409 results about "Smart factory" patented technology

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

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

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

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

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

The invention provides an intelligent factory fault diagnosis method and system based on an AI prediction model, and the method comprises the steps: obtaining an equipment monitoring data flow of a target production line of an intelligent factory, carrying out the diagnosis feature construction processing of the equipment monitoring data flow, generating a state evolution feature and a component correlation feature, and carrying out the fault diagnosis of the target production line of the intelligent factory; and inputting the state evolution characteristics and the component association characteristics into a pre-trained fault prediction model for fault prediction, and generating diagnosis result data containing fault risk levels. And the potential fault type and the propagation characteristic information are identified according to the diagnosis result data, and finally the maintenance guidance data containing the fault positioning identifier are generated based on the potential fault type and the propagation characteristic information and are transmitted to the factory operation and maintenance system to trigger the fault intervention operation, so that the accuracy and the maintenance efficiency of intelligent factory fault diagnosis are effectively improved.

The invention provides an intelligent factory process optimization method and system based on multi-modal data fusion, and the method comprises the steps: obtaining a multi-modal data set generated in the production process of an intelligent factory, carrying out the multi-modal fusion processing of the multi-modal data set, generating a fusion data set, and carrying out the process feature extraction operation based on the fusion data set. The method comprises the following steps: generating a process optimization feature set, carrying out dynamic optimization strategy matching processing according to the process optimization feature set, generating a process parameter optimization strategy, executing the process parameter optimization strategy, carrying out optimization adjustment on a process execution process of the intelligent factory, and monitoring optimized process execution state data in real time to trigger a strategy iteration updating operation. According to the method, the production quality stability and the process resource utilization efficiency can be synchronously improved.

The invention belongs to the technical field of lean manufacturing monitoring, and particularly relates to an anomaly detection and processing method for a production process of an intelligent factory, and the method comprises the steps: constructing a dual monitoring node chain and a layered anomaly association evaluation node chain based on a preset production process flow chain, and collecting the operation state and process parameter data of equipment in real time; the hierarchical anomaly association assessment node chain analyzes the equipment health degree through the operation state assessment sub-chain, mines a cross-process anomaly propagation relationship through the whole-process technology anomaly assessment sub-chain, generates a whole-process association anomaly assessment space, fuses real-time data and historical anomaly strategy index information based on a distributed algorithm, and finally performs the whole-process association anomaly assessment on the basis of the real-time data and the historical anomaly strategy index information. Dynamically constructing a difference exception processing strategy library, and realizing processing strategy synchronous feedback of positioning exception; according to the invention, through a dual monitoring architecture and hierarchical association evaluation analysis, the problems of fuzzy abnormal propagation path and response lag are solved, and the real-time performance and processing accuracy of abnormal detection are improved.

Provided is a concrete full lifecycle green quality control method based on Internet of Things and big data, comprising the following steps: step 1, on the basis of an information platform, selecting raw materials required by concrete, so as to achieve the control of the selection of the raw materials of concrete; step 2, on the basis of the information platform, performing engineering concrete mix ratio intelligent design, and obtaining the mix ratio of the raw materials required by concrete corresponding to a project under construction; step 3, on the basis of the information platform, taking into account the obtained concrete mix ratio to mix and prepare concrete by means of an intelligent concrete factory, and automatically testing and controlling the quality of the prepared concrete at the same time; and step 4, on the basis of the information platform, using the mixed and prepared concrete to perform intelligent construction. Automatic, intelligent and green control of concrete throughout the full lifecycle from raw materials, mix ratio design, production, and construction to the service life of the structure are achieved by controlling each stage of the full lifecycle of the concrete.

The invention discloses a smart factory management system and method based on MES, and relates to the technical field of smart manufacturing, and the method comprises the steps: calculating a health degree score based on equipment historical maintenance records, and generating a time-space correlation multi-dimensional analysis data set containing an equipment topological structure and health degree parameters in combination with a digital twin network; performing spatial-temporal feature coupling and topological weight dynamic adjustment on the spatial-temporal correlation multi-dimensional analysis data set through a dynamic topological analysis algorithm, and generating a high-risk equipment list and an anomaly control instruction set; based on the high-risk equipment list, constructing a standardized multi-dimensional abnormal feature vector, and generating an aging weighted danger level signal through an entropy weight method; according to the method, space-time alignment of equipment operation state data and physical topology data is realized through a multi-rate Kalman filtering algorithm and an iterative nearest point algorithm, a high-fidelity digital twin network is constructed in combination with a dynamic graph convolutional network, and accurate anomaly detection and health degree evaluation are supported.

The invention discloses a toxic and harmful substance leakage reinspection method, device and equipment based on an unmanned aerial vehicle, and a storage medium, relates to the technical field of Internet of Things and intelligent factories, and aims to solve the problem that the detection accuracy and efficiency of toxic and harmful substances are low. The method comprises the following steps: dividing a target area into a plurality of grids, and acquiring initial gas concentration data of each grid in real time through a fixed foundation sensing array; the grids with the initial gas concentration data larger than the preset concentration are determined as abnormal grids, and a reinspection flight path is dynamically generated according to the unmanned aerial vehicle parking position, the abnormal grid position and the environment parameters; the unmanned aerial vehicle is controlled to fly to the abnormal grid according to the reinspection flight path, and multi-dimensional data including visible light images, infrared thermal imaging data, gas component spectrums and space coordinate data of the abnormal grid are collected; and fusing the initial gas concentration data and the multi-dimensional data of the abnormal grid, constructing a three-dimensional dynamic concentration field model, and outputting leakage source position information and a diffusion trend.

The invention relates to the technical field of equipment cooperative control, and discloses a multi-equipment cooperative control method and system under an autonomous cooperation algorithm, and the method comprises the steps: collecting the physical attribute data, real-time operation data, production task data and a smart factory scene graph of operation equipment under a multi-equipment cooperative task in a smart factory application; based on the physical attribute data and the real-time operation data, identifying a space conflict mode and a time conflict mode of the operation equipment to obtain collaborative conflict information; according to the production task data and the collaboration conflict information, carrying out collaboration conflict decoupling on the operation equipment to obtain a feasible collaboration mode; performing obstacle avoidance reinforcement learning on the operation equipment by using the smart factory scene graph to construct an obstacle avoidance global path of the operation equipment in the smart factory; and based on the feasible coordination mode and the obstacle avoidance global path, constructing a target coordination control scheme of the operation equipment so as to perform coordination control on the operation equipment. According to the invention, the reliability of multi-device cooperative control under the autonomous cooperation algorithm can be improved.

The invention relates to the technical field of smart factory management platforms, and discloses a data real-time monitoring method based on a smart factory management platform system, and the method comprises the steps: carrying out the standard format conversion of supplier Excel data, carrier GPS track data flow and warehouse RFID goods location data in a smart factory ERP system, and obtaining standardized business data; calculating comprehensive scores of suppliers based on the standardized business data and generating a dynamic sorting list; receiving purchase order information and creating a logistics scheduling plan, and pre-allocating an optimal goods allocation for the warehousing commodities to obtain a warehousing pre-allocation scheme; monitoring the execution progress of the storage pre-distribution scheme in real time, and synchronously updating service state information; the auditing state of the purchase order information is monitored based on the service state information, and an intelligent collaborative decision-making scheme is generated, real-time synchronous updating of the service states of the supplier management module, the logistics scheduling module and the warehouse management module is ensured, and the supply chain collaborative management efficiency is improved.

The invention discloses an intelligent factory data intelligent analysis and management system, and relates to the technical field of data analysis. Multi-source data are uniformly accessed through an industrial gateway, and a knowledge graph is constructed after cleaning and standardization; an edge node deploys a lightweight AI model to realize real-time analysis such as equipment anomaly detection; intelligent distribution of cloud side tasks is realized based on a decision model; optimizing production scheduling and quality control by using an algorithm; a zero-trust architecture is adopted to guarantee safety, and efficient energy management is realized in combination with reinforcement learning; all the modules work cooperatively, and the intelligent level of a factory is improved. The operation efficiency and quality of the intelligent factory are effectively improved. Efficient data fusion processing is realized, and equipment anomaly detection is more accurate; the order delivery period is shortened, and the product reject ratio is reduced; network security protection is enhanced, and energy waste is reduced; the decision response speed is accelerated, the decision accuracy is improved, cost reduction and efficiency improvement of enterprises are comprehensively assisted, and the competitiveness is enhanced.

The invention discloses an AI-based intelligent factory dynamic production scheduling optimization method and system, and belongs to the technical field of factory dynamic production scheduling, and the method comprises the following steps: obtaining and integrating the working states of various types of equipment in a factory, the stock position states of different materials, and the manual data; the production order state, the process route requirement, the plan and the completion time of each process and the delivery date requirement of the customer are determined; generating an initial production scheduling plan through an AI optimization algorithm in combination with supply chain material data, factory storage space, equipment switching cost, production rules, each order process dependency relationship and a production target; according to the method, the initial scheduling plan is generated by acquiring and integrating multiple types of production data, the dynamic events are monitored, multiple schemes are generated through evaluation, the optimal scheme is selected through economic model evaluation, and production refinement, dynamic response and benefit optimization are achieved.

The invention relates to the technical field of data analysis, provides an intelligent factory automatic monitoring method and system based on knowledge base enhancement, and realizes more accurate anomaly analysis and more effective process adjustment of an intelligent factory. The method comprises the steps of performing knowledge enhancement fusion processing on an obtained real-time monitoring data set of an intelligent factory through a pre-constructed process knowledge base and a pre-constructed monitoring rule base, and generating a process knowledge graph; performing abnormal mode recognition processing on the process knowledge graph based on a semantic matching strategy, extracting feature description of an abnormal event and a semantic association path with a historical monitoring text, and generating an abnormal mode analysis result containing abnormal root cause inference; according to the abnormal mode analysis result and the dynamic incidence relation in the process knowledge graph, an automatic monitoring report containing root cause priority ranking and optimization operation guidance is generated, and the automatic monitoring report is fed back to the intelligent factory control terminal to trigger process adjustment operation.

The invention relates to the technical field of smart factory decision-making, and discloses a digital twin-driven smart factory AI intelligent decision-making system, which comprises a data acquisition and preprocessing module used for acquiring and preprocessing real-time and historical data of a production line; the twinborn modeling identification module is used for constructing a twinborn body and carrying out parameter identification and uncertainty quantification; the alignment evaluation credible module is used for comparing twin prediction data with real data and generating availability marks and trust scores; the strategy simulation optimization module is used for generating candidate strategies and risk evidences based on the constraints and the key performance indicators; the grayscale online publishing module is used for screening and grayscale publishing a strategy according to the trust score and the performance result; the operation evaluation auditing module is used for collecting operation data and generating an auditing packet; and the drifting detection updating module is used for detecting distribution drifting and recalibrating and updating the twinborn body and the strategy library. According to the invention, the reliability, robustness and continuous optimization of the production decision of the smart factory are realized.

The invention provides an intelligent factory management method and system based on the industrial internet, and the method comprises the steps: obtaining original data of a multi-source heterogeneous device, and generating a semantic event structure through preprocessing; the industrial event sequence is mapped into nodes, candidate edges are generated in combination with the physical topology connection relation of the equipment, and a target causal map is constructed; according to the target causal atlas, calculating a node risk score of each event node, and performing balance adjustment on the score by using a graph-level centrality value to generate a normalized node risk score of each event node; mapping the node risk scores to devices, and calculating a risk average value of each device; and converting a scoring matrix of the current task to all candidate devices into a scheduling strategy value, executing a scheduling strategy and collecting task execution feedback, and dynamically adjusting a target causal atlas and a node risk score according to the difference between an actual result and a predicted value to realize dual optimization of the target causal atlas and the scheduling strategy.

The embodiment of the invention provides an intelligent factory semantic decision generation method and system based on a knowledge graph, and the method comprises the steps: obtaining an operation data set of an intelligent factory, carrying out the semantic feature extraction of the operation data set, and generating the target semantic feature of an equipment operation parameter and the context correlation feature of a semantic description text; and based on a pre-constructed knowledge graph structure, performing dynamic semantic matching processing on the target semantic features and the context association features, generating a semantic decision instruction set corresponding to the equipment operation parameters, generating an equipment control strategy set according to instruction priorities and instruction execution conditions in the semantic decision instruction set, and sending the equipment control strategy set to a server. And feeding back the equipment control strategy set to a control system of the intelligent factory to trigger operation optimization operation, and updating a semantic node association relationship in the knowledge graph structure. According to the method, the problem of fragmentation of equipment operation state representation is effectively solved, and the interpretability of feature extraction is improved by utilizing a collaborative verification mechanism of numerical parameters and text description.

The invention provides an intelligent factory production optimization method and system based on AI scheduling, and belongs to the technical field of intelligent factory production management.The method comprises the steps that firstly, process disassembling is conducted on batch production orders, an order task atlas is obtained, a dynamic resource pool is constructed according to the real-time resource state of a factory, the order task atlas is input into a pre-trained scheduling AI model, and a scheduling AI model is established; a dynamic resource pool is combined to generate multiple groups of initial scheduling paths, conflict detection and conflict point identification are performed on the initial scheduling paths, an adaptive adjustment module is called to correct parameters based on conflict point types and influence ranges, a final production optimization scheme is generated and imported into a factory execution system, and intelligentization and high efficiency of production scheduling are realized. And the production efficiency and benefits are improved.

The invention belongs to the technical field of factory production scheduling, and discloses an MES (Manufacturing Execution System)-based capacity balance type intelligent factory scheduling system, which comprises a preprocessing module for realizing the function of intelligently selecting a production strategy according to a current production mode, and improving the flexibility and accuracy of production decision making. The data execution module ensures the stable execution of the production plan, and can timely discover and respond to the change of the production plan, thereby avoiding the confusion and delay in the production process. And secondly, through introduction of the change scheduling module and the plan change module, the production plan change process is more scientific and efficient. By storing and calling a change algorithm, the system can automatically analyze the relation between order performance indexes and reasonably adjust the weight of each performance index of production, so that a new production plan better meeting actual requirements is formulated.

The invention relates to the technical field of shipbuilding production site management, in particular to a digital intelligent factory management method and system, and the method comprises the steps: building a configurable product structure model, a work decomposition structure model and a technological process model for each ship type, and carrying out the real-time sensing and automatic data collection of parameters of a production site; establishing a uniform resource pool, and marking attributes of resources; dynamically evaluating the requirements of different ship types on shared resources in combination with plans and real-time production states; carrying out resource allocation by using an optimization algorithm; the execution state of each process task is monitored in real time, and key production data is automatically collected; and visually displaying each ship type, the construction progress of each section, the critical path, the resource state and the risk early warning information in real time through a billboard, an instrument panel and a report form. According to the invention, through multi-ship-type unified modeling, real-time data perception and intelligent optimization scheduling, collaborative management, cost reduction and benefit increase in the ship building process are realized.

The invention relates to the technical field of equipment control optimization, in particular to an intelligent factory equipment energy efficiency optimization system which comprises the following steps: acquiring wall thickness distribution data of a steel pipe, local residual stress and a temperature gradient field in a straightening process; and a preset plastic mechanical model is used for calculating the actual yield strength and the theoretical straightening energy threshold value of each section of the steel pipe, so that a straightening parameter set of the steel pipe straightening machine is generated. And further correcting the straightening parameter set according to the temperature gradient field so as to optimize and eliminate energy consumption. A steel pipe straightening area is divided, a heat sensitive area and a low-temperature hardening area are identified, correction weights are distributed according to the area difference of the areas, then a corrected straightening parameter set is generated, and the straightening process of the steel pipe is optimized.

The invention provides an intelligent factory manufacturing optimization method and system based on energy consumption prediction, and the method comprises the steps: carrying out the time sequence structural processing of historical energy consumption record data continuously collected in a factory manufacturing process, and generating an energy consumption sequence unit with a continuous time stamp through time window division and data correlation verification; constructing a manufacturing process-energy consumption association knowledge graph based on the energy consumption sequence unit, and establishing a structured knowledge representation containing node attributes and edge relationship weights by identifying a causal association relationship between process execution nodes and energy consumption fluctuation features; calling a pre-trained graph neural network model to carry out time sequence evolution prediction processing on the graph, and generating an energy consumption prediction sequence of a future preset manufacturing cycle; and according to the energy consumption prediction sequence, performing dynamic scheduling optimization on the execution sequence of the current manufacturing process and the resource allocation scheme, and generating an energy consumption optimization oriented manufacturing execution instruction. The energy consumption management level and the resource utilization efficiency in the manufacturing process of the intelligent factory are improved.

The invention relates to the technical field of intelligent manufacturing and industrial Internet of Things, in particular to an intelligent factory equipment safety management system and method, and the system comprises an identity authentication module, a dynamic safety interaction module, an intelligent monitoring response module and a self-adaptive optimization module. An identity authentication module; through fusion of multi-dimensional information authentication, block chain decentralized storage, intelligent algorithm behavior analysis, encrypted communication and fine-grained authority control, multi-source data threat detection and reinforcement learning driven strategy optimization, a whole-process security system is constructed. The method comprises the steps of identity authentication, security interaction, monitoring response and adaptive optimization. The problems that in a traditional industrial scene, equipment identity authentication is fragile, data transmission is not safe, threat response lags and strategies are staticized are solved, high-safety authentication, dynamic encryption communication, real-time threat response and strategy self-optimization are achieved, and the equipment safety protection capacity and the system intelligence level are improved.

The invention particularly relates to an intelligent factory integrated operation management method based on multi-system integration, and relates to the technical field of intelligent factory operation management. Performing intelligent planning and robustness decision making; performing production execution and monitoring; and carrying out equipment full-life-cycle management. According to the method, the limitation of a traditional static plan is thoroughly broken through through a collaborative mechanism of multi-dimensional risk index linkage, dynamic time buffering and a plan chain; on one hand, the DTB performs real-time calculation based on a supply chain risk index, an equipment health index and a process stability index, approaches reference buffer to avoid resource waste when the risk is low, automatically expands to absorb disturbance when the risk is high, and ensures dynamic balance of plan efficiency and disturbance resistance; and on the other hand, the pre-generated plan chain is rapidly triggered when the disturbance exceeds the limit, global rearrangement is not needed, and continuous production can be guaranteed only through local adjustment.

The invention discloses a smart factory energy efficiency optimization method, system and device based on the industrial Internet of Things, and relates to the technical field of the industrial Internet of Things, and the method comprises the steps: collecting production energy consumption data based on equipment operation parameters and production plan information; constructing a factory digital twinborn model, and determining an equipment start-stop time sequence and an energy distribution strategy; deploying edge computing nodes, monitoring the vibration spectrum of the equipment, generating a state prediction map in a time sliding window, and performing adaptive compensation optimization; and meanwhile, an energy flow network is drawn up based on the equipment start-stop time sequence and the energy distribution strategy, and energy dynamic adjustment is carried out. The technical problems of low energy utilization efficiency and insufficient equipment operation stability of a smart factory in the prior art are solved, and the technical effects of realizing smart factory energy efficiency optimization based on the industrial Internet of Things and improving the energy utilization efficiency and the equipment operation stability are achieved.

The invention relates to the technical field of computers, and provides an intelligent interaction guiding method and system based on user intention recognition. According to the embodiment of the invention, deep and comprehensive user interaction analysis is realized; the user demand dynamic state can be accurately captured by acquiring the interaction data set in real time; and context association features and interaction behavior features generated by multi-modal feature extraction provide a rich and meticulous information basis for intention analysis. On the basis, a preset intention recognition rule base is used for carrying out joint intention analysis processing, an intention label set and user portrait features are generated, accurate insight of the user intention is achieved, and then an interaction path guiding strategy is generated and pushed to a target user terminal in real time to update an interaction interface. Therefore, the method and the system can closely meet user requirements, provide personalized operation guidance, remarkably improve man-machine interaction efficiency and user operation accuracy, and enhance the intelligent service level of an intelligent factory.

The invention provides an intelligent management system and method based on multi-agent cooperation. The intelligent management system comprises a data acquisition module used for acquiring production field data in real time; the data processing module is used for data cleaning; the large model module is used for performing semantic analysis on a task input by a user based on a large model; the agent module is used for performing task scheduling based on a TSQlearning scheduling algorithm; and the RAG knowledge base module is used for constructing an RAG knowledge base and providing knowledge retrieval based on hierarchical indexing and a mixed retrieval mechanism. According to the method, user requirements are analyzed through the large model module, the task comprehensive scheduling capability is improved through the distributed decision of the agent module and the TSQlearning algorithm, the resource utilization rate is improved, the information retrieval accuracy is improved from 70% to 92% through the hierarchical indexing and mixed retrieval mechanism of the RAG knowledge base, and the management efficiency and the intelligent level of the smart factory are improved.