Remote automatic centralized control system

Abstract

The invention relates to the field of sewage treatment, in particular to a remote automatic centralized control system which integrates five innovation modules including intelligent environment perception, decision optimization, remote intelligent operation and maintenance, safety compliance monitoring and sustainable evaluation. Sewage treatment parameters are monitored in real time and adjusted in a self-adaptive mode through the Internet of Things and edge computing, the intelligent decision module optimizes a treatment strategy, the remote operation and maintenance module is combined with the AR / VR technology to achieve efficient maintenance, the safety module ensures that data are safe and transparent, the sustainable module evaluates and optimizes environmental influences, and an efficient, intelligent and green sewage treatment system is jointly constructed.

Description

Technical Field

[0001] The present application relates to the field of sewage treatment, and in particular to a remote automatic centralized control system. Background Art

[0002] In the current sewage treatment field, traditional remote monitoring and control systems face a series of technical challenges and limitations, which directly affect the efficiency, cost and environmental impact of sewage treatment.

[0003] Among them, in traditional sewage treatment systems, data collection and processing often rely on isolated sensors and control systems, resulting in poor information transmission and making it difficult for decision makers to obtain comprehensive and real-time data support, which affects the system's response speed and decision-making efficiency.

[0004] At the same time, most existing systems are controlled based on fixed rules and lack in-depth analysis and prediction capabilities of data, making it impossible to identify potential equipment failures or water quality changes in advance, resulting in increased maintenance costs and unstable treatment effects. Summary of the invention

[0005] In order to improve the intelligence level of sewage treatment and realize efficient, safe and environmentally friendly water resources management, the present application provides a remote automatic centralized control system.

[0006] In a first aspect, the present application provides a remote automatic centralized control system, comprising the following modules: The intelligent environmental perception and adaptive module uses IoT sensors and edge computing technology to monitor key parameters such as water quality, flow, and equipment status in real time. It also combines environmental data to conduct comprehensive analysis, predict the impact of environmental changes on sewage treatment, and automatically adjust treatment processes and parameters. Intelligent decision-making and optimization module, based on the data collected by the intelligent environmental perception and adaptive module, simulates the system performance under different treatment strategies and automatically optimizes the dosage of reagents, energy consumption distribution, and equipment operation time; The remote collaboration and intelligent operation and maintenance module combines AR and VR technologies to allow operation and maintenance personnel to remotely inspect equipment, diagnose faults and provide maintenance guidance. It also integrates an AI assistant to provide fault prediction and maintenance suggestions based on historical data, thus reducing unplanned downtime. Intelligent security and compliance monitoring module, access and monitor network security, monitoring data including basic information, panoramic view, process flow chart, configuration screen, inspection data, historical parameters, alarm data and management data, through blockchain technology, to achieve data immutability and transparency; Sustainability assessment and optimization module, which assesses the environmental impact of the sewage treatment process, monitors changes in the external environment, and adjusts system operation strategies; The intelligent environmental perception and adaptation module provides real-time data for the intelligent decision-making and optimization module. The intelligent decision-making and optimization module analyzes the results and adjusts the control strategy, and at the same time feeds back to the remote collaboration and intelligent operation and maintenance module to guide the operation and maintenance operations; the intelligent safety and compliance monitoring system ensures that the operation of all modules meets safety and compliance requirements, and collaborates with the sustainability assessment and optimization module.

[0007] A remote automatic centralized control method is implemented by using the above remote automatic centralized control system, and includes the following steps: S1: Use IoT sensors and edge computing technology to monitor water quality, flow, and equipment status parameters in real time, combine environmental data for comprehensive analysis, and predict the impact of environmental changes on sewage treatment; S2: Based on the data collected in step S1, the intelligent decision-making and optimization module simulates the system performance under different treatment strategies, automatically optimizes the dosage of reagents, energy consumption allocation and equipment operation time, so as to achieve optimal utilization of resources and improve treatment efficiency; S3: Combines AR and VR technologies to allow operation and maintenance personnel to remotely inspect equipment, diagnose faults and provide maintenance guidance through mobile terminals such as PCs, mobile phones or tablets. The integrated AI assistant provides fault prediction and maintenance suggestions based on historical data, reducing unplanned downtime. S4: By connecting PLC devices to industrial gateways, access and monitor network security, the monitoring data includes basic information, panoramic views, process flow charts, configuration screens, inspection data, historical parameters, alarm data, and management data. Blockchain technology is used to make data tamper-proof and transparent, ensuring that the operation of all modules meets security and compliance requirements. S5: Evaluate the impact of the sewage treatment process on the environment, monitor changes in the external environment, and adjust the system operation strategy to achieve the dual goals of resource optimization and environmental assessment; S6: Through the feedback mechanism, the analysis results of the intelligent decision-making and optimization module are fed back to the remote collaboration and intelligent operation and maintenance module to guide the operation and maintenance operations. At the same time, the intelligent security and compliance monitoring module ensures that the operation of all modules meets the security and compliance requirements, and cooperates with the sustainability assessment and optimization module to form a closed-loop control.

[0008] Optionally, the real-time monitoring of water quality, flow rate and equipment status parameters by IoT sensors and edge computing technology, combined with environmental data for comprehensive analysis, and prediction of the impact of environmental changes on sewage treatment include the following steps: S11: Through the IoT sensor network, key parameters such as water quality, flow, and equipment status are collected in real time; S12: Use edge computing technology to analyze the collected data in real time to identify anomalies and trends; S13: Based on historical data, construct a prediction model to predict the impact of environmental changes on the sewage treatment system; S14: Based on the prediction results, intelligently generate adaptive control strategies to adjust processing procedures and parameters to adapt to environmental changes.

[0009] Optionally, based on the data collected in step S1, the intelligent decision-making and optimization module simulates the system performance under different treatment strategies, automatically optimizes the dosage of reagents, energy consumption distribution and equipment operation time, so as to achieve optimal utilization of resources and improve treatment efficiency, including the following sub-steps: S21: Integrate the data of the intelligent environment perception module and perform preprocessing to ensure data quality; S22: Use the intelligent decision-making and optimization module to simulate system performance under different processing strategies and evaluate their impact on resource utilization and processing efficiency; S23: Apply optimization algorithms to automatically adjust the dosage of reagents, energy consumption distribution and equipment operation time to achieve resource optimization; S24: Execute the optimized strategy and monitor its effect in real time to ensure the efficiency and economy of the treatment process.

[0010] Optionally, the combination of AR and VR technologies allows operation and maintenance personnel to remotely perform equipment inspection, fault diagnosis and maintenance guidance through mobile terminals such as PCs, mobile phones or tablets, and integrates AI assistants to provide fault prediction and maintenance suggestions based on historical data to reduce unplanned downtime, including the following steps: S31: Using AR and VR technologies, remotely monitor equipment status through mobile terminals and conduct preliminary fault diagnosis; S32: Use AI algorithms to predict potential failures based on historical data; S33: Through AR technology, remotely guide on-site personnel to carry out equipment inspection and maintenance; S34: Continuously optimize operation and maintenance strategies based on fault predictions and maintenance records.

[0011] Optionally, the PLC device is connected to the industrial gateway to access and monitor network security. The monitoring data includes basic information, panoramic view, process flow chart, configuration screen, inspection data, historical parameters, alarm data and management data. The data is tamper-proof and transparent through blockchain technology to ensure that the operation of all modules meets security and compliance requirements, including the following steps: S41: Access and monitor network security through industrial gateways to ensure data transmission security; S42: Monitor various data including basic information, panoramic view, process flow chart, configuration screen, inspection data, historical parameters, alarm data and management data; S43: Use blockchain technology to ensure data immutability and transparency, and enhance data security and trust; S44: Regularly conduct compliance checks and generate reports to ensure that system operations meet industry standards and regulatory requirements.

[0012] Optionally, the process of evaluating the environmental impact of the sewage treatment process, monitoring changes in the external environment, and adjusting the system operation strategy to achieve the dual goals of resource optimization and environmental assessment includes the following steps: S51: Regularly assess the environmental impact of the wastewater treatment process, including emissions, resource consumption and impact on the ecosystem, monitor external environmental changes, including climate change and water resource conditions, and predict their impact on the system; S52: Based on the evaluation results, set the dual goals of resource optimization and environmental assessment to ensure the efficiency and environmental friendliness of the treatment process; S53: Adjust system operation strategies and optimize resource utilization based on environmental impact assessment and external environment changes; S54: Set sustainability indicators, including carbon footprint, resource recovery rate and ecological impact index, continuously monitor and improve, and generate reports regularly.

[0013] Optionally, the analysis results of the intelligent decision-making and optimization module are fed back to the remote collaboration and intelligent operation and maintenance module through a feedback mechanism to guide the operation and maintenance operations. At the same time, the intelligent security and compliance monitoring module ensures that the operation of all modules meets the security and compliance requirements, and cooperates with the sustainability assessment and optimization module to form a closed-loop control, including the following steps: S61: Integrate data from intelligent decision-making, operation and maintenance, and security modules to conduct in-depth analysis and identify system performance bottlenecks and optimization opportunities; S62: Train and apply machine learning models to predict system behavior and optimize control strategies; S63: Use VR and AR technologies to provide immersive training for operation and maintenance personnel to improve fault diagnosis and maintenance skills; S64: Predict system performance trends and implement preventive maintenance based on historical data and machine learning models.

[0014] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a remote automatic centralized control method as described in any one of the above items are implemented.

[0015] In a fourth aspect, the present application provides a computer storage medium, which adopts the following technical solution: A computer storage medium comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a remote automatic centralized control method as described in any one of the above items are implemented.

[0016] In summary, this application includes the following beneficial technical effects: 1. Through intelligent environmental perception and adaptive modules, the system can monitor and predict the impact of environmental changes on sewage treatment in real time, and automatically adjust the treatment process and parameters, thereby significantly improving system operation efficiency, reducing unplanned downtime, and ensuring the stability and continuity of the treatment process; 2. The remote collaboration and intelligent operation and maintenance module combines AR and VR technologies to enable operation and maintenance personnel to remotely inspect equipment, diagnose faults and provide maintenance guidance, greatly improving operation and maintenance efficiency and response speed; 3. The sustainability assessment and optimization module evaluates the impact of the sewage treatment process on the environment, monitors changes in the external environment, and adjusts the system operation strategy to ensure the environmental friendliness and sustainability of the sewage treatment process in line with the requirements of green development. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a principle block diagram of a remote automatic centralized control system in an embodiment of the present application.

[0018] Figure 2 It is a flow chart of a remote automatic centralized control method in an embodiment of the present application.

[0019] Figure 3 It is a flowchart of the sub-steps of step S1 in the embodiment of the present application.

[0020] Figure 4 It is a flowchart of the sub-steps of step S2 in the embodiment of the present application.

[0021] Figure 5 It is a flowchart of the sub-steps of step S3 in the embodiment of the present application.

[0022] Figure 6 It is a flowchart of the sub-steps of step S4 in the embodiment of the present application.

[0023] Figure 7 It is a flowchart of the sub-steps of step S5 in the embodiment of the present application.

[0024] Figure 8 It is a flowchart of the sub-steps of step S6 in the embodiment of the present application. DETAILED DESCRIPTION

[0025] The following is combined with Figure 1 and Figure 8This application is described in further detail.

[0026] Reference Figure 1 The present application embodiment discloses a remote automatic centralized control system, including the following modules: The intelligent environmental perception and adaptive module uses IoT sensors and edge computing technology to monitor key parameters such as water quality, flow, and equipment status in real time. It also combines environmental data to conduct comprehensive analysis, predict the impact of environmental changes on sewage treatment, and automatically adjust treatment processes and parameters. Intelligent decision-making and optimization module, based on the data collected by the intelligent environmental perception and adaptive module, simulates the system performance under different treatment strategies and automatically optimizes the dosage of reagents, energy consumption distribution, and equipment operation time; The remote collaboration and intelligent operation and maintenance module combines AR and VR technologies to allow operation and maintenance personnel to remotely inspect equipment, diagnose faults and provide maintenance guidance. It also integrates an AI assistant to provide fault prediction and maintenance suggestions based on historical data, thus reducing unplanned downtime. Intelligent security and compliance monitoring module, access and monitor network security, monitoring data including basic information, panoramic view, process flow chart, configuration screen, inspection data, historical parameters, alarm data and management data, through blockchain technology, to achieve data immutability and transparency; Sustainability assessment and optimization module, which assesses the environmental impact of the sewage treatment process, monitors changes in the external environment, and adjusts system operation strategies; The intelligent environmental perception and adaptation module provides real-time data for the intelligent decision-making and optimization module. The intelligent decision-making and optimization module analyzes the results and adjusts the control strategy, and at the same time feeds back to the remote collaboration and intelligent operation and maintenance module to guide the operation and maintenance operations; the intelligent safety and compliance monitoring system ensures that the operation of all modules meets safety and compliance requirements, and collaborates with the sustainability assessment and optimization module.

[0027] Correspondingly, in actual applications, for the treatment of garbage leachate, the remote automatic centralized control system monitors the water quality parameters in the regulating tank in real time through the intelligent environmental perception and adaptive module, such as pH value, suspended solids concentration, chemical oxygen demand (COD), etc., and collects environmental data such as temperature and humidity to evaluate the impact of external conditions on the treatment process. When the system detects that the water quality parameters exceed the preset range, the intelligent decision-making and optimization module automatically adjusts the dosage of chemical agents such as flocculants to promote the effective precipitation of garbage suspended solids and sludge, ensuring the stability and efficiency of the treatment process.

[0028] After the sludge settles in the sludge thickening tank, the intelligent decision-making and optimization module optimizes the sludge thickening process according to the real-time monitoring of sludge concentration and characteristics, and adjusts the operation parameters of the thickening tank, such as stirring speed, residence time, etc., to improve the sludge thickening efficiency. At the same time, the intelligent environmental perception and adaptive module monitors the treatment of garbage liquid in the flotation coagulation sedimentation tank, and automatically adjusts the oxidation treatment conditions, such as the amount of oxidant added, reaction time, etc., to ensure that the separated effluent, concentrated water and sludge achieve the expected treatment effect.

[0029] After the effluent enters the intermediate water tank and the stripping tower, the system automatically adjusts the operating parameters of the stripping tower, such as air flow and temperature, through the intelligent decision-making and optimization module to remove volatile organic compounds (VOCs) and other harmful substances in the effluent, ensuring that the effluent is safely discharged after reaching the water production standard. For sludge, the system monitors its precipitation in the sludge thickening tank through the intelligent environmental perception and adaptive module, and automatically adjusts the precipitation conditions to improve the sludge concentration efficiency. During the evaporation of concentrated water, the system automatically adjusts the operating parameters of the evaporator, such as temperature and pressure, to improve evaporation efficiency and reduce energy consumption.

[0030] For the waste gas generated, the remote automatic centralized control system automatically adjusts the operating parameters of the deodorization equipment through the intelligent decision-making and optimization module, such as the amount of activated carbon adsorption and the operating conditions of the biological filter, so as to remove the odor and harmful substances in the waste gas and ensure that the waste gas emissions meet environmental protection standards. For the drying process of solid waste liquid, the system monitors the drying conditions, such as temperature, humidity, wind speed, etc., through the intelligent environmental perception and adaptive module, and automatically adjusts the operating parameters of the drying equipment to ensure the efficient drying of solid waste liquid and form solid waste for subsequent treatment and disposal.

[0031] During the entire treatment process, the intelligent safety and compliance monitoring module continuously monitors the safety and compliance of the system operation to ensure that all operations comply with environmental regulations and safety standards. The sustainability assessment and optimization module evaluates the impact of the entire treatment process on the environment, monitors external environmental changes such as climate change and hydrological conditions, and adjusts the system operation strategy to reduce the negative impact on the environment and achieve the goals of environmental friendliness and resource conservation.

[0032] Therefore, through the coordinated work of the above modules, the remote automatic centralized control system realizes the intelligent and automated control of the landfill leachate treatment process, improves the treatment efficiency, reduces the operating costs, and ensures the environmental sustainability and safety of the treatment process.

[0033] Reference Figure 2 This embodiment also provides a remote automatic centralized control method, which is executed by using the above-mentioned remote automatic centralized control system, and includes the following steps: S1: Use IoT sensors and edge computing technology to monitor water quality, flow, and equipment status parameters in real time, combine environmental data for comprehensive analysis, and predict the impact of environmental changes on sewage treatment.

[0034] Reference Figure 3 , step S1 correspondingly includes the following sub-steps.

[0035] S11: Through the IoT sensor network, key parameters such as water quality, flow, and equipment status are collected in real time; S12: Use edge computing technology to analyze the collected data in real time to identify anomalies and trends; S13: Based on historical data, construct a prediction model to predict the impact of environmental changes on the sewage treatment system; S14: Based on the prediction results, intelligently generate adaptive control strategies to adjust processing procedures and parameters to adapt to environmental changes.

[0036] In practical applications, steps S11-S14 are described by way of example below.

[0037] First, the IoT sensor network is deployed in key locations such as regulating tanks, flotation coagulation sedimentation tanks, intermediate water tanks, stripping towers, sludge thickening tanks, and evaporators to monitor key parameters such as water quality parameters (such as pH, COD, BOD, ammonia nitrogen, total phosphorus, etc.), flow, and equipment status (such as agitator speed and pump operation status) in real time. These sensors include water quality sensors, flow meters, temperature sensors, pressure sensors, etc., to ensure comprehensive coverage of key links in the treatment process.

[0038] Furthermore, edge computing devices are deployed near data collection points to analyze the collected data in real time and identify abnormal situations and trends. For example, when the pH value or COD exceeds the preset range, the system immediately triggers an alarm and analyzes the cause of the abnormality, such as whether it is caused by changes in the composition of the landfill leachate or equipment failure. Edge computing devices can respond quickly, reduce data transmission delays, and improve processing efficiency.

[0039] Based on historical data, the system builds a prediction model to predict the impact of environmental changes on the sewage treatment system. For example, by analyzing the impact of seasonal changes, rainfall, temperature and other environmental factors on the treatment effect, the operating status of the sewage treatment system in the future can be predicted. The prediction model can be based on machine learning algorithms, such as time series analysis, neural networks, etc., to improve the accuracy and reliability of the prediction.

[0040] Finally, based on the prediction results, the system intelligently generates adaptive control strategies to adjust the treatment process and parameters to adapt to environmental changes. For example, when it is predicted that increased rainfall may lead to an increase in the amount of leachate, the system automatically adjusts the dosage of flocculants and increases the speed of the agitator to improve the removal efficiency of suspended solids. At the same time, the system adjusts the operating parameters of the evaporator, such as temperature and pressure, according to the predicted concentrated water production to ensure evaporation efficiency and energy utilization efficiency.

[0041] In summary, during the treatment of landfill leachate, the system automatically adjusts the dosage of flocculants by real-time monitoring of water quality parameters in the regulating tank to promote the effective precipitation of suspended solids and sludge. In the flotation coagulation sedimentation tank, the system automatically adjusts the oxidation treatment conditions, such as the dosage of oxidants, reaction time, etc., according to the real-time monitoring of the treatment of the garbage liquid, to ensure that the separated effluent, concentrated water and sludge achieve the expected treatment effect. After the effluent enters the intermediate water tank and the stripping tower, the system automatically adjusts the operating parameters of the stripping tower, such as air flow, temperature, etc., to remove VOCs and other harmful substances in the effluent, and ensure that the effluent is safely discharged after reaching the water production standard. For the generated waste gas, the system automatically adjusts the operating parameters of the deodorization equipment, such as the amount of activated carbon adsorption, the operating conditions of the biological filter, etc., to remove the odor and harmful substances in the waste gas, and ensure that the waste gas emissions meet environmental protection standards.

[0042] Replay Figure 2 , the following steps are also included after step S1.

[0043] S2: Based on the data collected in step S1, the intelligent decision-making and optimization module simulates the system performance under different treatment strategies, automatically optimizes the dosage of reagents, energy consumption distribution and equipment operation time, so as to achieve optimal utilization of resources and improve treatment efficiency.

[0044] Reference Figure 4 , for step S2, it includes the following sub-steps.

[0045] S21: Integrate the data of the intelligent environment perception module and perform preprocessing to ensure data quality; S22: Use the intelligent decision-making and optimization module to simulate system performance under different processing strategies and evaluate their impact on resource utilization and processing efficiency; S23: Apply optimization algorithms to automatically adjust the dosage of reagents, energy consumption distribution and equipment operation time to achieve resource optimization; S24: Execute the optimized strategy and monitor its effect in real time to ensure the efficiency and economy of the treatment process.

[0046] In practical applications, steps S21-S24 are described by way of example below.

[0047] The system first integrates the real-time data collected by the intelligent environmental perception module, including water quality parameters, flow rates, and equipment status at key locations such as regulating tanks, flotation coagulation sedimentation tanks, intermediate water tanks, air stripping towers, sludge thickening tanks, and evaporators. In the data preprocessing stage, the system cleans the data, removes outliers, fills in missing values, ensures data quality, and provides a reliable basis for subsequent analysis and decision-making.

[0048] Furthermore, the intelligent decision-making and optimization module simulates the system performance under different treatment strategies based on the integrated data, such as adjusting the flocculant dosage, oxidation treatment conditions, evaporator operating parameters, etc., to evaluate their impact on resource utilization and treatment efficiency. Through simulation, the system can predict key indicators such as energy consumption, reagent consumption, and treatment time under different strategies, providing a basis for optimization decisions.

[0049] In addition, the system applies optimization algorithms, such as genetic algorithms and particle swarm optimization, to automatically adjust the dosage of reagents, energy consumption distribution and equipment operation time to achieve resource optimization. For example, the system can automatically adjust the dosage of flocculants to minimize the consumption of reagents while ensuring the effective removal of suspended solids; adjust the temperature and pressure of the evaporator to improve evaporation efficiency and reduce energy consumption.

[0050] Finally, the optimized strategy is executed, and the system monitors its effect in real time, including key indicators such as treatment efficiency, energy consumption, and reagent consumption, to ensure the efficiency and economy of the treatment process. Through real-time monitoring, the system can adjust the strategy in time to respond to changes in the treatment process, such as fluctuations in the composition of landfill leachate and decline in equipment performance, so as to maintain the stability and optimization of the treatment effect.

[0051] Replay Figure 2 , the following steps are also included after step S2.

[0052] S3: Combining AR and VR technologies, it allows operation and maintenance personnel to remotely perform equipment inspection, fault diagnosis and maintenance guidance through mobile terminals such as PCs, mobile phones or tablets. The integrated AI assistant provides fault prediction and maintenance suggestions based on historical data, reducing unplanned downtime.

[0053] Reference Figure 5 , step S3 correspondingly includes the following sub-steps.

[0054] S31: Using AR and VR technologies, remotely monitor equipment status through mobile terminals and conduct preliminary fault diagnosis; S32: Use AI algorithms to predict potential failures based on historical data; S33: Through AR technology, remotely guide on-site personnel to carry out equipment inspection and maintenance; S34: Continuously optimize operation and maintenance strategies based on fault predictions and maintenance records.

[0055] Correspondingly, steps S31-S34 are described by way of example below.

[0056] Among them, operation and maintenance personnel use AR (augmented reality) technology to remotely monitor equipment status, such as operating parameters and environmental conditions of key equipment such as regulating tanks, flotation coagulation sedimentation tanks, and evaporators through mobile terminals such as tablets or smart glasses. AR technology superimposes real-time data and virtual information on the image of the actual equipment to help operation and maintenance personnel intuitively understand the equipment status. At the same time, through VR (virtual reality) technology, operation and maintenance personnel can simulate equipment operation in a virtual environment, conduct preliminary fault diagnosis, and improve the accuracy and efficiency of fault identification.

[0057] Furthermore, based on historical data, the system uses AI algorithms, such as machine learning and deep learning, to predict potential failures. The system analyzes the equipment's operating data, maintenance records, environmental conditions, etc., identifies failure modes and trends, and predicts the type and time of possible equipment failures. Through prediction, the system can take preventive measures in advance, such as adjusting equipment operating parameters, arranging preventive maintenance, etc., to reduce the occurrence of failures and improve the reliability and stability of the equipment.

[0058] In addition, when equipment fails, the system uses AR technology to remotely guide on-site personnel to inspect and repair the equipment. Operation and maintenance personnel use smart glasses or mobile terminals to overlay the equipment's maintenance manual, operating guide, troubleshooting steps and other information on the equipment in AR form to provide real-time guidance and help. At the same time, remote experts can use AR technology to observe the on-site situation in real time, provide professional technical support and suggestions, and improve maintenance efficiency and quality.

[0059] At the same time, the system continuously optimizes the operation and maintenance strategy based on fault prediction and maintenance records. By analyzing the fault type, frequency, and cause, the system can identify the weak links of the equipment, optimize the equipment design and operating parameters, and reduce the occurrence of faults. At the same time, the system can optimize the maintenance plan, such as adjusting the maintenance cycle, optimizing the spare parts inventory, etc., to improve maintenance efficiency and reduce maintenance costs.

[0060] In summary, during the treatment of landfill leachate, operation and maintenance personnel use AR technology through mobile terminals to remotely monitor the operating status of key equipment such as regulating tanks and flotation coagulation sedimentation tanks, and conduct preliminary fault diagnosis. Based on historical data, the system uses AI algorithms to predict potential faults, such as pump wear and agitator failure, and take preventive measures in advance. When equipment fails, the system uses AR technology to remotely guide on-site personnel to inspect and repair the equipment to improve maintenance efficiency and quality. Based on fault predictions and maintenance records, the system continuously optimizes operation and maintenance strategies, such as adjusting maintenance cycles, optimizing spare parts inventory, etc., to improve maintenance efficiency and reduce maintenance costs.

[0061] Replay Figure 2 , step S3 includes the following steps.

[0062] S4: By connecting PLC devices to industrial gateways, accessing and monitoring network security, the monitoring data includes basic information, panoramic views, process flow charts, configuration screens, inspection data, historical parameters, alarm data, and management data. Blockchain technology is used to make data tamper-proof and transparent, ensuring that the operation of all modules meets security and compliance requirements.

[0063] Reference Figure 6 , step S4 correspondingly includes the following sub-steps.

[0064] S41: Access and monitor network security through industrial gateways to ensure data transmission security; S42: Monitor various data including basic information, panoramic view, process flow chart, configuration screen, inspection data, historical parameters, alarm data and management data; S43: Use blockchain technology to ensure data immutability and transparency, and enhance data security and trust; S44: Regularly conduct compliance checks and generate reports to ensure that system operations meet industry standards and regulatory requirements.

[0065] Correspondingly, steps S41-S44 are described below by way of example.

[0066] The system is connected through the industrial gateway to achieve the connection between the equipment and the network, while monitoring the network security to ensure the security of data transmission. The industrial gateway has functions such as firewall, intrusion detection, and data encryption to prevent unauthorized access and data leakage, and protect the system from network attacks.

[0067] Furthermore, the system monitors various data including basic information, panoramic views, process flow charts, configuration screens, inspection data, historical parameters, alarm data, and management data. Through real-time monitoring and data management, the system can fully understand the status of the processing process, detect abnormal situations in a timely manner, and ensure the stability and safety of the processing process.

[0068] In addition, the system uses blockchain technology to ensure the immutability and transparency of data, and enhance data security and trust. Blockchain uses distributed ledgers and encryption algorithms to ensure the integrity and authenticity of data, prevent data from being maliciously tampered with or forged, and improve the credibility and reliability of data.

[0069] At the same time, the system regularly conducts compliance checks, including data security, network security, data management, etc., to ensure that the system operates in accordance with industry standards and regulatory requirements. The system automatically generates compliance reports, records inspection results and improvement measures, provides decision support for management, and serves as compliance proof to meet regulatory requirements.

[0070] Replay Figure 2 , step S4 includes the following steps.

[0071] S5: Assess the environmental impact of the sewage treatment process, monitor external environmental changes, and adjust system operation strategies to achieve the dual goals of resource optimization and environmental assessment.

[0072] Reference Figure 7 , step S5 correspondingly includes the following sub-steps.

[0073] S51: Regularly assess the environmental impact of the wastewater treatment process, including emissions, resource consumption and impact on the ecosystem, monitor external environmental changes, including climate change and water resource conditions, and predict their impact on the system; S52: Based on the evaluation results, set the dual goals of resource optimization and environmental assessment to ensure the efficiency and environmental friendliness of the treatment process; S53: Adjust system operation strategies and optimize resource utilization based on environmental impact assessment and external environment changes; S54: Set sustainability indicators, including carbon footprint, resource recovery rate and ecological impact index, continuously monitor and improve, and generate reports regularly.

[0074] Correspondingly, the following examples are given.

[0075] First, the system regularly evaluates the impact of the sewage treatment process on the environment, including the composition and concentration of emissions, resource consumption (such as water, electricity, and chemicals), and the impact on the ecosystem. At the same time, the system monitors changes in the external environment, such as climate change and water resources, and predicts their impact on the system, such as the impact of extreme weather on treatment efficiency and the impact of water shortage on treatment costs.

[0076] Based on the results of the environmental impact assessment, the system sets the dual goals of resource optimization and environmental assessment to ensure the efficiency and environmental friendliness of the treatment process. For example, the system can set goals such as reducing carbon emissions, improving resource recovery rates, and reducing chemical consumption, while ensuring that the treated emissions meet environmental standards and minimize the impact on the ecosystem.

[0077] Furthermore, according to environmental impact assessment and external environmental changes, the system adjusts the operation strategy and optimizes resource utilization. For example, the system can adjust the dosage of reagents, equipment operation time, energy consumption allocation, etc. to adapt to external environmental changes, such as reducing water consumption and improving water recovery when water resources are scarce; adjusting energy consumption allocation and reducing operating costs when energy prices fluctuate.

[0078] In addition, the system sets sustainability indicators, including carbon footprint, resource recovery rate, ecological impact index, etc., for continuous monitoring and improvement. The system generates reports regularly to record the changing trends of sustainability indicators, evaluate the environmental impact and resource utilization efficiency of the treatment process, provide decision support for management, and serve as a proof of sustainability to meet environmental regulations and social responsibility requirements.

[0079] Replay Figure 2 , after step S5, the following steps are also included.

[0080] S6: Through the feedback mechanism, the analysis results of the intelligent decision-making and optimization module are fed back to the remote collaboration and intelligent operation and maintenance module to guide the operation and maintenance operations. At the same time, the intelligent security and compliance monitoring module ensures that the operation of all modules meets the security and compliance requirements, and cooperates with the sustainability assessment and optimization module to form a closed-loop control.

[0081] Reference Figure 8 , step SS correspondingly includes the following sub-steps.

[0082] S61: Integrate data from intelligent decision-making, operation and maintenance, and security modules to conduct in-depth analysis and identify system performance bottlenecks and optimization opportunities; S62: Train and apply machine learning models to predict system behavior and optimize control strategies; S63: Use VR and AR technologies to provide immersive training for operation and maintenance personnel to improve fault diagnosis and maintenance skills; S64: Predict system performance trends and implement preventive maintenance based on historical data and machine learning models.

[0083] In this embodiment, steps S61 to S64 are described by way of example below.

[0084] Correspondingly, the remote automatic centralized control system of the treatment plant integrates data from intelligent decision-making, operation and maintenance, and safety modules to conduct in-depth analysis to identify system performance bottlenecks and optimization opportunities. The system analyzes equipment operation data, maintenance records, fault history, energy consumption data, etc. to identify periods of low equipment operation efficiency, equipment with abnormal energy consumption, and potential safety hazards. For example, the system found that a pump had abnormally high energy consumption under specific operating parameters. By adjusting the operating parameters, it successfully reduced energy consumption and improved operating efficiency.

[0085] By analyzing historical data, the machine learning model trained by the system can predict the operating status, failure probability, energy consumption trend, etc. of the equipment. For example, the system predicts that the failure probability of a certain equipment is high under specific operating conditions, and adjusts the operation strategy in advance to avoid failures and improve the reliability and stability of the equipment.

[0086] In addition, the system uses VR (virtual reality) and AR (augmented reality) technologies to provide immersive training for operation and maintenance personnel to improve their fault diagnosis and maintenance skills. By simulating equipment operation, fault scenarios, and maintenance processes in a virtual environment, operation and maintenance personnel can practice in a safe environment and improve their fault diagnosis and maintenance skills. For example, the system uses AR technology to guide operation and maintenance personnel to perform fault diagnosis on actual equipment, improving maintenance efficiency and accuracy.

[0087] Finally, based on historical data and machine learning models, the system performance trend is predicted and preventive maintenance is implemented. By analyzing the equipment's operating data, maintenance records, and failure history, the system predicts the equipment's performance trend, such as the equipment's operating efficiency, failure probability, and energy consumption trend. For example, the system predicts that a certain equipment's maintenance cycle is about to arrive, and arranges a maintenance plan in advance, avoiding sudden failures and reducing operation and maintenance costs.

[0088] This embodiment also provides an electronic device. The electronic device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for remote automatic centralized control is implemented: S1: Use IoT sensors and edge computing technology to monitor water quality, flow, and equipment status parameters in real time, combine environmental data for comprehensive analysis, and predict the impact of environmental changes on sewage treatment; S2: Based on the data collected in step S1, the intelligent decision-making and optimization module simulates the system performance under different treatment strategies, automatically optimizes the dosage of reagents, energy consumption allocation and equipment operation time, so as to achieve optimal utilization of resources and improve treatment efficiency; S3: Combines AR and VR technologies to allow operation and maintenance personnel to remotely inspect equipment, diagnose faults and provide maintenance guidance through mobile terminals such as PCs, mobile phones or tablets. The integrated AI assistant provides fault prediction and maintenance suggestions based on historical data, reducing unplanned downtime. S4: By connecting PLC devices to industrial gateways, access and monitor network security, the monitoring data includes basic information, panoramic views, process flow charts, configuration screens, inspection data, historical parameters, alarm data, and management data. Blockchain technology is used to make data tamper-proof and transparent, ensuring that the operation of all modules meets security and compliance requirements. S5: Evaluate the impact of the sewage treatment process on the environment, monitor changes in the external environment, and adjust the system operation strategy to achieve the dual goals of resource optimization and environmental assessment; S6: Through the feedback mechanism, the analysis results of the intelligent decision-making and optimization module are fed back to the remote collaboration and intelligent operation and maintenance module to guide the operation and maintenance operations. At the same time, the intelligent security and compliance monitoring module ensures that the operation of all modules meets the security and compliance requirements, and cooperates with the sustainability assessment and optimization module to form a closed-loop control.

[0089] When the computer program is executed by a processor, it can implement any one of the above method embodiments regarding the remote automatic centralized control system.

[0090] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: S1: Use IoT sensors and edge computing technology to monitor water quality, flow, and equipment status parameters in real time, combine environmental data for comprehensive analysis, and predict the impact of environmental changes on sewage treatment; S2: Based on the data collected in step S1, the intelligent decision-making and optimization module simulates the system performance under different treatment strategies, automatically optimizes the dosage of reagents, energy consumption allocation and equipment operation time, so as to achieve optimal utilization of resources and improve treatment efficiency; S3: Combines AR and VR technologies to allow operation and maintenance personnel to remotely inspect equipment, diagnose faults and provide maintenance guidance through mobile terminals such as PCs, mobile phones or tablets. The integrated AI assistant provides fault prediction and maintenance suggestions based on historical data, reducing unplanned downtime. S4: By connecting PLC devices to industrial gateways, access and monitor network security, the monitoring data includes basic information, panoramic views, process flow charts, configuration screens, inspection data, historical parameters, alarm data, and management data. Blockchain technology is used to make data tamper-proof and transparent, ensuring that the operation of all modules meets security and compliance requirements. S5: Evaluate the impact of the sewage treatment process on the environment, monitor changes in the external environment, and adjust the system operation strategy to achieve the dual goals of resource optimization and environmental assessment; S6: Through the feedback mechanism, the analysis results of the intelligent decision-making and optimization module are fed back to the remote collaboration and intelligent operation and maintenance module to guide the operation and maintenance operations. At the same time, the intelligent security and compliance monitoring module ensures that the operation of all modules meets the security and compliance requirements, and cooperates with the sustainability assessment and optimization module to form a closed-loop control.

[0091] When the computer program is executed by a processor, it can implement any one of the above method embodiments regarding the remote automatic centralized control method.

[0092] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0093] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0094] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail in the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A remote automatic centralized control system, characterized in that: Includes the following modules: The intelligent environmental perception and adaptive module uses IoT sensors and edge computing technology to monitor key parameters such as water quality, flow, and equipment status in real time. It also combines environmental data to conduct comprehensive analysis, predict the impact of environmental changes on sewage treatment, and automatically adjust treatment processes and parameters. Intelligent decision-making and optimization module, based on the data collected by the intelligent environmental perception and adaptive module, simulates the system performance under different treatment strategies and automatically optimizes the dosage of reagents, energy consumption distribution, and equipment operation time; The remote collaboration and intelligent operation and maintenance module combines AR and VR technologies to allow operation and maintenance personnel to remotely inspect equipment, diagnose faults and provide maintenance guidance. It also integrates an AI assistant to provide fault prediction and maintenance suggestions based on historical data, thus reducing unplanned downtime. Intelligent security and compliance monitoring module, access and monitor network security, monitoring data including basic information, panoramic view, process flow chart, configuration screen, inspection data, historical parameters, alarm data and management data, through blockchain technology, to achieve data immutability and transparency; Sustainability assessment and optimization module, which assesses the environmental impact of the sewage treatment process, monitors changes in the external environment, and adjusts system operation strategies; The intelligent environmental perception and adaptation module provides real-time data for the intelligent decision-making and optimization module. The intelligent decision-making and optimization module analyzes the results and adjusts the control strategy, and at the same time feeds back to the remote collaboration and intelligent operation and maintenance module to guide the operation and maintenance operations; the intelligent safety and compliance monitoring system ensures that the operation of all modules meets safety and compliance requirements, and collaborates with the sustainability assessment and optimization module.

2. A remote automatic centralized control method, implemented by a remote automatic centralized control system as claimed in claim 1, characterized in that: The following steps are involved: S1: Use IoT sensors and edge computing technology to monitor water quality, flow, and equipment status parameters in real time, combine environmental data for comprehensive analysis, and predict the impact of environmental changes on sewage treatment; S2: Based on the data collected in step S1, the intelligent decision-making and optimization module simulates the system performance under different treatment strategies, automatically optimizes the dosage of reagents, energy consumption allocation and equipment operation time, so as to achieve optimal utilization of resources and improve treatment efficiency; S3: Combines AR and VR technologies to allow operation and maintenance personnel to remotely inspect equipment, diagnose faults and provide maintenance guidance through mobile terminals such as PCs, mobile phones or tablets. The integrated AI assistant provides fault prediction and maintenance suggestions based on historical data, reducing unplanned downtime. S4: By connecting PLC devices to industrial gateways, access and monitor network security, the monitoring data includes basic information, panoramic views, process flow charts, configuration screens, inspection data, historical parameters, alarm data, and management data. Blockchain technology is used to make data tamper-proof and transparent, ensuring that the operation of all modules meets security and compliance requirements. S5: Evaluate the impact of the sewage treatment process on the environment, monitor changes in the external environment, and adjust the system operation strategy to achieve the dual goals of resource optimization and environmental assessment; S6: Through the feedback mechanism, the analysis results of the intelligent decision-making and optimization module are fed back to the remote collaboration and intelligent operation and maintenance module to guide the operation and maintenance operations. At the same time, the intelligent security and compliance monitoring module ensures that the operation of all modules meets the security and compliance requirements, and cooperates with the sustainability assessment and optimization module to form a closed-loop control.

3. A remote automatic centralized control method according to claim 2, characterized in that: The method uses IoT sensors and edge computing technology to monitor water quality, flow rate, and equipment status parameters in real time, combines environmental data for comprehensive analysis, and predicts the impact of environmental changes on sewage treatment, including the following steps: S11: Through the IoT sensor network, key parameters such as water quality, flow, and equipment status are collected in real time; S12: Use edge computing technology to analyze the collected data in real time to identify anomalies and trends; S13: Based on historical data, construct a prediction model to predict the impact of environmental changes on the sewage treatment system; S14: Based on the prediction results, intelligently generate adaptive control strategies to adjust processing procedures and parameters to adapt to environmental changes.

4. The remote automatic centralized control method according to claim 2, characterized in that: Based on the data collected in step S1, the intelligent decision-making and optimization module simulates the system performance under different treatment strategies, automatically optimizes the dosage of reagents, energy consumption distribution and equipment operation time, so as to achieve optimal utilization of resources and improve treatment efficiency, including the following sub-steps: S21: Integrate the data of the intelligent environment perception module and perform preprocessing to ensure data quality; S22: Use the intelligent decision-making and optimization module to simulate system performance under different processing strategies and evaluate their impact on resource utilization and processing efficiency; S23: Apply optimization algorithms to automatically adjust the dosage of reagents, energy consumption distribution and equipment operation time to achieve resource optimization; S24: Execute the optimized strategy and monitor its effect in real time to ensure the efficiency and economy of the treatment process.

5. The remote automatic centralized control method according to claim 2, characterized in that: The combination of AR and VR technologies allows operation and maintenance personnel to remotely perform equipment inspection, fault diagnosis and maintenance guidance through mobile terminals such as PCs, mobile phones or tablets, and integrates AI assistants to provide fault prediction and maintenance suggestions based on historical data to reduce unplanned downtime, including the following steps: S31: Using AR and VR technologies, remotely monitor equipment status through mobile terminals and conduct preliminary fault diagnosis; S32: Use AI algorithms to predict potential failures based on historical data; S33: Through AR technology, remotely guide on-site personnel to carry out equipment inspection and maintenance; S34: Continuously optimize operation and maintenance strategies based on fault predictions and maintenance records.

6. The remote automatic centralized control method according to claim 2, characterized in that: The PLC device is connected to the industrial gateway to access and monitor network security. The monitoring data includes basic information, panoramic view, process flow chart, configuration screen, inspection data, historical parameters, alarm data and management data. The data is tamper-proof and transparent through blockchain technology to ensure that the operation of all modules meets security and compliance requirements, including the following steps: S41: Access and monitor network security through industrial gateways to ensure data transmission security; S42: Monitor various data including basic information, panoramic view, process flow chart, configuration screen, inspection data, historical parameters, alarm data and management data; S43: Use blockchain technology to ensure data immutability and transparency, and enhance data security and trust; S44: Regularly conduct compliance checks and generate reports to ensure that system operations meet industry standards and regulatory requirements.

7. The remote automatic centralized control method according to claim 2, characterized in that: The process of evaluating the environmental impact of the sewage treatment process, monitoring changes in the external environment, and adjusting the system operation strategy to achieve the dual goals of resource optimization and environmental assessment includes the following steps: S51: Regularly assess the environmental impact of the wastewater treatment process, including emissions, resource consumption and impact on the ecosystem, monitor external environmental changes, including climate change and water resource conditions, and predict their impact on the system; S52: Based on the evaluation results, set the dual goals of resource optimization and environmental assessment to ensure the efficiency and environmental friendliness of the treatment process; S53: Adjust system operation strategies and optimize resource utilization based on environmental impact assessment and external environment changes; S54: Set sustainability indicators, including carbon footprint, resource recovery rate and ecological impact index, continuously monitor and improve, and generate reports regularly.

8. The remote automatic centralized control method according to claim 2, characterized in that: The analysis results of the intelligent decision-making and optimization module are fed back to the remote collaboration and intelligent operation and maintenance module through the feedback mechanism to guide the operation and maintenance operation. At the same time, the intelligent safety and compliance monitoring module ensures that the operation of all modules meets the safety and compliance requirements, and cooperates with the sustainability assessment and optimization module to form a closed-loop control, including the following steps: S61: Integrate data from intelligent decision-making, operation and maintenance, and security modules to conduct in-depth analysis and identify system performance bottlenecks and optimization opportunities; S62: Train and apply machine learning models to predict system behavior and optimize control strategies; S63: Use VR and AR technologies to provide immersive training for operation and maintenance personnel to improve fault diagnosis and maintenance skills; S64: Predict system performance trends and implement preventive maintenance based on historical data and machine learning models.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a remote automatic centralized control method as described in any one of claims 2 to 8 are implemented.

10. A computer storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a remote automatic centralized control method as described in any one of claims 2 to 8 are implemented.

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