1268393 results about "Industrial engineering" patented technology

The invention discloses a power transmission and distribution production task cooperation system and method based on an intelligent agent, and relates to the technical field of power distribution production task scheduling, the system comprises six modules: a natural language input interaction module processes a user instruction and multi-modal information, and generates structured data; the electric power field knowledge enhancement analysis module establishes mapping from a natural language to business data; the dynamic interaction context memory module stores historical interaction data and generates a context feature vector through a bidirectional LSTM and an attention mechanism; the intelligent task scheduling and conflict resolution module is used for disassembling instructions into sub-tasks, dynamically evaluating priorities in combination with three-dimensional indexes and resolving resource conflicts; the agent task execution and cooperation module drives agents to execute tasks according to priorities and synchronize states in real time; the system closed-loop feedback optimization module analyzes the execution log and automatically updates model parameters; according to the system, the problems of term analysis deviation, strategy staticization and insufficient self-optimization capability of a traditional scheduling system are solved.

The invention provides a big data platform scheduling task and data collaborative smooth migration method and system, and relates to the technical field of big data, and the method comprises the steps: constructing a digital twin environment of a target system, collecting task operation state data of a source system, extracting task features through a deep neural network, and carrying out the clustering and grouping. And deploying the task group to a digital twin environment, analyzing a task association relationship and a resource competition relationship based on a graph neural network, and generating a task stability score in combination with a task operation index. And constructing a migration risk prediction model to perform quantitative evaluation on the migration risk, and determining a migration priority and an execution sequence according to the quantitative evaluation. And making corresponding resource configuration, data synchronization and fault recovery plans for each task group, carrying out migration drilling in a digital twinning environment, and optimizing a migration scheme by utilizing a reinforcement learning algorithm. The migration risk can be effectively reduced, the smoothness and stability of the migration process are guaranteed, and the migration efficiency is improved.

The invention relates to the technical field of industrial automation control, and discloses a multi-machine collaborative industrial robot intelligent scheduling system and an application method.The system comprises a data acquisition module for acquiring real-time motion data through a multi-modal sensor; the feature fusion module is used for generating environment dynamic feature data based on graph neural network fusion data; the strategy generation module is used for generating collaborative strategy parameters by utilizing a collaborative reinforcement learning model; the trajectory optimization module is used for constructing a multi-target trajectory optimization model to plan an optimal synchronous trajectory; the motion control module is used for establishing a layered motion control model to realize cooperative motion control; a dynamic resource allocation module can also be included to optimize resource allocation. The application method sequentially executes corresponding steps according to the modules of the system. According to the method, the environment can be accurately sensed, the cooperation strategy can be optimized, the efficient track can be planned, accurate motion control and reasonable resource allocation can be realized, and the cooperative motion control capability and the production efficiency of the industrial robot in the complex environment can be effectively improved.

The invention provides an intelligent visual management method and system for enterprise big data. The method comprises the following steps: extracting a space-time association rule of an operation and maintenance report fault field and an equipment log error code, and generating a dynamic mapping data stream to drive a three-dimensional visual association topology; the method comprises the following steps: collecting cabinet vibration energy data, and synchronizing a highlight energy sudden increase area and error log entries according to a timestamp; dynamically distributing a vibration energy weight, and generating a risk probability matrix in combination with an error increment; fusing the time sequence characteristics and the physical topology path, constructing an abnormal event timestamp graph, and marking a fault propagation chain; and based on the map density gradient and the causal association strength, superimposing and rendering the penetrating thermodynamic diagram, associating topology, a fault chain and an equipment structure, and adaptively adjusting the color gradation highlighting abnormal region. According to the technical scheme provided by the invention, the multi-source fault correlation analysis efficiency is improved, and the abnormal risk is dynamically, visually and accurately positioned.

The invention provides a dynamic path planning method and system in an intelligent traffic system, and relates to the technical field of intelligent traffic. Constructing a dynamic road network traffic capacity model based on a space-time diagram convolutional network; generating a multi-branch trajectory prediction result with a confidence score, adaptively starting an encryption parameter synchronization mechanism in combination with vehicle density, and generating a candidate path set through multi-agent collaborative game optimization; performing multi-dimensional deviation degree evaluation based on actual driving data and planning expectation; synchronously generating a multi-mode cooperative guidance signal; the system comprises a multi-source data fusion unit, a space-time modeling engine, a hierarchical decision-making system, a path verification and execution module, a closed-loop feedback controller, an event response system and a multi-mode man-machine interface. According to the method, the global resource utilization efficiency is improved through cooperation of data-driven modeling and hierarchical game decision, and the real-time performance, the safety and the system adaptability of path planning are improved through a closed-loop feedback and event response mechanism.

Disclosed is a microgrid optimal scheduling method taking into consideration a system operation risk and user satisfaction, comprising: generating a power prediction curve of wind power and photovoltaic new energy output in a microgrid, using a Monte Carlo simulation method to obtain a prediction error range to generate a typical scene set, and by means of a data fitting method, acquiring a wind-photovoltaic prediction error probability density distribution function; using value-at-risk and conditional value-at-risk theories to quantify a microgrid operation risk which is increased by slightly high or low output caused by wind power and photovoltaics; obtaining the scheduling cost of an adjustable load in a microgrid system; taking the minimum sum of conditional value-at-risk cost and microgrid system operation cost as an objective function and a user satisfaction level under a demand-side response as a constraint, establishing a microgrid optimal scheduling model taking into consideration the operation risk and the user satisfaction; and using a genetic algorithm to solve the model, and obtaining an optimal result of grid-connected microgrid system optimal scheduling. The safety, economic efficiency, stability and reliability of the microgrid are enhanced.

The invention discloses a ring main unit monitoring method and system based on the Internet of Things, and the method comprises the steps: building a multi-source data collection network of a ring main unit, extracting the multi-dimensional time-frequency domain features of a signal through an edge computing device, generating a multi-source feature data set, and constructing a dynamic digital twinborn model, real-time mapping of the running state of the ring main unit is achieved through a physical-virtual synchronization algorithm, and twin data volumes are output; according to the node influence coefficient of the twin data volume, analyzing a topological structure of a ring network system by adopting a graph neural network, identifying a key fault propagation path and generating a priority monitoring node list; based on the priority monitoring node list and the twin data volume, dynamically generating an adaptive monitoring strategy through a reinforcement learning algorithm, executing the adaptive monitoring strategy and collecting feedback data in real time, and completing closed-loop iteration of the monitoring strategy through an online learning algorithm; through closed-loop control of multi-source perception-digital twinning-intelligent decision, high efficiency, intelligence and reliability of ring main unit monitoring are realized.

The invention discloses an intelligent management system for energy consumption optimization and fault self-diagnosis of cleaning equipment, and relates to the field of intelligent maintenance of the cleaning equipment, and the system comprises the steps: obtaining three groups of core parameters, i.e., a historical vibration spectrum, a motor current harmonic component and a bearing temperature gradient, constructing a dynamic failure mode knowledge graph, performing time sequence correlation analysis on the historical fault data to obtain failure mode analysis data; establishing a multi-dimensional analysis platform, identifying a high-risk component, and updating a fault threshold value; introducing a service time attenuation factor and a working condition correction coefficient, establishing an aging degree quantitative model, and calculating an aging coefficient; and constructing and developing an energy consumption-reliability joint optimization module, and adjusting equipment operation parameters. The method has the advantages that the dynamic knowledge graph and the time sequence analysis model are constructed by integrating multi-source sensor data, precise diagnosis and self-adaptive threshold adjustment of the coupling fault of the cleaning equipment are achieved, aging evaluation and task scheduling optimization are combined, the energy consumption efficiency is improved, and the maintenance cost is reduced.

The invention provides a method for participating in primary frequency modulation control of a power system based on a modular large-scale hydrogen production power supply, and the method comprises the steps: obtaining an original data flow from a high-precision synchronous phasor measurement unit, an electrolytic cell and a submodule, and carrying out the multi-scale decomposition and noise reduction processing after time synchronization and anomaly detection restoration; performing state estimation by using unscented Kalman filtering, extracting frequency disturbance characteristics in combination with empirical mode decomposition and Hilbert transform, and estimating the inertia of the power grid; an electrolytic cell dynamic model is established, and parameter identification and optimization are carried out through a recursive least square method; a control strategy is designed based on a multi-time scale control framework, virtual synchronous generator parameters are optimized through online Gaussian process regression, and frequency modulation control is realized in combination with scene tree random model predictive control and minimum-maximum regret learning; and finally, carrying out coordination control through a distributed consistency algorithm and carrying out multi-target performance evaluation optimization. And the frequency modulation performance and the operation efficiency of the system are improved.

The invention relates to the technical field of twinborn model simulation, and discloses a twinborn model simulation method and system for hot working of large forgings. The method comprises the following steps: collecting and preprocessing process parameters, quality data and environment information in a multi-source manner, and obtaining hot working characteristic data; performing correlation analysis to construct a process knowledge graph; calculating the distribution of a temperature field, a stress field and an organization field by using a self-sensing multi-field coupling var value neural network; comparing and analyzing to obtain deviation data and correction parameters; adjusting a network parameter optimization prediction result; and executing multi-objective optimization calculation, and generating a whole-process technological parameter and a control instruction. According to the method, full-process multi-physics field coupling calculation from smelting, casting, forging and pressing to heat treatment can be achieved, model parameters are dynamically adjusted according to real-time production data, the optimal process scheme is generated, and therefore the manufacturing quality and efficiency of large forgings are improved.

The invention provides a new material production process parameter optimization method and system based on artificial intelligence, and relates to the technical field of parameter optimization, and the method comprises the steps: constructing a multi-dimensional process parameter feature space, collecting and preprocessing process parameters, extracting correlation features through a deep convolutional neural network, and building a parameter feature space model; generating a technological parameter collaborative optimization model, constructing a multi-objective optimization function, and performing iterative optimization by using reinforcement learning and an improved ant colony algorithm; and performing process parameter adaptive control, constructing a dynamic control interval, collecting parameters in real time and performing adaptive adjustment to realize closed-loop control and model dynamic update.

The invention discloses a cloud edge cooperative control method, which comprises the following steps: establishing a cloud edge cooperative architecture, collecting data by edge nodes, and selecting a corresponding data compression strategy to compress the data; generating an edge-cloud collaborative uploading data stream; the cooperative control layer executes adaptive load balancing based on a cloud-side node state, task characteristics and a network condition which are monitored in real time so as to realize task allocation and scheduling between the cloud and the side; the cloud receives the data stream transmitted by the edge node, decompresses and processes the compressed data according to the metadata information, completes the task and feeds back an execution result; a node performance prediction model is constructed by using system state monitoring information, a task allocation strategy and a resource allocation and data compression strategy are adjusted in real time according to a prediction result, and cloud-side collaborative optimization is realized. Efficient data transmission, flexible task scheduling and self-adaptive resource management are realized, and the performance of the system in a complex and changeable environment is comprehensively improved.

The invention discloses a virtual power plant peak regulation optimization scheduling method and system, electronic equipment and a medium, and the method comprises the steps: obtaining multi-source data of distributed energy, energy storage equipment and a load side, and carrying out the cleaning and normalization processing; utilizing an LSTM neural network and an XGBoost model to carry out short-term and medium-and-long-term load prediction respectively; based on the prediction result and the resource state, generating a scheduling optimization strategy through a deep Q network (DQN) algorithm; according to an optimization strategy, distributed energy power generation, energy storage charging and discharging and load side response are dynamically managed, and multi-resource cooperative control is achieved; and through a feedback optimization module, DQN model parameters and strategy updating are automatically adjusted according to monitoring data, and the scheduling effect is optimized. The system comprises a data acquisition module, a data preprocessing module, a load prediction module, a scheduling decision module, a resource control module, and a monitoring and feedback optimization module. The method has the beneficial effects that the load prediction precision is improved, the resource scheduling is optimized, the operation cost is reduced, and the method is suitable for scheduling management of a multi-source energy system.

The invention provides a procurement supply chain collaborative intelligent management method and a procurement supply chain collaborative intelligent management system. Belongs to the technical field of supply chain management. Constructing a purchase demand prediction model; dynamically adjusting the purchasing strategy according to the prediction result; the record and the evaluation result are stored on the block chain; the system automatically sends order information to a supplier; the supplier confirms the order through the block chain platform and updates the production progress and the logistics information; the system adjusts an inventory strategy in real time; the logistics state is monitored in real time; the system analyzes data of each link of the supply chain in real time and identifies potential risks. Through dynamic path planning and real-time transportation optimization, the logistics path can be adjusted in real time according to traffic conditions, environmental factors and emergencies, the transportation time is shortened, the transportation cost is reduced, and therefore the overall efficiency of a supply chain is remarkably improved.

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

The invention discloses an edge computing resource allocation optimization method and system based on reinforcement learning, and relates to the technical field of edge computing resource management.The method comprises the steps that computing load data and network state data are collected, a historical load sequence is formed, a state space matrix is constructed, the historical load sequence is mapped into a discrete state value, and the discrete state value is obtained; establishing a deep neural network model; inputting the state space matrix into a deep neural network model, and generating a resource allocation strategy set according to the priority and time delay requirements of the calculation tasks; iteratively training the resource allocation strategy set by adopting a double Q learning algorithm, setting a reward function based on task completion time and a resource utilization rate, and selecting an optimal resource allocation strategy; and adjusting computing resource allocation of the edge nodes according to the optimal resource allocation strategy, and meanwhile, feeding back an execution result to the deep neural network model for online learning updating. According to the method, intelligent optimization of edge computing resource allocation is realized by introducing a deep reinforcement learning method.

Disclosed in the present invention are a fault identification method and system based on the operating condition of a continuous system in an open-pit mine. The method comprises: establishing a simulation model for a continuous system in an open-pit mine, and monitoring device data in real time; pre-processing the data, and performing potential fault identification; on the basis of a system pressure change rate and an adaptive adjustment mechanism of the continuous system in the open-pit mine, optimizing fault identification output; and designing a fault response and real-time adjustment mechanism to prevent fault occurrence. The fault identification method and system based on the operating condition of a continuous system in an open-pit mine provided in the present invention improve the speed and accuracy of fault diagnosis, particularly the rapid processing capability for complex data relationships. A breakthrough is achieved in fault prevention, thus enabling early warning and adaptive adjustment to be implemented before faults occur. Thus, the stability and safety of continuous systems in open-pit mines are significantly improved, and a more efficient technical solution is provided for operation management of modern open-pit mines.

The invention provides a coal mill fault diagnosis and prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting a vibration signal of a coal mill bearing through a vibration sensor, carrying out the multi-scale analysis through wavelet decomposition, extracting features, constructing a deep convolutional neural network model, and carrying out the fault diagnosis and prediction of a coal mill bearing. A maximum mean value difference adaptive layer is introduced to carry out feature alignment on data of a source domain and a target domain, identification of fault types and degrees is realized, a health state evaluation index including a fault severity coefficient and a development trend coefficient is established, and the remaining service life is calculated and early warning is carried out. According to the invention, the accuracy and generalization ability of fault diagnosis are improved, accurate prediction and timely early warning of the bearing fault of the coal mill are realized, and the service life of equipment is prolonged.

The present invention relates to the technical field of data transmission. Disclosed is an application-driven three-dimensional spatial data transmission method. The method comprises: determining system key performance indicators (KPIs) by means of qualitative and quantitative analysis; constructing an AI-driven adaptive three-dimensional data transmission mechanism, and dynamically adjusting a transmission strategy on the basis of a real-time network state, a device capability, an application scenario and the KPIs; developing an adaptive compression algorithm set oriented to three-dimensional data, so as to meet differentiated compression requirements of different application scenarios; performing loop execution of a test, and adjusting and optimizing the data transmission mechanism and the compression algorithm set on the basis of a test feedback result and the real-time network state; and deploying an optimized transmission method to a production environment, and collecting field data to optimize the system performance and verify the achievement of the KPIs. By constructing a qualitative and quantitative analysis framework based on machine learning, the present invention quantifies differentiated transmission requirements of different application scenarios and formulates transmission strategies meeting the scenario requirements.

The invention provides an industrial park multi-target collaborative optimization scheduling method and system based on artificial intelligence, relates to the technical field of intelligent scheduling of industrial parks, and aims to establish a digital twinborn model and a dynamic topological graph, perform resource flow relationship modeling by using a graph neural network, and perform resource demand analysis in combination with a multi-model prediction framework. A multi-agent reinforcement learning algorithm is adopted to generate a scheduling strategy, migration from a global strategy to a local decision module is realized through a strategy distillation technology, robustness of the scheduling strategy is simulated and verified in a digital twin model, the strategy is adjusted according to a simulation result, and finally efficient scheduling of park resources is realized. The method effectively improves the intelligentization and collaboration level of the operation of the industrial park, reduces the energy consumption and carbon emission, enhances the ability of the park to cope with complex scenes, and is suitable for the efficient management of the modern industrial park.

The invention discloses a computing resource scheduling method based on user demands and task priorities, which relates to the technical field of resource scheduling, and comprises the following steps: receiving a computing task request submitted by a user, analyzing and verifying explicit demand parameters and implicit demand parameters, and generating a standardized demand description object; acquiring cluster state data and external environment parameters in real time, constructing a user-task-environment three-dimensional feature tensor, and outputting a standardized feature vector group; and collecting a performance data flow of the container instance group, triggering an elastic scaling decision based on a pre-trained LSTM prediction model, dynamically adjusting cluster resource configuration and executing abnormal task rescheduling. According to the method, a user-task-environment three-dimensional feature tensor is constructed, and a dynamic mixed weighted priority score is generated in combination with a reinforcement learning model, so that space alignment and time sequence cumulative effect fusion of multi-dimensional features is realized.

The invention discloses a multi-modal fusion AGV dynamic path planning and cluster scheduling system, and relates to the technical field of multi-modal perception and data fusion, and the system comprises a multi-modal perception module which generates a dynamic obstacle confidence map through multi-source data fusion in combination with a hardware-level time synchronization and Transform feature fusion network; the dynamic path planning module adopts an improved rolling window algorithm, integrates an LSTM space-time conflict prediction model and an adaptive weight cost function, and realizes dynamic obstacle trajectory prediction and non-oscillation global path generation; the cluster scheduling control module is used for optimizing multi-AGV task allocation and conflict resolution in combination with a dynamic priority preemption mechanism and digital twinborn simulation rehearsal based on a distributed contract network protocol of edge computing; and the data conflict resolution module is used for triggering a multi-modal re-calibration process through confidence weighting and sliding window time sequence verification. According to the system, in logistics storage and intelligent manufacturing scenes, the dynamic obstacle avoidance success rate and the robustness and operation efficiency of an AGV cluster are improved.

The invention discloses an underground facility vibration monitoring method and system based on a distributed sensor network. The method comprises the following steps: acquiring initial data of the distributed sensor network; constructing an initial network topology structure; collecting sensor node state data; dynamic topology adjustment is executed; network performance is evaluated and optimized; an optimized network topology structure is obtained; receiving original data acquired by the sensor and performing data cleaning and standardization by using a network topology structure; a filtering algorithm is adopted to remove noise, and features are analyzed and extracted; and generating a preprocessed feature data set. A multi-source data fusion algorithm module is adopted to integrate information; constructing an anomaly prediction model and optimizing parameters, and performing anomaly detection by adopting the optimized anomaly prediction model; outputting the fused data and an anomaly detection result; and judging whether to trigger early warning or not according to a preset threshold value for an abnormal detection result. Efficient and reliable real-time vibration monitoring is realized, and the anomaly detection accuracy and the system adaptability are improved.

The invention discloses a dynamic sensitive data outbound risk assessment method and system based on multi-source risk information in the technical field of data cross-border. The system is mainly composed of a multi-source risk information acquisition module, a dynamic security identifier generation module, a risk collaborative assessment engine, a dynamic weight adjustment module, a disposal range dynamic calculation module and a flexible emergency disposal module, and an acquisition-identifier-assessment-calculation-disposal full-process closed-loop architecture is formed. A full-process closed-loop processing framework of collection-identification-evaluation-calculation-disposal is innovatively proposed, and by combining a dynamic risk evaluation model, a reinforcement learning intelligent technology and a block chain evidence storage technology, the problems of insufficient dynamic nature, lack of collaboration and lack of closed-loop capability in the prior art are systematically solved; and high-precision evaluation, real-time response and traceable management and control of the cross-border data flow risk are realized.

The invention provides an AI algorithm-based scenic spot car intelligent path establishing and planning method and system, and relates to the technical field of path planning, and the method comprises the steps: collecting scenic spot road geographic information data, and constructing a weighted road network model; collecting real-time positioning of the trolley and sensor data; using the reinforcement learning model to generate a real-time optimal path planning strategy; calculating an initial recommendation path; road condition changes are monitored, and a path is dynamically adjusted; and outputting the updated optimal path scheme and carrying out navigation. According to the method, intelligent and real-time path planning of the vehicles in the scenic area can be realized.

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 discloses a textile product defect detection method and system, and the method comprises the steps: collecting a textile surface image, and carrying out the preprocessing; constructing a feature extraction model, and extracting multi-modal features of the textile fabric surface defects; analyzing the extracted features, and classifying defect types; positioning defect positions, and marking defect areas and types; and outputting a defect detection report, and optimizing model parameters according to a detection result. Through multi-modal feature extraction and a feedback optimization mechanism, the classification accuracy and the positioning precision of textile fabric defect detection are remarkably improved, and particularly, the method has outstanding performance in detection of complex and low-contrast defects. According to the invention, the detection efficiency is greatly improved, false detection and missing detection are reduced, and an efficient and intelligent solution is provided for quality control of the textile industry.

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

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 solenoid valve precise control method based on flow dynamic adjustment, which comprises the following steps: acquiring working parameters in real time through a sensor, obtaining real-time working condition data of a solenoid valve, and carrying out noise reduction and normalization preprocessing on the data to generate a comprehensive working condition feature vector; and on the basis of the feature vectors and the target output parameters, flow, temperature and outlet pressure values of future working conditions are calculated through a prediction algorithm. And in combination with the calculated future working condition and real-time working condition data, an optimized control signal is generated and used for dynamically adjusting the opening degree of the electromagnetic valve, and initial dynamic control over the flow and the pressure is achieved. Outlet pressure and flow are monitored in real time through high-frequency sampling, the deviation between actual output and a target value is calculated, and a control signal is corrected based on the deviation. The electromagnetic valve is suitable for high-precision flow and pressure regulation under complex working conditions, has the characteristics of quick response and high regulation precision, and can be widely applied to the fields of environmental protection, industrial automation and fluid control.