13854results about "Programme total factory control" patented technology

The invention provides an intelligent building energy conservation and emission reduction digital twin management system and method, and relates to the field of building automation and energy conservation control. The intelligent building energy conservation and emission reduction digital twinborn management system comprises a digital twinborn model construction module, an energy consumption prediction and regulation module, a multi-device cooperative control module, a real-time feedback and self-learning module and an edge-cloud cooperative architecture. By integrating a building information model, real-time sensor data, meteorological data and the like, a three-dimensional visual dynamic twinborn model is constructed, historical data playback and thermodynamic diagram analysis are supported, omnibearing virtual mapping of a physical building is realized, a physical system and virtual model data are synchronized in real time through an Internet of Things sensor network, and the real-time real-time dynamic twinborn model is established. The consistency of a virtual model and an actual system is ensured, the precision and reliability of the model are remarkably improved, artificial illumination is dynamically compensated according to natural illumination, lamps are automatically turned off in an unmanned area through infrared induction, and equipment-level energy saving is achieved in combination with air conditioning system optimization.

The invention provides a real-time transmission method and system based on internet-of-things sensing data in a digital twinborn scene, and the method comprises the steps: collecting and processing vibration spectrum data, pressure gradient change data and temperature distribution data of equipment in an industrial production line, generating a preprocessing data package, carrying out the analysis to obtain an equipment state fingerprint feature and an environment working condition label, and carrying out the real-time transmission of the environment working condition label. A target virtual control parameter and a target error compensation amount are generated by using a dynamic parameter correction loop of the digital twin, an actual material flow state is generated in combination with material flow rate data, an optimization strategy sequence of multi-device collaborative operation is generated by using a distributed computing architecture, and the optimization strategy sequence is converted into a track compensation parameter of an end effector in the device; in combination with the target error compensation amount, data closed-loop transmission is carried out; according to the invention, millisecond-level time domain matching of vibration, pressure and temperature data and material flow velocity is realized through a dynamic timestamp alignment mechanism of 5G edge computing nodes, and the problem of synchronization error accumulation of traditional internet-of-things sensing data is solved.

In example embodiments, an industrial technology stack for an industrial environment includes a set of computational resources and a set of layers executed by the set of computational resources, the set of layers including a governance layer, an enterprise layer, an offering layer, a transaction layer, an operations layer, a network layer, a data layer, and a resource layer. In example embodiments, the industrial technology stack may include one or more artificial intelligence models for implementing one or more components of one or more layers of the set of layers.

The invention relates to a waterworks water production process unit dosing method based on intelligent sensing and self-adaptive regulation, and the method comprises the steps: collecting water quality parameters and environment working condition parameters through a multi-dimensional data sensing network, and building a multi-dimensional data flow; constructing a dosing demand prediction model, establishing a nonlinear mapping relationship between the dynamic change of water quality and the dosing amount through space-time correlation analysis, and outputting a real-time dosing amount prediction value; the predicted value and the working condition parameters are mapped to a fuzzy logic controller, fluctuation characteristics are quantized through a membership function, coupling reasoning is executed based on a fuzzy rule base, and a chemical adding amount adjustment value containing a trend compensation item is generated; after the optimized dosage output by the constructed dynamic adjustment model is applied, model parameter iterative updating, fuzzy rule triggering threshold dynamic calibration and membership function reconstruction are triggered through effluent quality deviation monitoring. According to the invention, through closed-loop linkage of machine learning prediction, fuzzy logic decision and dynamic adjustment, a whole-process intelligent dosing regulation and control system is constructed.

The invention relates to the technical field of factory management. The intelligent factory management method based on digital twinning comprises the following steps: performing dynamic identification and analysis processing on a multi-source heterogeneous communication protocol of factory equipment to obtain a standardized data stream; constructing a three-dimensional digital twinborn model comprising an equipment state and a technological process; generating a virtual control instruction according to an analog control strategy in the three-dimensional digital twin model; sandbox security verification processing is carried out on the virtual control instruction, and a compliance instruction set is screened out; converting the compliance instruction set into a protocol format supported by the target equipment, generating a reverse control instruction and issuing the reverse control instruction to the physical equipment; performing closed-loop feedback optimization processing according to the difference between the execution result of the physical equipment and the prediction result of the three-dimensional digital twinborn model, so as to solve the problem that the digital twinborn model lacks real-time sensor data injection and dynamic behavior constraint embedding, ensure that the model can reflect the real state of the equipment, and improve the real-time performance of the equipment. And the reliability and the execution security of the virtual control instruction are improved.

The invention relates to the technical field of production line control, and discloses an automatic control method and system for a plastic processing production line. The method comprises the steps of collecting technological parameters of a plastic processing production line and transmitting the technological parameters to a central control system to generate a database; multi-dimensional parameter correlation analysis is executed, a parameter and quality mapping relation is established through a CNN-LSTM hybrid network, and an optimization model is formed; calculating an optimal control parameter, generating a control strategy and issuing the control strategy to an execution unit; and monitoring a response result, updating the model in real time, and forming closed-loop adaptive control. According to the method, the mapping relation between the process parameters and the product quality is accurately established through the deep learning model, the optimal control parameters are automatically calculated, and closed-loop adaptive control based on production feedback is realized.

The invention belongs to the technical field of industrial equipment monitoring, and discloses an industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence, and the system collects multi-modal data in real time through a distributed sensor network, and carries out the real-time processing and feature extraction through the edge intelligence technology. Data fusion and optimization are carried out through a cloud deep learning model, a self-adaptive early warning and decision module dynamically adjusts a threshold value and optimizes an operation strategy, an environmental adaptability optimization module ensures the stability of equipment under extreme conditions, and an equipment health management and collaborative maintenance module realizes cross-equipment collaborative maintenance. The remote monitoring and interaction module provides man-machine interaction type control, and the cross-device collaborative learning module improves the model performance through federal learning. According to the system, the accuracy of equipment monitoring, the timeliness of early warning and the intelligent level of maintenance are remarkably improved.

The invention provides an intelligent factory data processing method and system based on the industrial Internet, and the method comprises the steps: firstly obtaining a real-time industrial data set collected by a multi-mode sensor in a target production region, covering various data, such as equipment vibration signals, carrying out the time sequence synchronization processing of the real-time industrial data set, and generating a synchronization data block; the method comprises the following steps of: extracting dynamic state characteristics of a production line from the data, calling a pre-trained anomaly detection model to carry out multi-dimensional anomaly detection, outputting an anomaly detection result, matching a preset expert knowledge base according to the anomaly detection result, and generating a dynamic control instruction set containing equipment adjustment parameters and the like; finally, production parameter configuration is adjusted based on the dynamic control instruction set and fed back to the industrial control terminal in real time, optimization of current production resource configuration is achieved, and the production efficiency and the resource utilization rate of an intelligent factory are effectively improved.

The invention provides an intelligent factory production scheduling method and system based on digital twinning, and the method comprises the steps: firstly obtaining a real-time monitoring data set, including equipment operation states, material flow tracks and environment sensing data, of a physical workshop; generating a three-dimensional digital twinborn model containing an equipment dynamic operation topology, a material real-time distribution thermodynamic diagram and an environment state simulation layer, then performing operation state correlation analysis on the three-dimensional digital twinborn model, and generating a feature set of equipment operation efficiency, material flow bottleneck, environment interference and the like; generating a dynamic scheduling parameter set containing equipment load balancing, material priority distribution and environment adaptation compensation parameters, and finally calling a preset scheduling strategy optimization algorithm to perform multi-objective optimization processing on the dynamic scheduling parameter set to generate an equipment scheduling, material distribution and environment regulation and control instruction set. And synchronizing to a physical workshop to execute real-time scheduling operation, thereby realizing intelligent and efficient production scheduling.

The invention relates to the technical field of intelligent control, in particular to a control method and system based on intelligent collaborative production. Comprising the following steps: aiming at a task cut-in influence range, traversing related nodes in a production network graph model, and detecting a device resource occupation conflict with an existing task to obtain a resource conflict detection result; recursively tracking the affected downstream process from the time delay predicted value along the dependence edge of the production network graph model to generate an influence propagation path; according to a weight dynamic quantification result, summarizing weight values of all affected nodes, generating a global view evaluation score, and determining an overall influence degree of new task cut-in; if the global view evaluation score exceeds a preset threshold, adjusting the cut-in node and time of the new task, and re-executing resource conflict detection to obtain an optimized task cut-in scheme; and extracting a process execution sequence and a resource allocation plan from the optimized task cut-in scheme, updating the production network diagram model, and generating a final collaborative production scheduling scheme.

The invention relates to the field of digital twinning technology and intelligent energy management, and discloses an industrial park dynamic resource regulation and control method based on digital twinning, which comprises the following steps of: constructing a digital twinning platform: acquiring operation states and environmental data of various devices in a park in real time through an Internet of Things sensor network, establishing a digital twin model of each device, and updating the virtual state of the device in real time through data transmission; real-time data of the devices are input into the platform, the state of each device is dynamically updated, synchronization of the digital twin model and the physical device is ensured, and accurate simulation of the current state of the device is provided. According to the invention, by integrating the park management system and the closed-loop control system, real-time collaborative scheduling of park equipment and energy management is realized, energy distribution is optimized, and balanced utilization of various energies is ensured; meanwhile, real-time feedback and error correction are realized through a closed-loop control mechanism.

The invention discloses a sewage plant total nitrogen concentration real-time prediction and process regulation and control method based on an optimization integration algorithm, and belongs to the technical field of environment monitoring and treatment. According to the method, sewage plant data are monitored and collected, a sliding window and a time sequence are combined to analyze and clean the data and reconstruct features, total nitrogen concentration strong correlation variables are screened, data quality is standardized and optimized, a plurality of machine learning algorithms are adopted to construct a prediction model, and an optimal model is optimized through cross validation and performance evaluation. The robustness is improved by global parameter adjustment in combination with optimization algorithms such as a particle swarm, process schemes such as aeration intensity and carbon source adding are generated through multi-objective optimization after containerization deployment, and a whole-process intelligent management and control system is constructed by integrating virtual verification, graded early warning and a self-adaptive feedback mechanism. According to the method, the problems of detection lag, insufficient model generalization ability, regulation response delay and the like of a traditional method are solved, and the operation energy consumption and the medicament cost are remarkably reduced while it is guaranteed that the effluent quality stably reaches the standard.

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

A process scheduling method and apparatus, a device and a storage medium, relating to the technical field of production scheduling. The method comprises: receiving a process scheduling request (S201); in response to the process scheduling request, determining an instruction being executed, an instruction to be executed, and scheduling resource information (S202); establishing an objective function and a constraint condition on the basis of the instruction being executed, the instruction to be executed, the scheduling resource information and corresponding actual scheduling information (S203); optimizing the objective function under the constraint condition by means of a swarm intelligence optimization algorithm to obtain an optimal solution of the objective function (S204); and using the optimal solution to determine an instruction execution policy, so as to perform process scheduling (S205). According to the process scheduling method, scheduling resources such as instruments and devices can be fully utilized, and the continuously growing demand for production scheduling is met.

The invention discloses an industrial scene-oriented data acquisition system and an industrial scene-oriented data acquisition method. According to the invention, through the multi-level dynamic optimization design, the data acquisition efficiency and the system reliability in a complex environment are significantly improved. The system dynamically allocates thread resources based on the real-time communication state of the equipment, realizes stable throughput under a high-concurrency scene in combination with core binding and polling scheduling strategies, and ensures that millisecond-level response is still maintained when 10000-level equipment is accessed. The memory preloading and protocol template technology eliminates jitter during operation, the hierarchical resource isolation mechanism provides deterministic guarantee for key control instructions, and interruption of the production process due to data delay or resource competition is avoided. The data flow delay is further reduced through multi-protocol efficient analysis and intelligent cache management, seamless compatibility of heterogeneous equipment in a hybrid networking scene is supported, and the strict requirements of continuous production industries such as steel and chemical engineering for real-time performance and stability are met.

The invention relates to the technical field of production debugging control, and discloses a production debugging control method and system for a plastic container. The method comprises the following steps: arranging a plurality of different sensors in plastic container blow molding equipment, and simultaneously collecting blow molding process parameter data; performing wavelet threshold denoising and anomaly identification on the blow molding process parameter data to obtain a process parameter anomaly identification result; establishing a transfer function matrix between the process parameters and quality indexes according to the process parameter anomaly identification result, and obtaining a target process parameter set through multi-target optimization; and inputting the target process parameter set into a double-integral enhanced recurrent neural network for parameter regulation and control calculation to obtain a process parameter adjustment amount. According to the method, early detection and accurate positioning of process abnormity in the blow molding process are realized, the quality fluctuation risk and the defective product rate are greatly reduced, and the technical problem that different response characteristic parameters are difficult to coordinate is solved.

The invention provides an intelligent sewage treatment process optimization method and system. The method comprises the following steps: continuously collecting the flux attenuation rate, transmembrane pressure difference and pollutant spectrum data of a ceramic membrane, carrying out time sequence analysis, constructing a pollution accumulation dynamic model, and predicting a critical threshold value; when a threshold value is reached, triggering an ultrasonic cavitation device, adjusting high-frequency vibration to generate a microbubble group, stripping pollutants by utilizing shock waves, and calculating membrane flux recovery data; fusing the spectral data and the recovery data to extract an actual pollution stripping efficiency value, and comparing a theoretical value to obtain a difference proportion; if the actual value is lower than the theoretical value, the cleaning period is shortened, the fluid pressure is increased to strengthen the stripping effect, and meanwhile, dynamic model parameters are reversely corrected based on the pressure adjustment amplitude to form closed-loop regulation and control. According to the invention, intelligent prediction, self-adaptive cleaning and closed-loop optimization of ceramic membrane pollution are realized, the sewage filtering efficiency is improved, and the energy consumption and cleaning cost are reduced.

The invention discloses an intelligent scheduling and optimization method for an industrial electrical automation system, and the method comprises the following steps: deploying a distributed sensor network to collect electrical parameters, an equipment vibration spectrum and production work order data in real time, and constructing a unified feature vector based on time-space alignment and confidence weighting; a production system-energy management-external power grid three-layer interaction model is established, and a dynamic carbon emission calculation engine and process deadlock detection module is embedded; an improved NSGA-III algorithm is adopted to solve a multi-target Pareto leading edge, and energy consumption, productivity and carbon emission target priorities are adjusted in real time in combination with a dynamic weight mechanism; distributed optimization is executed through an edge-cloud federated architecture, cross-system instruction synchronization is achieved, and closed-loop dynamic feedback is formed. According to the method, the limitation of traditional single system optimization is broken through, the energy consumption is reduced by 15%-30%, the carbon emission intensity is reduced by 12%-18%, the abnormal response speed is increased to 3 seconds, and intelligent decision making and green transformation in a complex industrial scene are supported.

The invention relates to the technical field of industrial equipment scheduling, in particular to a HarmonyOS industrial equipment intelligent scheduling and real-time cooperative control method, which comprises the following steps: S1, industrial equipment autonomous behavior design and dynamic role switching based on bionic swarm intelligence; by introducing bionic swarm intelligence, multi-modal perception, transfer learning and block chain technologies, the scheduling efficiency and task execution capability of industrial equipment in a complex dynamic environment are remarkably improved, and in the actual use process, the equipment can dynamically adjust task priority and resource allocation according to the real-time operation state and scene change, so that the service life of the equipment is prolonged. And meanwhile, environment data is collected by utilizing a multi-mode sensor, prediction and reasoning are performed through a machine learning model, and the equipment can pre-judge a fault risk and optimize a task chain execution scheme, so that the fault rate and energy consumption in the production process are reduced, and the production efficiency and stability are remarkably improved.

The invention discloses a multi-source process parameter mapping supervision system and method based on a big data model, and relates to the technical field of process parameter analysis. Initial process parameters in a process production line are collected, the initial process parameters are processed to obtain standardized process parameters, and the process production line is subjected to process stage division; analyzing a stage product deviation degree of the process stage, calculating correlation between the standardized process parameters and the product deviation degree of the process stage, analyzing the stage product deviation degree of the process stage, predicting the product deviation degree of the process stage in production, and predicting a product reject ratio in production based on the product deviation degree of the process stage. According to the method, the product deviation of each stage and the reject ratio of the whole product are continuously predicted, the production state is judged in real time, a dynamic quality control closed loop is constructed, and the pertinence and effectiveness of supervision are improved.

The invention discloses an industrial digital factory cooperative work method and a cloud platform, and the method comprises the steps: collecting factory multi-mode discrete source data, carrying out the cross-domain feature integration through a dynamic tensor fusion algorithm, and generating a global consistent factory situation fusion tensor; inputting the factory situation fusion tensor into a resource constrained collaborative decision engine, constructing a multi-device collaborative task optimization scheme based on a dynamic resource game model, and outputting a non-competitive multi-node collaborative scheduling instruction; performing abnormal situation recovery processing on the multi-node cooperative scheduling instruction, and outputting a continuous production control flow with enhanced stability; and inputting the continuous production control flow into a distributed edge collaborative architecture, dynamically configuring computing power resources based on an intelligent contract of a trusted execution environment, and generating a globally convergent factory collaborative control strategy set. By utilizing the embodiment of the invention, a high-robustness and self-adaptive cooperative working method can be provided for the industrial digital factory, the production efficiency is improved, and the energy consumption is reduced.

The invention relates to the technical field of workshop scheduling, in particular to a multi-online collaborative scheduling method and system for a flexible intelligent processing production line, and the method comprises the steps: building a numerical control parameter model for processing equipment, decomposing a processing program into basic process instruction units, combining the numerical control parameter model and the real-time operation state of the equipment to analyze the adaptation degree of the basic process instruction unit and the equipment, and generating a preliminary task allocation scheme; constructing a distributed control network among the devices, generating a local task sequence of each device based on the preliminary task allocation scheme and the adaptation degree, and obtaining a final task allocation scheme and a task execution plan set based on a contract network protocol algorithm; establishing a multi-constraint collaborative framework, and generating a collaborative production scheduling scheme under the multi-constraint collaborative framework; and establishing a heterogeneous equipment motion cooperative control model, and performing multi-level adjustment through event-driven feedback control. According to the invention, flexible cooperative scheduling of heterogeneous equipment can be realized.

The invention belongs to the technical field of fault prediction and management, and discloses a production line abnormity real-time diagnosis system based on industrial Internet of Things, which comprises a data acquisition and processing module, a distributed sensor network covering key equipment of a production line is constructed, multi-dimensional production line data is acquired, and the data acquisition and processing module is used for acquiring data of the production line; a self-adaptive sampling technology is adopted to dynamically adjust the multi-dimensional production line data acquisition frequency according to the equipment state, and preliminary multi-dimensional production line data processing is executed at the edge end; and the multi-scale time sequence management module adopts a hot, warm and cold three-level hierarchical storage architecture, compulsively switches sampling frequencies of key equipment parameters in combination with a multi-level safety threshold mechanism, performs resource allocation through a hierarchical calculation architecture, and introduces an abnormal sensitive new mode detection and double-track system template updating mechanism to identify a novel abnormal mode. The state change of the equipment is continuously monitored; it is ensured that resources can be efficiently scheduled in normal, early warning and abnormal states, and the anti-risk capacity of the system is improved.

The invention discloses a distributed intelligent warehouse scheduling system based on artificial intelligence, and belongs to the technical field of warehouse scheduling. Comprising a multi-source environment sensing module, a dynamic inventory management module, a distributed task scheduling module, an intelligent path planning module, a resource dynamic allocation module, an anomaly detection and emergency response module, an energy consumption optimization module, a supply chain collaboration module and a man-machine interaction and visualization module. A warehouse digital twinborn model is constructed, immersive display of a storage state and a scheduling strategy is realized, an AR scene is superposed through a color coding path, a thermodynamic diagram and a particle flow form, a manager can intuitively master inventory distribution, task progress and an abnormal region, eye movement tracking and a gesture recognition technology support an interactive decision, and the workload of the manager is reduced. The AR marking function can mark an abnormal area and synchronize the abnormal area to a decision making system, and through combination of AR and AI, a brand new interaction normal form is provided for intelligence and humanization of warehouse management.

The invention provides a digital workshop multi-device cooperative control method and system based on the Internet of Things, and relates to the technical field of data processing, and the method comprises the steps: obtaining the state information of a first device in a processing task execution process; when the process progress reaches a preset completion threshold value or the operation time exceeds a preset operation time threshold value, generating a state change event; organizing the various state change events into a queue according to a time sequence, and generating a to-be-scheduled event stream; determining a target state change event associated with the second equipment; instructing the second equipment to execute a subsequent operation task, calibrating execution starting time by taking the triggering time as a reference, and generating a second equipment action record; matching the second equipment action record with the target state change event, comparing the recording time of the second equipment action record with the trigger time, and if the time deviation exceeds a tolerance interval, generating adjustment instruction data; gradually correcting the control period of the second equipment; according to the invention, autonomy and accuracy of multi-device cooperative control are improved.

The invention provides a new energy automobile control circuit board production process optimization control method and system, and relates to the technical field of production control, and the method comprises the steps: collecting production process parameters in real time, inputting the parameters into a process digital twin model, and obtaining process deviation data; and inputting the deviation data into a process parameter optimization model based on a deep reinforcement learning algorithm to generate an adjustment instruction, performing real-time parameter adjustment on the production line, and feeding back to a digital twinborn model to form closed-loop control. According to the invention, accurate monitoring and intelligent optimization of the production process can be realized, the production quality and the production efficiency of the circuit board are improved, and the rate of defective products is reduced.

The disclosure provides an energy-saving control system and method for a boiler in a thermal power plant. The system includes: a prediction module, used for predicting current boiler operating efficiency; an optimization module, used for optimizing boiler input parameters based on a particle swarm algorithm, determining an optimal operating condition parameter combination, and setting adjustment steps of the optimal operating condition parameter combination; an emission detection module, used for obtaining NOx emission data of the boiler in thermal power plant and calculating a NOx emission fluctuation degree according to the NOx emission data; a parameter correction module, used for setting emission weight according to the fluctuation degree and setting a correction value of the optimal operating condition parameter combination according to the emission weight; a regulating and controlling module, used for regulating and controlling the boiler of the thermal power plant.

The invention relates to an intelligent manufacturing production line life cycle management and control method based on digital twinning and AI cooperation. The method comprises the following steps: transmitting acquired operation data of a physical production line to a preset digital twinning model; driving the digital twin model to perform simulation operation by using the operation data, and analyzing the operation state and potential problems of the physical production line to obtain a simulation analysis result; the simulation analysis result is input into an AI system, and intelligent decision making and optimization are carried out through the AI system; the optimization decision of the AI system is fed back to the physical production line, the equipment and the production process are controlled and adjusted, and the actual operation condition of the adjusted physical production line is fed back to the digital twin model for data verification and model updating, so that effective management and control of the life cycle of the intelligent manufacturing production line are realized; the problems of data islands, equipment fault prediction and maintenance limitation and the like in traditional production management are solved, the production efficiency and the product quality are improved, and the intelligent manufacturing level of enterprises is enhanced.

A conditional temporal diffusion model-based method and apparatus for generating a time series of an industrial device, including: acquiring parameter indicator data for the time series of the industrial device; using a noise at a target time instant in a target Gaussian noise distribution as an initial variable of the time series; inputting the parameter indicator data and the initial variable into a noise prediction model constructed based on a conditional temporal diffusion model, to obtain a predictive noise output by the noise prediction model; denoising the predictive noise according to the initial variable, to obtain a target variable of the time series located at a previous time instant of the target time instant; and inputting the target variable and the parameter indicator data into the noise prediction model for an iteration, to generate the time series of the industrial device.

The invention relates to the technical field of water treatment intelligent control, in particular to an AI-based intelligent urban water purification and sewage treatment process optimization system and method, and the method comprises the steps: obtaining related sensor data information, and monitoring and recording the change of key data in real time; establishing a water quality change prediction model by using a multiple linear regression model, wherein the water quality change prediction model predicts the water quality change according to the key data change acquired in real time; the dynamic agent AI model adjusts the dosage, and the water treatment chemical / biological reaction kinetics AI model predicts the pollutant degradation condition; a local lightweight AI model optimizer and a multi-target genetic algorithm are combined with energy consumption, medicament cost, processing efficiency and carbon emission to obtain an optimal control model; the optimal control model optimizes the operation parameters of the sewage treatment process according to the pollutant degradation condition. According to the method, the operation parameters of the sewage treatment process are optimized through the optimal control model, the energy consumption, the agent cost and the carbon emission are reduced, and the treatment efficiency is improved.