An integrated intelligent comprehensive management system, method, electronic device and storage medium for shale gas pollutants

Abstract

The present invention discloses an integrated intelligent comprehensive management system, method, electronic device and storage medium for shale gas pollutants, including: a pollutant monitoring module including a gas component sensor array unit, a liquid pollutant parameter detection unit, and a solid detection unit; an intelligent analysis module mainly used for constructing a digital twin shale gas pollutant simulation model and constructing a pollutant migration prediction model; a pollutant treatment module mainly used for taking treatment measures based on the types of pollutants; a model optimization module mainly used for calculating the difference between the predicted data and the actual data to optimize the model; a central control module mainly used for communicating with each module to process real-time optimization of parameters and energy efficiency balanced regulation; a database module mainly used for storing the monitored pollutant data and digital twin model data. The advantages of the present invention are as follows: Through the digital twin simulation model and the pollutant migration prediction model, the pollutant migration situation during the shale gas extraction process is accurately simulated, providing a scientific basis for pollutant management.

Description

Technical Field

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

[0002] With the rapid development of shale gas exploitation, the accompanying pollutant emission problems are becoming increasingly serious. In particular, the emissions and migrations of gaseous, liquid, and solid pollutants may have a significant impact on the surrounding environment and ecosystem. Therefore, there is an urgent need for a more intelligent and integrated pollutant management method to address the complex pollutant emission problems during shale gas exploitation.

[0003] Currently, the shale gas pollutant management methods on the market usually rely on manual monitoring, fixed equipment detection, and ex-post treatment means. By collecting real-time data and conducting routine analysis, they identify the sources and diffusion ranges of pollutants. These traditional methods generally lack dynamic prediction of the pollutant migration process and rely more on on-site data and feedback from monitoring equipment, usually mainly static and ex-post correction. Although certain control of pollution sources can be achieved, their disadvantages are lack of pertinence, slow response speed, and easy to miss the best treatment opportunity. In addition, traditional methods mostly focus on a single pollutant form or a single pollution source and cannot comprehensively address the complex migration laws and interactive effects of gaseous, liquid, and solid pollutants during shale gas exploitation. Therefore, these methods have problems such as poor prediction ability, lag in emergency response, and low treatment efficiency in practical applications. Summary of the Invention

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

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

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

[0007] A 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] An intelligent analysis module: The intelligent analysis module is mainly used to construct a digital twin shale gas pollutant simulation model based on historical data and construct 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 through an adaptive control algorithm, realize real-time optimization of processing parameters and energy efficiency balanced regulation;

[0012] Database module: The database module is mainly used to store the 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 key gases in key areas such as well sites, gas gathering stations, and treatment facilities through high-precision sensors, covers the diffusion path, and uploads 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 analyzes the composition and automatically classifies the collected surface settlement solid samples, 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] Shale gas pollutant digital twin simulation module, which constructs a digital twin simulation model based on shale gas extraction data to simulate shale gas extraction 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 from the simulation model, and constructs a pollutant migration prediction model.

[0021] Preferably, constructing a digital twin simulation model based on shale gas extraction data to simulate shale gas extraction 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 achieve multi-physical field coupling;

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

[0024] Construct a simulation model based on the three-dimensional spatial framework and the framework for the temporal variation of shale gas production;

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

[0026] Preferably, for the pollutant data obtained based on the simulation model, predicting the future migration direction, path, and rate of the pollutants, the construction of the pollutant migration prediction model specifically includes:

[0027] Based on the obtained parameter data of the pollutants, classify according to the pollutant form, and divide into gaseous pollutant data, liquid pollutant data, and solid pollutant data;

[0028] Based on the pollutant data in each form, respectively build prediction models for modeling, specifically including:

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

[0030] Assign weights to the simulated data and the real-time data to obtain unified data;

[0031] Calculate and simulate the migration behavior of gaseous pollutants over time through the convection-diffusion equation to obtain the diffusion direction and diffusion rate of gaseous pollutants;

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

[0033] Assign weights to the simulated data and the real-time data to obtain unified data;

[0034] Based on the properties of liquid pollutants, divide into oily pollutants and aqueous pollutants, and build prediction models for the two types of pollutants respectively through the multi-phase flow equation;

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

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

[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 forms of pollutants, a gaseous pollutant migration prediction model, a liquid pollutant migration prediction model, and a solid pollutant migration prediction model are respectively constructed.

[0053] Based on the pollutant migration prediction models in each form, a unified pollutant migration prediction model is obtained through fusion.

[0054] Based on the pollutant migration prediction model, the migration situations of pollutants in each form are predicted and simulated to obtain the migration direction, path, and rate of pollutants.

[0055] Based on the pollutant migration situation, treatment strategies are deployed in advance.

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

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

[0058] By combining the digital twin simulation model and the pollutant migration prediction model, the migration processes of different forms of pollutants during shale gas extraction can be comprehensively and accurately simulated and predicted. Through modeling based on extraction data and real-time collected data, the migration direction, path, and rate of pollutants can be grasped in real time, providing a scientific basis for pollutant management. Especially by constructing prediction models for gaseous, liquid, and solid pollutants respectively, more targeted and efficient management strategies can be provided according to the characteristics of different pollutants. At the same time, this method also optimizes the model through the difference between the predicted data and the actual data to ensure that the prediction results are continuously accurate, thereby improving the feasibility and response ability of the management strategy. This ability of accurate prediction and dynamic adjustment can effectively reduce the negative impact of pollutants on the environment, enhance the predictability and real-time nature of pollutant treatment, and thus realize the intelligent, accurate, and efficient management of pollutants during shale gas extraction. Brief Description of the Drawings

[0059] Figure 1 It is a schematic diagram of the system proposed by the present invention;

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

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

[0062] Figure 4 It is a working flowchart of the pollutant distribution data proposed by the present invention;

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

[0064] Figure 6 The working flowchart of the pollutant treatment module proposed by the present invention;

[0065] Figure 7 The working flowchart of the model optimization module proposed by the present invention;

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

[0067] Figure 9 The architecture diagram of the electronic device in this solution;

[0068] Figure 10 The schematic diagram of the structure of the computer-readable storage medium in this solution. Detailed implementation manners

[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 in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0070] Refer to 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 construct a digital twin simulation model of shale gas pollutants based on historical data, and construct 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 the error;

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

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

[0077] Refer to Figure 2 As shown, the pollutant monitoring module specifically includes:

[0078] Gas component sensor array unit: The gas component sensor array unit detects key gases in key areas such as well sites, gas gathering stations, and processing facilities through high-precision sensors, covers the diffusion path, and uploads the collected data to the central control module; Key gases, and covers the diffusion path, uploading 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 conducts component analysis and automatic classification on the collected surface settlement solid samples, and uploads the collected data to the central control module.

[0081] Specifically, some gases may dissolve in liquids, changing the composition of the liquid samples, some gas components may react with solid pollutants, changing the properties of the solid samples, and pollutants in the liquid may adhere to the solid surface, resulting in errors in the weight and component analysis of the solid samples. Therefore, after the mixture samples are collected, separation treatment is carried out first to extract or separate gases, liquids, and solids respectively, and then their respective pollutants are detected independently.

[0082] Refer to 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] Shale gas pollutant digital twin simulation module, which constructs a digital twin simulation model based on shale gas extraction data to simulate shale gas extraction 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 from the simulation model, and constructs a pollutant migration prediction model.

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

[0087] Refer to Figure 4As shown, a digital twin simulation model is constructed based on shale gas extraction data to simulate shale gas extraction, and pollutant distribution data is obtained based on the simulation model, specifically including:

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

[0089] Construct a framework for the time variation of shale gas extraction based on the extraction data;

[0090] Construct a simulation model based on the three-dimensional space framework and the time variation framework of shale gas extraction;

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

[0092] Specifically, the three-dimensional space framework considers important factors such as the underground geological structure, rock formation 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 during the extraction process can be more accurately simulated, thereby improving the reliability and accuracy of the simulation results. By combining actual extraction data, a time variation framework for shale gas extraction is constructed to reflect the dynamic changes during the extraction process, such as gas production changes, pore pressure fluctuations, etc., providing support in the time dimension for further simulation models.

[0093] Refer to Figure 5 As shown, based on the pollutant data obtained from the simulation model, predict the future migration direction, path, and rate of pollutants, and construct a pollutant migration prediction model, specifically including:

[0094] Based on the obtained parameter data of pollutants, classify them according to the pollutant form, and divide them into gaseous pollutant data, liquid pollutant data, and solid pollutant data;

[0095] Based on the pollutant data in each form, build prediction models respectively, specifically including:

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

[0097] Assign weights to the simulated data and real-time data to obtain unified data;

[0098] Calculate and simulate the migration behavior of gaseous pollutants over time through the convection-diffusion equation to obtain the diffusion direction and diffusion rate of gaseous pollutants;

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

[0100] Assign weights to the 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 aqueous pollutants, and prediction models are respectively constructed for the two types of pollutants through multiphase flow equations;

[0102] Based on the two obtained prediction models, the two models are fused through machine learning to obtain a unified prediction model for the migration of liquid pollutants;

[0103] Based on the prediction model for the migration of liquid pollutants, obtain the migration direction, path and rate of liquid pollutants;

[0104] Prediction model for the migration of solid pollutants: The model is constructed based on the simulated solid pollutant data and the solid pollutant data obtained in real time by the pollutant monitoring module;

[0105] Assign weights to the simulated data and real-time data to obtain unified data;

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

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

[0108] Based on the prediction models for the migration of pollutants in each form, summarize and merge to obtain the prediction model for the migration of pollutants.

[0109] It can be understood that when fusing the prediction models for the migration of pollutants in different forms (gaseous, liquid, solid), there may be significant differences in the parameters and results between different models, resulting in a decrease in the final comprehensive prediction accuracy. Multi-model fusion techniques such as weighted average method and ensemble learning (such as random forest, XGBoost, etc.) can be used, and different weights are assigned to each model according to the confidence and prediction effect of different pollutant models. In addition, cross-validation and model comparison can be carried out to ensure that the final comprehensive model has high accuracy and robustness.

[0110] Secondly, due to the complexity of the three-dimensional space framework and multi-physics field coupling, the entire simulation process may result in a large amount of calculations. Especially when dealing with large-scale mining areas, the running speed and efficiency of the model may not meet the requirements of real-time warning. The calculation efficiency of the model can be improved through parallel computing and high-performance computing technologies, 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 during 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 Figure 1, 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] Predict and simulate the migration of pollutants in various forms based on the pollutant migration prediction model to obtain the migration direction, path, and rate of the pollutants;

[0125] Based on the migration situation of the pollutants, deploy treatment strategies in advance;

[0126] Optimize the prediction model 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 implemented by means of Figure 9 the architecture of the electronic device shown. As Figure 9 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 the network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store an integrated intelligent comprehensive management system and management method for shale gas pollutants provided by the present application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 9 the architecture shown is only exemplary. When implementing different devices, one or more components shown in the Figure 9 electronic device may be omitted according to actual needs.

[0128] Figure 10 is a schematic diagram of the structure of a computer-readable storage medium provided by an embodiment of the present application. As Figure 10 shown, it is a computer-readable storage medium 600 according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are run by a processor, an integrated intelligent comprehensive management system and management method for shale gas pollutants according to the embodiment of the present application described with reference to the above drawings 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, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

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

[0130] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized.

[0131] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An integrated intelligent comprehensive management platform 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 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. It includes a shale gas pollutant digital twin simulation module and a pollutant migration prediction model module. A shale gas pollutant digital twin simulation module, wherein the shale gas pollutant digital twin simulation module 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: The pollutant migration prediction model 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; 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 shale gas pollutant integrated intelligent comprehensive management platform 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 components in key areas of 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; 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 shale gas pollutant integrated intelligent comprehensive management platform 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.

4. The shale gas pollutant integrated intelligent comprehensive management platform 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.

5. The shale gas pollutant integrated intelligent comprehensive management platform 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.

6. The shale gas pollutant integrated intelligent comprehensive management platform 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.

7. An integrated intelligent comprehensive management method for shale gas pollutants, characterized in that: include: A digital twin simulation model is built based on shale gas production data to simulate shale gas production conditions, and pollutant distribution data is obtained based on the simulation model; 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.

8. 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 drive the operation of an integrated intelligent comprehensive management platform for shale gas pollutants as described in any one of claims 1-6.

9. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by the processor, they drive the operation of an integrated intelligent comprehensive management platform for shale gas pollutants as described in any one of claims 1-6.

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