An intelligent operation and maintenance method and system for an environmental protection island of a coal-fired power plant

Abstract

The application discloses a kind of coal-fired power plant environmental protection island intelligent operation and maintenance method and system, comprising: collecting the working equipment operation data of denitration, dust removal, desulfurization system in environmental protection island;The multi-source data is preprocessed and fused;Fusion data is input state analysis model to obtain equipment state parameters and process parameters, state analysis model includes improved PSO-Attention-LSTM operating state prediction model;Combining expert knowledge base carries out fault precursor identification and early warning;Based on multi-link collaborative optimization model, collaborative optimization control strategy is generated, with pollutant ultra-low emission as constraint, with system total energy consumption and total material consumption minimization as target.The system includes multi-source data acquisition module, data fusion module, operating state analysis module, fault prediction and diagnosis module, collaborative optimization control module, digital twin module and man-machine interaction module, solves the technical problems that environmental protection island operation and maintenance data are dispersed, fault early warning is lagging, and collaboration is poor, realizes the predictive maintenance and collaborative optimization operation of environmental protection island.

Description

Technical Field

[0001] This invention relates to the technical field of operation and maintenance of coal-fired power plants, and in particular to an intelligent operation and maintenance method and system for the environmental protection island of a coal-fired power plant. Background Technology

[0002] A coal-fired power plant's environmental protection island refers to a core environmental protection facility system integrating functions such as desulfurization, denitrification, and dust removal. It bears the heavy responsibility of removing pollutants such as sulfur dioxide, nitrogen oxides, and particulate matter from flue gas, and its operational stability and efficiency directly determine whether the power plant can achieve environmental compliance. Currently, a coal-fired power plant's environmental protection island typically includes key equipment such as selective catalytic reduction (SCR) denitrification units, electrostatic precipitators, and wet desulfurization absorption towers. These devices are connected in series in the flue gas duct to collaboratively complete the pollutant removal task.

[0003] However, the existing operation and maintenance technology for environmental protection islands in coal-fired power plants has the following technical problems:

[0004] (1) Equipment operation data is scattered, resulting in an "information silo" phenomenon. The desulfurization tower, denitrification reactor, dust collector and other equipment inside the environmental protection island belong to different suppliers, and the data acquisition systems operate independently. This makes it difficult to integrate and analyze equipment operation data, environmental monitoring data and power grid load data. About 60% of the environmental protection island data of coal-fired power plants are scattered and stored in more than 5 independent databases. There is a lack of effective data integration and correlation analysis between the systems, making it impossible to form a comprehensive understanding of the operation status of the environmental protection island.

[0005] (2) Operation and maintenance rely on manual experience and passive maintenance mode. The existing operation and maintenance data of the environmental protection island are generally recorded and integrated manually, which is not timely. Moreover, the operation and maintenance mode is mainly based on passive response, which makes it difficult to achieve early warning and predictive maintenance of equipment failure, resulting in a high equipment failure rate and high maintenance costs -39.

[0006] (3) The control logic of desulfurization and denitrification is rigid and difficult to adapt to variable load conditions. The operating conditions of coal-fired power plants are affected by a variety of factors such as fuel quality fluctuations, grid load changes, and differences in meteorological conditions. Existing control systems cannot detect and respond to these dynamic changes in a timely manner. Taking the desulfurization system as an example, the traditional control system still operates according to preset parameters, which has problems such as high desulfurizing agent waste rate and large fluctuations in emission concentration.

[0007] (4) There is a lack of collaborative optimization mechanism among the subsystems of denitrification, dust removal and desulfurization in the environmental protection island. The optimization of a single link often comes at the cost of sacrificing the economy or emission stability of other links, and it is impossible to achieve the global optimum.

[0008] Therefore, there is an urgent need to develop an intelligent operation and maintenance method and system for the environmental protection island of coal-fired power plants that can integrate multi-source data, realize real-time monitoring of equipment status and fault prediction and early warning, and support multi-stage collaborative optimization. Summary of the Invention

[0009] The present invention aims to at least partially solve one of the technical problems in the related art.

[0010] Therefore, the purpose of this invention is to propose an intelligent operation and maintenance method and system for the environmental protection island of a coal-fired power plant, so as to solve the technical problems of scattered operation and maintenance data, delayed fault early warning, poor system coordination, and low operation and maintenance efficiency in the prior art.

[0011] To achieve the above objectives, this invention proposes an intelligent operation and maintenance method for the environmental protection island of a coal-fired power plant, comprising the following steps:

[0012] S1. Monitor all working equipment within the environmental protection island of the coal-fired power plant and collect operating data of all working equipment; the environmental protection island includes a denitrification system, a dust removal system, and a desulfurization system;

[0013] S2. The collected multi-source operational data are preprocessed and fused to obtain comprehensive data reflecting the true operational status of the environmental protection island;

[0014] S3. Input the comprehensive data into a preset state analysis model to obtain the state parameters and process parameters of the working equipment; the state analysis model includes an operating state prediction model based on the improved PSO-Attention-LSTM algorithm;

[0015] S4. Based on the state parameters and process parameters, and combined with a preset expert knowledge base, identify the early signs of failure for key equipment in the environmental protection island, and issue a warning message when an early sign of failure is identified.

[0016] S5. Based on a multi-stage collaborative optimization model, a collaborative optimization control strategy for the denitrification system, dust removal system, and desulfurization system is generated, with ultra-low pollutant emissions as the constraint and minimizing the total energy consumption and total material consumption of the system as the objective function.

[0017] In addition, the intelligent operation and maintenance method and system for the environmental protection island of a coal-fired power plant proposed above according to the present invention may also have the following additional technical features:

[0018] Specifically, the denitrification system is an SCR denitrification device, the dust removal system includes an electrostatic precipitator or a bag filter, and the desulfurization system is a limestone-gypsum wet desulfurization absorption system.

[0019] Specifically, the operational data includes equipment operating parameters, environmental monitoring data, power grid load data, and fuel data.

[0020] Specifically, the multi-source data fusion employs a Kalman filter fusion algorithm or a Bayesian estimation-based fusion algorithm.

[0021] Specifically, the improved PSO-Attention-LSTM runtime prediction model includes: an input layer, a PSO optimization layer, an Attention mechanism layer, an LSTM layer, and an output layer; wherein the PSO optimization layer uses the particle swarm optimization algorithm to adaptively optimize the hyperparameters of the LSTM network, and the Attention mechanism layer assigns different weights to features at different time steps.

[0022] Specifically, the state analysis model also includes a condition prediction fusion model based on random forest and time series algorithms, which is used to achieve short-term prediction of flue gas volume, flue gas temperature and pollutant concentration at the entrance of the environmental protection island.

[0023] The expert knowledge base is a dynamically updated knowledge base, which continuously updates the fault mode library and feature parameter thresholds based on newly added fault cases in actual operation through an incremental learning mechanism.

[0024] The optimized combination of slurry circulation pumps in the desulfurization system in the collaborative optimization control strategy adopts an optimization strategy based on particle swarm optimization algorithm. The goal is to minimize the total power consumption of the pump group and solve for the optimal combination of circulation pumps while ensuring desulfurization efficiency.

[0025] The method also includes a digital twin modeling step: constructing a digital twin model of the environmental island based on the geometric parameters, equipment attributes, and operating data of the physical system of the environmental island, realizing real-time synchronous mapping between the virtual system and the physical system, and supporting offline simulation and root cause tracing of faults.

[0026] A smart operation and maintenance system for an environmental protection island in a coal-fired power plant includes a multi-source data acquisition module, a data transmission module, a data preprocessing module, a multi-source data fusion module, an operation status analysis module, a fault prediction and diagnosis module, a control command execution module, a digital twin module, and a human-machine interaction module. The multi-source data acquisition module collects equipment operation data, environmental monitoring data, power grid data, and fuel data during the operation of the environmental protection island. The data transmission module transmits the data collected by the multi-source data acquisition module to a data processing center. The data preprocessing module performs cleaning, filtering, and normalization preprocessing operations on the transmitted raw data. The multi-source data fusion module uses data fusion algorithms to fuse the preprocessed data, establish a data fusion model, and obtain comprehensive data reflecting the actual operation status of the environmental protection island. The operation status analysis module uses big data analysis and machine learning algorithms to analyze the operation status of the environmental protection island based on the fused data. The system includes a real-time analysis and evaluation module, a fault prediction and diagnosis module with an improved PSO-Attention-LSTM operating status prediction model and an expert knowledge base, used for fault precursor identification and early warning of environmental island equipment; a collaborative optimization control module, used to generate collaborative optimization control commands based on the evaluation results of the operating status analysis module and the diagnostic information of the fault prediction and diagnosis module, combined with preset control objectives and optimization strategies, using model predictive control and reinforcement learning algorithms; a control command execution module, used to transmit the control commands generated by the collaborative optimization control module to each actuator of the environmental island, realizing automated control of the environmental island equipment and providing feedback on the execution status of control commands; a digital twin module, used to construct a digital twin model of the environmental island, realizing real-time synchronous mapping between the virtual system and the physical system; and a human-machine interaction module, used to provide a visual operation interface for operators to view operating data, operating status analysis results, fault early warning information, and control command execution status.

[0027] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0028] (1) It solves the problem of "information silos" in environmental protection island data. Through the multi-source data acquisition and fusion module, the operation data, environmental monitoring data, power grid data and other data scattered in the denitrification, dust removal and desulfurization subsystems are integrated into a unified data view, forming a comprehensive understanding of the operation status of the environmental protection island and providing a complete data foundation for intelligent operation and maintenance.

[0029] (2) It has realized the transformation from passive maintenance to proactive predictive maintenance. The operation status prediction model constructed by improving the PSO-Attention-LSTM algorithm can accurately predict the operation status of desulfurization and denitrification within an accuracy range of more than 84%, and provide early warning before equipment failure occurs, helping maintenance personnel to reasonably arrange maintenance plans and avoid unplanned downtime.

[0030] (3) The coordinated optimization control of multiple links in the environmental protection island has been realized. By establishing a coordinated optimization model with ultra-low emissions of pollutants as a constraint and minimizing the total energy consumption and total material consumption of the system as the goal, the barriers of independent control of each subsystem of denitrification, dust removal and desulfurization have been broken, so that the entire environmental protection island can respond to changes in operating conditions as an organic whole, and significantly reduce energy consumption and material consumption while ensuring that emissions meet the standards.

[0031] (4) The digital twin module enables visualized operation and maintenance management of the entire life cycle of the environmental protection island, supports offline simulation and root cause tracing, reduces the dependence on the experience level of operation and maintenance personnel, and improves the overall operation and maintenance efficiency and intelligence level.

[0032] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0033] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0034] Figure 1 This is a schematic diagram of the intelligent operation and maintenance method for the environmental protection island of a coal-fired power plant according to the present invention.

[0035] Figure 2 This is a schematic diagram of the intelligent operation and maintenance system for the environmental protection island of a coal-fired power plant according to the present invention.

[0036] As shown in the figure: 1. Multi-source data acquisition module; 2. Data transmission module; 3. Data preprocessing module; 4. Multi-source data fusion module; 5. Operation status analysis module; 6. Fault prediction and diagnosis module; 7. Control command execution module; 8. Digital twin module; 9. Human-computer interaction module. Detailed Implementation

[0037] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the invention, and should not be construed as limiting the invention. Rather, embodiments of the invention include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0038] The intelligent operation and maintenance method and system for the environmental protection island of a coal-fired power plant according to embodiments of the present invention will be described below with reference to the accompanying drawings.

[0039] like Figure 1 As shown in the figure, the intelligent operation and maintenance method for the environmental protection island of a coal-fired power plant according to an embodiment of the present invention may include:

[0040] S1. Monitor all working equipment within the environmental protection island of the coal-fired power plant and collect operating data from all working equipment; the environmental protection island includes a denitrification system, a dust removal system, and a desulfurization system;

[0041] S2. The collected multi-source operational data are preprocessed and fused to obtain comprehensive data reflecting the true operational status of the environmental protection island;

[0042] S3. Input the comprehensive data into the preset state analysis model to obtain the state parameters and process parameters of the working equipment; the state analysis model includes an operating state prediction model based on the improved PSO-Attention-LSTM algorithm;

[0043] S4. Based on state parameters and process parameters, combined with a preset expert knowledge base, identify the early signs of failure of key equipment in the environmental protection island, and issue early warning information when the early signs of failure are identified.

[0044] S5. Based on a multi-stage collaborative optimization model, a collaborative optimization control strategy for the denitrification system, dust removal system, and desulfurization system is generated. With ultra-low pollutant emissions as a constraint and minimizing total system energy consumption and total material consumption as the objective function, a combination of model predictive control and reinforcement learning is used to dynamically adjust the operating parameters of each stage. Specifically, the denitrification system dynamically adjusts the ammonia injection rate based on the predicted SCR inlet concentration; the dust removal system automatically tracks and adjusts the electrostatic precipitator power output parameters based on unit load, flue gas volume, and pollutant concentration; and the desulfurization system automatically optimizes the start-up and shutdown of the slurry circulation pump combination and the slurry supply rate based on the real-time SO2 inlet concentration, thereby achieving optimal desulfurization efficiency and energy-saving operation.

[0045] Specifically, after the system starts, the multi-source data acquisition module collects real-time operating data of all working equipment within the environmental protection island (S1), including equipment operating parameters, environmental monitoring data, power grid load data, and fuel data. The data transmission module securely transmits the collected raw data to the data processing center. The data preprocessing module cleans, filters, and normalizes the raw data, and the multi-source data fusion module fuses the preprocessed data (S2) to obtain comprehensive data reflecting the true operating status of the environmental protection island. Based on the fused comprehensive data, the operating status analysis module uses big data analysis and machine learning algorithms to perform real-time analysis and evaluation of the environmental protection island's operating status (S3). The fault prediction and diagnosis module starts simultaneously, using an improved PSO-Attention-LSTM model to predict the operating status of key equipment, identify fault precursors, and output early warning information (S4). If an abnormal equipment status or fault precursors are detected, an early warning is immediately issued to maintenance personnel, and a fault diagnosis report and suggested maintenance plan are output. Based on the operational status analysis results and fault diagnosis information, the collaborative optimization control module, combined with a multi-stage collaborative optimization model constrained by ultra-low pollutant emissions and aimed at minimizing total system energy consumption and total material consumption, generates collaborative optimization control commands (S5) for each stage of denitrification, dust removal, and desulfurization. The control command execution module transmits the collaborative optimization control commands to each actuator in the environmental protection island, realizing automated closed-loop control and providing real-time feedback on the execution status of the control commands.

[0046] In the above process, operational status analysis, fault prediction and diagnosis, and collaborative optimization control form a closed loop, continuously iterating to ensure the environmental island always maintains its optimal operating state. All real-time operational data, status analysis results, fault warning information, and collaborative control commands are displayed in a visual form on the operation interface (S6) through the human-machine interaction module. Maintenance personnel can view the system status, confirm warning information, or manually intervene in control commands through the interactive interface.

[0047] In one embodiment of the present invention, such as Figure 1 As shown, the denitrification system is an SCR denitrification device, the dust removal system includes an electrostatic precipitator or a bag filter, and the desulfurization system is a limestone-gypsum wet desulfurization absorption tower.

[0048] In one embodiment of the present invention, such as Figure 1 As shown, the operational data includes equipment operating parameters, environmental monitoring data, power grid load data, and fuel data.

[0049] In one embodiment of the present invention, such as Figure 1 As shown, multi-source data fusion employs either a Kalman filter fusion algorithm or a Bayesian estimation-based fusion algorithm.

[0050] In one embodiment of the present invention, such as Figure 1As shown, the improved PSO-Attention-LSTM runtime prediction model includes: an input layer, a PSO optimization layer, an Attention mechanism layer, an LSTM layer, and an output layer. The PSO optimization layer uses the particle swarm optimization algorithm to adaptively optimize the hyperparameters of the LSTM network, and the Attention mechanism layer assigns different weights to features at different time steps.

[0051] In one embodiment of the present invention, such as Figure 1 As shown, the state analysis model also includes a condition prediction fusion model based on random forest and time series algorithms, which is used to achieve short-term prediction of flue gas volume, flue gas temperature and pollutant concentration at the entrance of the environmental protection island.

[0052] In one embodiment of the present invention, such as Figure 1 As shown, the expert knowledge base is a dynamically updated knowledge base. It continuously updates the fault mode library and feature parameter thresholds based on newly added fault cases in actual operation through an incremental learning mechanism.

[0053] In one embodiment of the present invention, such as Figure 1 As shown, the optimization of the slurry circulation pump combination in the desulfurization system in the collaborative optimization control strategy adopts an optimization strategy based on the particle swarm optimization algorithm. The goal is to minimize the total power consumption of the pump group and solve for the optimal circulation pump operation combination under the premise of ensuring desulfurization efficiency.

[0054] In one embodiment of the present invention, such as Figure 1 As shown, a digital twin model of the environmental island is constructed based on the geometric parameters, equipment attributes, and operational data of the physical system of the environmental island, realizing real-time synchronous mapping between the virtual system and the physical system, and supporting offline simulation and root cause tracing of faults.

[0055] A smart operation and maintenance system for the environmental protection island of a coal-fired power plant, such as Figure 2 As shown, it includes a multi-source data acquisition module, a data transmission module, a data preprocessing module, a multi-source data fusion module, an operational status analysis module, a fault prediction and diagnosis module, a control command execution module, a digital twin module, and a human-computer interaction module.

[0056] The multi-source data acquisition module is used to collect equipment operation data, environmental monitoring data, power grid data, and fuel data during the operation of the environmental protection island of a coal-fired power plant.

[0057] The system includes several modules: a data transmission module to transmit data collected by the multi-source data acquisition module to the data processing center; a data preprocessing module to clean, filter, and normalize the transmitted raw data; a multi-source data fusion module to fuse the preprocessed data using data fusion algorithms, establish a data fusion model, and obtain comprehensive data reflecting the actual operating status of the environmental island; an operating status analysis module to perform real-time analysis and evaluation of the environmental island's operating status based on the fused data using big data analysis and machine learning algorithms; and a fault prediction and diagnosis module, which incorporates an improved PSO-Attention-LSTM operating status prediction model and an expert knowledge base, for identifying early signs of faults in the environmental island's equipment. The system includes a collaborative optimization control module, which generates collaborative optimization control commands based on the evaluation results of the operation status analysis module and the diagnostic information of the fault prediction and diagnosis module, combined with preset control objectives and optimization strategies, using model predictive control and reinforcement learning algorithms. The control command execution module transmits the control commands generated by the collaborative optimization control module to the various actuators on the environmental protection island, achieving automated control of the island's equipment and providing feedback on the execution status of the control commands. The digital twin module constructs a digital twin model of the environmental protection island, enabling real-time synchronous mapping between the virtual and physical systems. The human-machine interaction module provides a visual operating interface for operators to view operating data, operation status analysis results, fault warning information, and control command execution status.

[0058] It should be noted that by using the geometric parameters, equipment attributes, and operational data of the environmental protection island to construct a digital twin model, the virtual system is kept highly synchronized with the physical system, ensuring the authenticity and effectiveness of the simulation results. The digital twin model supports offline simulation and deduction, which helps to simulate various working conditions before actual operation and maintenance, discover potential hidden dangers, and thus take measures in advance to avoid failures.

[0059] By setting the above-mentioned technical features, the present invention not only realizes comprehensive monitoring and precise analysis of the environmental protection island of coal-fired power plants, but also improves the overall performance and stability of the system through optimized control and digital twin technology, ensuring the dual improvement of environmental protection and economic benefits.

[0060] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0061] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0062] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for intelligent operation and maintenance of the environmental protection island in a coal-fired power plant, characterized in that, Includes the following steps: S1. Monitor all working equipment within the environmental protection island of the coal-fired power plant and collect operating data of all working equipment; the environmental protection island includes a denitrification system, a dust removal system, and a desulfurization system; S2. The collected multi-source operational data are preprocessed and fused to obtain comprehensive data reflecting the true operational status of the environmental protection island; S3. Input the comprehensive data into a preset state analysis model to obtain the state parameters and process parameters of the working equipment; the state analysis model includes an operating state prediction model based on the improved PSO-Attention-LSTM algorithm; S4. Based on the state parameters and process parameters, and combined with a preset expert knowledge base, identify the early signs of failure for key equipment in the environmental protection island, and issue a warning message when an early sign of failure is identified. S5. Based on a multi-stage collaborative optimization model, a collaborative optimization control strategy for the denitrification system, dust removal system, and desulfurization system is generated, with ultra-low pollutant emissions as the constraint and minimizing the total energy consumption and total material consumption of the system as the objective function.

2. The intelligent operation and maintenance method for the environmental protection island of a coal-fired power plant according to claim 1, characterized in that, The denitrification system is an SCR denitrification device, the dust removal system includes an electrostatic precipitator or a bag filter, and the desulfurization system is a limestone-gypsum wet desulfurization absorption tower.

3. The intelligent operation and maintenance method for the environmental protection island of a coal-fired power plant according to claim 1, characterized in that, The operational data includes equipment operating parameters, environmental monitoring data, power grid load data, and fuel data.

4. The intelligent operation and maintenance method for the environmental protection island of a coal-fired power plant according to claim 1, characterized in that, The multi-source data fusion employs a Kalman filter fusion algorithm or a Bayesian estimation-based fusion algorithm.

5. The intelligent operation and maintenance method for the environmental protection island of a coal-fired power plant according to claim 1, characterized in that, The improved PSO-Attention-LSTM runtime prediction model includes: an input layer, a PSO optimization layer, an Attention mechanism layer, an LSTM layer, and an output layer; wherein the PSO optimization layer uses the particle swarm optimization algorithm to adaptively optimize the hyperparameters of the LSTM network, and the Attention mechanism layer assigns different weights to features at different time steps.

6. The intelligent operation and maintenance method for the environmental protection island of a coal-fired power plant according to claim 1, characterized in that, The state analysis model also includes a condition prediction fusion model based on random forest and time series algorithms, which is used to achieve short-term prediction of flue gas volume, flue gas temperature and pollutant concentration at the entrance of the environmental protection island.

7. The intelligent operation and maintenance method for the environmental protection island of a coal-fired power plant according to claim 1, characterized in that, The expert knowledge base is a dynamically updated knowledge base, which continuously updates the fault mode library and feature parameter thresholds based on newly added fault cases in actual operation through an incremental learning mechanism.

8. The intelligent operation and maintenance method and system for the environmental protection island of a coal-fired power plant according to claim 1, characterized in that, The optimized combination of slurry circulation pumps in the desulfurization system in the collaborative optimization control strategy adopts an optimization strategy based on particle swarm optimization algorithm. The goal is to minimize the total power consumption of the pump group and solve for the optimal combination of circulation pumps while ensuring desulfurization efficiency.

9. The intelligent operation and maintenance method for the environmental protection island of a coal-fired power plant according to claim 1, characterized in that, The method also includes a digital twin modeling step: constructing a digital twin model of the environmental island based on the geometric parameters, equipment attributes, and operating data of the physical system of the environmental island, realizing real-time synchronous mapping between the virtual system and the physical system, and supporting offline simulation and root cause tracing of faults.

10. An intelligent operation and maintenance system for the environmental protection island of a coal-fired power plant, characterized in that... It includes a multi-source data acquisition module, a data transmission module, a data preprocessing module, a multi-source data fusion module, an operational status analysis module, a fault prediction and diagnosis module, a control command execution module, a digital twin module, and a human-computer interaction module. The multi-source data acquisition module is used to collect equipment operation data, environmental monitoring data, power grid data, and fuel data during the operation of the environmental protection island of a coal-fired power plant. The data transmission module is used to transmit the data collected by the multi-source data acquisition module to the data processing center; The data preprocessing module is used to perform cleaning, filtering, and normalization preprocessing operations on the transmitted raw data. The multi-source data fusion module is used to fuse preprocessed data using data fusion algorithms, establish a data fusion model, and obtain comprehensive data that reflects the actual operating status of the environmental protection island. The operational status analysis module is used to perform real-time analysis and evaluation of the operational status of the environmental protection island based on the fused data, using big data analysis and machine learning algorithms. The fault prediction and diagnosis module has an improved PSO-Attention-LSTM operation status prediction model and an expert knowledge base built in, which is used to identify and warn of fault precursors in the environmental protection island equipment; the collaborative optimization control module is used to generate collaborative optimization control commands by combining the evaluation results of the operation status analysis module and the diagnostic information of the fault prediction and diagnosis module with preset control objectives and optimization strategies, using model predictive control and reinforcement learning algorithms. The control command execution module is used to transmit the control commands generated by the collaborative optimization control module to each actuator of the environmental protection island, so as to realize the automated control of the environmental protection island equipment and provide feedback on the execution status of the control commands; The digital twin module is used to build a digital twin model of the environmental island, enabling real-time synchronous mapping between the virtual and physical systems; The human-computer interaction module provides a visual operating interface for operators to view operating data, operating status analysis results, fault warning information, and control command execution status.

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