Intelligent cooperative control method and system for multi-area environmental protection treatment of coal power plant area

Abstract

The invention belongs to the technical field of environmental protection, and particularly relates to an intelligent cooperative control method and system for multi-area environmental protection management of a coal power plant area, and the method comprises the following steps: S1, collecting monitoring data of multiple areas in a plant and outside the plant, and transmitting the monitoring data to a control unit; s2, adjusting a preset threshold value based on the seasonal characteristic parameters; s3, comparing the data with a threshold value to judge abnormity, if the data is normal, performing classified storage and returning monitoring, and if the data is abnormal, entering S4; s4, judging whether the normal regulation range is exceeded or not according to the abnormal region and type, if not, autonomously controlling equipment, and if so, manually intervening and entering S5; s5, an abnormal warning is sent to an operation and maintenance terminal, and manual intervention is carried out; s6, adjusting the feedback frequency, judging whether the abnormity is eliminated or not, if yes, recovering storage, and otherwise, returning to S5; and S7, classifying and storing abnormal data and processing flows, repeating the steps S1-S6, and improving the treatment precision and efficiency by realizing cooperative monitoring, dynamic threshold adjustment and abnormal closed-loop processing of multiple areas in a plant and outside the plant, so as to adapt to a coal power plant scene.

Description

A method and system for intelligent collaborative control of environmental governance in multiple areas of a coal-fired power plant. Technical Field

[0001] This invention belongs to the field of environmental protection technology, and in particular relates to an intelligent collaborative control method and system for multi-area environmental protection management in coal-fired power plant areas. Background Technology

[0002] With increasingly stringent environmental regulations and the green transformation of the coal industry, the requirements for environmental governance in coal-fired power plant areas are constantly rising. These plant areas encompass multiple functional zones, each with significantly different governance needs, and the spread of pollutants outside the plant is influenced by natural factors, making the governance scenarios complex.

[0003] The existing governance solutions have obvious shortcomings. The monitoring system is fragmented, lacks collaborative monitoring between the plant and the outside, is prone to blind spots, and the threshold adjustment is lagging behind. It does not adapt to dynamic factors, resulting in low governance accuracy, chaotic data management, and weak traceability and optimization capabilities. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned technical problems by providing an intelligent collaborative control method and system for multi-area environmental governance in coal-fired power plants. This system achieves collaborative monitoring of multiple areas within and outside the plant, dynamically adjusts thresholds based on seasonal characteristic parameters to improve threshold adaptability and avoid lag, and classifies and stores data throughout the entire process to enhance tracking and optimization capabilities.

[0005] In view of this, the present invention provides an intelligent collaborative control method for multi-area environmental governance in a coal-fired power plant, comprising the following steps: S1: Real-time collection of monitoring data from multiple areas within the plant and outside the plant, and feedback to the control unit; S2: The control unit adjusts the preset threshold of the monitoring data based on seasonal characteristic parameters in the monitoring data; S3: The control unit compares the collected monitoring data with the adjusted preset threshold and determines whether there is abnormal data. If abnormal data is found, step S4 is executed; if normal data is found, step S1 is returned, and the monitoring data is classified and stored according to various areas and data types; S4: Based on the area and data type of the abnormal data, it is determined whether it exceeds the normal control parameters. If the range is specified, proceed to S5; otherwise, generate control instructions and control the power and start / stop of the waste treatment equipment, production equipment, and auxiliary equipment in the corresponding area, then proceed to S6. S5: Based on the area and type of abnormal data, generate an abnormal alarm instruction and send it to the maintenance personnel terminal for manual intervention. S6: The control unit increases the feedback frequency of the area where the abnormal data is located and judges whether the abnormality has been eliminated based on the real-time feedback information. If yes, the feedback frequency is restored and proceed to S7; otherwise, proceed to S5. S7: The control unit classifies and stores the abnormal data and abnormal data processing flow according to each area and data type, and repeats S1-S6.

[0006] In the above technical solution, further, in step S1: the multiple areas within the plant include a core production area, a waste treatment area, an auxiliary production area, and an office area; wherein, the monitoring data includes at least one of ambient temperature, humidity, air pollutant concentration, water quality parameters, dust concentration, or soil heavy metal content.

[0007] In the above technical solution, further, in step S2: the seasonal characteristic parameters include at least one of ambient temperature, humidity or wind speed.

[0008] In the above technical solution, further, in step S2: the preset threshold adjustment includes: the control unit compares the seasonal characteristic parameters in the monitoring data with the preset seasonal characteristic parameters corresponding to the current season. When the difference is within the preset range, the preset threshold is kept unchanged. If the difference exceeds the preset range, the preset threshold of the parameters in the monitoring data related to the current season is increased or decreased; the preset threshold of the parameters not related to the current season remains unchanged.

[0009] In the above technical solution, further, in step S4: the conventional control range includes: the control unit determines the correlation between the current area and the abnormal data and the devices in the current area, and adjusts the power of the devices related to the abnormal data to increase or decrease or turn on or off; devices unrelated to the abnormal data maintain their power or on / off state unchanged.

[0010] In the above technical solution, further, in step S6: the feedback frequency adjustment includes: the control unit determines the correlation between the current area and the abnormal data and other monitoring data in the current area, and adjusts the feedback frequency of the monitoring data related to the abnormal data in the current area to increase by 2-3 times; the feedback frequency of the detection data unrelated to the abnormal data remains unchanged.

[0011] This invention provides a smart and collaborative control system for multi-area environmental governance in a coal-fired power plant, comprising: a monitoring unit for acquiring monitoring data from multiple areas within the plant and outside the plant, and feeding it back to a control unit; a control unit for receiving the monitoring data fed back by the monitoring unit, and based on the monitoring data, completing the collaborative control of multiple areas within the plant and outside the plant; an execution unit for receiving execution instructions from the control unit, and executing power adjustment or start / stop operations of waste treatment equipment, production equipment, and auxiliary equipment; and an operation and maintenance terminal for receiving and displaying abnormal alarm instructions, while operation and maintenance personnel can input manual control instructions and feed them back to the control unit.

[0012] In the above technical solution, further: the monitoring unit includes multiple monitoring sub-units, which are respectively deployed in the core production area, the waste treatment area, the auxiliary production area, the office area and the external area, for collecting detection data in the plant and monitoring data of the external all-wind-direction related area, the downstream water source area and the surrounding soil area.

[0013] In the above technical solution, further: an analysis submodule is used for comparing characteristic parameters and determining the correlation between abnormal data and equipment or other monitoring data; an adjustment submodule is used for adjusting preset thresholds and feedback frequencies; an anomaly determination submodule is used for determining abnormal data and judging whether it exceeds the normal control range; and an instruction generation submodule is used for generating control instructions and anomaly alarm instructions.

[0014] Furthermore, in the above technical solution, the operation and maintenance terminal also has a data retrieval function, which is used to retrieve monitoring data, abnormal data processing procedures, historical control instructions and manual intervention operation records, and supports querying and exporting by time, region or data type.

[0015] The beneficial effects of this invention are as follows: 1. It enables collaborative monitoring of multiple areas within the plant and outside the plant, breaking the fragmented monitoring pattern, comprehensively covering governance scenarios, eliminating monitoring blind spots, and providing complete data support for precise governance; 2. It dynamically adjusts thresholds based on seasonal characteristic parameters, avoiding over-governance or untimely governance after threshold adjustment, thus reducing governance costs; 3. Through a closed-loop governance process of monitoring data, anomaly judgment, automatic control, manual intervention, feedback verification, and data storage, it standardizes anomaly handling logic and significantly improves response speed and governance efficiency; 4. By dynamically adjusting the feedback frequency, it strengthens the real-time monitoring accuracy of anomaly areas, ensuring accurate verification of anomaly elimination effects and improving governance stability; 5. It classifies and stores data throughout the entire process, providing a data foundation for subsequent governance effect evaluation, fault tracing, and solution optimization, enabling the governance process to be traceable and optimizable. Attached Figure Description

[0016] Figure 1 is a schematic flowchart of the control method of Embodiment 1 of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0018] Example 1: This example provides an intelligent collaborative control method for multi-area environmental governance in a coal-fired power plant, including the following steps: S1: Real-time collection of monitoring data from multiple areas within the plant and outside the plant, and feedback to the control unit; S2: The control unit adjusts the preset threshold of the monitoring data based on seasonal characteristic parameters in the monitoring data; S3: The control unit compares the collected monitoring data with the adjusted preset threshold and determines whether there is abnormal data. If abnormal data is found, step S4 is executed; if normal data is found, step S1 is returned, and the monitoring data is classified and stored according to various areas and data types; S4: Based on the area and data type of the abnormal data, it is determined whether it exceeds the normal adjustment range. S1: If the control range is specified, proceed to S5; otherwise, generate control instructions and control the power and start / stop of the waste treatment equipment, production equipment, and auxiliary equipment in the corresponding area, then proceed to S6. S5: Based on the area and type of the abnormal data, generate an abnormal alarm instruction and send it to the maintenance personnel's terminal for manual intervention. S6: The control unit increases the feedback frequency of the area where the abnormal data is located and determines whether the abnormality has been eliminated based on the real-time feedback information. If yes, the feedback frequency is restored, and proceed to S7; otherwise, proceed to S5. S7: The control unit classifies and stores the abnormal data and abnormal data processing flow according to various areas and data types, and repeats S1-S6.

[0019] This embodiment demonstrates that it enables collaborative monitoring of multiple areas within the plant and external environments, breaking the fragmented monitoring pattern, comprehensively covering governance scenarios, eliminating monitoring blind spots, and providing complete data support for precise governance. Dynamically adjusting thresholds based on seasonal characteristic parameters avoids over- or untimely governance, improving threshold adaptability and reducing governance costs. A closed-loop governance process encompassing monitoring data, anomaly detection, automatic control, manual intervention, feedback verification, and data storage standardizes anomaly handling logic, significantly improving response speed and governance efficiency. Dynamic adjustment of feedback frequency enhances real-time monitoring accuracy in anomaly areas, ensuring accurate verification of anomaly elimination effects and improving governance stability. Full-process data is categorized and stored, providing a data foundation for subsequent governance effect evaluation, fault tracing, and solution optimization, achieving traceability and optimizability of the governance process.

[0020] Example 2: This example provides a method for intelligent collaborative control of environmental protection in multiple areas of a coal-fired power plant. In addition to the technical solutions of the above examples, it also has the following technical features: In step S1, the multiple areas within the plant include a core production area, a waste treatment area, an auxiliary production area, and an office area; wherein the monitoring data includes at least one of ambient temperature, humidity, air pollutant concentration, water quality parameters, dust concentration, or soil heavy metal content.

[0021] As can be seen from this embodiment, by accurately defining the monitoring area, covering the core pollution sources of coal-fired power plants and non-production sensitive areas, the monitoring is ensured to be targeted. At the same time, by acquiring data from multiple dimensions, the comprehensiveness of the monitoring data is guaranteed.

[0022] Example 3: This example provides a method for intelligent collaborative control of environmental protection in multiple areas of a coal-fired power plant. In addition to the technical solutions of the above examples, it also has the following technical features: In step S2, the seasonal characteristic parameters include at least one of ambient temperature, humidity or wind speed.

[0023] As can be seen from this embodiment, by screening the core meteorological parameters that significantly affect the diffusion and concentration changes of pollutants in coal-fired power plants, a precise and effective basis for judgment can be provided for dynamic threshold adjustment, thereby improving the adaptability and accuracy of threshold adjustment.

[0024] Example 4: This example provides a smart collaborative control method for multi-area environmental governance in a coal-fired power plant area. In addition to the technical solutions of the above examples, it also has the following technical features. In step S2: the preset threshold adjustment includes: the control unit compares the seasonal characteristic parameters in the monitoring data with the preset seasonal characteristic parameters corresponding to the current season. When the difference is within the preset range, the preset threshold remains unchanged. If the difference exceeds the preset range, the preset threshold of the parameters related to the current season in the monitoring data is increased or decreased. The preset threshold of parameters not related to the current season remains unchanged. The preset seasonal characteristic parameters are characteristic parameters such as temperature, humidity, and wind speed that are preset for different regions. The preset range of the difference is also adaptively adjusted according to different regions. These are all existing conventional operations and are routine adjustments that can be made by those skilled in the art based on the differences in different regions. They will not be described in detail here. At the same time, the initial preset threshold is set manually according to the current season. The specific increase or decrease adjustment of the preset threshold is set manually by those skilled in the art. For example, when the temperature exceeds the preset range in summer, the preset threshold for the temperature parameter will be decreased, while the preset threshold for the humidity parameter will be increased. These are conventional settings and will not be described in detail here.

[0025] As can be seen from this embodiment, by establishing a quantitative threshold adjustment judgment standard to replace subjective experience judgment, the accuracy and objectivity of threshold adjustment are improved; and by adjusting the threshold only for seasonally related parameters while keeping irrelevant parameters unchanged, ineffective adjustment actions are reduced, the computational load of the control unit is reduced, and the system operating efficiency is improved; and by setting a preset difference range, frequent threshold adjustments caused by small fluctuations in seasonal characteristic parameters are avoided, thereby improving the system's operational stability.

[0026] Example 5: This example provides a method for intelligent collaborative control of environmental governance in multiple areas of a coal-fired power plant. In addition to the technical solutions of the above examples, it also has the following technical features. In step S4: the conventional control range includes: the control unit determines the correlation between the current area and abnormal data and the equipment in the current area, and adjusts the power of the equipment related to the abnormal data to increase or decrease or turn on / off; the equipment unrelated to the abnormal data maintains its power or on / off state unchanged; wherein, the increase or decrease or on / off of the power of the equipment related to the abnormal data is set manually by those skilled in the art. For example, when the dust parameters are abnormal, the power of the exhaust fan or ventilation fan can be increased or the exhaust fan or ventilation fan that is in the off state can be turned on to guide the exhaust gas containing environmental dust to the waste treatment area for treatment. This is a conventional setting and will not be described in detail here.

[0027] As can be seen from this embodiment, by achieving precise control of equipment, blind start-up or power adjustment of all equipment is avoided, thereby reducing equipment energy consumption and operation and maintenance costs; the logic for determining the correlation between equipment and anomalies is clarified, ensuring that the control actions are highly targeted, and improving the accuracy and efficiency of anomaly management; unrelated equipment maintains its original state, ensuring the normal production order and stability of auxiliary functions in the plant area, and avoiding unnecessary interference to production by management actions.

[0028] Example 6: This example provides a method for intelligent collaborative control of environmental protection in multiple areas of a coal-fired power plant. In addition to the technical solutions of the above examples, it also has the following technical features: In step S6, the feedback frequency adjustment includes: the control unit determines the correlation between the current area and abnormal data and other monitoring data in the current area, and adjusts the feedback frequency of the monitoring data related to the abnormal data in the current area to increase by 2-3 times; the feedback frequency of the detection data unrelated to the abnormal data remains unchanged.

[0029] As can be seen from this embodiment, by precisely increasing the feedback frequency of anomaly-related data, the real-time acquisition of key information in anomaly areas is strengthened, providing accurate and high-frequency data support for anomaly elimination determination; irrelevant data maintains its original frequency, avoiding communication link congestion and data redundancy caused by high-frequency transmission of all data, and reducing system computing and communication costs; at the same time, by precisely increasing the feedback frequency of anomaly-related data, the real-time acquisition of key information in anomaly areas is strengthened, and the feedback frequency of monitoring data related to anomaly data in the current area is also increased, avoiding the impact of anomaly of a certain parameter on the monitoring of related parameters. For example, when the temperature parameter is abnormal, it is easy to cause changes in the humidity parameter. Therefore, the comprehensiveness of anomaly monitoring can be improved, providing accurate, high-frequency and complete data support for anomaly elimination determination.

[0030] Example 7: This example provides an intelligent collaborative control system for multi-area environmental governance in a coal-fired power plant. In addition to the technical solutions described in the above examples, it also features the following technical characteristics: a monitoring unit for acquiring monitoring data from multiple areas within the plant and outside the plant, and feeding it back to the control unit; a control unit for receiving the monitoring data from the monitoring unit and, based on the monitoring data, performing collaborative control of multiple areas within the plant and outside the plant; an execution unit for receiving execution commands from the control unit and performing power adjustment or start / stop operations on waste treatment equipment, production equipment, and auxiliary equipment; and a maintenance terminal for receiving and displaying abnormal alarm commands, while maintenance personnel can input manual control commands to feed them back to the control unit. The units and terminals can be connected via communication, using either signal lines or wireless methods, both of which are conventional choices and will not be elaborated upon here. Furthermore, the specific structures and models of the monitoring unit, control unit, execution unit, and maintenance terminal are all existing mature technologies, known to those skilled in the art from traditional monitoring units, control units, execution units, and maintenance terminals, and will not be elaborated upon here.

[0031] As can be seen from this embodiment, by constructing a full-link modular system architecture of "monitoring-control-execution-operation and maintenance", the functional boundaries of each unit are clear, which facilitates subsequent maintenance, upgrades and functional expansion. At the same time, it provides hardware support for the control method, ensuring that the various functions of the method can be accurately implemented through hardware units, realizing the synergistic optimization of the method and the system, and improving the overall governance effect.

[0032] Example 8: This example provides an intelligent collaborative control system for multi-area environmental governance in a coal-fired power plant. In addition to the technical solutions of the above examples, it also has the following technical features: The monitoring unit includes multiple monitoring sub-units, which are respectively deployed in the core production area, the waste treatment area, the auxiliary production area, the office area, and the external area. They are used to collect monitoring data within the plant and monitoring data from the external all-wind-direction related area, the downstream water source area, and the surrounding soil area. The monitoring sub-units use different monitoring devices depending on the different monitoring parameters. Their specific structures, types, and models are all existing mature technologies, which are routinely selected by those skilled in the art based on the type of parameters to be monitored. They will not be described in detail here.

[0033] As can be seen from this embodiment, the plant sub-units accurately cover each functional area, ensuring the relevance and comprehensiveness of the plant monitoring data; and the plant external sub-units cover the entire wind direction related area, avoiding blind spots in pollutant diffusion monitoring caused by wind direction changes, while also covering downstream water sources and surrounding soil, realizing multi-media collaborative monitoring.

[0034] Example 9: This example provides an intelligent collaborative control system for multi-area environmental governance in a coal-fired power plant. In addition to the technical solutions of the above examples, it also has the following technical features: the control unit includes: an analysis submodule for comparing characteristic parameters and determining the correlation between abnormal data and equipment or other monitoring data; an adjustment submodule for adjusting preset thresholds and feedback frequencies; an anomaly determination submodule for determining abnormal data and whether it exceeds the normal control range; and an instruction generation submodule for generating control instructions and anomaly alarm instructions. The specific structures and models of the submodules, adjustment submodule, anomaly determination submodule, and instruction generation submodule are all existing mature technologies, which can be conventionally selected by those skilled in the art according to their needs, and will not be elaborated here.

[0035] As can be seen from this embodiment, by adopting a modular sub-module design, each sub-module has a single and focused function, reducing the complexity of the core control logic and facilitating subsequent function optimization and fault diagnosis. Furthermore, the analysis sub-module provides accurate basis for subsequent adjustment and judgment actions, the adjustment sub-module realizes dynamic control, the anomaly judgment sub-module ensures accurate judgment, and the instruction generation sub-module ensures instruction standardization. The collaborative work of each sub-module improves the computing efficiency and reliability of the control unit.

[0036] Example 10: This example provides an intelligent collaborative control system for multi-area environmental governance in coal-fired power plants. In addition to the technical solutions of the above examples, it also has the following technical features: The operation and maintenance terminal also has a data retrieval function, which is used to retrieve monitoring data, abnormal data processing procedures, historical control instructions and manual intervention operation records, and supports querying and exporting by time, region or data type.

[0037] This embodiment demonstrates that by centrally retrieving and managing data throughout the entire process, it provides convenience for operations and maintenance personnel to evaluate governance effectiveness, trace the causes of faults, and optimize governance solutions. It also supports multi-dimensional querying and exporting by time, region, and data type, improving data retrieval efficiency and adapting to data usage needs in different scenarios. Furthermore, it strengthens data traceability capabilities, meeting environmental regulations' requirements for data traceability in governance and reducing environmental compliance risks caused by data loss. Simultaneously, it enhances the intelligence level of operations and maintenance work, reduces the data processing workload for operations and maintenance personnel, and lowers operations and maintenance costs.

[0038] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for intelligent collaborative control of multi-area environmental governance in a coal-fired power plant area, characterized in that, Includes the following steps: S1: Real-time collection of monitoring data from multiple areas within the plant and outside the plant, and feedback to the control unit; S2: The control unit adjusts the preset threshold of the monitoring data based on the seasonal characteristic parameters in the monitoring data; S3: The control unit compares the collected monitoring data with the adjusted preset threshold and determines whether there is abnormal data. If there is abnormality, step S4 is executed. If normal, step S1 is returned, and the monitoring data is classified and stored according to various areas and data types. S4: Based on the location and data type of the abnormal data, determine whether it exceeds the normal control range. If so, proceed to S5. If not, generate a control command and control the power and start / stop of the waste treatment equipment, production equipment and auxiliary equipment in the corresponding area before proceeding to S6. S5: Based on the region and data type of the abnormal data, generate an abnormal alarm command and send it to the maintenance personnel's terminal for manual intervention and adjustment; S6: The control unit increases the feedback frequency of the area where the abnormal data is located, and judges whether the abnormality has been eliminated based on the real-time feedback information. If yes, the feedback frequency is restored and proceeds to S7; otherwise, proceeds to S5. S7: The control unit classifies and stores abnormal data and abnormal data processing procedures by region and data type, and repeats S1-S6.

2. The intelligent collaborative control method for multi-area environmental protection governance in coal-fired power plant areas according to claim 1, characterized in that, In step S1: the plant includes multiple areas such as the core production area, the waste treatment area, the auxiliary production area and the office area; wherein, the monitoring data includes at least one of the following: ambient temperature, humidity, air pollutant concentration, water quality parameters, dust concentration or soil heavy metal content.

3. The intelligent collaborative control method for multi-area environmental protection governance in coal-fired power plant areas according to claim 1, characterized in that, In step S2: the seasonal characteristic parameters include at least one of ambient temperature, humidity or wind speed.

4. The intelligent collaborative control method for multi-area environmental protection governance in coal-fired power plant areas according to claim 1, characterized in that, In step S2: the preset threshold adjustment includes: the control unit compares the seasonal characteristic parameters in the monitoring data with the preset seasonal characteristic parameters corresponding to the current season. When the difference is within the preset range, the preset threshold remains unchanged. If the difference exceeds the preset range, the preset threshold of the parameters in the monitoring data related to the current season is increased or decreased. The preset threshold of the parameters unrelated to the current season remains unchanged.

5. The intelligent collaborative control method for multi-area environmental protection governance in coal-fired power plant areas according to claim 1, characterized in that, In step S4: the conventional control range includes: the control unit determines the correlation between the current area and the abnormal data and the devices in the current area, and adjusts the power of the devices related to the abnormal data to increase or decrease or turn on or off; devices unrelated to the abnormal data maintain their power or on / off status.

6. The intelligent collaborative control method for multi-area environmental protection governance in coal-fired power plant areas according to claim 1, characterized in that, In step S6: the feedback frequency adjustment includes: the control unit determines the correlation between the current area and abnormal data and other monitoring data in the current area, and adjusts the feedback frequency of the monitoring data related to the abnormal data in the current area to increase by 2-3 times; the feedback frequency of the detection data unrelated to the abnormal data remains unchanged.

7. A control system employing the intelligent collaborative control method for multi-area environmental protection governance in coal-fired power plant areas as described in any one of claims 1-6, characterized in that, include: The monitoring unit is used to acquire monitoring data from multiple areas within the plant and outside the plant, and feed it back to the control unit; the control unit is used to receive the monitoring data fed back by the monitoring unit, and based on the monitoring data, to complete the coordinated control of multiple areas within the plant and outside the plant; the execution unit is used to receive the execution instructions from the control unit, and to perform power adjustment or start / stop operations on the waste treatment equipment, production equipment, and auxiliary equipment; the operation and maintenance terminal is used to receive and display abnormal alarm instructions, and at the same time, operation and maintenance personnel can feed back manual control instructions to the control unit.

8. The control system of the intelligent collaborative control method for multi-area environmental protection governance in coal-fired power plant areas according to claim 7, characterized in that: The monitoring unit includes multiple monitoring sub-units, which are respectively deployed in the core production area, waste treatment area, auxiliary production area, office area and external area, to collect detection data in the plant and monitoring data of the external all-wind-direction related area, downstream water source area and surrounding soil area.

9. The control system of the intelligent collaborative control method for multi-area environmental protection governance in coal-fired power plant areas according to claim 7, characterized in that, The control unit includes: an analysis submodule for comparing characteristic parameters and determining the correlation between abnormal data and equipment or other monitoring data; an adjustment submodule for adjusting preset thresholds and feedback frequencies; an anomaly determination submodule for determining abnormal data and whether it exceeds the normal control range; and an instruction generation submodule for generating control instructions and anomaly alarm instructions.

10. The control system of the intelligent collaborative control method for multi-area environmental protection governance in coal-fired power plant areas according to claim 7, characterized in that: The maintenance terminal also has a data retrieval function, which can be used to retrieve monitoring data, abnormal data processing procedures, historical control instructions, and manual intervention operation records, and supports querying and exporting by time, region, or data type.

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