728 results about "Control network" patented technology

The invention discloses an automatic control method and system for secondary granulation of a high-voltage zinc oxide resistor disc, relates to the technical field of intelligent manufacturing of power equipment, and solves the problems of out-of-control particle morphology caused by dynamic coupling parameter identification lag and control instability caused by multi-physical field parameter coupling in an existing method. According to the invention, a dynamic physical property parameter matrix is generated in real time based on multi-band dielectric relaxation spectrum analysis and terahertz wave tomography; predicting a fluidized phase change threshold value and an energy gathering area through multi-physics field coupling modeling; a time sequence attention deep reinforcement learning algorithm is adopted to generate a multi-field cooperative adjustment instruction; positioning a parameter conflict source and triggering decoupling compensation by combining a high-frequency vibration and acoustic emission combined monitoring module; performing closed-loop correction on the control network weight based on the laser spectrum data and a partial least squares regression model; the real-time performance of fluidization parameter identification, the stability of multi-field coupling control and the recovery efficiency of abnormal working conditions are remarkably improved, and meanwhile the batch consistency of the electrical performance of the resistor discs is guaranteed.

The invention discloses a security risk autonomous sensing system and method based on a digital twin industrial control network. The method comprises the following steps: performing four-dimensional multi-modal modeling on an industrial control network, and constructing a twin model integrating entities, rules, services and behaviors; constructing a high-fidelity simulation network by utilizing a containerization technology based on the model; deploying a gateway at an actual network boundary, collecting traffic characteristics in real time and identifying suspicious behaviors; performing protocol restoration and behavior reconstruction on the suspicious traffic to generate a behavior sequence; performing anomaly recognition and attack classification on the behavior sequence through a deep learning model; evaluating a risk level; and matching and verifying a defense strategy according to an evaluation result, and automatically synchronizing the defense strategy to a master control network to realize a detection-response-protection closed loop. According to the invention, autonomous identification of suspicious behaviors in the industrial control network and intelligent decision-making of security policies are realized, and the method has the advantages of high simulation, low invasion, closed-loop controllability and the like.

The invention relates to the technical field of computer networks, in particular to a monitoring method and system based on industrial computer network fault data, and the method comprises the steps: collecting the heterogeneous fault data of each layer of equipment in an industrial control network in real time through distributed probe nodes; performing multi-modal normalization processing on the original fault data; constructing a fault knowledge graph, and dynamically associating an equipment topological relation, a historical fault mode and a current production task context; fault root cause analysis is carried out by adopting a hybrid inference engine, and a potential fault propagation path is predicted in combination with a rule-based expert system and an LSTM-GNN joint model; generating a grading alarm strategy, triggering a self-adaptive fault-tolerant mechanism, and dynamically adjusting network bandwidth allocation or starting redundant equipment switching according to the fault grade; according to the invention, by constructing the industrial knowledge graph and the adaptive fault-tolerant mechanism, efficient, accurate and interpretable fault diagnosis and prediction are realized, and the reliability and operation and maintenance efficiency of an industrial network are improved.

The invention discloses a spatial-temporal joint modeling system and method for watershed water quality prediction. The system comprises a data acquisition module, a feature extraction module, a feature fusion module, a model training module and a prediction output module. In the watershed water quality prediction process, the combined influence of time and space is considered, a space-time position coding combined embedded layer is designed, an attention mechanism guided by hydrological characteristics is combined, a door control network dynamically fused with space-time characteristics is constructed, and the space-time coupling influence of a watershed topological structure on pollutant diffusion is considered; the attention weight is dynamically adjusted by quantifying the topological importance of the monitoring points in the network, a feature channel which is most effective for a current prediction task is highlighted, and noise or redundant information is suppressed; in the model training process, a simplified gating mechanism is adopted, the gradient dispersion problem is reduced, the nonlinearity of a layer is kept, convergence is accelerated, a dynamic graph learning device is used, physical rules are respected, data changes are self-adapted, and more accurate water quality modeling is achieved.

The invention belongs to the technical field of communication, and discloses a network transmission management method and system, and the method comprises the steps that a data collection module collects multi-dimensional data in real time; the decision planning module optimizes a network route; the decision planning module performs protocol and encryption switching; the execution module dynamically adjusts the transmission strategy; the safety protection module performs four-dimensional dynamic protection on the data; the hybrid cloud deployment module guarantees the confidentiality of core data; the multi-active data center module realizes data synchronization among a plurality of data centers; the cross-domain collaborative module realizes privacy protection collaborative training between edge nodes through federated learning; and the block chain auditing traceability module records the key operation log. According to the method, routing and flow prediction are optimized through a mixed quantum classical calculation engine, multi-protocol heterogeneous scheduling and neural mimicry congestion control are combined, the network transmission efficiency is greatly improved, the data transmission speed is improved, and the data leakage risk is reduced through application of a zero-trust security matrix and a quantum key distribution technology.

The invention discloses a power monitoring system anomaly detection method based on a multi-model voting mechanism, and the method mainly comprises the following steps: firstly, collecting network flow data and equipment behavior data in a power monitoring system, and converting the network flow data and the equipment behavior data into multi-dimensional feature vectors; secondly, multiple types of anomaly detection models are constructed, and each model independently judges the anomaly of the input data; performing fusion processing on judgment results of the models through a majority voting or weighted voting mechanism, and outputting a comprehensive anomaly judgment result; and finally, dynamically adjusting the weight and detection parameters of each model based on a feedback mechanism, outputting alarm information when a fusion result exceeds a set threshold value, and linking a system response module. The power monitoring system anomaly detection method provided by the invention for the first time has the advantages of high detection accuracy, low false alarm and missing report rate, dynamic adjustability, high adaptability, flexible deployment and the like, and is particularly suitable for industrial control network security protection scenes in key infrastructures.

The invention provides a drilling formation identification and adaptive parameter adjustment method and system, and the method comprises the steps: converting multi-source sensing data into structured formation feature representation through constructing a formation response mode library, and achieving the precise identification of formation interface distribution; a parameter response area distribution analysis and missing information reconstruction technology is adopted, recessive stratum characteristics are deeply excavated, and the internal law of stratum changes is revealed; an adjusting point optimization selection and enhanced drilling path planning mechanism is introduced, and an efficient drilling control network is constructed; potential field distribution is constructed through parameter change features, and coupling correlation between the parameter features and spatial positions is established; intelligent configuration and self-adaptive adjustment of drilling parameters are realized by combining harmonic force analysis and execution scheme generation; and finally, a complete drilling stratum identification and parameter control system is formed, an accurate and efficient technical solution can be provided for drilling operation in a complex stratum environment, and the drilling efficiency and the operation quality are improved.

The invention provides an urban low-altitude AI collaborative management and control method and system based on a Beidou grid code and a block chain, and the method comprises the steps: obtaining the information of an unmanned plane, and searching a corresponding evidence storage record in the block chain; the unmanned aerial vehicle information is obtained through a low-altitude intelligent flight control box compatible with various unmanned aerial vehicle protocols; verifying the qualification of the unmanned aerial vehicle through the evidence storage record, and bringing the unmanned aerial vehicle into a distributed collaborative management and control network for unified scheduling when the unmanned aerial vehicle passes the qualification authentication; setting flight authority according to airspace classification of the airspace map and generating a corresponding geofence to limit the flight range of each unmanned aerial vehicle; the airspace map is divided into space-time grids with specified resolution based on Beidou grid codes, state information of each grid unit in the space-time grids is analyzed based on an AI reinforcement learning engine, and flight paths of the unmanned aerial vehicles are dynamically optimized. Flight proxy of the unmanned aerial vehicles is realized through the low-altitude intelligent flight control box, unified scheduling of the multi-source heterogeneous unmanned aerial vehicles is realized, and the airspace use efficiency is improved.

The invention discloses an industrial control network anomaly detection method considering priori knowledge, and the method comprises the steps: enabling a spatial feature extraction module to consider the function attributes, communication specifications and topological structure features of equipment in an industrial control network, introducing equipment role perception coding and topological perception position coding, improving the self-attention through protocol constraint, and achieving the detection of the anomaly of the industrial control network. The spatial dependency relationship of an industrial control scene can be better understood, and the recognition capability of the model on key spatial features is improved. Meanwhile, a periodic sensing door is arranged in the time sequence feature extraction module, historical state information is aggregated to obtain scene context vectors, so that an attention mechanism is updated, weighted features are obtained, features of different time scales are subjected to weighted fusion in combination with a multi-scale time window, and finally, the three features are spliced together, so that a scene is obtained. And final time feature representation is formed. According to the method, the prior knowledge can be effectively utilized, the interaction mode between devices is captured, the time sequence dependency relationship is analyzed, and the anomaly detection effect is improved.

Systems and methods of generating fraud detection models and controlling network permissions of one or more systems within a network environment are disclosed. A network activity dataset comprising data representative of network activity within a network environment is received and at least one co-controlled system in the network activity dataset is identified by implementing a trained fraud detection model configured to receive the network activity dataset and output a fraud determination for each system having at least a first role in the network activity data. The fraud determination represents a likelihood of a system having the first role engaging in a co-controlled network activity. In response to identifying the at least one co-controlled system, one or more permissions of the at least one co-controlled system for operating within the network environment are modified.

The invention is suitable for the field of industrial safety monitoring, and provides an industrial control safety monitoring method and system, and the method comprises the steps: collecting the network flow data of an industrial control network in real time based on an industrial protocol analysis engine; synchronously acquiring operation state parameters of the key equipment by adopting an edge computing node; constructing a coupling relation quantitative model of the network flow and the equipment operation state, and calculating a dynamic correlation coefficient between the network flow and the equipment operation state through the coupling relation quantitative model; constructing reference correlation coefficient distribution in a normal production state based on historical data, and dynamically updating a safety threshold value; when the dynamic correlation coefficient exceeds a safety threshold value, triggering a multi-stage early warning mechanism; according to the method and the system, deep fusion monitoring of the industrial control network and the equipment operation state is realized, and the limitation of traditional split type monitoring is broken through. Through dynamic quantification of a coupling relationship, adaptive threshold adjustment and multi-level early warning, the timeliness and accuracy of abnormal risk identification are significantly improved, and network attacks and equipment fault hazards are effectively reduced.

The invention relates to the technical field of air conditioning system control, and discloses an air conditioning system intelligent flexible regulation and control method based on an AI algorithm, and the method comprises the steps: obtaining an original environment parameter flow containing temperature, humidity and energy consumption signals, inputting the original environment parameter flow into a pre-trained intelligent flexible regulation and control network to generate a regulation and control strategy matching result set, and determining each parameter regulation and control instruction set according to the regulation and control result set. The network is trained by historical operation data, and the data comprises historical regulation and control features (fusing time sequences and periodic features), operation mode labels and weight coefficients extracted from continuous air conditioner operation parameters. Periodic characteristics are extracted through periodic decomposition, an operation mode label is determined based on a decision algorithm, and network parameters are dynamically updated according to a weight coefficient. The system is provided with a multi-modal fusion module, a time sequence prediction module, a multi-target optimization module and a hierarchical regulation mechanism, influence factors can be dynamically distributed, multi-target self-adaptive regulation and control are achieved, and the regulation and control precision, the energy efficiency and the environmental adaptability of the air conditioning system are improved.

The invention discloses a dynamic identification system and method for fault nodes of a heat pump measurement and control network, and aims to solve the problems of one-sided weight distribution, lack of directional modeling and insufficient dynamic adaptability in the prior art. The system comprises an energy transfer topology network model building module which is used for respectively quantifying the physical connection strength and the directional energy transfer path of the heat pump system by building an undirected graph and directed graph bimodal model; the multi-dimensional weight distribution module adopts a subjective and objective fusion strategy and combines a complex network theory, an entropy weight method and an analytic hierarchy process to distribute comprehensive weights for nodes and explicit modeling directivity dependence; the dynamic robustness analysis module is used for simulating and positioning structural hub nodes through static attacks, simulating and tracking cascade failure and topology reconstruction processes through dynamic attacks, and comprehensively evaluating the robustness of the system; and the fault simulation verification module verifies weight rationality and method effectiveness from four dimensions of objectivity, center matching degree, interpretability and robustness based on a fuzzy comprehensive evaluation framework. According to the method, through fusion topology modeling, dynamic weight distribution and dynamic and static combination robustness analysis, key fault nodes are accurately recognized, the system vulnerability is revealed, a data driving basis is provided for redundancy design, intelligent operation and maintenance and reliability improvement of the heat pump system, and the operation and maintenance cost is remarkably reduced.

The invention relates to the technical field of thermal equipment operation state dynamic test and compensation control, in particular to an AI-based boiler air pre-heater temperature uniformity regulation and control system, which comprises a collaborative closed-loop control network of a data acquisition module, an AI prediction module, a heat storage system control module, a temperature compensation control module and a thermal stress analysis module. The data acquisition module acquires flue gas temperature, coal quality parameters and load instructions in real time, the flue gas temperature, the coal quality parameters and the load instructions are transmitted to the AI prediction module through an industrial bus, XGBoost, LightGBM and a deep neural network model are used for carrying out spatial-temporal feature extraction on the evolution trend of a temperature field, and the heat storage system control module dynamically switches heat storage and release modes according to the load state. The heat distribution proportion among the multiple units is adjusted by combining the three-dimensional stress field distribution data of the thermal stress analysis module; the bottleneck of lack of thermal inertia regulation of an existing system is broken through, dynamic balance of a temperature field under the condition that thermal stress of a heat transfer element is controllable is achieved, and the heat exchange efficiency and operation stability of the air pre-heater are improved.

The invention relates to the technical field of high-pressure hydrogenation reactor control, and discloses a multi-stage pressure optimization control method and system for a high-pressure hydrogenation reactor, and the method comprises the steps: obtaining temperature, pressure and flow monitoring data, generating original parameter data, and deploying a distributed control network; collecting and analyzing multi-stage pressure sensor data, and generating a pressure change influence factor; sensing a valve state, and generating a valve change influence factor; collecting data of a flow sensor, and dividing a control area after calibration; and calculating a pressure optimization index based on the control area data, integrating multiple factors to generate comprehensive control data, and further generating a pressure control signal and outputting an instruction. The system comprises a control network deployment module, a pressure analysis module, a valve analysis module, a control area analysis module and a pressure control signal generation module. Through multi-dimensional data fusion, a distributed control network and dynamic optimization calculation, the accuracy and stability of pressure control of the high-pressure hydrogenation reactor are improved, and the method is suitable for efficient and safe production in the field of petrochemical engineering.

Disclosed in the present application are an aircraft final assembly and integration testing platform and testing method. On the basis of a rational platform architecture, structured process data preparation, model-based operation guideline document preparation, and dispatch assistance decision-making are realized by means of an application layer, such that uniform data sources in the field of final assembly can be achieved. Data uploading and issuing between an industrial control network and an internal network, and data interaction control between layers are implemented by means of the mutually collaborative operation of the application layer, a storage layer, an edge layer and an execution layer, thereby improving the information acquisition efficiency and the operating efficiency. The edge layer and the execution layer collaboratively operate, so as to realize the interactive collaboration between the assembly guidance work for operators and automatic testing, thereby improving the efficiency of data information acquisition, operation instruction and fault handling during a final assembly and testing process of an aircraft.

The invention provides an intelligent layout method, system and device for a land survey control network and a medium, and the method comprises the steps: building a space-time dynamic terrain model based on the obtained measurement data of a construction region; generating a candidate control point set in the construction area according to a preset layout density requirement; based on the space-time dynamic terrain model, performing stability analysis on each candidate control point in the candidate control point set to obtain a stability index of each candidate control point; based on the space-time dynamic terrain model, any two candidate control points in the candidate control point set are used as candidate control point pairs for time sequence intervisibility analysis, and time sequence intervisibility indexes of the candidate control point pairs are obtained; establishing a multi-objective optimization model according to the stability index and the time sequence intervisibility index; and solving the multi-objective optimization model by adopting a multi-objective evolutionary algorithm, and outputting a control network layout scheme set. By adopting the method, the point distribution quality of the control network can be improved.

The invention provides a scanning method and system for hidden asset identification in an electric power industrial control network, and aims to solve the problem that silent, non-registered or illegal access equipment is difficult to discover by the existing asset surveying and mapping means. According to the method, multiple protocol induced detection messages are injected into a target network, equipment response data are collected, feature vectors are extracted, and abnormal equipment behaviors are identified by using an unsupervised clustering and anomaly detection algorithm; and meanwhile, in combination with a graph neural network and a hidden Markov model, modeling is performed on a network communication chain structure and a historical behavior sequence, and potential hidden asset nodes are deduced. According to the method, active discovery and risk reasoning of hidden assets can be realized on the premise of not influencing industrial control services, and the method is suitable for industrial control scenes such as transformer substations and dispatching centers in the power industry and has high safety, intelligence and feasibility.

The invention discloses a complex space truss positioning system and method based on a three-dimensional coordinate acquisition technology, and relates to the technical field of truss positioning. The complex space truss positioning system and method based on the three-dimensional coordinate acquisition technology comprises the following steps: S1, three-dimensional modeling and pre-compensation; s2, constructing and dynamically calibrating a three-dimensional control network; s3, on-site preliminary measurement and data acquisition; s4, contour trajectory speculation and connection complementation; s5, performing dynamic multi-view measurement and data integration; s6, data analysis and positioning adjustment; measuring equipment is selected according to the characteristics of the truss, node coordinates are preliminarily measured and collected, shielded parts are recorded, interference of environmental factors is controlled, and the quality of basic measurement data is guaranteed. An image processing and mode recognition technology is utilized to analyze a contour track, connection and node positions of a shielding part are speculated in combination with structural characteristics, and verification is performed after mechanical analysis optimization, so that the measurement problem caused by shielding is effectively solved, and data integrity is improved.

The invention discloses a 5G-TSN cross-domain scheduling method, which comprises the following steps of: constructing a 5G-TSN hierarchical control network architecture, and carrying out global resource unified regulation and allocation; a circular queue forwarding mechanism is combined and adopted, a synchronous time slot cross-domain transmission mechanism is constructed, a multi-level service quality mapping strategy is implemented, 5G and TSN network priorities are matched, and an isolation transmission mechanism is used to meet differentiation requirements; according to differentiated demands of 5G and TSN networks, constructing a mixed service scene and determining demand attributes of different services; in combination with a synchronous time slot cross-domain transmission mechanism, the scheduling process of different service quality services is analyzed, and a scheduling and time delay model is constructed; according to cross-domain scheduling resource allocation based on time delay perception, scheduling of hybrid services is realized through three-dimensional collaborative optimization of time slot offset injection, wireless resource block allocation and base station power regulation and control. According to the invention, the overall performance of the 5G-TSN network can be obviously improved, and efficient transmission guarantee is provided for diversified services.

The invention discloses an intelligent networked unmanned ship cluster control method and system, and relates to the technical field of intelligent control and cluster collaboration, and the method comprises the steps: constructing a multi-layer distributed unmanned ship cooperative control network structure, and dividing nodes according to the task attributes of unmanned ships. The method comprises the steps of establishing a connection topological graph in a cluster according to initial function distribution, calibrating nodes through the connection topological graph, collecting the energy state, the load capacity, the communication quality and the task pressure of each unmanned ship, establishing a multi-dimensional state matrix, and calculating a comprehensive decision value of each node according to the multi-dimensional state matrix. And switching node operation through an adjacent topological relation and a comprehensive decision value, synchronizing the updated node state and the connection diagram to an unmanned ship cluster, and carrying out self-adaptive distribution and network structure optimization. According to the method disclosed by the invention, the management orderliness of a cluster structure and the task cooperation efficiency among the nodes are improved by constructing a three-layer cooperative network of the control nodes, the execution nodes and the sensing nodes.

The invention provides an addressing method for a strategy control network element, an application side network element and a control plane network element, and the method comprises the steps: obtaining a device address of a strategy binding network element retrieval database network element based on the local static data configuration information of the application side network element; retrieving a device address of a database network element according to the policy binding network element, and sending the control plane network element query message to a corresponding target policy binding network element retrieval database network element, so as to query an address of a target control plane network element in the target policy binding network element retrieval database network element according to the control plane network element query information; according to the address of the target control plane network element, sending the policy control network element address query request to the target control plane network element; and receiving a query result corresponding to the policy control network element address query request sent by the target control plane network element. According to the invention, the problem that the application side network element cannot acquire the IP address of the policy control network element according to the user IP address information is avoided.

The invention provides an intelligent sensor cooperative control method and system for a microbiological detection environment, and relates to the technical field of sensor control, and the method comprises the steps: carrying out the hierarchical cloud edge control architecture layout of a production workshop according to equipment distribution information and production activity information, and obtaining a cascaded hierarchical control network; the K-level cooperative control secondary center executes partition autonomous parameter mapping and outputs a K-layer distributed environment compensation strategy; the microbial environment cooperative control center carries out transition area buffer control and outputs a global cooperative control strategy; and the microbial environment cooperative control center issues a global cooperative control strategy to the environment stability maintaining equipment array based on the spatial topological relevance of the environment stability maintaining equipment, and low-fluctuation environment steady state maintenance is carried out. The technical problem that the stability of a microbiological detection environment is affected due to the fact that most microbiological detection environment adjusting systems in the prior art are based on fixed parameter setting and lack of a quick response mechanism for sudden environment changes is solved.

The invention discloses a multi-unmanned aerial vehicle confrontation task execution method and device based on model reinforcement learning, and a medium, and the method comprises the following steps: inputting the current local observation into a reinforcement learning control network for each airframe in an own unmanned aerial vehicle group, and outputting meta-actions (including movement and attack), performing confrontation interaction with the enemy unmanned aerial vehicle group to obtain finite confrontation experience data; the collected historical interaction experience is used for supervised learning of a world model, so that the world model can approach probability distribution of an interaction rule of a real environment; data expansion is carried out on real interaction data by using a world model, an action network and an evaluation network are trained on expanded empirical data, and strategy optimization and parameter updating of a multi-unmanned aerial vehicle system are realized; repeating the process, and sequentially carrying out experience collection, world model training and enhancement strategy updating; and finally, the trained action network is reserved and is used for outputting a specific action strategy in an actual confrontation task.

The invention discloses an industrial control network flow abnormity real-time detection method based on a P4 programmable switch, and the method comprises the steps: extracting network flow characteristics related to a timestamp in a data plane in real time, carrying out high-line-speed preliminary detection on the traffic by using an industrial control network traffic anomaly detection algorithm based on a threshold rule to discover suspicious traffic, and uploading a preliminary detection result to a control plane; the control plane obtains suspicious traffic characteristic data based on the periodically maintained key traffic characteristic data and uploads the suspicious traffic characteristic data to the detection plane; the detection plane performs fine-grained detection on the suspicious traffic feature data by using a traffic anomaly detection model based on machine learning to obtain a final detection result; and when the difference between the final detection result and the preliminary detection result exceeds a given value, generating a new preliminary detection threshold rule based on an optimization algorithm or deep reinforcement learning, and updating to optimize the detection accuracy. According to the method, the real-time detection delay of the flow abnormity of the industrial control network can be greatly reduced, and the detection accuracy is remarkably improved.

The embodiment of the invention discloses an intelligent content generation method and device based on a large language model and a medium, and relates to the technical field of large language models.The method comprises the steps that text description information input by a user is obtained, cross-modal semantic analysis is conducted on the text description information through the large language model, and a multi-dimensional semantic embedding vector is generated; the multi-dimensional semantic embedding vector comprises a biological feature semantic embedding vector, an environment element semantic embedding vector and a physical parameter semantic embedding vector; on the basis of a multi-dimensional semantic embedding vector, carrying out dynamic reasoning complementation on missing interaction parameters, and generating optimized structured description data; and according to the structured description data and a preset multi-mode control network, generating initial image content, verifying the initial image content, and determining target image content. Potential information of multi-subject interaction is automatically complemented and inferred, it is ensured that the generated image and text description are kept highly consistent, and the problem of generation distortion caused by semantic errors is remarkably reduced.

The invention discloses a video generation method based on a body mask, belongs to the technical field of artificial intelligence, and can solve the problems that an existing body world model is inconsistent in an action space and a pixel space, is sensitive to the change of a visual angle of a camera, and is not uniform in architecture among different body structures. The method comprises the following steps: S1, determining a mask sequence with a body according to a target video; s2, encoding the body mask sequence and the initial frame image of the target video respectively to correspondingly obtain body mask features and image encoding features; s3, inputting the body mask features into a control network module to obtain injection features, and inputting the image coding features into a backbone network of a video generation model to obtain backbone layer features; and S4, fusing the injection feature and the trunk layer feature to obtain a fused feature, and generating a prediction video according to the fused feature. The method is used for generating the predictive video with the body.

The invention provides an intelligent cabinet heat dissipation control method and system, and belongs to the technical field of cabinet heat dissipation control. According to the method, data items all have acquisition time and cabinet area identifiers; the method comprises the following steps: dividing a cabinet hot area by using fuzzy clustering based on a historical sample, dynamically modeling by combining an adaptive PID control algorithm, optimizing control parameters through a particle swarm optimization algorithm, adjusting a hot area control model weight coefficient according to real-time feedback, generating a hot area-equipment association map, and constructing an adaptive heat dissipation control network; predicting a temperature trend through a decision tree model by using a correlation map and real-time operation characteristics generated by the network, and generating a fan rotating speed adjusting strategy, an air conditioner power distribution scheme and a multi-stage heat dissipation control strategy according to the temperature trend; inputting a heat dissipation control strategy and an execution result as feedback into the self-adaptive heat dissipation control network, and dynamically optimizing a correlation map and control parameter modeling; and multi-cycle feedback closed-loop optimization control is realized through multi-time feedback and parameter adjustment.

The invention discloses a multi-branch industrial ventilation intelligent regulation and control system and method based on a PLC. According to the system, a closed-loop control network is formed by an infrared sensor, a wind speed sensor, a PLC (Programmable Logic Controller), an air door control cabinet, an electric valve, a frequency converter, a frequency conversion fan and an upper computer. The infrared sensor is responsible for monitoring the running state of each production line in real time. The wind speed sensor is used for detecting the wind speed in the wind pipe and verifying wind quantity balance. The PLC processes all sensor data and outputs a control instruction. On one hand, the opening degree of the electric valve is adjusted through the air door control cabinet, and on the other hand, the rotating speed of the fan is adjusted in real time through the frequency converter. The upper computer is used for system parameter monitoring, threshold setting and data interaction with the manufacturing execution system. According to the system, on-demand adjustment of multi-branch ventilation is achieved through intelligent closed-loop control, the problem of response lag of a traditional control mode is effectively solved, the dust removal efficiency and the energy utilization rate are remarkably improved, and safe and efficient operation of the industrial environment is guaranteed.

The invention provides a routing control method, which is applied to network equipment with multi-link access capability, and comprises the following steps: carrying out service identification on a to-be-forwarded data flow of the network equipment to obtain service type information of the data flow; obtaining evaluation indexes of a plurality of candidate links of the network equipment; evaluating the plurality of candidate links based on the evaluation index and the service type information to obtain a comprehensive quality score; selecting a target link from the plurality of candidate links according to the service type information, the evaluation index and the comprehensive quality score; and controlling the network equipment to transmit the data flow through the target link. The invention further provides a multi-service parallel routing method, electronic equipment, a computer readable medium and a computer program product. According to the invention, differentiated routing control of multiple service types is realized, quality fluctuation caused by switching of operators is reduced, and the stability and the intelligent level of network transmission are remarkably improved.