereby improving the reliability and accuracy of the simulation results. By combining actual mining data, a time-varying framework for shale gas mining is constructed to reflect the dynamic changes in the mining process, such as changes in gas production and pore pressure fluctuations, providing support in the time dimension for further simulation models.

[0093] See also Figure 5 As shown in the figure, based on the pollutant data obtained by the simulation model, the future migration direction, path and rate of pollutants are predicted. The construction of the pollutant migration prediction model specifically includes:

[0094] Based on the acquired pollutant parameter data, the pollutants are classified according to their forms, and divided into gaseous pollutant data, liquid pollutant data, and solid pollutant data;

[0095] Based on the pollutant data in various forms, modeling and prediction models are constructed separately, including:

[0096] Gaseous pollutant migration prediction model: The model is constructed based on simulated gaseous pollutant data and gaseous pollutant data acquired in real time by the pollutant monitoring module;

[0097] Weights are assigned to simulated data and real-time data to obtain unified data;

[0098] The migration behavior of gaseous pollutants over time is obtained through calculation and simulation of the convection-diffusion equation, and the diffusion direction and diffusion rate of gaseous pollutants are obtained;

[0099] Liquid pollutant migration prediction model: The model is constructed based on simulated liquid pollutant data and liquid pollutant data acquired in real time by the pollutant monitoring module;

[0100] Weights are assigned to simulated data and real-time data to obtain unified data;

[0101] Based on the properties of liquid pollutants, they are divided into oily pollutants and watery pollutants, and prediction models are constructed for the two types of pollutants using multiphase flow equations.

[0102] Based on the two prediction models obtained, the two models are integrated through machine learning to obtain a unified liquid pollutant migration prediction model;

[0103] Obtain the migration direction, path and rate of liquid pollutants based on the liquid pollutant migration prediction model;

[0104] Solid pollutant migration prediction model: The model is constructed based on simulated solid pollutant data and solid pollutant data acquired in real time by the pollutant monitoring module;

[0105] Weights are assigned to simulated data and real-time data to obtain unified data;

[0106] Based on the mass of solid pollutants, the mass gradient is divided;

[0107] Based on the mass gradient and the unified data of solid pollutants, the motion equations of solid pollutants under different masses are constructed to obtain the migration direction, path and rate of solid pollutants;

[0108] Based on the pollutant migration prediction models under various forms, the pollutant migration prediction models are summarized and combined to obtain the pollutant migration prediction models.

[0109] It is understandable that when the migration prediction models of pollutants in different forms (gaseous, liquid, solid) are integrated, the parameters and results of different models may be quite different, resulting in a reduction in the final comprehensive prediction accuracy. Multi-model fusion techniques such as weighted averaging and ensemble learning (such as random forest, XGBoost, etc.) can be used, and different weights can be assigned to each model based on the reliability and prediction effect of different pollutant models. In addition, cross-validation and model comparison can be performed to ensure that the final comprehensive model has high accuracy and robustness.

[0110] Secondly, due to the complexity of the three-dimensional spatial framework and multi-physics field coupling, the entire simulation process may result in a huge amount of calculations, especially when dealing with large-scale mining areas, the running speed and efficiency of the model may not meet the needs of real-time warning. The model calculation efficiency can be improved through parallel computing and high-performance computing technology, or simplified numerical methods (such as finite element method, finite difference method, etc.) can be used to optimize the calculation process and reduce the consumption of computing resources. In addition, machine learning methods can be combined to perform preprocessing and data dimensionality reduction in the model training stage to accelerate real-time calculations.

[0111] See also Figure 6 As shown, the pollutant treatment module specifically includes:

[0112] Based on the migration prediction data of pollutants in various forms obtained by the pollutant migration prediction model, the technical coupling strategy is implemented, including: linking the waste scrubber and the wastewater treatment system for gas-liquid coordinated treatment, deploying a permeable reaction wall at the front of the pollution plume, and controlling the solid-liquid interface;

[0113] Dynamic risk assessment is conducted based on the amount of pollutants, and response priorities are divided into:

[0114] Continuous monitoring and natural attenuation assessment will be conducted in low-risk areas, restoration plans will be initiated in medium-risk areas, and automatic emergency mechanisms will be implemented in high-risk areas.

[0115] See also Figure 7 As shown in the figure, the model optimization module specifically includes:

[0116] Based on the pollutant migration data obtained in real time, the error between the pollutant migration data and the predicted data of the pollutant migration prediction model is calculated;

[0117] Based on the error value, if it exceeds the threshold, the acquired real-time prediction data will be substituted into the pollutant migration prediction model under the corresponding form to optimize the model parameters.

[0118] Specifically, optimization methods usually include algorithms based on error minimization, such as the least squares method, genetic algorithm or Bayesian optimization, which automatically adjust model parameters to enable it to dynamically respond to environmental changes and improve the ability to predict future pollutant migration.

[0119] See also Figure 8 As shown, an integrated intelligent comprehensive management method for shale gas pollutants includes:

[0120] Build a digital twin simulation model of shale gas production based on shale gas production data and real-time acquisition data;

[0121] Based on the shale gas extraction digital twin simulation model, obtain the migration simulation data and pollutant property data of the shale gas extraction process;

[0122] Based on the pollutant migration simulation data and pollutant property data, according to the form of pollutants, gaseous pollutant migration prediction model, liquid pollutant migration prediction model, and solid pollutant migration prediction model are constructed respectively;

[0123] Based on the pollutant migration prediction models under various forms, a unified pollutant migration prediction model is obtained by fusion;

[0124] Based on the pollutant migration prediction model, the migration of pollutants in various forms is predicted and simulated to obtain the migration direction, path and rate of pollutants;

[0125] Deploy treatment strategies in advance based on the migration of pollutants;

[0126] The prediction model is optimized based on the difference between the predicted data and the actual data.

[0127] Furthermore, the method according to the embodiment of the present application can also be performed by Fig. 9 The electronic device architecture shown in FIG. Fig. 9 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as ROM 503 or hard disk 507, may store a shale gas pollutant integrated intelligent comprehensive management system and management method provided by the present application. The electronic device 500 may also include a terminal interface 508. Of course, Fig. 9 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Fig. 9 One or more components of an electronic device are shown.

[0128] Fig.10 Schematic diagram of a computer-readable storage medium structure provided by an embodiment of the present application. Fig.10 As shown, a computer-readable storage medium 600 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by the processor, an integrated intelligent comprehensive management system and management method for shale gas pollutants according to an embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0129] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0130] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0131] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An integrated intelligent comprehensive management system for shale gas pollutants, characterized in that: include: Pollutant monitoring module: The pollutant monitoring module mainly includes a gas component sensor array unit, a liquid pollutant parameter detection unit, and a solid detection unit; Intelligent analysis module: The intelligent analysis module is mainly used to build a digital twin shale gas pollutant simulation model based on historical data, and to build a pollutant migration prediction model based on the simulation model; Pollutant treatment module: The pollutant treatment module is mainly used to take targeted treatment measures based on the types of shale gas pollutants; Model optimization module: The model optimization module is mainly used to calculate the difference between the predicted data and the actual data, continuously optimize the prediction model, and reduce errors; Central control module: The central control module is mainly used to communicate with each module and realize real-time optimization of processing parameters and balanced regulation of energy efficiency through adaptive control algorithms; Database module: The database module is mainly used to store monitored pollutant data and digital twin model data.

2. The integrated intelligent comprehensive management system for shale gas pollutants according to claim 1 is characterized in that: The pollutant monitoring module specifically includes: Gas component sensor array unit: The gas component sensor array unit detects gas composition in well sites, gas gathering stations, and processing facilities through high-precision sensors. Gas, and cover the diffusion path, and upload the collected data to the central control module; Liquid pollutant parameter detection unit: The liquid pollutant parameter detection unit obtains liquid pollutant samples through an automatic sampling pump, obtains liquid pollutant data after multi-parameter detection, and uploads it to the central control module; Solid detection unit: The solid detection unit collects surface sedimentation solid samples, performs component analysis and automatic classification, and uploads the collected data to the central control module.

3. The integrated intelligent comprehensive management system for shale gas pollutants according to claim 1 is characterized in that: The intelligent analysis module specifically includes: The intelligent analysis module is electrically connected to the central control module, the database module, and the pollutant treatment module, obtains historical data and real-time data from the database module, and obtains execution instructions from the central control module; A shale gas pollutant digital twin simulation module, which constructs a digital twin simulation model based on shale gas production data to simulate shale gas production conditions, and obtains pollutant distribution data based on the simulation model; Pollutant migration prediction model module: This module predicts the future migration direction, path, and rate of pollutants based on the pollutant data obtained by the simulation model, and constructs a pollutant migration prediction model.

4. The integrated intelligent comprehensive management system for shale gas pollutants according to claim 1 is characterized in that: The digital twin simulation model is constructed based on shale gas production data to simulate shale gas production conditions, and pollutant distribution data is obtained based on the simulation model, specifically including: Construct a three-dimensional spatial framework based on the geological data of the mining area and realize multi-physical field coupling; Construct a temporal variation framework of shale gas production based on production data; Construction of simulation model based on three-dimensional space framework and shale gas production time variation framework; Based on the simulation data of the simulation model, parameter data of pollutants are obtained.

5. The integrated intelligent comprehensive management system for shale gas pollutants according to claim 1 is characterized in that: The pollutant data obtained based on the simulation model is used to predict the future migration direction, path, and rate of pollutants, and the pollutant migration prediction model is constructed, specifically including: Based on the acquired pollutant parameter data, the pollutants are classified according to their forms, and divided into gaseous pollutant data, liquid pollutant data, and solid pollutant data; Based on the pollutant data in various forms, modeling and prediction models are constructed separately, including: Gaseous pollutant migration prediction model: The model is constructed based on simulated gaseous pollutant data and gaseous pollutant data acquired in real time by the pollutant monitoring module; Weights are assigned to simulated data and real-time data to obtain unified data; The migration behavior of gaseous pollutants over time is obtained through calculation and simulation of the convection-diffusion equation, and the diffusion direction and diffusion rate of gaseous pollutants are obtained; Liquid pollutant migration prediction model: The model is constructed based on simulated liquid pollutant data and liquid pollutant data acquired in real time by the pollutant monitoring module; Weights are assigned to simulated data and real-time data to obtain unified data; Based on the properties of liquid pollutants, they are divided into oily pollutants and watery pollutants, and prediction models are constructed for the two types of pollutants using multiphase flow equations. Based on the two prediction models obtained, the two models are integrated through machine learning to obtain a unified liquid pollutant migration prediction model; Obtain the migration direction, path and rate of liquid pollutants based on the liquid pollutant migration prediction model; Solid pollutant migration prediction model: The model is constructed based on simulated solid pollutant data and solid pollutant data acquired in real time by the pollutant monitoring module; Weights are assigned to simulated data and real-time data to obtain unified data; Based on the mass of solid pollutants, the mass gradient is divided; Based on the mass gradient and the unified data of solid pollutants, the motion equations of solid pollutants under different masses are constructed to obtain the migration direction, path and rate of solid pollutants; Based on the pollutant migration prediction models under various forms, the pollutant migration prediction models are summarized and combined to obtain the pollutant migration prediction models.

6. The integrated intelligent comprehensive management system for shale gas pollutants according to claim 1 is characterized in that: The pollutant treatment module specifically includes: Based on the migration prediction data of pollutants in various forms obtained by the pollutant migration prediction model, the technical coupling strategy is implemented, including: linking the waste scrubber and the wastewater treatment system for gas-liquid coordinated treatment, deploying a permeable reaction wall at the front of the pollution plume, and controlling the solid-liquid interface; Dynamic risk assessment is conducted based on the amount of pollutants, and response priorities are divided into: Continuous monitoring and natural attenuation assessment will be conducted in low-risk areas, restoration plans will be initiated in medium-risk areas, and automatic emergency mechanisms will be implemented in high-risk areas.

7. The integrated intelligent comprehensive management system for shale gas pollutants according to claim 1 is characterized in that: The model optimization module specifically includes: Based on the pollutant migration data obtained in real time, the error between the pollutant migration data and the predicted data of the pollutant migration prediction model is calculated; Based on the error value, if it exceeds the threshold, the acquired real-time prediction data will be substituted into the pollutant migration prediction model under the corresponding form to optimize the model parameters.

8. An integrated intelligent comprehensive management method for shale gas pollutants, characterized in that: include: Build a digital twin simulation model of shale gas production based on shale gas production data and real-time acquisition data; Based on the shale gas extraction digital twin simulation model, obtain the migration simulation data and pollutant property data of the shale gas extraction process; Based on the pollutant migration simulation data and pollutant property data, according to the form of pollutants, gaseous pollutant migration prediction model, liquid pollutant migration prediction model, and solid pollutant migration prediction model are constructed respectively; Based on the pollutant migration prediction models under various forms, a unified pollutant migration prediction model is obtained by fusion; Based on the pollutant migration prediction model, the migration of pollutants in various forms is predicted and simulated to obtain the migration direction, path and rate of pollutants; Deploy treatment strategies in advance based on the migration of pollutants; The prediction model is optimized based on the difference between the predicted data and the actual data.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an integrated intelligent comprehensive management method for shale gas pollutants as described in any one of claims 1-7.

10. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, an integrated intelligent comprehensive management method for shale gas pollutants according to any one of claims 1-7 is implemented.

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