4050 results about "Management efficiency" patented technology

The invention relates to the technical field of intelligent building management, in particular to an intelligent building full-life-cycle monitoring management method and system, and the system comprises a data collection module, a multi-source data fusion and analysis module, a full-life-cycle management model construction module, an intelligent early warning module, a self-adaptive feedback regulation and control module, a data storage module and a remote visual monitoring module. According to the system, environment parameters and equipment operation state data of a building are acquired in real time through the data acquisition module, key operation indexes are extracted by using the multi-source data fusion and analysis module, and accurate risk assessment and early warning are realized under the support of the full-life-cycle management model. The self-adaptive feedback regulation and control module can dynamically adjust key operation parameters according to real-time data to ensure stable operation of equipment under different environment conditions, the data storage module supports long-term performance analysis, and the remote visual monitoring module realizes remote monitoring and multi-party collaborative management. According to the invention, the operation safety, stability and management efficiency of hydraulic facilities are effectively improved.

The invention relates to the technical field of information security, in particular to a power distribution Internet of Things cloud side end collaborative interaction security protection system, which comprises a network flow monitoring module, an abnormal flow identification module, a security threat response module, a network attack early warning module, a behavior pattern analysis module and an access authority management module. According to the method, the network flow data is collected and analyzed in real time, the abnormal flow recognition speed and accuracy are enhanced, the security threat response speed and efficiency are improved, measures are taken in time at the early stage of a problem, the risks of data leakage and network attacks are reduced, and the timeliness of defense measures is improved by combining security threat response and network attack early warning; by analyzing the relevance between threat events, the complex multi-stage attack activity is effectively predicted and identified, the access data and the communication mode of the equipment are monitored in real time, and the access authority and the trust level are adjusted, so that the security protection is more targeted and flexible, and the management efficiency of the risk equipment is improved.

The invention relates to the technical field of food storage management, in particular to a food storage management system, which comprises a data acquisition layer, is composed of a plurality of devices and is used for acquiring food, environment and user operation data; the data transmission layer transmits data by using wired and wireless network technologies; the data storage layer stores various types of data in combination with a relational database and a non-relational database; the data processing layer comprises a plurality of functional modules, such as an intelligent warehousing management module, and is used for realizing automatic input verification and batch management of food information; the intelligent ex-warehouse management module is used for optimizing ex-warehouse sequence and tracking food directions; the expiration and waste management module is used for carrying out expiration reminding and surplus value evaluation; the data analysis and purchase optimization module is used for predicting demands and analyzing loss; and the dish and inventory association module is used for matching the dish formula and the inventory and predicting the sales allocation inventory. The system improves the management efficiency, reduces the waste, optimizes the purchase decision, enhances the dish and inventory association management, guarantees the system safety, and provides data support to assist the decision.

The invention discloses an urban greening system based on big data and an optimization method thereof, relates to the technical field of artificial intelligence, and solves the problems of difficulty in heterogeneous data integration, insufficient real-time analysis capability of dynamic data and low management efficiency in an existing urban greening system. Multi-source heterogeneous data are collected through an API interface and Internet of Things communication, and are cleaned, aligned, converted and fused through a distributed real-time data stream processing platform; image recognition and a machine learning algorithm are used for monitoring diseases and insect pests in real time, and a growth prediction model is trained in combination with an LSTM network, so that the quick response capability of the system to environmental changes is enhanced; according to the method, target weights are dynamically adjusted and optimized through the deep Q network, a multi-target optimization model is constructed in combination with mixed integer linear programming, context awareness and a genetic algorithm, intelligent optimization of irrigation, fertilization, pest control and greening layout is achieved, and the management efficiency and the resource utilization rate of an urban greening system are remarkably improved.

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

The invention discloses a multi-terminal vehicle management system based on an intelligent algorithm, relates to the technical field of vehicle management, and is used for solving the problem of insufficient real-time performance and cooperative efficiency of vehicle management. According to the method, a multi-modal sensor fusion technology is combined with Gaussian process regression, complex environment data is modeled in real time, dynamic target and static background features are accurately extracted, environment changes are analyzed and predicted in combination with a time sequence, accurate input is provided for path planning and task allocation, a path is dynamically adjusted through an incremental optimization algorithm, emergencies are coped, and the path planning and task allocation efficiency is improved. According to a distributed path optimization mechanism, multi-vehicle task path conflicts are eliminated, the cooperation efficiency is improved, a priority model is constructed based on the task emergency degree, the time constraint and the resource matching degree, the weight is dynamically adjusted, vehicle and task matching is optimized in combination with reinforcement learning, the task execution efficiency is ensured, resource competition and path conflicts are solved, and the task execution efficiency is improved. Efficient cooperation in a complex dynamic environment is realized, and the real-time performance and management efficiency of vehicle management are improved.

The invention discloses a building intelligent operation and maintenance management system and method based on big data analysis, and relates to the field of building intelligent operation and maintenance, and the system comprises a data collection layer including high-precision multi-type sensors and adaptive collection and transmission, a data storage layer including distributed storage and high-redundancy cache, and a big data analysis layer including data cleaning and data storage. The operation and maintenance decision-making layer is used for generating a strategy according to analysis and performing dynamic adjustment; the user interaction layer is used for performing visual interface and convenient feedback; the intelligent early warning module is used for performing dynamic threshold and accurate early warning; the analog simulation optimization module is used for performing model simulation and VR / AR auxiliary decision making; and the operation and maintenance management level and the energy utilization efficiency are improved. The system integrates building data, monitors equipment in real time, accurately predicts faults and energy consumption, optimizes operation and maintenance decisions, improves the management efficiency and the energy utilization rate, reduces the cost, and improves the user experience.

The invention relates to the technical field of data processing, in particular to a cloud backup platform data deduplication method based on distributed storage. Comprising the following steps of: performing self-adaptive blocking on data by utilizing a dynamic blocking algorithm, generating a unique identification code and extracting multi-dimensional feature information; establishing a global index through a distributed hash table, mapping a data position and maintaining index consistency; performing redundancy analysis and similarity matching based on a deep learning algorithm, and executing a duplicate removal strategy; according to the access frequency and the load condition, distributed storage of data blocks is realized by adopting a hierarchical graph division algorithm; and a partitioning strategy, index frequency and storage layout are dynamically optimized by using a reinforcement learning algorithm, and global optimization of performance and resources is realized. According to the invention, high efficiency, accuracy and dynamic optimization of data deduplication are realized, and the data storage and management efficiency of the cloud backup platform is improved.

The invention discloses a poultry breeding monitoring management system based on big data, and relates to the technical field of poultry breeding supervision, the poultry breeding monitoring management system monitors the environment in a poultry house in real time, immediately triggers a behavior monitoring module when abnormality is detected, generates a specific environmental abnormality score, and combines visual monitoring and infrared thermal imaging to realize the monitoring and management of poultry breeding. According to the method, individual behavior characteristics of each poultry are effectively extracted, an individual behavior data stream is generated, individual potential health risks are identified, a health archive is adopted to accumulate long-term behaviors and environmental data of each poultry, and a time sequence analysis model is utilized to identify individual behavior modes and health change trends. Possible health risks are predicted based on historical data and models, management perspectiveness and disease prevention and control efficiency are effectively improved, meanwhile, abnormal features with the most influence are automatically recognized through comprehensive risk scoring and contribution degree analysis and are pushed to management personnel, the health archive of each poultry is updated in real time, and the management efficiency is improved. And the overall operation efficiency of the farm is improved.

The invention relates to the technical field of raw material management systems, in particular to a raw material management system for building engineering construction. The management system comprises a raw material information management module, a purchase management module, an inventory management module, a use management module, an optimization allocation module, a raw material quality evaluation module, an environmental impact evaluation and material selection module, a supply chain transparency module, a life cycle management module and a raw material demand prediction and inventory optimization module. An automatic purchase and supplier collaborative optimization module; the system uniformly manages data information of links of raw material purchasing, inventory, use and the like through a centralized data platform, forms a complete material management chain, avoids information dispersion and lag, realizes real-time sharing of data of each link, improves transparency of management and accuracy of decision making, and meanwhile, greatly improves management efficiency. The complexity and error rate of manual operation can be remarkably reduced through automatic purchase order generation, intelligent inventory management and supplier collaboration.

The invention discloses a health monitoring method and system for an intelligent building, and belongs to the field of building construction health monitoring, and the method comprises the steps: obtaining internal and external multi-dimensional data of the intelligent building in real time through a plurality of sensors, the multi-dimensional data comprising environment data, structure data of the intelligent building, equipment state data and personnel activity data; preprocessing the multi-dimensional data to obtain preprocessed multi-dimensional data; according to the preprocessed multi-dimensional data, a health condition analysis result of the intelligent building is output by utilizing a pre-trained health monitoring model, and the health condition analysis result comprises the suitability degree of environmental conditions, the stability degree of a building structure, the equipment health degree and the risk degree of personnel activities; when the health condition analysis result indicates that the intelligent building is abnormal in health, an early warning signal is sent out; and determining a repair scheme of the intelligent building according to the type of the health abnormality. According to the method, the safety, comfort and management efficiency of the building can be improved.

The invention discloses a multi-protocol intelligent interaction heterogeneous device full interconnection method and system, and belongs to the technical field of computers, and the method comprises the steps: S1, collecting data, and deploying edge computing nodes; s2, preprocessing the collected data; s3, performing classification according to task requirements of the data; s4, after classification, determining an optimal communication protocol suitable for the current network condition; s5, realizing data format standardization and communication protocol unification by adopting an OPC UA protocol; s6, on the basis of an OPC UA publishing / subscribing mechanism and a QoS priority mechanism, allowing the equipment to publish or subscribe data in real time and ensuring priority transmission of high-priority data; s7, performing data matching and association, and fusing the data; s8, realizing a global decision; according to the multi-protocol intelligent interaction heterogeneous equipment full interconnection method and system provided by the invention, by introducing protocol unification, intelligent interaction and distributed optimization technologies, the interoperability of multi-protocol equipment and the overall management efficiency of a traffic system can be effectively improved.

The invention relates to the technical field of smart energy management, in particular to a public energy consumption equipment operation and maintenance management system suitable for a smart park. The system comprises an equipment operation state sensing module, an energy consumption characteristic dynamic analysis module, an operation parameter optimization module, an equipment state balance regulation and control module, an equipment operation safety constraint verification module and an equipment cooperative operation execution module. According to the invention, through combination of time sequence analysis and load fluctuation trend extraction, dynamic monitoring and trend arrangement are realized, the accuracy and comprehensiveness of operation data are improved, the operation diagnosis capability is improved, the parameter adjustment flexibility is enhanced, multi-device cooperative regulation and control and load balancing are realized, energy consumption fluctuation is reduced, and the system efficiency is improved. And in combination with safety verification and offset risk analysis, potential risk areas are adjusted in real time, the operation reliability is enhanced, a global regulation and control scheme is dynamically generated, energy consumption waste is reduced, the management efficiency of public energy consumption equipment is comprehensively improved, and the efficient energy-saving target of the smart park is achieved.

The invention relates to the technical field of petroleum and natural gas engineering, and discloses a wellbore life cycle management system and method, and the system comprises multiple modules of wellbore design, construction monitoring, integrity evaluation and the like. Accurate design parameters are generated by obtaining geological exploration data, multi-sensor data are collected in real time to guarantee construction safety, integrity is evaluated according to multi-source data, the corrosion rate is predicted, a time-space correlation algorithm is used for integrating data to recognize an abnormal coupling effect, then a risk level signal and an early warning instruction set are generated, and an optimization maintenance scheme is formulated. According to the method, the design accuracy and stability of the shaft can be remarkably improved, the condition of the shaft is comprehensively and accurately evaluated, corrosion is accurately predicted, and risk early warning and maintenance decision making are scientifically carried out. Meanwhile, the management efficiency is improved through visual display, many problems of traditional shaft management are effectively solved, the operation and maintenance cost is reduced, and the safety and economical efficiency of energy exploitation are improved.

The invention relates to the technical field of artificial intelligence, in particular to a multi-agent cooperative communication and real-time resource optimization management method and system based on large model driving, and the method comprises the following steps: semantic understanding and task analysis, communication protocol and mechanism design, resource demand prediction and allocation scheduling, information sharing and interaction, and feedback and evaluation mechanism. The method has the beneficial effects that efficient reliability allocation and feedback error correction are realized through a multi-agent reinforcement learning framework by utilizing powerful reasoning and generalization capabilities of a large model, so that the communication and cooperation capabilities among multiple agents are remarkably improved, and meanwhile, the method is combined with a real-time resource optimization management technology, and through reasonable resource allocation and scheduling, the real-time resource optimization management efficiency is improved. And maximum utilization of resources is realized.

The invention discloses a data circulation method and device for an integrated platform and a finance and tax integrated platform, and the method comprises the steps: enabling an RPA module to convert multi-source data into structural data, and recognizing the financial information in the structural data; the RPA module classifies and sorts the financial information and determines the processing sequence of the classified and sorted financial information; the CL module analyzes the financial information according to the processing sequence, determines risk data and key data of the financial information, and generates a first processing strategy and a second processing strategy; the iPaaS module receives the risk data and the key data and transfers the risk data and the key data to the data security monitoring module; and the data security monitoring module carries out risk investigation on the risk data by adopting a first processing strategy, encrypts the key data by adopting a second processing strategy, and transfers the risk data and the encrypted key data to a corresponding service processing platform. The financial management efficiency can be effectively improved, and safe data circulation between systems is achieved.

The invention relates to the technical field of bridge construction, solves the technical problem that progress or quality data cannot be updated in real time due to difficulty in direct synchronous data feedback between a construction site and a management system, and particularly relates to a BIM-based intelligent two-dimensional code bridge construction quality and progress management and control system. The geometric information, the construction progress and the quality standard of the bridge structure are recorded in detail, and a unique two-dimensional code is generated for each construction node. A constructor obtains real-time node information by scanning the two-dimensional code, updates the construction progress and uploads quality detection data, and analyzes and feeds back the construction data in real time, so that efficient management and control of the construction progress and quality are ensured. The system overcomes the defects in the prior art, has the characteristics of timely information updating, accurate data, automatic management, high traceability and the like, and can remarkably improve the management efficiency and the quality control level of bridge construction.

The invention provides a building equipment operation data intelligent analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: extracting historical positioning data in a space calibration interval, and calculating a position offset vector set between a predicted trajectory point and an actual trajectory point according to the trajectory point on a predicted motion trajectory; taking the weighted sum of squares of minimizing the position offset vector as an optimization target to obtain a three-dimensional space transformation parameter optimization model; according to the optimization model, a quasi-Newton optimization algorithm is adopted to solve the optimization model, and a final calibration parameter set is generated; and based on the final calibration parameter set, correcting the predicted motion trajectory, performing spatial relationship analysis on the corrected trajectory and a spatial risk field, calculating a collision risk probability and an accident severity level, generating a comprehensive risk score, and triggering a response action according to a preset threshold value. The construction safety level and the management efficiency are improved.

The invention discloses a data management method based on a distributed storage system, relates to the technical field of data management, and is used for solving the problem of storage management behavior strategy mismatch. On the basis of dynamic perception of data access behaviors in a distributed storage system, a behavior feature vector fusing an access mutation rate, periodicity and an access span is constructed, behavior pattern recognition and strategy structure generation are completed, optimal deployment and hierarchical storage of copies are achieved through node resource state perception and construction of a cost function, and the strategy structure generation efficiency is improved. And after compression and consistency configuration are executed and the strategy falls to the ground, the compressibility and the offset trend of the behavior path are analyzed, scheduling management information is extracted, and a stable execution or strategy adjustment signal is generated, so that the problem of strategy execution mismatching caused by behavior perception deficiency in the distributed storage system is reduced, and the strategy execution efficiency is improved. The strategy closed-loop control and the resource scheduling optimization under behavior driving are realized, so that the data management efficiency and the strategy adaptation capability of the distributed storage system are improved.

The invention discloses an intelligent water quality monitoring device based on Internet of Things, relates to the technical field of environmental monitoring, and aims to analyze a complex nonlinear relationship in water quality data, accurately predict a water quality change trend and identify potential water quality problems in advance through a deep learning model and a system. The dynamic weight calculation module can dynamically adjust the importance of water quality parameters according to real-time data and environmental changes, the monitoring precision and adaptability are improved, an intelligent feedback mechanism automatically adjusts the sampling frequency and the sensor position when an early warning signal is triggered, accurate monitoring of the water quality changes is ensured, and the monitoring accuracy is improved. The Internet of Things technology supports real-time transmission of water quality data and analysis results to the cloud, managers can check water quality states, historical trends and early warning records through a remote monitoring interface, the response capability and management efficiency of the system are improved, and the intelligent water quality monitoring device has the advantages of being high in precision, high in intelligence and high in adaptability.

The invention provides a distributed storage and data warehouse permission synchronization method and system, and relates to the technical field of permission management, and the method comprises the steps: analyzing user operation data of distributed storage, training a machine learning model, and generating a directory permission intelligent mapping table; analyzing the structure information of the data table in the data warehouse, and generating a multi-dimensional table authority evaluation model; generating a self-adaptive permission rule base by combining the two, and deploying the self-adaptive permission rule base to each node of distributed storage; when the permission is changed, the system calculates an influence range according to the self-adaptive permission rule base and generates a permission updating instruction sequence; and the system monitors the instruction execution state, identifies abnormal nodes, and performs repair based on the permission propagation tree to ensure the permission consistency. According to the method, automatic synchronization of distributed storage and data warehouse authority can be realized, the authority management efficiency is improved, the manual intervention cost is reduced, and the data security is enhanced.

The invention relates to the technical field of power systems, in particular to a power grid operation risk level visualization intelligent management tool system. The system obtains power grid operation data through an Internet of Things sensor network, wherein the power grid operation data comprises current, voltage, equipment operation state and other multi-dimensional information; performing real-time analysis on the acquired data by adopting multi-level clustering analysis, a predictive maintenance algorithm and a deep learning model to realize abnormal behavior identification and risk prediction; a dynamic risk map is constructed based on a GIS technology, and accurate positioning and real-time display of risk levels are realized in combination with regional feature recognition and personalized threshold setting. According to the invention, multi-source data acquisition, intelligent analysis and visual presentation are creatively and organically integrated to form a closed-loop risk management system, so that the system has adaptive learning ability. Practical application shows that the system can early warn potential risks 30 minutes in advance, the operation and maintenance response time is shortened to 3 minutes, and the power grid operation risk monitoring and management efficiency is remarkably improved.

The invention provides an intelligent media resource multi-modal knowledge graph management method based on a large model. The method comprises the following steps: S1, collecting and preprocessing media resource multi-modal data; step S2, adopting a multi-modal entity relationship extraction method to extract a triple from the preprocessed media asset multi-modal data; step S3, according to the triple obtained by extraction, constructing a media asset multi-modal knowledge graph; and S4, performing iterative management on the media asset multi-modal knowledge graph through a large model. Aiming at the problems of large order of magnitude, various modes and inconvenience in manual management of the media asset information, the invention provides an intelligent solution method based on a large model, and the management efficiency of the media asset information is effectively improved.

The invention discloses an intelligent building safety management system based on big data, and belongs to the technical field of intelligent buildings. Comprising the following modules: a multi-source data fusion and preprocessing module, which adopts intelligent data standardization and feature extraction technologies to realize efficient integration and semantic conversion of cross-source heterogeneous data and ensure data quality and consistency; the security risk dynamic knowledge graph construction module is used for constructing a semantic association network of building security elements through a graph neural network so as to continuously self-learn and dynamically update a risk feature relationship; the intelligent risk assessment and early warning module is used for carrying out comprehensive modeling, accurate positioning, layered assessment and intelligent early warning on building safety risks; the safety decision support module is used for providing an executable risk assessment report and an emergency decision suggestion; and the safety management visualization module adopts an interactive multi-dimensional visualization technology to intuitively present the dynamic evolution process of the building safety risk, so that the risk perception and management efficiency is remarkably improved.

The present application relates to the technical field of management, and in particular to an order whole-process management method and system, a computer device, and a storage medium. The method comprises: S1, entering order information and establishing an order database; S2, performing order scheduling calculation on the basis of the order information and generating an order production sequence list; S3, adjusting modification requirements of production equipment on the basis of the order production sequence list; S4, recording operator information and operation information in a production process, and automatically initiating a feeding request when the production equipment is short of raw materials; and S5, in the production process, monitoring the yield of the production equipment, determining whether the yield can meet the requirements of a delivery cycle, if yes, continuing to monitor the production equipment, and if not, carrying out early warning reminding. The present application makes the process controllable by monitoring workers and the whole process of cloth production, thereby improving the working efficiency of the workers and the cloth production management efficiency.

The invention relates to the field of human resource management, and discloses a tobacco enterprise human resource management auxiliary calibration method based on big data, and the method comprises the steps: obtaining multi-source heterogeneous data related to enterprise internal human resources, and constructing a structured human resource data model in combination with a data standardization processing mechanism and an abnormality elimination strategy; post portrait modeling is carried out on the structured human resource data model, a capability dimension nesting analysis method is introduced, key capability factors and weight distribution required by each post are extracted, and a post capability demand graph is constructed; based on the post capability demand map, fusing the staff portraits and the historical job data, and identifying the deviation between posts and the staff through a multi-dimensional feature matching algorithm to form a preliminary calibration suggestion set; and a dynamic service association analysis method is introduced, and key matching parameters in the preliminary calibration suggestion set are dynamically corrected in combination with latest service demand data and real-time task assignment information. The method has the advantage of improving the management efficiency.

The invention provides a flow limiting method and system based on a distributed cache strategy, belongs to the technical field of distributed flow limiting, and is applied to a distributed cache system which comprises a plurality of cache partitions. The method comprises the following steps: acquiring real-time traffic and historical traffic of each cache partition; inputting the real-time traffic and the historical traffic into a traffic prediction model to obtain predicted traffic at the next moment; and determining a flow limiting parameter of the cache partition based on the predicted flow, and limiting the flow at the next moment based on the flow limiting parameter. According to the method, the flow limiting parameters are automatically adjusted by using the machine learning technology, the data flow can be accurately controlled, the system overload is prevented, the resource waste caused by excessive limitation is avoided, and the flow management efficiency and effect of the distributed cache system are remarkably improved.

The invention provides a computing power network task scheduling method and device, a storage medium and a program product, and relates to the technical field of computers.The method comprises the steps that a task scheduling optimization problem is obtained by defining an objective function between network energy consumption of a computing power network and network carbon emission of the computing power network and constraint conditions corresponding to the objective function; the optimization objective of the objective function is to reduce the network energy consumption and the network carbon emission by optimizing the computing power node, the forwarding path and the forwarding time corresponding to the computing power task; solving the task scheduling optimization problem through a preset optimization algorithm to obtain a task scheduling method of the computing power network; the task scheduling method is used for indicating a forwarding path of a computing power task in a computing power network, a computing power node for executing the computing power task and forwarding time corresponding to the computing power node; according to the method, the problems of relatively low expandability and management efficiency of task allocation can be solved; and the task allocation flexibility and management efficiency are improved.

The invention relates to the technical field of the Internet of Things and artificial intelligence, and discloses an equipment maintenance fault intelligent analysis method and system based on the Internet of Things, and the method comprises the steps: representing an entity and a relation as a time function, and constructing a time sequence knowledge graph; monitoring vector change in the knowledge embedding space, detecting a concept drift phenomenon and mining an equipment performance evolution mode; constructing an equipment state transition model by using a recurrent neural network, predicting the future state of the equipment and evaluating the fault risk; collecting feedback processing knowledge conflicts and updating the model; generating multi-time-scale fault risk early warning and maintenance suggestions; according to the method, the knowledge acquisition efficiency is improved, dynamic knowledge support of the full life cycle of the equipment is provided, early recognition of gradient faults is realized, the method has adaptive knowledge updating capability, multi-dimensional fault analysis is provided, maintenance resource configuration is optimized, and the equipment management efficiency is improved.

The invention provides a continuous casting quality control method based on a meta-cognitive coordination architecture agent cluster, and relates to the technical field of ferrous metallurgy intelligent manufacturing. The continuous casting quality control method comprises the steps of data input and standardization, center coordination and intelligent agent cluster operation and maintenance. In the central coordination process, the meta-cognitive coordination agent serves as a core to coordinate six kinds of functional agents including a semantic analysis agent, a data perception agent, a defect prediction agent, a root cause analysis agent, a process optimization agent and a digital twinborn agent, and task scheduling and state monitoring of the whole system are achieved. Three key functions of task decomposition, data scheduling and closed-loop management and control are completed; in the closed-loop management and control step, risk early warning, defect prediction, root cause analysis, process optimization and digital twinborn verification and feedback are carried out for continuous casting quality. Compared with a traditional scheme, optimization is carried out in the aspects of whole process, multiple modes, intelligence, collaboration and the like, the management efficiency is improved, and economic benefits can also be increased.