2360 results about "Multi objective optimization algorithm" patented technology

The invention provides a network attack and defense decision support method and system based on artificial intelligence, and relates to the technical field of network attack and defense, and the method comprises the steps: obtaining network topology information and equipment configuration information, constructing a network environment digital twinborn model, collecting network flow data and security logs, and extracting features through a deep learning model. Generating a network security situation assessment report, analyzing the attack graph by using a graph neural network, identifying a target node and generating an initial defense strategy set, generating a plurality of candidate defense schemes by using a multi-target optimization algorithm based on a validity assessment result of a defense strategy, and performing security and feasibility analysis on the candidate defense schemes, the method comprises the steps of selecting an optimal defense scheme, refining the optimal defense scheme into specific implementation steps, generating alternative schemes for coping with different attack conditions, presenting the optimal defense scheme and the alternative schemes through a visual interface, and providing a decision basis and a risk assessment report.

The invention relates to the technical field of communication command, in particular to an intelligent emergency command system and method based on scene twinning. Comprising a data acquisition module, a scene twinborn management module, an emergency situation analysis module, an emergency response decision module and a resource scheduling command module. Multi-source data of an emergency scene are collected through a sensor network, a scene twinning model is constructed by using augmented reality and digital twinning technologies, and real-time rendering of a virtual scene is realized. According to the method, emergency situation analysis, risk detection and trend prediction are performed in combination with a random matrix theory and a graph neural network algorithm, an emergency response strategy is generated by using a decision tree based on an analysis report, resources are scheduled and allocated through a multi-objective optimization algorithm, and an optimal emergency command scheme is ensured. According to the invention, the efficiency and scientificity of emergency command are improved, and the rapidity and accuracy of emergency response are enhanced.

The invention discloses a multi-dimensional intelligent management method and system for whole-process cost, and the method comprises the steps: generating a time-space associated structured cost data cube according to heterogeneous cost data of the stages of project planning, design, construction and completion; outputting a dynamic cost prediction curve and deviation sensitive nodes based on the structured cost data cube; according to the dynamic cost prediction curve, performing multi-party task allocation optimization by using a block chain enabled BIM / CIM collaboration platform, and generating a collaboration instruction set of smart contract coding; outputting a risk probability matrix and an advanced early warning signal based on the collaborative instruction set and the real-time engineering data flow; and according to the risk probability matrix, adopting a multi-objective optimization algorithm to generate an anti-interference decision scheme set, and outputting an optimal cost control strategy after digital twinborn simulation verification. By using the embodiment of the invention, the cost data of each stage of the project can be efficiently integrated, dynamic cost prediction is realized, and collaborative decision and effective risk management are optimized.

The invention relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraint, and the method specifically comprises the following steps: 1, synchronously collecting spectral information, DO, COD, temperature, pH and other data at a key monitoring station, constructing a hydrodynamic water quality coupling equation, simulating the spatial-temporal dynamic distribution of water quality parameters, and calculating the water quality change trend; 2, outputting a water quality sensitive area through a hydrodynamic force-water quality model, screening sensor layout point positions in combination with information entropy evaluation and spatial clustering, realizing low-cost water quality sensor network deployment through a multi-objective optimization algorithm, and calculating a water body global water quality distribution diagram by adopting a spatial interpolation method, 3, synchronously collecting spectral information according to key monitoring sites, analyzing main pollution sources, and adopting a principal component analysis and attention mechanism neural network; 4, based on real-time optical characteristic value-DO data, in combination with a spatial topology network, a water quality gradient and a cross-regional covariance, capturing water quality parameter spatial correlation among different sites, and determining the water quality parameter spatial correlation among different sites; an optical characteristic value-DO-COD dynamic prediction model is constructed; a COD predicted value is corrected by combining pollution traceability and spectral characteristics, and multi-source data is assimilated by adopting ensemble Kalman filtering, so that the model precision is improved.

The invention, which relates to the technical field of medicine inventory management, discloses an intelligent medicine inventory management and optimization system comprising a demand prediction module, an inventory monitoring module, a purchase decision module and a medicine supply module. The demand prediction module is used for performing drug demand prediction according to historical sales data, seasonal factors and market trends; and analyzing and predicting by adopting a machine learning algorithm. According to the intelligent medicine inventory management and optimization system, based on a multi-objective optimization algorithm, in combination with the inventory turnover rate, the purchase cost and the medicine validity period, a scientific and reasonable purchase replenishment strategy is generated, including purchase time, quantity and supplier selection, and the scientificity and the accuracy of a purchase decision are improved; by integrating historical sales data, seasonal factors and market trends, demand prediction is performed by using a machine learning algorithm, and an accurate drug future demand trend prediction result can be generated.

The invention provides a chemical industrial park illegal behavior intervention and tracing system based on edge calculation, and aims to solve the problems that an existing system is single in perception capability, insufficient in intelligent analysis, limited in edge collaboration, passive in intervention, weak in tracing capability and the like. According to the system, an improved ResNet-FPN and Transform architecture is adopted to realize multi-modal data fusion, and violation behavior analysis is carried out through a double-flow intention understanding framework and a graph neural network in deep combination with chemical process knowledge; a self-adaptive edge node networking and federated learning mechanism is adopted to realize dynamic scheduling of computing resources and knowledge migration; an intervention decision model based on risk entropy supports predictive intervention, and a multi-objective optimization algorithm ensures balanced safety and efficiency of an intervention strategy; a complete evidence chain is constructed by adopting a multi-modal trajectory reconstruction technology and a distributed evidence storage network, and credible tracing of violation behaviors is realized. The technical bottleneck of a traditional system in the aspects of professional scene understanding and real-time processing is broken through, and the intelligent level of safety management of the chemical industry park is remarkably improved.

The invention discloses a smart road driving time prediction method based on multi-source data fusion and deep learning, and the method comprises the steps: constructing a multi-source data collection and fusion platform, developing a spatial-temporal feature deep learning model, deploying a real-time data processing and anomaly detection system, building a self-adaptive prediction optimization mechanism, and constructing a smart traffic application service platform. Comprehensive acquisition, deep analysis, real-time processing, accurate prediction and intelligent service of traffic data are realized. The method specifically comprises the following steps: deploying an Internet of Things sensor network to collect multi-source data, and designing an adaptive weighted fusion algorithm to construct a multi-dimensional dynamic traffic database; constructing an LSTM-GNN hybrid neural network architecture to extract spatio-temporal features; real-time data processing and anomaly detection are carried out by adopting a streaming computing framework and various technologies; designing a multi-objective optimization algorithm and a reinforcement learning model to realize adaptive prediction optimization; and developing a visual decision support system and a personalized navigation service. The system effectively improves the traffic management efficiency and the public travel experience.

The invention discloses an intelligent analysis and remote control algorithm based on digital twinning. The intelligent analysis and remote control algorithm comprises a digital twinning model building and dynamic updating module, an intelligent analysis algorithm module based on digital twinning, a self-adaptive remote control algorithm module and an algorithm process and implementation module. Through multi-dimensional modeling and data fusion, the precision of the digital twin model is improved, and the system state can be predicted more accurately. In combination with deep learning and a multi-objective optimization algorithm, intelligence and optimization of a control strategy are realized, and the operation efficiency and safety of the system are improved. Based on model predictive control and a distributed architecture, real-time and accurate remote control of a physical system is realized, and the dynamic adaptability of the system is enhanced. The algorithm framework has good generalization ability, can be suitable for different types of complex systems, and supports to adapt to new application scenarios through modular extension.

The invention discloses an electric bicycle lithium battery data real-time monitoring and early warning system and method, and relates to the technical field of battery management, and the system comprises a multi-dimensional data collection module which is used for obtaining a multi-source heterogeneous data set of a lithium battery system; the collaborative feature extraction module is used for generating a comprehensive evaluation parameter set; the dynamic threshold generation module is used for constructing a self-adaptive early warning boundary model according to the comprehensive evaluation parameter set; the intelligent decision module is used for generating a hierarchical control instruction set based on a multi-objective optimization algorithm; and the cloud collaboration module is used for synchronizing the hierarchical control instruction set to the edge computing node and the cloud management platform, and triggering a multi-level linkage protection mechanism based on the game theory when the thermal runaway risk index is detected to exceed a first dynamic threshold value. According to the electric bicycle lithium battery data real-time monitoring and early warning system and method provided by the invention, the safety and reliability of a battery system are improved.

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

The invention discloses a labeling task assignment method and device based on artificial intelligence, and the method comprises the steps: obtaining historical behavior data, and constructing a multi-dimensional user portrait; receiving a task description document, a data sample and a quality requirement document to obtain a multi-dimensional task feature vector; based on the multi-dimensional user portraits and the multi-dimensional task feature vectors, a matching degree score is calculated through a multi-objective optimization algorithm, and an optimal task allocation scheme is generated; optimizing the task structure through a fireworks algorithm based on student t distribution, and generating an optimized task unit structure; real-time monitoring is carried out through the anomaly detection model and the quality prediction model, and quality control measures are triggered; model parameters are updated through a reinforcement learning algorithm, and a personalized feedback and capability improvement strategy is generated. According to the method, accurate matching between the annotators and the tasks is realized, the processing efficiency of complex tasks is improved, the annotation quality is improved, the expansibility and the response speed of a platform are enhanced, and an effective solution is provided for large-scale and high-quality data annotation.

The invention discloses a data analysis platform management method and system, and belongs to the technical field of computer data processing, and the method comprises the steps: obtaining multi-source heterogeneous data from a medical information system; establishing a cross-system data association model, performing dynamic feature extraction and weight distribution on multi-source heterogeneous data, and outputting a resource supply-demand relationship graph; analyzing the resource supply-demand relation graph, and identifying business conflicts in medical resource scheduling; generating a conflict solution of the business conflict based on a multi-objective optimization algorithm, and generating an execution instruction; and issuing an execution instruction to the target medical system, verifying an execution result of the execution instruction, and updating system parameters. According to the method, a cross-system dynamic modeling and multi-objective optimization strategy is adopted, and a closed-loop verification mechanism and a multi-source heterogeneous data fusion technology are combined, so that intelligent prediction, accurate scheduling and self-learning optimization of medical resource conflicts can be realized, full-process traceable execution of high-value consumables is ensured, and the resource utilization efficiency and the emergency response capability are comprehensively improved.

The invention relates to a lithium battery energy storage system fire-fighting ventilation and explosion venting safety assessment method based on multi-dimensional simulation. The method comprises the following steps: constructing an energy storage system digital twinborn model fusing structure parameters, material attributes and environmental parameters; generating a multi-mode failure scene set covering multiple temperature domains and aging states through mode recognition; simulating and quantifying dynamic interaction of a temperature field, a flow field and a stress field in the thermal runaway evolution process based on thermal-fluid-solid multi-physics field coupling; constructing a space-time associated dynamic safety evaluation matrix, and combining fuzzy comprehensive evaluation and Monte Carlo sampling to generate risk quantitative indexes; and iteratively correcting parameters of the fire-fighting ventilation and explosion venting system through a multi-objective optimization algorithm to form a graded safety assessment conclusion. According to the method, the technical bottlenecks of environmental parameter splitting and single failure scene in a traditional method are broken through, the thermal runaway suppression efficiency is improved, the combustible gas concentration control error is reduced, and collaborative optimization of explosion venting pressure fluctuation suppression and ventilation response is realized through a closed-loop evaluation mechanism.

The invention discloses an industrial equipment remote operation method based on distributed identification and control optimization. The method comprises the following steps: S1, constructing a hierarchical adaptive distributed computing architecture; s2, collecting multi-modal data of industrial equipment at an edge layer, and deploying an adaptive sparse convolutional neural network to carry out state detection and fault diagnosis; s3, deploying a distributed computing node cluster in a local layer, and performing local optimization on the control task through a distributed near-end strategy optimization algorithm; s4, processing data by utilizing cloud computing resources in a global layer, and generating a control strategy by adopting a multi-objective optimization algorithm; s5, carrying out data sharing and feedback among the hierarchies; s6, dynamically adjusting task distribution through a task migration mechanism; s7, introducing a hierarchical collaborative optimization mechanism, and optimizing a control strategy; and S8, performing data analysis by using a data-driven analysis technology to form closed-loop control. According to the invention, a distributed calculation and multi-objective optimization method is adopted, and efficient control of remote operation of industrial equipment is realized.

The invention provides a knowledge graph construction and product recommendation method and system based on user data, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an initial knowledge graph through user historical behavior data, generating an optimization graph through a multi-layer neural network comprising a multi-granularity layer, a graph attention layer and a graph convolution layer, and performing bidirectional random walk sampling based on real-time behavior data of a target user to obtain a related sub-graph, calculating a product node importance score, and performing sorting pushing by adopting a multi-target optimization algorithm. According to the method, multi-dimensional accurate description of user interests can be realized, the recommendation accuracy and diversity are improved, and meanwhile, the commercial value is considered.

The invention provides an electromagnetic interference suppression method and system for a chip, and relates to the technical field of intelligent chips, and the method comprises the steps: obtaining layout data of a target chip, and based on the layout data, extracting structure parameters and device parameters through an image segmentation algorithm, and constructing a multi-physics field coupling simulation model; the method comprises the following steps: determining an optimal excitation source corresponding to a target chip through a machine learning algorithm, calculating to obtain transient multi-physical field distribution by adopting a time domain finite element method and through an adaptive time step control strategy, extracting time-frequency domain multi-scale distribution characteristics, and constructing a spectrum fingerprint database of electromagnetic interference by utilizing a deep convolutional neural network. Generating an electromagnetic interference identification result; and based on an electromagnetic interference identification result, determining an optimal suppression circuit topology through a suppression topology search algorithm, determining optimal suppression device parameters through a multi-objective optimization algorithm in combination with electromagnetic compatibility index constraints, and generating an electromagnetic interference suppression strategy.

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

A multi-objective optimization method and system for a shield construction parameter. The method comprises: acquiring an initial shield construction parameter; and optimizing the shield construction parameter by using a multi-objective optimization algorithm, during the optimization of the shield construction parameter, on the basis of the shield construction parameter, obtaining a tunneling speed by means of performing prediction on the basis of random forest, calculating a shield tunneling energy consumption ratio on the basis of the tunneling speed and the shield construction parameter, and taking the maximization of the tunneling speed and the minimization of the shield tunneling energy consumption ratio as two fitness functions of the multi-objective optimization algorithm. The multi-objective optimization method and system for a shield construction parameter take construction efficiency into consideration and reduce the construction cost while ensuring construction safety.

The invention discloses an intelligent water quality regulation and control system and method based on multi-parameter real-time monitoring, and belongs to the technical field of water quality monitoring. According to the intelligent water quality regulation and control system, the water quality data of the water body is obtained in real time through the multi-parameter sensor array, and the monitoring regulation and control server can quickly generate an abnormal report and a water quality regulation and control scheme. The data processing module performs feature extraction on the water quality data to obtain target features; the water quality evaluation module is used for accurately evaluating the water quality by using a pre-trained deep neural network model; the abnormity identification module can timely judge whether the water quality has a pollution risk and generate an abnormity report; and the regulation and control module generates a water quality regulation and control scheme by adopting a multi-objective optimization algorithm. The system realizes real-time performance, accuracy and intelligence of water quality monitoring, can quickly respond to water quality changes, effectively reduces pollution risks, and improves the efficiency and effect of water quality regulation and control.

The invention relates to the technical field of digital factory production management, and discloses a digital factory-oriented multi-target production plan optimization method and system, and the method comprises the steps: decomposing an enterprise order into a plurality of subtasks through a production task decomposition module, and extracting related information; the resource dynamic evaluation module collects data in real time to generate a resource state matrix; the multi-objective optimization algorithm module constructs a model and adopts an improved non-dominated sorting genetic algorithm to solve; the dynamic priority distribution module adjusts the priorities of the sub-tasks according to the real-time data; and the conflict resolution unit is used for solving resource allocation conflicts. The method can accurately decompose tasks, evaluate resources in real time, collaboratively optimize multiple targets, flexibly deal with abnormities, efficiently resolve conflicts, comprehensively improve the production efficiency and benefits of the digital factory, and effectively solve the complex problems in the production plan making and optimizing process of the digital factory.

The invention relates to the technical field of network operation and maintenance supervision, in particular to a network operation and maintenance environment risk assessment method and system based on a mimicry security technology, and the method comprises the steps: carrying out the collection and preprocessing of network operation and maintenance environment data, and generating standardized security data; abnormal feature data and disturbance response data are extracted through real mimicry analysis and active challenge mimicry analysis, and associated threat feature data are generated through fusion; based on the associated threat feature data, constructing a heterogeneous twin model, performing state modeling, threat modeling and resource modeling, and generating risk assessment data; and generating an optimal response strategy through a multi-objective optimization algorithm, including flow control, node isolation and resource scheduling, and dynamically updating a risk state through a feedback regulation mechanism. Through dynamic threat identification and real-time optimization response, the risk assessment accuracy and the security defense level of the network operation and maintenance environment are improved, and the method has the advantages of being efficient, intelligent and dynamic.

The invention discloses an agent platform resource management method and device based on a cloud native architecture and a medium, and the method comprises the steps: packaging an agent application into an independent container instance based on a containerization technology, and deploying the container instance to a target node; acquiring task demand information of the intelligent agent in real time, and generating a dynamic scheduling scheme by combining the resource state data and through a multi-target optimization algorithm so as to allocate the task to a target container instance; according to a matching function of the capability vector of the intelligent agent and the task demand vector, calculating the integrating degree of the intelligent agent and the task so as to generate a collaborative decision-making result and issue the collaborative decision-making result to the target intelligent agent; the resource utilization rate and the task execution state of the intelligent agent are monitored, an elastic telescoping mechanism or task rescheduling is triggered according to feedback data monitored in real time, and a resource allocation strategy is dynamically adjusted.

The invention relates to the technical field of model optimization, in particular to an e-commerce inventory dynamic optimization model system based on real-time big data, which comprises an inventory information sharing unit, an intelligent replenishment algorithm unit and an inventory distribution strategy unit. In combination with edge calculation and block chain technologies, real-time collection, processing and secure sharing of inventory data are realized, an intelligent replenishment algorithm unit obtains data from a block chain platform, a demand prediction model is constructed by using a gating circulation module, and an automatic replenishment module sets a replenishment threshold and adjusts replenishment amount in combination with risk factors. The multi-objective optimization algorithm selects an optimal supplier, and the inventory distribution strategy unit formulates and optimizes an inventory distribution rule by applying a reinforcement learning algorithm based on real-time data, adjusts an inventory distribution proportion and uploads the inventory distribution proportion to the block chain, so that the inventory management efficiency is improved, the cost is reduced, and dynamic inventory optimization is realized.

The invention relates to the technical field of factory energy consumption management, in particular to a smart factory dynamic optimization management system based on digital twinning and big data analysis. Comprising a data acquisition and fusion module, a digital twinning construction module, a data analysis module, a dynamic optimization decision module and an anomaly diagnosis module. Constructing a digital twinborn model of a factory physical entity according to the collected data; constructing an energy consumption prediction model based on deep learning frameworks such as LTSM; when the energy consumption deviation exceeds the limit, abnormal root causes are positioned; and generating an energy consumption scheduling scheme based on a multi-objective optimization algorithm, and issuing an instruction to realize dynamic energy consumption adjustment. Through deep fusion of digital twinning and big data technologies, comprehensive and accurate simulation, multi-target collaborative optimization, rapid abnormality diagnosis and dynamic control of factory energy consumption are realized, the energy utilization efficiency is effectively improved, the cost is reduced, the intelligent level is improved, and the method has remarkable economic benefits and environmental benefits.

The invention relates to the technical field of enterprise finance and taxation risk management and control, and discloses an enterprise finance and taxation integrated risk management and control platform based on multi-source data fusion. The platform collects multi-source heterogeneous finance and taxation data in real time through a finance and taxation data collection module, and a data fusion preprocessing module generates a fusion data cube by using federal learning and a cross-domain data alignment algorithm. The risk feature modeling module constructs a multi-dimensional risk feature map based on a graph convolutional network and a dynamic Bayesian network, and the risk dynamic assessment module assesses risks in real time through an adaptive weighted ensemble learning algorithm and a risk conduction model. And the risk management and control decision module generates a management and control scheme by adopting a multi-objective optimization algorithm and a game theory strategy. In addition, the finance and tax data security storage module ensures data security. According to the platform, multi-source data fusion and efficient risk management and control are realized, risks can be accurately evaluated, scientific decisions are provided, enterprise finance and taxation data security is guaranteed, and enterprise finance and taxation management level is improved.

The invention relates to the technical field of battery energy storage, and discloses an energy storage operation optimization method and system considering battery life loss, and the method comprises the steps: initializing battery energy storage electrical parameters, constructing a battery life loss model, and obtaining the health state change of a battery; constructing a battery operation cost model, defining a multi-objective optimization algorithm and constraint conditions, and optimizing the battery operation cost; and carrying out adaptive scheduling through a model prediction control and reinforcement learning algorithm, and determining an optimal charging and discharging strategy of the energy storage system. By constructing the battery life loss model and the multi-target optimization algorithm, economical operation of the energy storage system and prolonging of the battery life are achieved, the charging and discharging strategy of the energy storage system can be dynamically adjusted according to the power market cost fluctuation, the load requirement and the battery health state, and the operation cost of the energy storage system is reduced.

The embodiment of the invention provides an airspace fine management method and device based on acceptable risks, and a risk assessment model is constructed by creatively adopting a grid airspace division mode and fusing ground attributes, historical accidents, meteorological environments and aircraft activity multi-source data. The self-adaptive adjustment of the risk tolerance threshold is realized through a deep reinforcement learning method, and a differentiated regional access standard is established. And through combination of task grading evaluation and a risk early warning mechanism, accurate evaluation of the operation safety of the unmanned aerial vehicle is realized. A multi-objective optimization algorithm is adopted to carry out route planning, cooperative management and control are realized in cooperation with an emergency avoidance strategy, and a prevention and control mechanism is continuously optimized through risk traceability analysis. According to the method, the defects of the traditional technology in the aspects of fine management, risk assessment, collaborative management and control and the like are effectively overcome, and the safety and efficiency of urban low-altitude airspace management are remarkably improved.

The invention relates to the technical field of sewage treatment, and particularly discloses a sewage and wastewater treatment control system and method based on an intelligent optimization algorithm. Water quality data are collected through a data acquisition module, and a water quality characteristic matrix is generated through preprocessing. And the prediction module analyzes the feature matrix by using the trained water quality dynamic prediction model to obtain a water quality prediction result. And processing the prediction result by using a multi-objective optimization algorithm to obtain an initial control parameter. And the parameter optimization module calculates a water load fluctuation ratio, a model confidence coefficient and an equipment state according to the sewage and wastewater treatment data, inputs the water load fluctuation ratio, the model confidence coefficient and the equipment state into the adaptive fuzzy network and generates a multi-target parameter optimization suggestion. And the dynamic optimization module adjusts the multi-objective optimization algorithm parameters according to the parameters, and processes the prediction result again to obtain optimization control parameters. And the control module regulates and controls sewage and wastewater treatment according to the optimized parameters. The system realizes closed-loop management from data acquisition, prediction and optimization to control, can dynamically adapt to water quality change, and operates stably and efficiently.

The invention relates to the technical field of welding, in particular to a corrosion steel welding cooperative control method and system. Comprising the following steps that welding seam geometric parameters, molten pool dynamic characteristic parameters and welding heat input parameters in the corrosion steel welding process are collected in real time through a multi-dimensional sensor array; constructing a corroded steel welding seam feature space model based on the welding seam geometric parameters, and determining material corrosion grade distribution and mechanical property parameters of a welding seam area in combination with a preset corroded steel material database; a molten pool form evolution prediction model is established through an adaptive Kalman filtering algorithm by utilizing the dynamic characteristic parameters of the molten pool and the welding heat input parameters, and the solidification behavior and the welding seam forming trend of the molten pool are predicted in real time; according to the material corrosion grade distribution, the mechanical property parameters and the molten pool forming trend, a dynamic adjustment strategy of the welding process parameters is generated through a multi-objective optimization algorithm; the reliability and safety of the corrosion steel welding joint can be improved.

The invention provides an oil smoke real-time concentration monitoring and intelligent adjusting method and system, and relates to the technical field of intelligent control, and the method comprises the steps: collecting real-time air quality data of a catering area through a plurality of environment sensors, including oil smoke concentration, PM2.5 concentration, carbon monoxide concentration, temperature and humidity; and the control center calculates an oil smoke concentration spatial and temporal distribution field based on real-time data in combination with spatial interpolation, considers smoke exhaust energy consumption, smoke exhaust efficiency and regional oil smoke concentration, and determines an optimal cooperative control strategy by utilizing a multi-target optimization algorithm, and the optimal cooperative control strategy comprises the fan rotating speed of each range hood, the smoke exhaust direction and the air supply parameters of the fresh air system. And the control center issues the control strategy to a range hood controller to adjust operating parameters of the range hood. Dynamic optimization control over the air quality of the catering area can be achieved, the oil smoke concentration is reduced, energy is saved, and the smoke exhaust efficiency is improved.