An intelligent collaborative system and method for controlling and managing water and soil pollution in production enterprises

Abstract

A smart collaborative system and method for water and soil pollution control in operating enterprises is disclosed. The system includes a production subsystem, an identification subsystem, and a control subsystem. The data output terminal of the production subsystem is connected to the data input terminal of the identification subsystem. The data terminals of the identification subsystem and the control subsystem are bidirectionally connected. The data terminals of the control subsystem and the production subsystem are also bidirectionally connected. The system is supported by an embedded integrated remote control architecture, forming a four-level collaborative architecture of "client-cloud server-embedded edge terminal-execution layer". The purpose of this invention is to address the shortcomings of existing technologies in terms of production and control coordination, control accuracy, and remote control adaptability, in order to meet the core needs of operating enterprises for "simultaneous production and control". This smart collaborative system and method for water and soil pollution control in operating enterprises is applicable to the control needs of operating enterprises of different sizes and with different types of pollution.

Description

Technical Field

[0001] This invention relates to the fields of environmental protection and pollution prevention technology, and in particular to the collaborative technology of pollution control and production operation in operating enterprises, specifically to an intelligent collaborative system and method for water and soil pollution control in operating enterprises. Background Technology

[0002] Production processes at operating enterprises encompass multiple stages, including raw material pretreatment, core process execution, and product storage and transportation. Over long-term operation, these sites are prone to soil and groundwater pollution due to equipment leaks and pipeline corrosion. Such sites are characterized by high requirements for production continuity and strong coupling between pollution migration and production conditions. Traditional pollution control technologies struggle to balance production operations with pollution remediation needs, and existing technologies still have many limitations. For example, patent application CN117725741A proposes a "design method for pollution control and collaborative remediation schemes for industrial solid waste sites." This technology calculates the service life of the coverage barrier system using analytical models and selects the lowest-cost scheme that meets the remediation time requirements, solving the problem of high costs and reliance on numerical simulation in the design of pollution control and remediation schemes for industrial solid waste sites. However, it suffers from a technical deficiency: it does not consider the dynamic synergy between production and control at operating enterprises. The scheme only addresses static pollution scenarios at solid waste landfills and cannot adapt to the actual needs of continuous production and dynamic changes in operating conditions at operating enterprises. Ultimately, this leads to conflicts between the control scheme and production operations, making it difficult to achieve simultaneous production and control.

[0003] Patent application publication number CN118735278A proposes "a method for differentiated management of site pollution prevention and control in chemical industrial parks." This technology divides pollution risk areas by constructing a scoring system and formulates targeted management strategies, solving the problem of difficult overall control caused by the large area and complex hydrogeology of chemical industrial parks. However, it has technical defects such as a static control strategy and a lack of real-time data linkage. It does not involve a dynamic adaptation mechanism between production conditions and pollution control, and cannot adjust and optimize control measures according to changes in production load and pollution migration paths. Ultimately, this results in low control accuracy and delayed response, making it difficult to meet the real-time requirements of pollution prevention and control for enterprises in operation.

[0004] Furthermore, patent application publication number CN120673356A proposes an "embedded port edge intelligent identification method, identification terminal, equipment, and control system." This technology achieves rapid local identification of multiple targets in port scenarios through an improved YOLOv8 model, solving the problems of high identification latency and high deployment costs in traditional centralized architectures. However, it has technical defects such as not being adapted to the water and soil pollution control scenarios of operating enterprises and lacking a remote collaboration mechanism for production and control. It only focuses on local identification and control and cannot achieve remote dynamic adaptation between production conditions and pollution control, ultimately making it difficult to meet the needs of operating enterprises for remote production and control in different locations.

[0005] To address the above shortcomings, the applicant proposed an intelligent collaborative system and method for water and soil pollution control in operating enterprises. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies in terms of production and control coordination, control accuracy, and remote control adaptability, so as to meet the core needs of enterprises in production to "manage and control simultaneously with production". This invention proposes an intelligent collaborative system and method for water and soil pollution control in enterprises in production, which is applicable to the control needs of enterprises of different sizes and different types of pollution.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A smart collaborative system for water and soil pollution control in operating enterprises, the system includes a production subsystem, an identification subsystem, and a control subsystem; the data output terminal of the production subsystem is connected to the data input terminal of the identification subsystem; the data terminal of the identification subsystem is bidirectionally connected to the data terminal of the control subsystem; and the data terminal of the control subsystem is bidirectionally connected to the data terminal of the production subsystem.

[0008] The system is supported by an embedded integrated remote control architecture, forming a four-level collaborative architecture of "client-cloud server-embedded edge terminal-execution layer". The production subsystem is used to execute the entire production process, outputting production information to the identification subsystem, and simultaneously receiving operating condition adjustment instructions from the control subsystem and feeding back production information to the control subsystem. The identification subsystem is used for site pollution information collection and underground 3D reconstruction, outputting pollution information to the control subsystem and receiving control effect data feedback, while simultaneously receiving production information from the production subsystem and feeding back operating condition adjustment instructions to the production subsystem. The control subsystem is used to realize zoning-classification-grading control of site pollution, outputting operating condition adjustment instructions to the production subsystem and receiving production information, while simultaneously receiving pollution information provided by the identification subsystem and feeding back control effect data. The embedded integrated remote control architecture is used to build a full-link of "remote command - data interaction - collaborative decision-making - execution feedback", realizing remote integrated control and data interaction of production, identification, and control.

[0009] The production subsystem includes: a raw material pretreatment module, a core process execution module, a product storage and transportation module, an operating condition monitoring module, and a production database; the data terminals of the raw material pretreatment module and the core process execution module are bidirectionally connected; the data terminals of the core process execution module and the product storage and transportation module are bidirectionally connected; the data output terminals of the raw material pretreatment module, the core process execution module, and the product storage and transportation module are connected to the data input terminals of the operating condition monitoring module; and the data output terminal of the operating condition monitoring module is connected to the data input terminal of the production database.

[0010] The raw material pretreatment module is used to purify, dehydrate, and desalinate the raw materials, and output qualified raw materials to the core process execution module, while simultaneously recording the raw material requirements and process load fed back by the core process execution module. The core process execution module is used to complete core production processes such as catalytic cracking and catalytic reforming, and simultaneously collects production operation parameters. The product storage and transportation module is used to realize the storage and transportation of products, and synchronously records the operating status of the storage tank and the loading and unloading operation sequence information. The operating condition monitoring module is used to collect operating condition data such as production load, equipment operating status, pipeline pressure, and raw material / product turnover in real time, and simultaneously carry out production process risk monitoring to ensure data timeliness and production safety. The production database is used to store operating condition data and risk monitoring data, and synchronously transmits the stored production information to the identification subsystem and the control subsystem.

[0011] The identification subsystem includes: a real-time monitoring module, a sampling module, a low-disturbance drilling module, a high-fidelity sampling module, a rapid detection module, a 3D reconstruction module, and a pollution database. The data output terminal of the real-time monitoring module is connected to the data input terminal of the sampling module; the data output terminal of the sampling module is connected to the data input terminals of the low-disturbance drilling module and the high-fidelity sampling module; the data terminals of the low-disturbance drilling module and the high-fidelity sampling module are bidirectionally connected to the data terminal of the pollution database; the data output terminals of the low-disturbance drilling module and the high-fidelity sampling module are connected to the data input terminal of the rapid detection module; the data output terminal of the rapid detection module is connected to the data input terminal of the 3D reconstruction module; and the data output terminals of the 3D reconstruction module and the rapid detection module are connected to the data input terminal of the pollution database.

[0012] The real-time monitoring module is used to monitor the site soil and groundwater in real time by deploying multimodal sensors based on the production information provided by the production subsystem. The sampling module is used to carry out the work according to the process of "real-time monitoring → sample point confirmation → preliminary sampling". Specifically, it determines the sampling point by combining the real-time monitoring data of the site and the data feedback of the control subsystem. After completing the preliminary sampling, it calls the feedback results of the pollution database to optimize the sampling point and detection frequency. The low-disturbance drilling module and the high-fidelity sampling module are used to collect soil and groundwater samples during production breaks or maintenance windows using low-disturbance drilling and high-fidelity sampling techniques, thereby reducing interference with production operations. The rapid detection module is used to monitor soil, groundwater and their pollution characteristics, to perform in-situ detection of heavy metals and organic pollutants in groundwater and soil, and to quickly output pollutant type and concentration data; simultaneously, it obtains necessary parameters such as soil skeleton, organic matter content and porosity through laboratory testing. The three-dimensional reconstruction module is used to integrate pollutant type, concentration data, key physicochemical parameters and site geological data to complete the three-dimensional spatial distribution of soil and groundwater pollutants, generate a three-dimensional pollution distribution map to the control subsystem, and feed back operating condition adjustment instructions to the production subsystem. The pollution database is used to store data from the entire process of sampling, detection, and reconstruction.

[0013] The control subsystem includes: a zone control module, a category control module, an emergency response module, a pollution risk assessment module, a hierarchical control module, an effectiveness evaluation module, a control database, and an embedded fault self-healing system. The data output terminal of the zone control module is connected to the data input terminal of the category control module; the data output terminal of the category control module is connected to the data input terminals of the emergency response module and the pollution risk assessment module; the data output terminal of the emergency response module is connected to the data input terminal of the pollution risk assessment module; the data output terminal of the pollution risk assessment module is connected to the data input terminal of the hierarchical control module; the data output terminal of the hierarchical control module is connected to the data input terminals of the effectiveness evaluation module and the control database; and the data output terminal of the effectiveness evaluation module is connected to the data input terminal of the control database.

[0014] The zoning control module is used to receive the three-dimensional pollution distribution map output by the identification subsystem, and, in combination with the production function attributes of the producing enterprises, divide the site into a core production area, a storage and transportation buffer area, a decommissioning and treatment area, and a surrounding sensitive area. The classification and control module is used to determine the urgency of pollution and identify control targets based on the pollution information output by the identification subsystem: sudden and high-risk emergency situations are identified as short-term emergency control targets and enter the emergency response module; cumulative and low-risk non-emergency situations are identified as medium- and long-term control targets and enter the pollution risk assessment module. The emergency response module is used to execute emergency response measures and respond to short-term emergency control needs. After the emergency response is completed, it enters the determination stage of "whether it can enter medium- and long-term control": if the pollution risk is reduced to a controllable range, it enters medium- and long-term control; if it does not meet the standards, it continues to implement emergency response measures until the medium- and long-term control access conditions are met. The pollution risk assessment module is used to conduct pollution risk assessments on soil and groundwater pollutants during the medium- and long-term management phase, and classify the risk levels into high risk, medium risk, and low risk. The hierarchical control module is used to match control levels and measures according to risk level results, and the data after execution is fed back to the identification subsystem for effect evaluation. The control measures include Level 1 control, Level 2 control, and Level 3 control; The effect evaluation module is used to quantitatively detect and determine the compliance of the control effect: if the pollution risk reaches the lower limit of the low risk level after control, it is determined to meet the standard, and the process will proceed to the later environmental supervision and output the production subsystem with operating condition adjustment instructions such as production load adjustment and equipment switching sequence optimization; if it does not meet the standard, the control level will be re-determined and the corresponding control measures will be implemented until the standard is met. The control database is used to store control plans, effect evaluation data, and operational status data of sensors, actuators, and communication links. At the same time, based on the pollution distribution density and production activity intensity in each area, it dynamically feeds back sampling point layout optimization suggestions to the identification subsystem. The embedded fault self-healing system is used to realize real-time monitoring, diagnosis and self-healing of faults in the control subsystem, ensuring the continuity of the production and control process.

[0015] This invention also includes an intelligent collaborative method for water and soil pollution control in operating enterprises. This method can address the shortcomings of existing technologies in terms of production and control coordination, control accuracy, and remote control adaptability, thereby meeting the core needs of operating enterprises for "simultaneous production and control." The method includes the following steps: Step 1: Complete the module startup and parameter initialization of the production subsystem, identification subsystem, and control subsystem, and verify the effectiveness of the communication link and hardware connection of the embedded integrated remote control architecture; Step 2: The production subsystem starts the raw material pretreatment, core process execution, and product storage and transportation modules according to the process. The working condition monitoring module collects information on the entire process, such as production load, process parameters, and risk monitoring data, in real time and stores it in the production database. At the same time, it synchronizes production information to the identification subsystem and the control subsystem. Step 3: The identification subsystem accurately acquires site pollution information according to the process of "real-time monitoring → sample point confirmation → preliminary sampling → low-disturbance drilling → high-fidelity sampling → rapid detection → three-dimensional reconstruction", and the pollution information is synchronized to the control subsystem. Step 4: Based on production and pollution data, the control subsystem divides the site into core production area, storage and transportation buffer area, decommissioning and treatment area, and surrounding sensitive area, clarifies the boundaries of the control area, and determines whether the pollution is an emergency situation with sudden and high risk of diffusion. If so, then determine the control objective as short-term emergency control and proceed to step 5; If not, proceed to step 6; Step 5: Take emergency response measures to quickly stop the spread of pollution, and then determine whether medium- to long-term control measures can be implemented. If possible, proceed directly to step 6; If not, continue implementing emergency response measures until the pollution no longer meets the emergency conditions of suddenness and high risk of diffusion, then proceed to step 6; Step 6: Determine the control target as medium- to long-term control, conduct soil and groundwater pollution risk level assessment using quantitative assessment methods, and match the corresponding control level and remediation measures based on the assessment results; If the soil / groundwater pollution risk assessment result is high risk, then Level 1 control measures will be implemented; If the soil / groundwater pollution risk assessment result is medium risk, then level two control measures will be adopted; If the soil / groundwater pollution risk assessment result is low risk, then Level 3 control measures will be adopted; Step 7: Dynamically optimize control points based on embedded edge terminals to provide accurate location data for control execution; Step 8: Issue remote control commands through the embedded integrated remote control architecture to drive the control and execution mechanism to implement control measures and adjust production conditions; Step 9: After the control measures are implemented, the control effect is reassessed based on the updated three-dimensional pollution distribution map to determine whether the pollution risk assessment after control measures has reached the lower limit of the low risk level. If the target is met, the control effect is deemed to be satisfactory, and the subsequent environmental monitoring can proceed. All data in the control subsystem is synchronously stored in the control database. If the target is not met, the control effect is deemed unsatisfactory, and corresponding control measures need to be reimplemented until the control effect meets the target. Step 10: In the later stage of environmental supervision, the monitoring points are continuously and dynamically adjusted based on the embedded edge terminal to ensure that pollution does not rebound and achieve full-cycle, blind-spot-free control.

[0016] Compared with the prior art, the present invention has the following technical effects: 1) This invention innovatively constructs a collaborative system of "production-identification-control", combining a four-level remote architecture of "client-cloud server-embedded edge terminal-execution layer" to achieve a closed loop of "dynamic feedback of production conditions-accurate identification of pollution-real-time response of control and management-optimization of production parameters". It can fundamentally solve the problems of production capacity loss and secondary pollution caused by the traditional "shutdown treatment" model, and at the same time completely solve the core pain point of lack of coordination between production and control in the existing technology, and achieve deep adaptation between the two. 2) This invention integrates low-disturbance drilling, high-fidelity sampling, rapid in-situ detection and underground three-dimensional reconstruction technologies, combined with the dynamic control point layout driven by embedded edge terminals. It can achieve accurate characterization of pollutant type, concentration and three-dimensional spatial distribution under the premise of minimizing interference with site ecology and production facilities. It breaks through the limitations of existing technologies where the control strategy is too static and the control accuracy is low due to insufficient sampling accuracy. It also solves the problems of discontinuous coverage of fixed points and insufficient control targeting. 3) This invention establishes a “zoning-classification-grading” control strategy, combined with a dynamic control point layout driven by data on “pollution migration-production facilities-operating condition changes”, to achieve precise matching between control plans and pollution risks and production needs; at the same time, it has a built-in embedded fault self-healing system that can automatically complete the diagnosis and autonomous repair of hardware / software faults in the control subsystem, and can quickly restore control functions without manual intervention, filling the gap in existing technologies where control is easily interrupted after a fault, and ensuring that the process of control while production is carried out is continuous and uninterrupted. 4) This invention, through deep data linkage between the production subsystem and the control subsystem, can dynamically adjust the control intensity and measures according to changes in production load and process parameters, solving the problem that existing technologies cannot adapt to complex production scenarios of enterprises in operation and that control response is lagging. At the same time, by using conflict entropy calculation and self-organizing optimization methods, it can achieve collaborative decision-making between production condition parameters and control measure parameters, which is applicable to the control needs of enterprises of different sizes and different pollution types, and significantly improves the adaptability and effectiveness of pollution prevention and control. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the overall logical architecture of the present invention; Figure 2 This is a schematic diagram of the production subsystem process in an embodiment of the present invention; Figure 3 This is a schematic diagram of the identification subsystem process in an embodiment of the present invention; Figure 4 This is a schematic diagram of the control subsystem process in an embodiment of the present invention; Figure 5 This is a flowchart illustrating the dynamic layout of control points in an embodiment of the present invention. Figure 6 This is a flowchart illustrating the fault self-healing process of the control subsystem in an embodiment of the present invention. Figure 7 This is a schematic diagram of the embedded integrated remote control architecture in an embodiment of the present invention; Figure 8 This is a schematic diagram illustrating the specific technical implementation process in an embodiment of the present invention. Detailed Implementation

[0018] like Figure 1 As shown, an intelligent collaborative system for water and soil pollution control in an operating enterprise specifically includes: a production subsystem, an identification subsystem, and a control subsystem; the data output terminal of the production subsystem is connected to the data input terminal of the identification subsystem; the data terminal of the identification subsystem is bidirectionally connected to the data terminal of the control subsystem; the data terminal of the control subsystem is bidirectionally connected to the data terminal of the production subsystem; as shown... Figure 8As shown, the system is supported by an embedded integrated remote control architecture, forming a four-level collaborative architecture of "client-cloud server-embedded edge terminal-execution layer"; The production subsystem is used to execute the entire production process, output production information to the identification subsystem, and receive working condition adjustment instructions issued by the control subsystem and feed back production information to the control subsystem. The identification subsystem is used for site pollution information collection and underground three-dimensional reconstruction, outputs pollution information to the control subsystem and receives control effect data feedback, and at the same time receives production information from the production subsystem and feeds back operating condition adjustment instructions to the production subsystem. The control subsystem is used to implement zoned, classified, and graded control of site pollution, output operating condition adjustment instructions to the production subsystem and receive production information, and at the same time receive pollution information provided by the identification subsystem and feed back control effect data. The embedded integrated remote control architecture is used to build a complete chain of "remote command - data interaction - collaborative decision-making - execution feedback" to realize remote integrated control and data interaction for production, identification, and management.

[0019] Preferably, the production subsystem includes: a raw material pretreatment module, a core process execution module, a product storage and transportation module, an operating condition monitoring module, and a production database; the data terminals of the raw material pretreatment module and the core process execution module are bidirectionally connected; the data terminals of the core process execution module and the product storage and transportation module are bidirectionally connected; the data output terminals of the raw material pretreatment module, the core process execution module, and the product storage and transportation module are connected to the data input terminals of the operating condition monitoring module; and the data output terminal of the operating condition monitoring module is connected to the data input terminal of the production database. The raw material pretreatment module is used to purify, dehydrate, and desalinate raw materials such as crude oil, and output qualified raw materials to the core process execution module, while simultaneously recording basic data such as raw material properties and processing volume; The core process execution module is used to complete core production processes such as catalytic cracking and catalytic reforming, and simultaneously collect operating parameters such as process temperature, pressure, and flow rate; The product storage and transportation module is used to realize the storage and transportation of products, and synchronously record information such as the operating status of the storage tank and the loading and unloading operation sequence. The operating condition monitoring module is used to collect operating condition data such as production load, equipment operating status, pipeline pressure, and raw material / product turnover in real time, and simultaneously carry out production process risk monitoring to ensure data timeliness and production safety. The production database is used to store operating condition data and risk monitoring data, and synchronously transmits the stored information to the identification subsystem and the control subsystem.

[0020] Preferably, the identification subsystem includes: a real-time monitoring module, a sampling module, a low-disturbance drilling module, a high-fidelity sampling module, a rapid detection module, a 3D reconstruction module, and a pollution database; the data output terminal of the real-time monitoring module is connected to the data input terminal of the sampling module; the data output terminal of the sampling module is connected to the data input terminals of the low-disturbance drilling module and the high-fidelity sampling module; the data terminals of the low-disturbance drilling module and the high-fidelity sampling module are bidirectionally connected to the data terminal of the pollution database; the data output terminals of the low-disturbance drilling module and the high-fidelity sampling module are connected to the data input terminal of the rapid detection module; the data output terminal of the rapid detection module is connected to the data input terminal of the 3D reconstruction module; and the data output terminals of the 3D reconstruction module and the rapid detection module are connected to the data input terminal of the pollution database. The real-time monitoring module is used to monitor the site soil and groundwater in real time by deploying multimodal sensors based on the production information provided by the production subsystem. The sampling module is used to carry out the work according to the process of "real-time monitoring → sample point confirmation → preliminary sampling". Specifically, it determines the sampling point by combining the real-time monitoring data of the site and the data feedback of the control subsystem. After completing the preliminary sampling, it calls the feedback results of the pollution database to optimize the sampling point and detection frequency. The low-disturbance drilling module and the high-fidelity sampling module are used to collect soil and groundwater samples during production breaks or maintenance windows using low-disturbance drilling and high-fidelity sampling techniques, thereby reducing interference with production operations. The rapid detection module is used to monitor soil, groundwater and their pollution characteristics. It employs equipment including but not limited to portable GC-MS, in-situ water quality sensors, membrane interface detectors, and X-ray fluorescence spectrometers to perform in-situ detection of heavy metals and organic pollutants in groundwater and soil, and rapidly output pollutant type and concentration data. Simultaneously, it obtains necessary parameters such as soil skeleton, organic matter content, and porosity through laboratory testing. The three-dimensional reconstruction module is used to integrate pollutant type, concentration data, key physicochemical parameters and site geological data. Through CT scanning, filtering and noise reduction, MODFLOW numerical simulation and other technologies, it completes the underground three-dimensional spatial distribution of soil and groundwater pollutants, generates a three-dimensional pollution distribution map to the control subsystem, and feeds back the operating condition adjustment instructions to the production subsystem. The pollution database is used to store data from the entire process, including sampling, detection, and reconstruction.

[0021] Preferably, the control subsystem includes: a zone control module, a category control module, an emergency response module, a pollution risk assessment module, a hierarchical control module, an effectiveness evaluation module, a control database, and an embedded fault self-healing system; the data output terminal of the zone control module is connected to the data input terminal of the category control module; the data output terminal of the category control module is connected to the data input terminals of the emergency response module and the pollution risk assessment module; the data output terminal of the emergency response module is connected to the data input terminal of the pollution risk assessment module; the data output terminal of the pollution risk assessment module is connected to the data input terminal of the hierarchical control module; the data output terminal of the hierarchical control module is connected to the data input terminals of the effectiveness evaluation module and the control database; and the data output terminal of the effectiveness evaluation module is connected to the data input terminal of the control database. The zoning control module is used to receive the three-dimensional pollution distribution map output by the identification subsystem, and, in combination with the production function attributes of the producing enterprises, divide the site into a core production area, a storage and transportation buffer area, a decommissioning and treatment area, and a surrounding sensitive area. The classification and control module is used to determine the urgency of pollution and the control objectives based on the real-time pollution concentration, diffusion rate and other information output by the identification subsystem: sudden and high diffusion risk emergency situations are determined as short-term emergency control objectives and enter the emergency response module; cumulative and low diffusion risk non-emergency situations are determined as medium- and long-term control objectives and enter the pollution risk assessment module. The emergency response module is used to execute emergency measures such as rapid sealing, temporary seepage prevention, and mobile adsorption to respond to short-term emergency control needs. After the emergency response is completed, it enters the determination stage of "whether it can enter medium- and long-term control": if the pollution risk is reduced to a controllable range, it enters medium- and long-term control; if it does not meet the standards, it continues to implement emergency response measures until the medium- and long-term control access conditions are met. The pollution risk assessment module is used to conduct pollution risk assessments on soil and groundwater pollutants during the medium- and long-term management phase, and classify the risk levels into high risk, medium risk, and low risk. The hierarchical control module is used to match control levels and measures according to risk level results, and the data after execution is fed back to the identification subsystem for effect evaluation. The control measures include Level 1 control, Level 2 control, and Level 3 control; The first-level control is used to remediate high-risk soil and groundwater, including but not limited to using pre-oxidation technology, biological mud and other technologies to remediate soil pollution, using ion exchange, membrane separation and other technologies to remediate groundwater pollution, and sending temporary load reduction or partial shutdown instructions to the production subsystem when necessary. The secondary control measures are used to remediate soil and groundwater at medium risk levels. They adopt conventional remediation measures adapted to production conditions and are initiated during periods of low production. These measures include, but are not limited to, using pre-oxidation technology to remediate soil pollution and using adsorption precipitation, chemical oxidation, and other processes to remediate groundwater pollution. The three-level control is used to remediate low-risk soil and groundwater. Routine control measures are adopted without adjusting the production process. This includes, but is not limited to, using technologies such as biocomposting to remediate soil pollution and using processes such as adsorption precipitation and biodegradation to remediate groundwater pollution. The effect evaluation module is used to quantitatively detect and determine the compliance of the control effect: if the pollution risk reaches the lower limit of the low risk level after control, it is determined to meet the standard, and the process will proceed to the later environmental supervision and output the operating condition adjustment instructions such as production load adjustment and equipment switching sequence optimization to the production subsystem; if it does not meet the standard, the control will be carried out again until it meets the standard. The control database is used to store control plans, effect evaluation data, and operational status data of sensors, actuators, and communication links. At the same time, based on the pollution distribution density and production activity intensity in each area, it dynamically feeds back sampling point layout optimization suggestions to the identification subsystem. The embedded fault self-healing system is used to realize real-time monitoring, diagnosis and self-healing of faults in the control subsystem, ensuring the continuity of the production and control process.

[0022] Preferably, the embedded fault self-healing system specifically comprises: initiating embedded real-time monitoring to continuously collect operational status data of sensors, actuators, and communication links within the control subsystem; inputting the collected status data into a fault threshold dynamic judgment stage; if the data does not exceed a preset threshold, returning to the embedded real-time monitoring stage for continuous monitoring; if the data exceeds the threshold, entering the primary / redundant data comparison stage, comparing the monitoring data of the primary device and the redundant device to distinguish the fault type as "equipment fault" (such as actuator jamming) or "sensor fault" (such as signal distortion); and the embedded diagnostic module receiving the fault type output by the fault detection unit. After obtaining the fault information, a precise analysis is performed on the fault type (hardware / communication / software), location (specific sensor / actuator / link), and severity (mild / moderate / severe) to provide a basis for self-healing operations. The self-healing strategy matching stage then proceeds, invoking a pre-defined strategy library (e.g., "switch redundant sensor" for sensor fault matching, "start backup actuator" for equipment fault matching). The matched self-healing operation is executed to complete the autonomous fault repair. Upon completion, the control function is restored, and the self-healing results are simultaneously uploaded to the control database. The current control status is also fed back to the identification subsystem to ensure the continuity of the data link and control process. Figure 6 As shown.

[0023] Preferably, the embedded integrated remote control architecture includes: multiple clients including client 1 to client n, a cloud server, an embedded edge terminal, and an execution layer; the data terminals of the clients are bidirectionally connected to the data terminals of the cloud server; the data terminals of the cloud server are bidirectionally connected to the data terminals of the embedded edge terminals; and the data output terminals of the embedded edge terminals are connected to the data input terminals of the execution layer. The client is used to allow engineers in multiple locations to input remote control commands such as operating condition adjustment parameters and control measures parameters, while also receiving system information such as pollution identification information, pollution control results, and equipment operating status from the cloud server; The cloud server is used to realize data relay and command forwarding, forwarding remote control commands issued by the client to the embedded edge terminal, and receiving pollution identification information, pollution control results, equipment operation status and other data uploaded by the embedded edge terminal, and feeding the information back to each client. The embedded edge terminal is used to receive and transmit control commands forwarded by the cloud server. As the data processing core, it receives multimodal raw data and industrial camera image data transmitted by the recognition subsystem, processes them through the NPU processor, and uploads them to the cloud server. At the same time, it sends hardware control signals to the execution layer. The execution layer adjusts the operating conditions of the production subsystem's equipment based on hardware control signals, sends execution drive signals to the execution mechanisms of the control subsystem, and completes the dynamic layout of control points to achieve remote control.

[0024] Preferably, the dynamic layout of the control points specifically involves: inputting four types of data into the embedded edge terminal, including a 3D pollution distribution map, a production facility layout map, real-time production condition data, and a groundwater pollutant migration model; performing fusion calculations on the input data through the embedded edge terminal: combining the production facility layout map to avoid core production areas and key facilities, avoiding explosion-proof areas in Zone 0 / 1 and core production facilities such as pipe corridors and tank areas, to prevent control points from interfering with production operations; calculating the spacing between control units based on the pollution migration path: integrating the groundwater pollutant migration model, in the explosion-proof Zone 2 and green belt areas downstream of the pollution plume, setting the spacing between control units to 1 / 2 of the daily migration distance of pollutants, ensuring that control points cover the pollution diffusion range; dynamically adjusting the number of points according to changes in operating conditions: when production conditions are adjusted (such as increased process load) or the pollution migration path changes, the spacing and number of control points are adjusted synchronously to ensure continuous and complete control coverage; outputting the coordinates of the dynamic control points and generating a continuous control zone network scheme to achieve blind-spot-free coverage of the control area.

[0025] When the intelligent collaborative system for water and soil pollution control in operating enterprises is in operation, the following steps can be adopted: Step S1. Complete the module startup and parameter initialization of the production subsystem, identification subsystem, and control subsystem, and verify the effectiveness of the communication link and hardware connection of the embedded integrated remote control architecture; Step S2. The production subsystem initiates the raw material pretreatment, core process execution, and product storage and transportation modules according to the process flow. The operating condition monitoring module collects real-time information on the entire process, including production load, process parameters, and risk monitoring data, and stores it in the production database. Simultaneously, it synchronizes production information with the identification and control subsystems. Figure 2 As shown; Step S3. The identification subsystem accurately acquires site pollution information according to the process of "real-time monitoring → sample point confirmation → preliminary sampling → low-disturbance drilling → high-fidelity sampling → rapid detection → 3D reconstruction". The pollution information is synchronized to the control subsystem, such as... Figure 3 As shown; Step S4. Based on production and pollution data, and combined with site function delineation, the control subsystem divides the area into a core production area, a storage and transportation buffer area, a decommissioning and treatment area, and surrounding sensitive areas. It then clarifies the boundaries of the control area and determines whether the pollution is a sudden, high-risk emergency. Figure 4 As shown; If so, then the control objective is determined to be short-term emergency control, and proceed to step S5; If not, proceed to step S6; Step S5. Take emergency response measures, including but not limited to rapid sealing, temporary seepage prevention, and mobile adsorption, to quickly block the spread of pollution. After completion, determine whether it can enter medium- and long-term management. If possible, proceed directly to step S6; If not, continue to implement emergency response measures until the pollution no longer meets the emergency conditions of suddenness and high risk of diffusion, then proceed to step S6; Step S6. Determine the control objective as medium- to long-term control, conduct soil / groundwater pollution risk level assessment using quantitative assessment methods, and match the corresponding control level and remediation measures based on the assessment results; If the risk assessment result of soil / groundwater pollution is high, then Level 1 control measures will be taken, such as using pre-oxidation technology and biological mud to remediate soil pollution, and using processes such as ion exchange and membrane separation to remediate groundwater pollution. If necessary, temporary load reduction or partial shutdown instructions can be sent to the production subsystem. If the risk assessment result of soil / groundwater pollution is medium risk, then secondary control measures will be adopted, and remediation measures will be initiated during the production trough period. Soil pollution will be remediated using pre-oxidation technology, and groundwater pollution will be remediated using adsorption precipitation, chemical oxidation and other processes. If the risk assessment result of soil / groundwater pollution is low, then Level 3 control measures will be adopted, without the need to adjust the production process. Soil pollution will be remediated using technologies such as biocomposting, and groundwater pollution will be remediated using processes such as adsorption precipitation and biodegradation. Step S7. Dynamically optimize control points based on embedded edge terminals to provide precise location data for control execution, such as... Figure 5 As shown; Step S8. Issue remote control commands through the embedded integrated remote control architecture to drive the control and execution mechanism to perform control measures and adjust production conditions, such as... Figure 7 As shown; Step S9. After the control measures are implemented, the control effect is reassessed based on the updated three-dimensional pollution distribution map to determine whether the pollution risk assessment after control measures has reached the lower limit of the low risk level. If the target is met, the control effect is deemed to be satisfactory, and the subsequent environmental monitoring can proceed. All data in the control subsystem is synchronously stored in the control database. If the target is not met, the control effect is deemed unsatisfactory, and corresponding control measures need to be reimplemented until the control effect meets the target. Step S10. In the later stage of environmental supervision, the monitoring points are continuously and dynamically adjusted based on the embedded edge terminal to ensure that pollution does not rebound and to achieve full-cycle, blind-spot-free control.

[0026] Step S1 includes the following sub-steps: S1-1. Start the production subsystem, identification subsystem, and control subsystem, and complete the hardware interface adaptation between the multi-channel analog switch chip, IoT communication module, NPU processor, and the three subsystems integrated into the embedded edge terminal, such as... Figure 8 As shown; S1-2. Perform hardware self-tests on sensors, actuators, communication modules, etc., and simultaneously activate the embedded fault self-healing system to ensure the system operates without faults; S1-3. The production subsystem calibrates the raw material pretreatment threshold, process load range, and product storage and transportation safety parameters; the identification subsystem calibrates parameters such as the detection accuracy of embedded sensors, the temperature measurement range and temperature resolution of infrared thermal imagers, and the shooting frame rate and image resolution of industrial cameras; and the control subsystem sets the trigger conditions for short-term emergency control. S1-4. Establish data communication between the production database, pollution database, control database and embedded edge terminal to realize real-time data access and updates.

[0027] Step S3 includes the following sub-steps, such as Figure 3 As shown: S3-1. Continuously collect and monitor site soil and groundwater environmental parameters through the deployment of multimodal sensors; S3-2. Call the production information output by the production subsystem and the historical pollution data stored in the pollution database to determine the preset sample points and reconstructed sample points. Use low-disturbance drilling and high-fidelity sampling technology to complete the collection of soil and groundwater samples. S3-3. Rapid detection of pollutant types and concentrations is achieved using portable GC-MS, membrane interface detector (MIP), and portable X-ray fluorescence spectroscopy (XRF); simultaneously, some pre-set sample points are sent to the laboratory to obtain key physicochemical parameters such as soil skeleton, organic matter content, and porosity. S3-4. Integrate pollutant type, concentration data, key physicochemical parameters, and site hydrological and geological characteristic data to conduct underground three-dimensional reconstruction; S3-5. Classify and store the data from the entire process of sampling, detection, and reconstruction into the pollution database. At the same time, transmit the generated three-dimensional pollution distribution map to the control subsystem and receive feedback instructions from the control subsystem to dynamically optimize the layout of sampling points and detection frequency.

[0028] In step S4, the triggering conditions for short-term emergency control can be set as follows: (1) The soil pollutant concentration increases by ≥200% from the initial value within 1 hour, or the groundwater pollutant concentration exceeds twice or more the corresponding limit of the Groundwater Quality Standard (GB / T 14848-2017); (2) The diffusion rate of the soil / groundwater pollution plume is ≥0.5 m / h, or the extent of the pollution area expands by more than 20% of the initial pollution area within 2 hours; (3) The pressure / flow sensors of core production equipment such as reactors, pipelines, and storage tanks trigger abnormal alarms, such as a sudden drop in pipeline pressure ≥0.1MPa / minute, a sudden drop in storage tank liquid level ≥5% / minute, and simultaneously detect that the concentration of surrounding pollutants exceeds the standard; If any one of the above conditions is met, it is determined to be an emergency situation with a sudden and high risk of spread, triggering short-term emergency control.

[0029] Step S6 includes the following sub-steps: S6-1. Retrieve the measured concentrations of soil pollutants and groundwater pollutants stored in the identification subsystem, including the detection values ​​of target pollutants such as petroleum hydrocarbons, benzene series compounds, and heavy metals; S6-2. Determine the assessment benchmark values. For soil pollutant screening values, it is recommended to refer to the "Soil Environmental Quality Construction Land Soil Pollution Risk Control Standard (GB 36600-2018)" and the "Groundwater Pollutant Limits" are recommended to refer to the "Groundwater Quality Standard (GB / T 14848-2017)" to ensure that the units of the benchmark values ​​are consistent with the units of the measured concentrations. S6-3. Calculate the individual pollution indices for soil and groundwater respectively, and quantify the pollution degree of a single pollutant; Calculation of Soil Single Pollution Index ( P i The formula is: ; In the formula, C i To investigate pollutants in the soil at the survey sites i The measured concentration, mg / kg; S i Screening values ​​for soil pollutants, in mg / kg; Groundwater single pollution index ( P j The calculation formula is: ; In the formula, W j To investigate groundwater pollutants at the site j Measured concentration, mg / L; B j The limit for groundwater pollutants is mg / L; when P i (or P j When the concentration of pollutant is ≤1.0, it indicates that the pollutant is not exceeding the standard; when P i (or P j When the value is greater than 1.0, it indicates that the pollutant has exceeded the standard. S6-4. The Nemerow Comprehensive Pollution Index is adopted ( P N The formula for quantifying the comprehensive pollution risk of soil / groundwater is as follows: ; In the formula, P max The maximum value among all individual pollution indices reflects the degree of pollution from the most severe pollutant. P ave It is the arithmetic mean of all individual pollution indices, reflecting the overall pollution level; S6-5. According to P N The calculation results determine the soil / groundwater pollution risk level. When 1.0 < P N When the value is ≤2.0, it is considered low risk; When 2.0 < P N If the value is ≤3.0, it is classified as medium risk; when PN A value greater than 3.0 is considered high risk.

[0030] S6-6. Based on the risk level assessment results, determine the corresponding control level and remediation measures.

[0031] Step S7 includes the following sub-steps, such as Figure 5 As shown: S7-1. Embedded edge terminals deployed in key locations such as the periphery of production facilities and potential pollution diffusion paths call up four types of core input data: three-dimensional pollution distribution map, production facility layout map, real-time production condition data, and groundwater pollutant migration model. Spatiotemporal alignment and redundancy verification are performed based on the terminal, and invalid data is removed by mean filtering method. S7-2. The terminal has a built-in improved YOLOv8 target detection model to ensure that the pollutant identification latency is controlled within 200ms; the terminal establishes a data transmission link with industrial cameras and multimodal sensors through a gigabit Ethernet interface, and communicates with control and execution agencies through an RS485 interface; S7-3. Employ spatial buffer zone analysis algorithms to avoid core production areas and critical facilities, such as setting safety avoidance distances of ≥10m for Zone 0 / Zone 1 explosion-proof areas and ≥5m for pipe racks / tank areas; calculate control unit spacing based on pollution migration paths using the formula "control unit spacing = daily pollutant migration distance × control response coefficient" (control response coefficient is taken as 0.5); dynamically adjust the number of monitoring points according to changes in operating conditions, increasing the number of monitoring points by 30% during full-load operation and decreasing the number of monitoring points by 20% during low-load operation; S7-4. After calculation, the embedded edge terminal outputs the coordinates of the dynamic control points and generates a continuous control zone network scheme according to the mode of "serial networking of core pollution area + parallel networking of edge diffusion area". The scheme is synchronously transmitted to the cloud server as the location basis for control execution, so as to achieve blind-spot-free coverage of the control area.

[0032] Step S8 includes the following sub-steps, such as Figure 7 As shown: S8-1. During the operation of the embedded integrated remote control architecture, production data, pollution data, and control data are written to the embedded edge terminal in real time. The S8-2. The embedded edge terminal's built-in IoT communication module receives raw data collected by multimodal sensors in the identification subsystem and image data collected by industrial cameras in real time, and transmits it to the NPU processor. The S8-3.NPU processor preprocesses the received data, removes noise from the sensor data through mean filtering, crops and scales the image data, and calls the improved YOLOv8 model to complete pollution identification. S8-4. The embedded edge terminal transmits pollution identification results, dynamic control point coordinates, and equipment status to the client via a cloud server; S8-5. The client associates the current pollution risk level, control level, and dynamic control points, generates remote control commands for control execution and remote control commands for operating condition adjustment, and submits them to the cloud server; S8-6. After receiving the instruction, the cloud server forwards the instruction to the embedded edge terminal using the SSL / TLS encryption protocol; S8-7. The embedded edge terminal analyzes the received remote control commands, verifies the compatibility of the command parameters with the rated operating range of the production and control equipment based on historical correlation data of production and control parameters and the executability of dynamic control points, and conducts preliminary analysis through conflict entropy to pre-determine whether there are potential conflicts in the commands. The formula for calculating conflict entropy is: ; In the formula, H For production operating parameters, M For control measures parameters, For production operating parameters H Control measures parameters G The same probability of occurrence For production operating parameters H Marginal probability distribution For control measures parameters G Marginal probability distribution; like S_conflict If the value is greater than 0.6, a conflict is determined to exist. The optimal coordination scheme is then solved using S8-8 and fed back to the client via the cloud server. The client then regenerates the remote control commands until a conflict is determined to be non-existent. If not, it is determined that there is no conflict, and the execution drive signal is sent to the repair execution mechanism of the control subsystem and the working condition adjustment equipment of the production subsystem through the built-in multi-channel analog switch chip to execute S8-9; S8-8. Solve for the optimal coordination scheme using the self-organizing optimization method. The objective function is: ; In the formula, α Weighting of control effectiveness; β Weights for production intentions; γ Punishment for conflict; This is a function for measuring the quality of the control effectiveness after the merger. This is the merged production intent function, used to measure the degree to which the merged production intent is achieved; The conflict entropy after the merger is used to quantify the degree of conflict between the control effect and the production intention in the merger scheme. S8-9. After receiving the execution signal, the control and production subsystem adjusts the specific operating parameters such as aeration intensity, reagent injection amount, raw material input amount, process parameters, and storage and transportation routes according to the dynamic control points, and simultaneously records the instruction execution timestamp, associated parameter data, and point execution status. S8-10. After the production and control equipment completes the execution of the instructions, it feeds back the operating status information and the execution effect of the points to the embedded edge terminal and uploads it to the cloud server, completing the closed loop of the entire chain of "point optimization-instruction-execution-feedback".

[0033] Step S9 includes the following sub-steps: S9-1. Based on the updated three-dimensional pollution distribution map and combined with the execution data of dynamic control points, conduct a new assessment of the control effectiveness; S9-2. Determine whether the pollution risk assessment after control measures have reached the lower limit of the low-risk level; If the target is met, the control effect is deemed to have met the standard, and the process will proceed to the later stage of environmental supervision in S10. If the target is not met, the control effect is deemed unsatisfactory. The process returns to S6 to re-determine the control level and measures, and simultaneously returns to S7 to optimize the control points. The subsequent procedures continue until the target is met.

[0034] Step S10 includes the following sub-steps: S10-1. In the later stage of environmental supervision, the embedded edge terminal continuously calls up the three-dimensional pollution distribution map, the production facility layout map, real-time production condition data, and the groundwater pollutant migration model, and dynamically adjusts the spacing and number of monitoring points. S10-2. Regularly generate environmental monitoring reports and synchronize them to the control database and client to ensure that pollution does not rebound and achieve full-cycle control without blind spots.

[0035] S1-2 includes the following sub-steps, such as... Figure 6 As shown: S1-2-1. Start embedded real-time monitoring to continuously collect operational status data of sensors, actuators, and communication links within the control subsystem, such as sensor signal strength, actuator response time, and link transmission delay. S1-2-2. Input the collected status data into the fault threshold dynamic judgment link. The preset threshold is set based on the equipment factory standard and site operation experience, such as sensor signal loss ≥5min, actuator response delay ≥10s, link transmission error rate ≥1%, etc. If the data does not exceed the preset threshold, it will return to the embedded real-time monitoring stage for continuous monitoring. If the data exceeds the threshold, the process will proceed to the main / redundant data comparison stage. By comparing the monitoring data of the main device and the redundant device, the fault type will be distinguished as "device fault" or "sensor fault". S1-2-3. After receiving the fault type information output by the fault detection unit, the embedded diagnostic module performs precise fault information analysis on the fault type (hardware / communication / software), location (specific sensor / actuator / communication link number), and severity (mild: no impact on core control functions / moderate: some control functions are limited / severe: core control functions are interrupted) based on at least one diagnostic method among fault tree analysis, expert system, or support vector machine. S1-2-4. Based on a pre-trained self-healing strategy library, a three-dimensional mapping relationship between "fault type-location-severity" and self-healing strategies is established to automatically match self-healing strategies; if a new type of fault is encountered, fuzzy reasoning can be used to make decisions under uncertain conditions and adaptively adjust. S1-2-5. Execute the self-healing operation after matching to complete the autonomous fault repair.

[0036] S3-4 specifically includes the following sub-steps: S3-4-1. A spiral CT scanner was used to perform tomographic scanning on the reconstructed soil samples. The nonlocal mean method was used for noise reduction, and Gaussian high-pass filtering was used to segment the pores, organic matter and soil skeleton. S3-4-2. Construct a composite machine learning model based on SVR+Lasso regression, using preset sample point parameters as the training set and reconstructed sample point parameters as the test set. Train the model using the MSE loss function, and combine the CNN model with the inverse distance weighting method for secondary correction to output the porosity correction value and the organic matter volume fraction correction value. S3-4-3. Based on the moving cube algorithm, the pore structure is generated, and the in-situ pore model is restored by compression test to construct a spherical shell model of soil organic matter. S3-4-4. Combine MODFLOW numerical simulation technology to carry out three-dimensional modeling and characterize the spatial distribution pattern of pollutants in soil and groundwater; S3-4-5. Generate a three-dimensional distribution map of pollution to clarify the diffusion range, diffusion rate, concentration gradient, etc. of the pollution plume.

[0037] To better understand the application of the above-described process of the present invention in practical cases, the following description will be provided in conjunction with two actual production sites. It should be noted that, unless otherwise specified, the embodiments and features of the present invention can be combined with each other.

[0038] Example 1: This example describes an operating enterprise within a large-scale petrochemical production facility. The core production processes include cracking, separation, and polymerization, with continuous operation and a stable process load of 85%-90%. Preliminary site investigations indicated that due to aging pipeline flange seals and equipment leaks, organic pollutants such as benzene, toluene, and total petroleum hydrocarbons (TPH), as well as heavy metal pollutants such as hexavalent chromium and lead, were detected in the soil and groundwater, with a pollution plume area of ​​approximately 260 m². 2 The contamination depth is between 1.5m and 5m. Specific site characteristics are shown in the table below:

[0039] Based on the "production-identification-control" collaborative system and method described in this invention, a production-while-control scheme is implemented, operating in a closed loop throughout the entire process. During the system deployment and initialization phase, after starting the three main subsystems, eight embedded edge terminals are deployed around the pyrolysis unit area, near the polymerization reactor, and in the explosion-proof zone 2 downstream of the contamination plume, completing hardware interface adaptation; the embedded fault self-healing system is activated, completing the calibration of the main / redundant sensors (deviation ≤5%), preset fault thresholds, and setting short-term emergency control trigger conditions; real-time communication between the database and the embedded edge terminals is established.

[0040]

[0041] If any of the above conditions are met, short-term emergency control measures will be triggered.

[0042] In the production information collection phase, the production subsystem starts the operation of each module. The raw material pretreatment module has a raw material processing capacity of 50t / h. The core process execution module collects the cracking furnace pressure of 0.12-0.15MPa and the reaction temperature of 880-900℃. The operating condition monitoring module collects the production load of 88% and the pipeline pressure of 0.10-0.12MPa in real time. The data is written to the production database in real time and synchronized to the identification subsystem and the control subsystem.

[0043] During the pollution identification and 3D reconstruction process, multimodal sensors continuously collect environmental parameters to determine 12 preset sampling points and 8 reconstruction sampling points. Low-disturbance drilling and high-fidelity sampling are adopted during the production and maintenance window. The concentration of pollutants is quickly detected by portable GC-MS and XRF, and samples are sent to the laboratory simultaneously to obtain key parameters. Combined with CT scans and MODFLOW numerical simulation, a 3D pollution distribution map is generated, and the data is stored in the pollution database and synchronized to the control subsystem.

[0044] The site was divided into a core production area, a storage and transportation buffer area, and a surrounding sensitive area. In the core contaminated area, the soil benzene concentration increased by 220% within one hour, meeting the requirements for short-term emergency control. Emergency measures such as rapid sealing and temporary seepage prevention were implemented. After 30 days of continuous monitoring, the pollution plume diffusion rate decreased to 0.2 m / h, the benzene concentration in the core area soil decreased to 5-7 mg / kg, and the benzene concentration in groundwater decreased to 110-125 μg / L. No abnormal alarms were detected in the core equipment, thus meeting the conditions for transitioning to medium- and long-term control. During the medium- and long-term control and risk classification process, the measured concentrations of characteristic pollutants stored in the identification subsystem were retrieved. Soil pollutant screening values ​​were referenced to GB36600-2018 Class II land use standards, and groundwater limits were referenced to GB / T14848-2017 and T / SBX 11-2019. The core contaminated area was calculated... P max =3.75、 P ave =2.19、 P N =3.07 (High Risk), Pollution Plume Dispersion Area P max =2.5、 P ave =1.44、 P N =2.04 (medium risk), surrounding sensitive areas P max =1.9、 P ave =0.82、 P N =1.46 (low risk), ultimately matching Level 1, Level 2, and Level 3 control measures. Differentiated control measures are matched for different risk levels: Level 1 control: Soil adopts in-situ chemical oxidation + biological mud enhanced remediation process, adding persulfate oxidant and functional bacteria agent; groundwater adopts ion exchange resin + membrane separation combination process, and simultaneously sends instructions to the production subsystem to temporarily reduce the pyrolysis furnace process load to 80%-85% to avoid the core control period; Level 2 control: Start during the production off-peak period, soil adopts in-situ pre-oxidation remediation process, groundwater adopts adsorption precipitation + chemical oxidation synergistic process, no need to adjust core production parameters; Level 3 control: Adopt normalized control measures, soil adopts biological composting technology, groundwater adopts adsorption precipitation + biodegradation combination process, without interfering with the production process.

[0045]

[0046] During the remote control phase, production, pollution, and control data are written to the embedded edge terminal in real time. The client generates control commands, which are forwarded to the terminal via the cloud server, and verification confirms no conflicts. S_conflict=0.4) After that, the actuator is driven to operate, completing the closed loop of the entire link. The system ran continuously for 180 days. The soil concentration in the primary control area was 2-3 mg / kg benzene, 350-680 mg / kg toluene, 1200-2300 mg / kg total petroleum hydrocarbons, 3.2-5.5 mg / kg hexavalent chromium, and 650-780 mg / kg. The groundwater concentration was 8-10 μg / L benzene, 0.5-0.65 mg / L toluene, 0.4-0.5 mg / L total petroleum hydrocarbons, 0.03-0.04 mg / L hexavalent chromium, and 0.006-0.009 mg / L lead, all of which met the corresponding standards. In terms of production indicators, the product output fluctuation was ≤2.5%, energy consumption decreased by 2.8%, and there was no production interruption.

[0047]

[0048] Example 2: This example describes an operating chemical site located near a petrochemical solid waste landfill. The core processes include catalytic cracking and distillation purification, with a daily process load of 75%-85%. Due to damage to the impermeable layer of the surrounding solid waste landfill, leachate seeped into the site's soil and groundwater, resulting in the detection of organic pollutants such as 2,4-dinitrotoluene and naphthalene, as well as heavy metal pollutants such as hexavalent chromium, lead, and arsenic. The pollution plume covered an area of ​​approximately 390 m². 2 The contamination depth is between 3m and 8m. Specific site characteristics are shown in the table below:

[0049] When collecting production information, the production subsystem starts modules such as raw material pretreatment and catalytic cracking, and collects the cracking furnace pressure (0.10-0.13MPa), the operating condition monitoring module collects the production load (80%) and pipeline pressure (0.09-0.11MPa) in real time. All data is written to the production database and synchronized to the identification subsystem and control subsystem.

[0050] During the pollution identification and 3D reconstruction process, multimodal sensors continuously collected environmental parameters, identifying 11 preset sampling points and 6 reconstructed sampling points. Low-disturbance drilling sampling was conducted during production breaks. The concentrations of 2,4-dinitrotoluene and naphthalene in soil and groundwater were rapidly detected using a portable GC-MS, while the concentrations of chromium (hexavalent), lead, and arsenic were detected in situ using a portable X-ray fluorescence spectrometer (XRF). The reconstructed soil samples were subjected to tomographic scanning using a CT scanner. After filtering, noise reduction, and threshold segmentation, a 3D pollution distribution map was generated using MODFLOW numerical simulation.

[0051] The control subsystem divides the site into a core production area, a storage and transportation buffer area, a decommissioning and treatment area, and surrounding sensitive areas, as well as a core contaminated area. P N=3.2 (>3.0), close to the sensitive area, and 2,4-dinitrotoluene is a high-risk, difficult-to-migrate pollutant, thus it is judged as a sudden, high-diffusion-risk emergency situation, and short-term emergency control measures are implemented. In-situ chemical reduction, heavy metal stabilization, and anti-seepage curtains are adopted to block pollution diffusion. After 45 days of emergency treatment, the conditions for medium- and long-term control are met. During the medium- and long-term control and risk classification process, the detection data of the identification subsystem is used, and the assessment benchmark value is determined with reference to GB36600-2018, GB / T14848-2017, and T / SBX 11-2019, and the core area is calculated. P N =3.2 (High Risk), Transition Zone P N =2.3 (medium risk), sensitive area P N =1.6 (Low Risk), matching Level 1, Level 2, and Level 3 control measures. Differentiated control measures are matched for different risk levels: Level 1 control: Soil adopts a combination of in-situ chemical reduction and heavy metal stabilization process, adding sulfide stabilizers and reducing agents; groundwater is equipped with a combination of photocatalytic enhanced bioreactor core and permeable reaction wall, and the raw material pretreatment process is adjusted simultaneously to increase the efficiency of the water washing desulfurization process; Level 2 control: Soil adopts a combination of bioremediation and light heavy metal stabilization process, adding functional degradation bacteria; groundwater is equipped with ordinary bioreactor cores, and maintenance is carried out during low production load periods to avoid affecting product output; Level 3 control: mainly based on routine monitoring and light control, with supporting soil vapor phase extraction devices, and groundwater monitoring wells for regular sampling and analysis, without the need to adjust production process parameters throughout the process.

[0052]

[0053] After the dynamic control points were optimized, 6 photocatalytic reaction cores and a permeable reaction wall were deployed in the core area, 3 reaction cores were deployed in the transition area, and 2 monitoring wells were deployed in the sensitive area, forming a continuous control zone. The system operated continuously for 190 days. In the primary control zone, the soil concentrations of 2,4-dinitrotoluene were 1.8-4.5 mg / kg, naphthalene 28-55 mg / kg, hexavalent chromium 3.5-5.6 mg / kg, lead 690-790 mg / kg, and arsenic 45-58 mg / kg. In the groundwater, the concentrations of 2,4-dinitrotoluene were 3-5 μg / L, naphthalene 25-40 μg / L, hexavalent chromium 0.03-0.045 mg / L, lead 0.007-0.009 mg / L, and arsenic 0.007-0.009 mg / L, all meeting the corresponding standards. Regarding production indicators, product purity remained above 99.5%, output fluctuation was ≤2.5%, energy consumption decreased by 2.1% compared to before system operation, and no production safety accidents or environmental exceedances occurred.

[0054]

[0055] Example 3: This invention also includes an intelligent collaborative method for water and soil pollution control in operating enterprises. This method can address the shortcomings of existing technologies in terms of production and control coordination, control accuracy, and remote control adaptability, thereby meeting the core needs of operating enterprises for "simultaneous production and control." The method includes the following steps: Step 1: Complete the module startup and parameter initialization of the production subsystem, identification subsystem, and control subsystem, and verify the effectiveness of the communication link and hardware connection of the embedded integrated remote control architecture; Step 2: The production subsystem starts the raw material pretreatment, core process execution, and product storage and transportation modules according to the process. The working condition monitoring module collects information on the entire process, such as production load, process parameters, and risk monitoring data, in real time and stores it in the production database. At the same time, it synchronizes production information to the identification subsystem and the control subsystem. Step 3: The identification subsystem accurately acquires site pollution information according to the process of "real-time monitoring → sample point confirmation → preliminary sampling → low-disturbance drilling → high-fidelity sampling → rapid detection → three-dimensional reconstruction", and the pollution information is synchronized to the control subsystem. Step 4: Based on production and pollution data, the control subsystem divides the site into core production area, storage and transportation buffer area, decommissioning and treatment area, and surrounding sensitive area, clarifies the boundaries of the control area, and determines whether the pollution is an emergency situation with sudden and high risk of diffusion. If so, then determine the control objective as short-term emergency control and proceed to step 5; If not, proceed to step 6; Step 5: Take emergency response measures to quickly stop the spread of pollution, and then determine whether medium- to long-term control measures can be implemented. If possible, proceed directly to step 6; If not, continue implementing emergency response measures until the pollution no longer meets the emergency conditions of suddenness and high risk of diffusion, then proceed to step 6; Step 6: Determine the control target as medium- to long-term control, conduct soil and groundwater pollution risk level assessment using quantitative assessment methods, and match the corresponding control level and remediation measures based on the assessment results; If the soil / groundwater pollution risk assessment result is high risk, then Level 1 control measures will be implemented; If the soil / groundwater pollution risk assessment result is medium risk, then level two control measures will be adopted; If the soil / groundwater pollution risk assessment result is low risk, then Level 3 control measures will be adopted; Step 7: Dynamically optimize control points based on embedded edge terminals to provide accurate location data for control execution; Step 8: Issue remote control commands through the embedded integrated remote control architecture to drive the control and execution mechanism to implement control measures and adjust production conditions; Step 9: After the control measures are implemented, the control effect is reassessed based on the updated three-dimensional pollution distribution map to determine whether the pollution risk assessment after control measures has reached the lower limit of the low risk level. If the target is met, the control effect is deemed to be satisfactory, and the subsequent environmental monitoring can proceed. All data in the control subsystem is synchronously stored in the control database. If the target is not met, the control effect is deemed unsatisfactory, and corresponding control measures need to be reimplemented until the control effect meets the target. Step 10: In the later stage of environmental supervision, the monitoring points are continuously and dynamically adjusted based on the embedded edge terminal to ensure that pollution does not rebound and achieve full-cycle, blind-spot-free control.

[0056] Step 1 includes the following sub-steps: Step 1-1: Start the production subsystem, identification subsystem, and control subsystem, and complete the hardware interface adaptation between the multi-channel analog switch chip, IoT communication module, NPU processor, and the three subsystems integrated in the embedded edge terminal; Steps 1-2: Perform hardware self-tests on sensors, actuators, and communication modules, and simultaneously activate the embedded fault self-healing system to ensure fault-free system operation; Steps 1-3: The production subsystem calibrates the raw material pretreatment threshold, process load range, and product storage and transportation safety parameters; the identification subsystem calibrates the embedded sensor detection accuracy, infrared thermal imager temperature measurement range and temperature resolution, and industrial camera shooting frame rate and image resolution parameters; and the control subsystem sets the trigger conditions for short-term emergency control. Steps 1-4: Establish data communication between the production database, pollution database, control database and embedded edge terminal to realize real-time data access and updates.

[0057] Steps 1-2 include the following sub-steps: Step 1-2-1: Start embedded real-time monitoring to continuously collect operational status data of sensors, actuators, and communication links within the control subsystem; Step 1-2-2: Input the collected status data into the fault threshold dynamic judgment stage. The preset threshold is set based on the equipment's factory standard and site operation experience. If the data does not exceed the preset threshold, it will return to the embedded real-time monitoring stage for continuous monitoring. If the data exceeds the threshold, the process will proceed to the main / redundant data comparison stage. By comparing the monitoring data of the main device and the redundant device, the fault type will be distinguished as "device fault" or "sensor fault". Steps 1-2-3: After receiving the fault type information output by the fault detection unit, the embedded diagnostic module performs precise fault information analysis based on at least one diagnostic method among fault tree analysis, expert system, or support vector machine, on the fault type (hardware / communication / software), location (specific sensor / actuator / communication link number), and severity (mild: no impact on core control functions / moderate: some control functions are limited / severe: core control functions are interrupted). Steps 1-2-4: Establish a three-dimensional mapping relationship between "fault type-location-severity" and self-healing strategies based on the pre-trained self-healing strategy library to automatically match self-healing strategies; if a new type of fault is encountered, fuzzy reasoning can be used to make decisions under uncertainty conditions and adaptively adjust. Steps 1-2-5: Perform the self-healing operation after matching to complete the autonomous fault repair.

[0058] Step 3 includes the following sub-steps: Step 3-1: Continuously collect and monitor site soil and groundwater environmental parameters using deployed multimodal sensors; Step 3-2: Call the production information output by the production subsystem and the historical pollution data stored in the pollution database to determine the preset sampling points and reconstructed sampling points. Use low-disturbance drilling and high-fidelity sampling technology to complete the collection of soil and groundwater samples. Step 3-3: Complete the rapid detection of pollutant type and concentration; simultaneously send some pre-set sample points to the laboratory to obtain key physicochemical parameters such as soil skeleton, organic matter content, and porosity; Steps 3-4: Integrate pollutant type, concentration data, key physicochemical parameters, and site hydrological and geological characteristic data to conduct underground three-dimensional reconstruction; Steps 3-5: Classify and store the data from the entire process of sampling, detection, and reconstruction into the pollution database. Simultaneously, transmit the generated 3D pollution distribution map to the control subsystem and receive feedback instructions from the control subsystem to dynamically optimize the layout of sampling points and detection frequency.

[0059] Step 7 includes the following sub-steps: Step 7-1: Embedded edge terminals deployed in key locations such as the perimeter of the site's production facilities and potential pollution diffusion paths call up four types of core input data: 3D pollution distribution map, production facility layout map, real-time production condition data, and groundwater pollutant migration model. Based on the terminals, spatiotemporal alignment and redundancy verification are performed, and invalid data is removed. Step 7-2: The terminal integrates an improved YOLOv8 target detection model based on the proposed "Embedded Port Edge Intelligent Identification Method, Identification Terminal, Equipment and Control System" to control the pollutant identification latency; the terminal establishes a data transmission link with industrial cameras and multimodal sensors through an industrial-grade Ethernet interface, and communicates with control and execution mechanisms through an industrial-grade serial communication interface; Step 7-3: Use spatial buffer analysis algorithms to avoid core production areas and critical facilities; calculate the spacing between control units based on pollution migration paths; dynamically adjust the number of control points according to changes in operating conditions; Step 7-4: After calculation, the embedded edge terminal outputs the coordinates of the dynamic control points and generates a continuous control zone network scheme according to the mode of "serial networking of core pollution area + parallel networking of edge diffusion area". The scheme is synchronously transmitted to the cloud server as the location basis for control execution, so as to achieve blind-spot-free coverage of the control area.

[0060] Step 8 includes the following sub-steps: Step 8-1: During the operation of the embedded integrated remote control architecture, production data, pollution data, and control data are written to the embedded edge terminal in real time; Step 8-2: The IoT communication module built into the embedded edge terminal receives raw data collected by the multimodal sensors in the identification subsystem and image data collected by the industrial camera in real time, and transmits it to the NPU processor. Step 8-3: The NPU processor preprocesses the received data, removes interference signals from the sensor data through noise suppression methods, performs size adaptation processing on the image data (including but not limited to cropping and scaling), and calls the improved YOLOv8 target detection model to complete pollution identification; Step 8-4: The embedded edge terminal transmits the pollution identification results, dynamic control point coordinates, and equipment status to the client via the cloud server; Step 8-5: The client associates the current pollution risk level, control level, and dynamic control points, generates remote control commands for control execution and remote control commands for operating condition adjustment, and submits them to the cloud server; Step 8-6: After receiving the instruction, the cloud server forwards the instruction to the embedded edge terminal using an industrial-grade secure encrypted transmission protocol; Step 8-7: The embedded edge terminal analyzes the received remote control commands, verifies the compatibility of the command parameters with the rated operating range of the production and control equipment based on historical correlation data of production and control parameters and the executability of dynamic control points, and conducts preliminary analysis through conflict entropy to pre-determine whether there are potential conflicts in the commands. The formula for calculating conflict entropy is: ; In the formula,H For production operating parameters, M For control measures parameters, For production operating parameters H Control measures parameters G The same probability of occurrence For production operating parameters H Marginal probability distribution For control measures parameters G Marginal probability distribution; like S_conflict > S 0 ( S If 0 is the threshold, it is determined that there is a conflict. Step 8-8 is executed to find the optimal coordination solution and feeds it back to the client through the cloud server. The client regenerates the remote control command until it is determined that there is no conflict. If not, it is determined that there is no conflict. The built-in multi-channel analog switch chip sends the execution drive signal to the repair execution mechanism of the control subsystem and the working condition adjustment equipment of the production subsystem, and executes steps 8-9. Step 8-8: Solve for the optimal coordination scheme using the self-organizing optimization method. The objective function is: ; In the formula, α Weighting of control effectiveness; β Weights for production intentions; γ Punishment for conflict; This is a function for measuring the quality of the control effectiveness after the merger. This is the merged production intent function, used to measure the degree to which the merged production intent is achieved; The conflict entropy after the merger is used to quantify the degree of conflict between the control effect and the production intention in the merger scheme. Steps 8-9: After receiving the execution signal, the control and production subsystem adjusts the specific operating parameters such as aeration intensity, reagent injection amount, raw material input amount, process parameters, and storage and transportation routes according to the dynamic control points, and simultaneously records the instruction execution timestamp, related parameter data, and point execution status. Steps 8-10: After the production and control equipment completes the execution of the instructions, it feeds back the operating status information and the execution effect of the points to the embedded edge terminal and uploads it to the cloud server, completing the closed loop of the entire chain of "point optimization-instruction-execution-feedback".

[0061] Step 9 includes the following sub-steps: Step 9-1: Based on the updated 3D pollution distribution map and combined with the execution data of dynamic control points, conduct a new assessment of the control effectiveness; Step 9-2: Determine whether the pollution risk assessment after control measures has reached the lower limit of the low-risk level; If the target is met, the control effect is deemed to have been achieved, and the process proceeds to step 10, post-construction environmental monitoring. If the target is not met, the control effect is deemed unsatisfactory. Return to step 6 to re-determine the control level and measures, and simultaneously return to step 7 to optimize the control points. Continue to execute the subsequent process until the target is met.

[0062] Step 10 includes the following sub-steps: Step 10-1: In the later stage of environmental monitoring, the embedded edge terminal continuously calls the three-dimensional pollution distribution map, production facility layout map, real-time production condition data, and groundwater pollutant migration model to dynamically adjust the spacing and number of monitoring points. Step 10-2: Regularly generate environmental monitoring reports and synchronize them to the management database and client to ensure that pollution does not rebound and achieve full-cycle, blind-spot-free management.

[0063] The proposed method integrates low-disturbance drilling, high-fidelity sampling, rapid in-situ detection, and underground 3D reconstruction technologies. Combined with the dynamic control point layout driven by embedded edge terminals, it can accurately characterize the type, concentration, and 3D spatial distribution of pollutants while minimizing interference with the site ecology and production facilities. This overcomes the limitations of existing technologies, such as static control strategies and insufficient sampling accuracy leading to low control precision, and solves the problems of discontinuous coverage of fixed points and insufficient control targeting.

Claims

1. A smart collaborative system for water and soil pollution control in operating enterprises, characterized in that, The system includes a production subsystem, an identification subsystem, and a control subsystem; the data output terminal of the production subsystem is connected to the data input terminal of the identification subsystem; the data terminal of the identification subsystem is bidirectionally connected to the data terminal of the control subsystem; and the data terminal of the control subsystem is bidirectionally connected to the data terminal of the production subsystem.

2. The system according to claim 1, characterized in that, The system is supported by an embedded integrated remote control architecture, forming a four-level collaborative architecture of "client-cloud server-embedded edge terminal-execution layer"; The production subsystem executes the entire production process, outputs production information to the identification subsystem, and simultaneously receives operating condition adjustment instructions from the control subsystem and feeds back production information to the control subsystem. The identification subsystem collects site pollution information and performs underground 3D reconstruction, outputs pollution information to the control subsystem and receives control effect data feedback, and simultaneously receives production information from the production subsystem and feeds back operating condition adjustment instructions to the production subsystem. The control subsystem implements zoned, classified, and graded control of site pollution, outputs operating condition adjustment instructions to the production subsystem and receives production information, and simultaneously receives pollution information provided by the identification subsystem and feeds back control effect data. The embedded integrated remote control architecture is used to construct a full-link system of "remote command—data interaction—collaborative decision-making—execution feedback," realizing remote integrated control and data interaction of production, identification, and control.

3. The system according to claim 1 or 2, characterized in that, The production subsystem includes: a raw material pretreatment module, a core process execution module, a product storage and transportation module, an operating condition monitoring module, and a production database; the data terminals of the raw material pretreatment module and the core process execution module are bidirectionally connected; the data terminals of the core process execution module and the product storage and transportation module are bidirectionally connected; the data output terminals of the raw material pretreatment module, the core process execution module, and the product storage and transportation module are connected to the data input terminals of the operating condition monitoring module; and the data output terminal of the operating condition monitoring module is connected to the data input terminal of the production database.

4. The system according to claim 3, characterized in that, The raw material pretreatment module is used to purify, dehydrate, and desalinate the raw materials, and output qualified raw materials to the core process execution module, while simultaneously recording the raw material requirements and process load fed back by the core process execution module. The core process execution module is used to complete core production processes such as catalytic cracking and catalytic reforming, and simultaneously collects production operation parameters. The product storage and transportation module is used to realize the storage and transportation of products, and synchronously records the operating status of the storage tank and the loading and unloading operation sequence information. The operating condition monitoring module is used to collect operating condition data such as production load, equipment operating status, pipeline pressure, and raw material / product turnover in real time, and simultaneously carry out production process risk monitoring to ensure data timeliness and production safety. The production database is used to store operating condition data and risk monitoring data, and synchronously transmits the stored production information to the identification subsystem and the control subsystem.

5. The system according to claim 1, 2, or 4, characterized in that, The identification subsystem includes: a real-time monitoring module, a sampling module, a low-disturbance drilling module, a high-fidelity sampling module, a rapid detection module, a 3D reconstruction module, and a pollution database. The data output terminal of the real-time monitoring module is connected to the data input terminal of the sampling module; the data output terminal of the sampling module is connected to the data input terminals of the low-disturbance drilling module and the high-fidelity sampling module; the data terminals of the low-disturbance drilling module and the high-fidelity sampling module are bidirectionally connected to the data terminal of the pollution database; the data output terminals of the low-disturbance drilling module and the high-fidelity sampling module are connected to the data input terminal of the rapid detection module; the data output terminal of the rapid detection module is connected to the data input terminal of the 3D reconstruction module; and the data output terminals of the 3D reconstruction module and the rapid detection module are connected to the data input terminal of the pollution database.

6. The system according to claim 5, characterized in that, The real-time monitoring module is used to monitor the site soil and groundwater in real time by deploying multimodal sensors based on the production information provided by the production subsystem. The sampling module is used to carry out the work according to the process of "real-time monitoring → sample point confirmation → preliminary sampling". Specifically, it determines the sampling point by combining the real-time monitoring data of the site and the data feedback of the control subsystem. After completing the preliminary sampling, it calls the feedback results of the pollution database to optimize the sampling point and detection frequency. The low-disturbance drilling module and the high-fidelity sampling module are used to collect soil and groundwater samples during production breaks or maintenance windows using low-disturbance drilling and high-fidelity sampling techniques, thereby reducing interference with production operations. The rapid detection module is used to monitor soil, groundwater and their pollution characteristics, to perform in-situ detection of heavy metals and organic pollutants in groundwater and soil, and to quickly output pollutant type and concentration data; simultaneously, it obtains necessary parameters such as soil skeleton, organic matter content and porosity through laboratory testing. The three-dimensional reconstruction module is used to integrate pollutant type, concentration data, key physicochemical parameters and site geological data to complete the three-dimensional spatial distribution of soil and groundwater pollutants, generate a three-dimensional pollution distribution map to the control subsystem, and feed back operating condition adjustment instructions to the production subsystem. The pollution database is used to store data from the entire process of sampling, detection, and reconstruction.

7. The system according to claim 1, 2, 4, or 6, characterized in that, The control subsystem includes: a zone control module, a category control module, an emergency response module, a pollution risk assessment module, a hierarchical control module, an effectiveness evaluation module, a control database, and an embedded fault self-healing system. The data output terminal of the zone control module is connected to the data input terminal of the category control module; the data output terminal of the category control module is connected to the data input terminals of the emergency response module and the pollution risk assessment module; the data output terminal of the emergency response module is connected to the data input terminal of the pollution risk assessment module; the data output terminal of the pollution risk assessment module is connected to the data input terminal of the hierarchical control module; the data output terminal of the hierarchical control module is connected to the data input terminals of the effectiveness evaluation module and the control database; and the data output terminal of the effectiveness evaluation module is connected to the data input terminal of the control database.

8. The system according to claim 7, characterized in that, The zoning control module is used to receive the three-dimensional pollution distribution map output by the identification subsystem, and, in combination with the production function attributes of the producing enterprises, divide the site into a core production area, a storage and transportation buffer area, a decommissioning and treatment area, and a surrounding sensitive area. The classification and control module is used to determine the urgency of pollution and identify control targets based on the pollution information output by the identification subsystem: sudden and high-risk emergency situations are identified as short-term emergency control targets and enter the emergency response module. Non-emergency situations with cumulative and low diffusion risks are identified as medium- to long-term control targets and enter the pollution risk assessment module; The emergency response module is used to execute emergency response measures and respond to short-term emergency control needs. After the emergency response is completed, the next step is to determine whether medium- and long-term control measures can be implemented: if the pollution risk is reduced to a controllable range, medium- and long-term control measures will be implemented; if the standards are not met, the emergency response measures will continue to be implemented until the medium- and long-term control access conditions are met. The pollution risk assessment module is used to conduct pollution risk assessments on soil and groundwater pollutants during the medium- and long-term management phase, and classify the risk levels into high risk, medium risk, and low risk. The hierarchical control module is used to match control levels and measures according to risk level results, and the data after execution is fed back to the identification subsystem for effect evaluation. The control measures include Level 1 control, Level 2 control, and Level 3 control; The effect evaluation module is used to quantitatively detect and determine the compliance of the control effect: if the pollution risk reaches the lower limit of the low risk level after control, it is determined to meet the standard, and the process will proceed to the later environmental supervision and output the production subsystem with operating condition adjustment instructions such as production load adjustment and equipment switching sequence optimization; if it does not meet the standard, the control level will be re-determined and the corresponding control measures will be implemented until the standard is met. The control database is used to store control plans, effect evaluation data, and operational status data of sensors, actuators, and communication links. At the same time, based on the pollution distribution density and production activity intensity in each area, it dynamically feeds back sampling point layout optimization suggestions to the identification subsystem. The embedded fault self-healing system is used to realize real-time monitoring, diagnosis and self-healing of faults in the control subsystem, ensuring the continuity of the production and control process.

9. The system according to claim 8, characterized in that, The embedded fault self-healing system specifically involves: starting embedded real-time monitoring to continuously collect operational status data of sensors, actuators, and communication links within the control subsystem; inputting the collected status data into the fault threshold dynamic judgment stage; if the data does not exceed the preset threshold, returning to the embedded real-time monitoring stage for continuous monitoring; If the data exceeds the threshold, the process will proceed to the main / redundant data comparison stage. By comparing the monitoring data of the main device and the redundant device, the fault type will be distinguished as "device fault" or "sensor fault". After receiving the fault type information output by the fault detection unit, the embedded diagnostic module performs precise analysis on the type, location, and severity of the fault, providing a basis for self-healing operations. It then enters the self-healing strategy matching stage, calling the preset strategy library. After executing the matched self-healing operation, it completes the autonomous fault repair. Once the repair is complete, it triggers the restoration of the control function, synchronously uploads the self-healing results to the control database, and simultaneously feeds back the current control status to the identification subsystem, ensuring the continuity of the data link and control process.

10. The system according to claim 1 or 2, characterized in that, The embedded integrated remote control architecture includes: multiple clients (client 1 to client n), a cloud server, an embedded edge terminal, and an execution layer; the data terminals of the clients and the cloud server are bidirectionally connected; the data terminals of the cloud server and the embedded edge terminal are bidirectionally connected; and the data output terminal of the embedded edge terminal is connected to the data input terminal of the execution layer. The client is used to allow engineers in multiple locations to input remote control commands, and at the same time to receive system information such as pollution identification information, pollution control results, and equipment operating status fed back from the cloud server; The cloud server is used to realize data relay and command forwarding, forwarding remote control commands issued by the client to the embedded edge terminal, and receiving pollution identification information, pollution control results, and equipment operation status data uploaded by the embedded edge terminal, and feeding the information back to each client. The embedded edge terminal is used to receive and transmit control commands forwarded by the cloud server. As the data processing core, it receives multimodal raw data and industrial camera image data transmitted by the recognition subsystem, processes them through the NPU processor, and uploads them to the cloud server. At the same time, it sends hardware control signals to the execution layer. The execution layer adjusts the operating conditions of the production subsystem's equipment based on hardware control signals, sends execution drive signals to the execution mechanisms of the control subsystem, and completes the dynamic layout of control points to achieve remote control. The dynamic layout of the control points is specifically as follows: four types of data are input into the embedded edge terminal, including a 3D pollution distribution map, a production facility layout map, real-time production condition data, and a groundwater pollutant migration model; the embedded edge terminal performs fusion calculations on the input data: combining the production facility layout map to avoid core production areas and key facilities, avoiding explosion-proof areas in Zone 0 / Zone 1 and core production facilities, so as to avoid control points interfering with production operations; calculating the spacing between control units based on pollution migration paths; dynamically adjusting the number of points according to changes in operating conditions; outputting the coordinates of dynamic control points and generating a continuous control zone network scheme to achieve blind-spot-free coverage of the control area.

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