25422results about "Total factory control" patented technology

The invention relates to the technical field of smart home, and discloses an automatic coordinated regulation and control system and method for smart home equipment, and the system integrates a plurality of functional modules such as equipment access, environment perception and user behavior analysis. The equipment access module supports access of multiple protocols and endows the equipment with a unique identifier; the environment sensing module collects environment parameters in real time and compares the environment parameters with thresholds. The user behavior analysis module generates a user scene preference model by means of an algorithm, the cooperation strategy generation module synthesizes multiple factors to formulate a regulation and control strategy, and the dynamic priority management module allocates weights according to equipment and scene conditions. And the equipment control module executes the instruction and feeds back the state, and the abnormity detection module monitors the equipment state and triggers a strategy regeneration mechanism when abnormity occurs. The data storage module securely stores various data, and the user interaction module efficiently analyzes instructions. According to the invention, many problems of an existing smart home system are solved, automatic coordinated regulation and control of equipment can be realized, and user interaction experience is optimized.

The invention provides a pressure sensor data precision compensation method and system for industrial control, and the method comprises the steps: firstly obtaining an original pressure signal sequence outputted by a target pressure sensor in a preset collection period, the original pressure signal sequence containing a plurality of pressure signals arranged according to a timestamp; meanwhile, a real-time environment parameter set, covering temperature, humidity and mechanical vibration parameters in an acquisition period are detected, then the real-time environment parameter set is input into a pre-trained compensation parameter generation model, a corresponding dynamic compensation parameter set is generated, then each signal of an original pressure signal sequence is adjusted step by step according to the dynamic compensation parameter set, and a dynamic compensation parameter set is generated; and finally, the compensated pressure signal sequence is output to a signal processing module of an industrial control system to drive an execution mechanism, so that the data precision of the pressure sensor can be dynamically and accurately compensated by integrating multiple environmental factors, and the accuracy and stability of industrial control are improved.

The invention relates to the technical field of multi-axis servo systems, and discloses a synchronous control method, device and equipment for a multi-axis servo system, and the method comprises the steps: measuring a mechanical coupling relation parameter, a motor dynamic characteristic parameter and a sampling delay time parameter of the multi-axis servo system, and substituting the parameters into a state equation modeling formula, calculating to obtain a discrete time system state matrix; performing Z transformation processing on the discrete time system state matrix and calculating frequency response characteristics to obtain a transfer function matrix and a target coupling axis pair set; executing hierarchical optimization to obtain a preliminary control instruction; a trajectory tracking error and an inter-axis synchronization error are calculated and substituted into the optimization objective function for sampling delay compensation processing, and a compensation control instruction is obtained; the compensation control instruction is processed in real time, synchronous control signals are output to the servo shafts to achieve multi-shaft coordinated movement, and the synchronous control precision in the multi-shaft servo system is improved by accurately recognizing the key coupling shaft pairs and the sensitive frequency points.

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

The embodiment of the invention provides an industrial real-time data transmission guarantee method and system based on a TSN. The method is applied to the field of industrial communication and intelligent scheduling, and comprises the steps of obtaining industrial field network node information, completing TSN topology deployment and time synchronization, extracting communication characteristics and historical load data of various task flows, constructing a unified time sequence, and inputting a prediction model to generate a load prediction result. And in combination with task parameters, a scheduling strategy list and a prediction result, inputting a TSN digital twin network model to carry out analogue simulation, evaluating performance indexes under multiple strategies, selecting an optimal scheduling strategy and issuing the optimal scheduling strategy to TSN switching equipment, thereby realizing accurate scheduling control configuration of various task flows. According to the scheme, intelligent evaluation and automatic optimization issuing of the scheduling strategy are realized, the real-time performance and the deterministic communication capability of an industrial network are remarkably improved, the resource utilization efficiency and the adaptability of the scheduling decision are enhanced, and the key task flow is ensured to be reliably transmitted on time.

The invention discloses a household Internet of Things integrated fire alarm system and a linkage control method thereof, and relates to the technical field of smart home security. A first sensing recognition module, a second deep recognition module, a linkage control module, a cloud management platform module and a system self-checking module are integrated; the problems that in an existing fire alarm system, the false alarm rate is high, linkage equipment is prone to false triggering, linkage control logic is complex, and errors are prone to occurring are effectively solved. According to the invention, multi-modal data fusion and a deep learning algorithm are adopted to carry out fire identification, a dynamic threshold adjustment algorithm is combined to optimize the sensitivity of a sensor, and intelligent linkage configuration and an anomaly detection function are provided through a cloud management platform; the accuracy of fire identification and the stability of the system are greatly improved, the credibility of a user to the system and the comprehensiveness of family safety protection are remarkably improved, and resource waste and unnecessary panic caused by false alarm are effectively reduced.

The invention relates to the technical field of the Internet of Things, and discloses a machine room equipment environment monitoring method and system based on the Internet of Things, and the method comprises the steps: constructing an edge calculation hardware system of the Internet of Things to obtain an integrated terminal which integrates a communication unit, an acquisition unit and a control unit; performing multi-channel parallel acquisition and edge calculation analysis on equipment and environment parameters in the machine room to obtain a standard data tuple and an analysis data packet; executing local hierarchical storage and logic control to obtain a control instruction and storage data; executing three-layer network communication interaction on the storage data to obtain a remotely accessible monitoring view; according to the method, intelligent alarm and system self-maintenance are achieved, graded and classified alarm information and a system state report are obtained, the data transmission amount is greatly reduced, meanwhile, the system response delay is reduced, the control decision accuracy is improved, and the misoperation risk is reduced.

The invention provides an intelligent factory monitoring method and system based on multi-sensor fusion, and the method comprises the steps: firstly obtaining real-time monitoring data streams of multiple types of sensors in an intelligent factory, including operation state data collected by an equipment state sensor and scene state data collected by an environment state sensor; performing basic synchronization processing on the real-time monitoring data stream to obtain a standardized monitoring data stream, calling a pre-trained multi-sensor association analysis model to perform cross-source feature fusion processing on the standardized monitoring data stream to generate a fusion feature sequence, and performing abnormal mode detection processing based on the fusion feature sequence to generate an abnormal detection result; and finally, generating a monitoring intervention instruction containing equipment positioning information according to an anomaly detection result, and sending the monitoring intervention instruction to a factory control system to trigger a state adjustment operation, thereby effectively improving the monitoring precision and anomaly processing efficiency of the intelligent factory.

The invention relates to the technical field of data processing, and discloses an industrial one-stop multi-source data acquisition and monitoring system and equipment, and the system comprises a data acquisition module which is used for obtaining multi-source industrial monitoring data and generating a data stream input node; the data preprocessing module is used for processing the input data in the data flow model to generate a standardized data flow; the data conversion module is used for dynamically converting data nodes in the standardized data stream according to different data protocols to generate a compatible data stream; the task scheduling module is used for optimizing the data flow and distributing the data flow to a target node according to the compatible data flow in combination with a node priority and a dynamic scheduling strategy in the data flow model; and the exception detection module is used for carrying out exception state analysis on the optimized data stream and judging whether an exception exists or not according to a preset rule. According to the technical scheme, the compatibility and reliability of the monitoring system can be improved.

The invention relates to the technical field of hot working control, and discloses a hot working management and control method, system and device based on digital twinning and a storage medium. The method comprises the steps of obtaining technological parameters and quality data of smelting, casting, forging and pressing and heat treatment procedures through a multi-sensor fusion internet-of-things data acquisition system, constructing a hot working full-process data set and a multi-dimensional technological data platform, achieving parameter mapping and grid conversion through a self-adaptive interface algorithm, establishing a collaborative simulation platform, and obtaining a multi-dimensional technological data platform. And constructing a digital twinborn model and deploying the digital twinborn model to an edge computing gateway to generate a visual management and control system. And calculating a process parameter combination and a resource priority allocation scheme. According to the invention, on the basis of obtaining a large amount of process data in real time, cross-process collaborative optimization and intelligent decision can be realized, especially under the conditions of multi-physics field coupling and complex time-varying characteristics, how to effectively monitor and adjust process parameters in real time and ensure the optimization of product quality and production efficiency.

The invention provides a coal dressing whole process monitoring decision method and system based on Internet of Things sensing, and the method comprises the steps: firstly obtaining a real-time sensing data set of a plurality of monitoring nodes of a coal dressing process, covering equipment vibration, medium density and process flow data, and then carrying out the feature extraction, the method comprises the following steps of: acquiring equipment state, medium dynamic and flow stability characteristics of each monitoring node, performing abnormal correlation analysis on the characteristics based on a pre-trained monitoring decision model, generating abnormal probability distribution and flow adjustment parameters, determining a priority processing queue according to the abnormal probability distribution, and processing the flow according to the priority processing queue. And finally, the coal dressing process optimization strategy is issued to the edge execution terminal, coal dressing process adjustment operation is triggered, effective monitoring and decision optimization of the whole coal dressing process are achieved, and the stability and efficiency of the coal dressing process are improved.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine safety data comprehensive analysis and early warning system which comprises a data integration module, a three-dimensional visualization module, a risk assessment module, a linkage control module, a model training module and a central processing unit and can further comprise a decision support module, a storage cluster and a communication gateway. The data integration module constructs a multi-source heterogeneous data acquisition channel and performs dynamic topology modeling; the three-dimensional visualization module dynamically renders the monitoring data based on the space-time reference axis; the risk assessment module generates a danger situation map through space-time correlation analysis; the linkage control module establishes a multi-level response mechanism; the model training module optimizes the risk prediction model; and the central processing unit schedules each module to operate. According to the system, integrated analysis, dynamic visualization, risk prediction and cross-system linkage disposal of coal mine safety data are achieved, and the intelligent level and emergency capacity of coal mine safety monitoring are improved.

The invention relates to the technical field of artificial intelligence, can be applied to service scenes such as pension service, financial science and technology and medical health, and discloses a multi-agent cooperative task processing method, device and equipment and a medium, and the method comprises the steps: obtaining a task instruction, analyzing a core target, and decomposing the core target into a plurality of subtasks; obtaining environment information, dividing task areas, and generating a cooperation framework in combination with agent capability and area weight; real-time states of the agents are obtained, and the optimal agents are matched based on the cooperation framework to generate a task allocation table; a task distribution table is issued to control the intelligent agent to execute the task and upload execution information; monitoring an execution process, and performing dynamic adjustment and updating a task allocation table when detecting path conflicts or equipment faults; and after the subtask is completed, obtaining environment completion state data, and comparing the data with a preset standard model for acceptance. According to the method, efficient task decomposition and intelligent distribution are realized by fusing task semantics, environment information and intelligent agent capability, and the cooperation stability is improved by introducing a real-time state perception and self-adaptive mechanism.

The invention relates to the technical field of machining control, and discloses a forging part machining process control method and system based on digital twinning. The method comprises the following steps: acquiring temperature field and stress field data streams of large oil press equipment and forgings at multiple points in different areas; performing organization-defect association mode recognition on the data stream to generate a forging process database; a constitutive unit nesting method is adopted to construct a digital twinborn model, and a microstructure evolution unit is embedded in a macroscopic unit; performing nonlinear progressive simulation to obtain a critical deformation condition and a defect threshold value; and carrying out reverse solution on the process window to obtain an optimal process parameter combination. According to the method, the forging digital twin model is established through the constitutive unit nesting method, bidirectional coupling of macroscopic deformation and microstructure evolution is achieved, a stable process parameter combination is determined through the process window reverse solution technology, and therefore the quality stability and production efficiency of forged parts are improved.

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 provides a modal layering enhanced multi-agent cooperation control method and a related device, and relates to the technical field of multi-agent dynamic confrontation and cooperation. Collecting data in real time through a multi-mode sensor, and generating high-order environment state representation; on the basis of high-order environment state representation, dynamic roles are allocated to all agents through a dynamic role migration network; constructing a multi-level strategy system, decomposing a decision into a high-level strategy layer, a middle-level cooperation layer and a bottom-level control layer, and respectively generating a tactical intention, a cooperation relationship and a physical control instruction; low-delay strategy synchronization under key events is realized based on an event triggering mechanism; introducing an antagonism element learning mechanism, and performing strategy prototype retrieval and online fine tuning; executing the bottom-layer control instruction and feeding back an execution state in real time to form closed-loop optimization; role distribution and strategy weight are dynamically adjusted according to feedback data, continuous evolution of the multi-agent cooperation system is achieved, and top-speed adaptation and continuous strategy self-evolution of novel opponents are achieved.

The invention relates to the technical field of industrial data processing, in particular to a suspension spring multi-station production line collaborative scheduling system based on digital twinning, which comprises a station control programming module, a multi-station autonomous collaborative module, a quality pre-control module, a manufacturing process tracing module and a multi-machine execution safety module, according to the system, through real-time data acquisition and monitoring of an integrated digital twin platform, self-adaptive generation of control programs of all stations and programmed issuing of production instructions are carried out; when the production disturbance is sensed, dynamic optimization of a task sequence and real-time self-adaptive adjustment of processing parameters are executed; feedforward control and active compensation are realized in combination with a quality prediction model; constructing a digital thread to support tracing and analysis of the whole manufacturing process; and dynamic program verification of security interlocking and resource conflicts is cooperatively executed on multiple machines. Intelligentized, high-efficiency and high-quality integrated optimization control over the multi-station production process of the suspension spring is achieved.

The invention provides a monitoring information identification method based on the Internet of Things and a server, and the method comprises the steps: firstly obtaining multi-modal perception data which are collected by a plurality of Internet of Things monitoring nodes in a target monitoring area in real time and comprise images, sounds and environment parameters, and then carrying out the cross-modal feature extraction and fusion processing of the multi-modal perception data; the method comprises the steps of generating a comprehensive spatio-temporal characteristic spectrum, determining abnormal event types and spatio-temporal distribution information in a region through dynamic mode matching based on a preset abnormal mode library, generating an adaptive regulation and control instruction set according to the abnormal event types and the spatio-temporal distribution information, and determining control parameters and execution priorities of different Internet of Things execution devices. And finally, distributing the instruction set to an associated Internet of Things execution device cluster, triggering the cluster to execute control parameters according to priorities, eliminating the influence of abnormal events, and realizing comprehensive and intelligent monitoring and exception handling of a target monitoring area.

The invention provides a dust concentration monitoring and treatment method and system for an intelligent furniture production workshop, and relates to the technical field of intelligent furniture, and the method comprises the steps: collecting workshop dust concentration data through arranging a sensor, building a workshop dust concentration distribution three-dimensional model through employing a convolutional neural network, predicting the dust concentrations of different regions and different time periods, and obtaining the dust concentration of the workshop. And according to a prediction result, dividing dust danger level areas. And when the dust concentration exceeds the standard, generating early warning information and sending the early warning information to a manager terminal. And the control center determines the treatment priority according to the dust concentration distribution three-dimensional model, controls the ventilation system and the movable dust removal device to perform graded filtration, optimizes the three-dimensional model on line according to the real-time dust concentration data, and adjusts the treatment strategy. According to the invention, precise monitoring and efficient treatment of workshop dust concentration are realized, dust harm is reduced, and health of workers is guaranteed.

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

The invention provides an automatic production line multi-parameter real-time monitoring and fault diagnosis early warning method and system, and relates to the technical field of automatic production line monitoring, and the method comprises the steps: obtaining the data of a multi-source sensor, carrying out the data quality evaluation and correction, carrying out the monitoring of the performance parameters of an equipment assembly, and calculating the degradation rate; fault types are analyzed and identified in combination with time-frequency domain features, and a fault propagation link is established based on a physical connection relation to determine a fault source. According to the invention, equipment abnormity can be found in time, the fault source component is accurately positioned, the accuracy and timeliness of fault diagnosis are improved, and the equipment maintenance cost is effectively reduced.

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

The invention discloses a multi-mechanical-arm space-time synchronization control method for coiled pipe welding, and belongs to the technical field of automation control, and the method comprises the steps: in a primary laser and TIG hybrid welding execution stage, carrying out real-time data acquisition on a welding area of a workpiece, carrying out real-time deviation detection, and once real-time welding deviation is detected, carrying out time-space synchronization on the welding area of the workpiece; a local correction mechanism is triggered immediately, and deviation is prevented from being accumulated in the single welding process; after one-time welding is completed, the workpiece enters a transition area, global contour reconstruction is conducted on a welded section of the workpiece through three-dimensional scanning equipment, global deviation recognition is conducted, and unprocessed hysteresis deformation and accumulative errors are covered and corrected in real time; on the basis of the analysis result of the global deviation recognition, double correction is conducted on secondary welding, and cooperative control over space track correction and technological parameter adaptation is achieved; and after secondary welding is completed, the correction effect is evaluated through a double-layer evaluation mechanism, and the stability of the correction process is ensured.

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

The invention relates to the technical field of building engineering automation, and discloses a building robot collaborative operation system, a BIM modeling module is used for establishing a three-dimensional model of building engineering and dividing the three-dimensional model into a plurality of operation areas; the information acquisition module is used for acquiring state information and working environment information of the robot; the central control module is used for performing task allocation and operation planning by using an ant colony algorithm; the information sharing module is used for carrying out real-time communication between the robots through wireless communication in the operation process, and sharing operation progress, state information and environment information; the operation evaluation module is used for monitoring the operation progress and state of each robot in real time, evaluating the operation effect of the robot, adjusting task allocation and planning strategies in time according to the evaluation result, optimizing the collaborative operation process of the robot, and improving the operation efficiency of the robot. According to the invention, the problems of unreasonable task allocation, low communication efficiency, difficult operation coordination and the like in the collaborative operation of the existing building robot are solved.

The invention is suitable for the technical field of machine rooms, and provides a machine room environment management and control method based on a digital twinning technology, which comprises the following steps: constructing a digital twinning machine room model based on a three-dimensional structure of a machine room and a machine room equipment layout; real-time load data of each IT device, real-time power data of each cooling device and environmental parameter data of each type of sensor are obtained, thermal load distribution of the machine room is predicted, a preset environmental control strategy is combined, corresponding working conditions are simulated and generated for the machine room in a digital twin machine room model, PUE values of the machine room under the working conditions are obtained through calculation, and the PUE values of the machine room under the working conditions are obtained. Dynamically adjusting an environment control strategy; running state parameters of the IT equipment and video image data in the machine room are obtained in real time, the running state parameters and the video image data are analyzed, and potential abnormal equipment and abnormal areas are determined in the digital twin machine room model. According to the invention, comprehensive optimization of machine room energy efficiency, operation and maintenance efficiency and environment resource configuration is realized, and the intelligent level of machine room management and control is improved.

The invention provides a personnel monitoring method and system based on an intelligent door lock and a storage medium. The method comprises the following steps: in response to a first operation or a second operation, capturing a first face image by a first intelligent door lock, determining that the first face image is a preset category member corresponding to the first intelligent door lock, starting a personnel monitoring function, and recording and sending a first monitoring message to a door lock management platform; if the first intelligent door lock does not capture the first face image within a preset time after the personnel monitoring function is started, a second monitoring message is sent to a door lock management platform; and sending a second monitoring message to a first family member APP associated with the first intelligent door lock ID according to the door lock management platform. Compared with the prior art, the door lock is used for judging the going-out and leaving records of the preset type of personnel based on the personnel monitoring method of the intelligent door lock, so that real-time monitoring is facilitated; through cooperation between the door locks, cooperative attention between the door locks is realized.

The invention relates to the technical field of logistics management, in particular to an intelligent logistics terminal equipment collaborative management and control system based on AI edge computing, and the system comprises an edge data fusion and state recognition module which is deployed at an edge node, collects multi-source data of an operation state, environment perception, a communication link and the like, and generates an equipment state representation vector through fusion; the intelligent prediction and task scheduling decision module uploads the state vector to a cloud, predicts task completion capability and fault probability, and generates a task scheduling decision packet; the scheduling strategy issuing and edge execution collaboration module issues a scheduling packet through a multi-protocol gateway, and an edge node completes task distribution, communication switching and resource scheduling and caches a key strategy. According to the invention, the real-time sensing of the state of the logistics terminal equipment, the intelligent prediction and resource optimization of task scheduling, and the quick response and fault-tolerant control in a fault scene are realized, and the operation efficiency, the intelligent level and the stability of the system are remarkably improved.

The invention discloses an industrial data real-time acquisition monitoring system fusing edge computing and 5G, and belongs to the technical field of industrial Internet of Things. The system is composed of a multi-source heterogeneous data acquisition module, an edge computing node cluster, a 5G communication network and a cloud analysis platform, an edge-cloud collaborative architecture is innovatively adopted, a multi-protocol adapter is integrated to realize unified access of heterogeneous data of industrial equipment, a low-delay transmission channel is constructed by using a 5G network slicing technology, and the heterogeneous data of the industrial equipment is transmitted to the cloud analysis platform. And transmitting the preprocessed data to the edge computing node in parallel. And the edge layer realizes dynamic resource scheduling by adopting a containerization technology, and realizes millisecond-level response and local decision feedback. And meanwhile, through an edge-cloud data synchronization mechanism, a distributed time sequence database is constructed, and visual monitoring and deep analysis of multi-dimensional data are supported. The system improves the real-time processing capability of industrial field data and the reliability of the system, and has the technical advantages of low time delay, high concurrency and optimized resource utilization rate.

The invention discloses a modular reconfigurable production line control system integration method, which relates to the technical field of industrial automation, and comprises the following steps: establishing virtual mapping based on physical attribute parameters to form a production line digital twin basic model framework; collecting data in real time based on an on-site sensor, and establishing a digital twinborn dynamic mapping mechanism synchronous with a physical production line state; a reconstruction scheme is imported into a virtual environment, and key performance indexes are analyzed through a production line digital twin model rehearsal module combination process. A virtual production line model is constructed through a digital twin technology, a rehearsal and verification reconstruction scheme in a virtual environment is supported, trial and error time and cost required by traditional physical debugging are remarkably reduced, a reconstruction strategy is further optimized through a cloud AI algorithm, closed-loop optimization from virtual verification to physical execution is achieved in combination with edge end real-time control, and the real-time performance of the virtual production line is improved. The production line can quickly complete local or overall reconstruction according to production requirements, and the response speed and flexibility of the production line are greatly improved.