2826 results about "Message queue" patented technology

The invention discloses a data sharing system and method for realizing multi-agent interaction, and belongs to the technical field of agents. According to the method, production command instructions are labeled, a large language model is trained, and task types are identified; disassembling the production command instruction into a subtask sequence; constructing a knowledge relationship graph through the graph database; establishing a unified data interface, and constructing a data sharing center; dividing agent types according to the power transmission and distribution service scene, and determining a function boundary of each agent; a multi-agent simulation environment is used for testing the cooperation efficiency of the subtasks, and the execution sequence of the subtask sequence is optimized; sub-tasks are dynamically allocated according to the intelligent agent load and the skill matching degree; the intelligent agent accesses the knowledge relation graph through a unified data interface, and real-time pushing of data change is achieved through a message queue; the decision of the large language model is optimized through cue word engineering, specific operation is executed by utilizing a tool library component, and the intelligent agent executes operation according to a subtask sequence.

The invention provides a supply chain collaborative material management system and method, and relates to the technical field of material management, and the method comprises the steps: receiving a multi-enterprise heterogeneous material data flow, and constructing an industry knowledge graph based on the field-level semantic mapping of a dynamic ontology library; a standardized data table is generated; standardized data is injected into a distributed message queue for parallel processing, dynamic cache partitions are created according to material object labels, timestamp indexes are built in the dynamic cache partitions, a sliding window mechanism is adopted to scan data, and multi-strategy resolution is performed on conflict data in a window; performing conjoint analysis on the cleaned data to generate an enhanced analysis report, and activating a case transfer learning mechanism to update a model when the similarity between new data and historical cases exceeds a threshold value; constructing a material state transfer matrix for the abnormal data, simulating a correction operation in a digital twin environment, and triggering automatic correction; the problems of multi-enterprise heterogeneous data integration, dynamic data processing, intelligent analysis and the like are solved, and the collaboration efficiency and the management level of a supply chain are improved.

The invention discloses a real-time interactive digital human system supporting high concurrency and an implementation method thereof, and relates to the technical field of digital human interaction.The system comprises a model instance pool module used for loading the weight of a deep learning model to a shared memory area through the memory mapping technology; the multi-thread scheduling module is used for scheduling user requests by adopting a lock-free queue and a dynamic priority algorithm; the asynchronous pipeline processing module is composed of decoupled micro-services, and the modules are connected in series through asynchronous message queues; the client SDK is used for dynamically switching a rendering mode according to terminal hardware performance and network conditions; the elastic capacity expansion and contraction module is used for monitoring resource loads in real time and automatically adjusting the number of service instances; and the audio and video synchronization calibration module is used for ensuring that the synchronization error of the audio and video frames is lower than a preset threshold value through a timestamp alignment algorithm. According to the scheme, core challenges in a high-concurrency digital human interaction scene can be systematically solved, and innovative support is provided for large-scale real-time application.

The embodiment of the invention provides a kafka storage and calculation separated data processing method and device, and the method comprises the steps: constructing a multi-layer data processing architecture which comprises four layers: network communication, task distribution, processing scheduling and storage. Cold and hot data separation is realized through hierarchical design of a log pre-writing area and a main storage area, and intelligent scheduling of system resources is realized in combination with a copy manager, a coordinator and a load predictor. An adaptive compression algorithm and a verification mechanism are adopted to ensure the data transmission efficiency and integrity, a message cache region is created to store hotspot data, and flexible access of cold and hot data is realized based on message sites. According to the method, the defects of the traditional technology in storage and calculation coupling, storage efficiency, load balancing and the like are effectively overcome, and the performance and expandability of a message queue system are remarkably improved.

According to the artificial intelligence-based data analysis system and method provided by the invention, a set of novel data analysis system architecture is designed, and key technical components such as natural language processing, a large language model, database query optimization, multi-modal visualization and a semantic knowledge graph are fused; data transmission between modules is realized through unified intermediate data objects such as a structured query intention, an analytic tree and a structured query language template object, and asynchronous collaboration is realized through event driving and a message queue mechanism, so that a non-professional user can input an analysis request through a natural language; semantic analysis, structured query language query statement generation, data query and result visualization presentation are automatically completed, the method can be widely applied to data analysis scenes in the industries of government affairs, traffic, finance, education and the like, the data use efficiency is improved, the technical threshold is reduced, and the digital decision-making ability is enhanced.

The invention discloses a power equipment data acquisition and processing system, which comprises a multi-protocol compatible acquisition module, an intelligent network detection module, a system service performance monitoring module and a high-concurrency processing module, the multi-protocol compatible acquisition module is responsible for adaptation and data acquisition of different power equipment protocols and normalization processing of the acquired data; the intelligent network detection module monitors a network link state, diagnoses a network fault reason and pushes fault information in real time; the system service performance monitoring module collects key performance indexes of a server, displays performance data through a visual interface, and triggers an alarm when the indexes are abnormal; the high-concurrency processing module compresses the collected data, realizes efficient pushing and priority processing of the data through a message queue, and guarantees the data processing efficiency in a high-concurrency scene; comprehensive monitoring of power equipment communication and system performance is realized, and data acquisition efficiency and system reliability are improved.

The invention discloses a multi-platform Internet of Things equipment unified access management method, equipment and a storage medium, and relates to the technical field of Internet of Things integration. The method comprises the step of solving the fragmentation problem of cross-platform equipment access through dynamic configuration adaptation. The method comprises the following steps: starting from receiving a front-end equipment operation request, indexing a pre-configured mapping relation database through an equipment unique identifier, and obtaining a target platform type, an API address and an encryption certificate; dynamically calling a protocol adapter based on a platform type, and injecting an account certificate into a request header or a message body according to a platform specification; converting the original parameters into a format supported by a target platform through a protocol conversion engine, and combining the original parameters with the authentication information to generate a complete request; and realizing high-reliability communication by using an asynchronous message queue, and finally returning a platform response to a front end. According to the invention, through mapping management of the configuration driver and dynamic adapter scheduling, equipment access unification across the Internet of Things platform is realized.

The invention provides a thermal power plant environment monitoring and early warning method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: forming a preprocessing data stream for collected environment monitoring data through an edge calculation node; and carrying out real-time data stream processing by utilizing a distributed message queue, constructing a hierarchical distributed data storage structure, and carrying out homomorphic encryption and desensitization processing on data to form an encrypted data set. Based on an encrypted data set, a long-short term memory network, a random forest algorithm and a limit gradient boosting tree are adopted to construct a layered anomaly detection engine, environmental parameter spatial-temporal characteristics are extracted through a multi-head attention mechanism and residual connection, an early warning threshold value is iteratively calculated based on a Bayesian optimization algorithm, and an early warning level judgment standard is dynamically adjusted. And generating early warning information. And finally, through a three-dimensional visualization module and a knowledge inference engine based on a graph neural network and a deep reinforcement learning algorithm, visualization of early warning information and generation of a self-adaptive emergency processing strategy are realized respectively.

The invention discloses a hierarchical optimization scheduling method for deep peak regulation of thermal power generating units, and the method comprises the following steps: system layering: dividing a power system into a plurality of subsystems, each subsystem comprising a thermal power generating unit cluster and differentiated load demands; subsystem-level prediction: generating a basic scheduling plan; dynamic preference-driven multi-target collaborative optimization: receiving multi-target data from each subsystem, carrying out multi-target collaborative optimization, modeling three targets of a power grid company, an environmental protection department and a terminal user as game participants, quantifying the priority of each party by adopting a fuzzy membership function, generating a dynamic game solution through a Nash equilibrium solver, and carrying out multi-target collaborative optimization; screening out a Pareto optimal solution giving consideration to interests of multiple parties; performing online verification on the dynamic security domain; virtual synchronous machine cooperative support: simulating operation characteristics of a synchronous generator; and global coordination and iterative optimization: uploading the optimal scheduling scheme of each subsystem to a scheduling center through a message queue telemetry transmission protocol, and performing global constraint verification.

The present invention relates to the technical field of computers, and provides a log processing method and system, a log management platform, and an electronic device. The method comprises: acquiring real-time log data of each service system, and storing the log data in a message queue; according to a preset time window, writing the log data in the message queue into a target file of a specified storage directory; and in response to completion of data writing of a current time window, on the basis of the storage directory of the target file, calling a log analysis interface of a log analysis service to perform log analysis on the target file. The log processing method in the present invention reduces the risk incident rate of service systems, and improves the localization accuracy of an anomaly condition of a service log.

The invention relates to the technical field of big data and the field of financial science and technology, and discloses a data hierarchical storage and query processing method, which comprises the following steps of: dividing business data into offline data and real-time data, and respectively storing the offline data and the real-time data in an offline database and a real-time database; real-time data processing tasks are generated through a set time interval, and are distributed to a message queue according to priorities; the task processor obtains the task from the message queue, extracts real-time data and stores the real-time data in a distributed cache; the query request obtains data through the distributed cache, and if the data are not found, the data are searched from the real-time database and a result is returned. Through hierarchical storage, priority management and implementation of a cache mechanism on the data, the direct query pressure on the database is reduced, the response speed of the system in a high-concurrency scene is improved, and the real-time performance and stability of data query are ensured.

The invention relates to the technical field of long and short term memory and natural language processing, and discloses a session processing method and device based on long and short term memory, equipment and a medium. Using the updated large language model to respond to the input verbal skill based on the stored and downloaded long-term memory data to form interactive session information, and storing the interactive session information as short-term memory data in real time; wherein the large language model is updated through federal learning according to the short-term memory data; determining a theme according to the short-term memory data, screening important memory data in the short-term memory data based on a preset priority, and incrementally storing the important memory data as long-term memory data of the corresponding theme; and releasing the long-term memory data subjected to incremental storage to the message queue middleware according to the theme, so that other service line servers subscribing to the theme download the long-term memory data. The problems of memory decline, poor memory consistency and poor integration of a session system are solved.

The embodiment of the invention discloses a method and system for identifying transaction risks, and the method comprises the following steps: obtaining multi-source heterogeneous data, and initializing a dynamic transaction graph according to a multi-source heterogeneous data flow; the multi-source heterogeneous data comprises invoice data, fund flow data, an enterprise stock equity relationship and geographic data; receiving a multi-source event stream through a message queue, and updating the dynamic transaction graph in real time based on the multi-source event stream; for enterprise nodes affected by updating in the dynamic transaction graph, feature aggregation is executed, topological risk features are extracted, and risk scores of the enterprise nodes are output; verifying the consistency of the invoice hash and the fund flow hash through a relay chain protocol, and obtaining a cross-chain verification result; and according to the risk score and the cross-chain verification result, identifying a transaction risk. According to the embodiment of the invention, systematic risk identification is realized through data integration, graph construction, risk mining and cross-chain verification, and the accuracy of invoice risk identification can be improved.

The invention discloses a virtualized container environment monitoring device and method based on eBPF, and aims to solve the problems of poor isolation, insufficient security and platform adaptation deficiency in a virtualized container environment. According to the method, time sequence data of monitored events in a virtualized container environment are collected in real time through an eBPF technology, the time sequence data are transmitted to a data analysis module through eBPF mapping and message queues, data are analyzed through a long-short-term memory network, the deviation degree of the current time sequence data and historical data is calculated, abnormal behaviors are judged, and an alarm is given. According to the method, the user-defined program is operated under the condition that kernel codes do not need to be modified, intelligent analysis and abnormal behavior recognition are conducted on the collected data, comprehensive monitoring and dynamic detection of complex threats are achieved, the safety, stability and compatibility of the system are improved, and the method is suitable for virtual environments under cloud computing and big data scenes.

The invention provides a dynamic network topology updating method based on intelligent event filtering, which comprises the following steps of: S1, monitoring network equipment and link states in real time, and realizing asynchronous event processing by using a message queue; s2, intelligently identifying and screening the collected events, filtering noise events, and classifying effective events according to priorities; s3, based on the event priority, triggering incremental topology update of a corresponding type; and S4, verifying the consistency of topological updating, and confirming the legality of the topological structure. According to the invention, through technologies of machine learning intelligent event screening, priority-driven incremental updating, consistency verification and the like, high efficiency and accuracy of dynamic updating of the network topology are realized, system resource consumption is reduced, and reliability and real-time response capability of network operation and maintenance are remarkably improved.

The invention relates to an ERP non-core service migration method based on K8s dynamic resource scheduling, and belongs to the technical field of cloud computing containerization and ERP system optimization. The method comprises the following steps: identifying a non-core module based on business criticality, and generating a migration list; an internal interface is transformed into a standard API gateway route, and strong dependency communication is asynchronously decoupled through a message queue. Historical resource load data is collected, a periodic rule is extracted by using a time sequence decomposition model, a reference value is set, and a K8s scheduling parameter is generated. Adopting multi-stage construction to separate compilation and operation environments, and generating a lightweight mirror image; and implementing security reinforcement, and dynamically updating the health examination probe and configuration. Selecting a controller according to stateless and persistent requirements; the locality of NUMA domain resources is optimized through topology awareness scheduling; dynamic storage binds a persistent volume according to QoS; the network policy implements container-level traffic isolation. And dynamically adjusting the resource quota and the number of copies. The efficient and safe migration of the ERP non-core business in the K8s environment is realized.

The invention relates to the technical field of message queues, in particular to a message controllable optimization method and system based on kafka, and the method comprises the steps: obtaining a data attribute, and calculating a data priority value according to the data attribute; the method comprises the steps of establishing a position index cache mechanism based on priority data, persisting partition-offset data in a cache pool to an index table according to the position index cache mechanism, and performing priority consumption of high-priority messages based on persistent priority indexes, including setting a consumption control program architecture, performing priority consumption on the high-priority messages, and performing priority consumption on the high-priority messages based on the persistent priority indexes. Isolating the consumption queue and introducing a message de-duplication operation; according to the method, full-link performance adjustment and optimization are carried out on the premise of preferential consumption of high-priority messages, service scene extension is carried out based on the adjusted and optimized full links, the messages are pulled directionally according to the priorities through a consumption control program, the disorder of parallel consumption of Kafka partitions is broken through, the high-priority messages can directly jump to corresponding positions to be processed preferentially, and the service scene extension efficiency is improved. The consumption delay is obviously shortened.

The invention discloses a cross-platform equipment alarm information acquisition method, and particularly relates to the field of information acquisition, the method adapts to different platform equipment through a standardized interface layer, the interface layer comprises an equipment connection protocol library and a unified data conversion unit, and an original log of the equipment is analyzed in real time based on a preset alarm rule template; extracting key information and generating an alarm event object; converting the alarm key information into an alarm event in a unified format, and adding equipment identification metadata; alarm events are asynchronously transmitted to a central processing server by utilizing a high-reliability message queue, and the transmission reliability is guaranteed by adopting various mechanisms; performing duplicate removal, priority classification and persistent storage on the alarm events in the central server, and ensuring data consistency by adopting a hierarchical storage architecture; the whole processing link is provided with perfect monitoring indexes, and automatic capacity expansion and contraction are achieved.

The invention relates to the technical field of data processing and the field of financial science and technology, and discloses a business index monitoring and warning method, which comprises the following steps of: collecting basic business index data in a business node, sending the data to a monitoring server through a message queue, and accumulatively receiving the basic business index data in a preset time window; aggregation processing is carried out when the time window ends, and a statistical result is generated; determining an alarm threshold by combining trend analysis according to the historical statistical data; and then processing the statistical result, comparing the statistical result with an alarm threshold value, generating alarm information, and finally sending the alarm information through an alarm server. According to the invention, through real-time acquisition, processing and aggregation of the business index data, the change of a complex business scene can be quickly responded; the system can generate alarm information more accurately and transmit the alarm information to a related service system in time by utilizing a dynamically adjusted alarm threshold value and trend analysis; the processing efficiency of the monitoring data can be improved.

The invention relates to the technical field of data processing, in particular to a regulation and control management method and system based on a Kafka message queue. According to the method, a time feature vector is constructed based on historical message traffic and event tags, and a node load coefficient is predicted through a graph neural network in combination with a Kafka Broker network topology; performing queue scheduling according to the message priority and the node load coefficient, and optimizing a message processing sequence; performing decoupling management on calculation, storage and network resources according to the resource use indexes, and judging whether capacity expansion and shrinkage, copy migration or current limiting adjustment is triggered or not; a lightweight Kafka instance is deployed at an edge node, hierarchical storage of the edge and the center is realized in combination with data activeness, and data synchronization is asynchronously completed; and constructing a recovery strategy model through reinforcement learning, and realizing automatic identification and regulation response of node abnormity.

The invention relates to the technical field of data processing, and discloses a real-time data stream processing method and device, electronic equipment and a storage medium. The method comprises the following steps: distributing a received to-be-processed data stream to a first distributed node through a preset load balancer; fragmenting the received distribution data stream at the first distributed node according to a preset fragmentation rule, then writing the fragmented data stream into a message queue, and performing parallel processing on fragmented data in the message queue through multiple threads; storing the intermediate state data in the fragmented data processing process, generating snapshot data according to the fragmented data, and synchronizing the snapshot data to the distributed storage system; acquiring load data of the first distributed node, and dynamically updating a data flow distribution strategy of the load balancer according to the load data through the resource use prediction model; and writing a processing result of the to-be-processed data stream into persistent storage through an asynchronous batch mechanism, and returning the processing result through a preset real-time communication protocol. The method improves the data stream processing efficiency and stability.

The invention relates to the technical field of digital relays, and discloses a digital relay telephone traffic real-time acquisition and intelligent distribution system for emergency scheduling, which executes the following steps through five modules: receiving and normalizing UDP (User Datagram Protocol) data through a time domain buffer management module, completing space-time matching of telephone traffic and media streams through a cross-protocol association engine, and completing real-time transmission of the telephone traffic and the media streams. And unsuccessful data triggers a reverse tracing service for remedy, the intelligent scheduling controller performs grading processing according to the emergency degree and dynamically deploys resources, and finally the data is intelligently distributed to a message queue cluster through an elastic load balancing module. By constructing a multi-level heterogeneous data processing framework and dynamic redundancy transmission, intelligent space-time alignment and elastic resource scheduling mechanisms, accurate routing of first-aid instructions and zero-loss guarantee of key data are achieved, meanwhile, the self-adaptive fault-tolerant capability of the system to abnormal working conditions such as equipment clock skew and burst flow is enhanced, and the system reliability is improved. And high-availability technical support is provided for digital upgrading of emergency dispatching.

Embodiments generate state elements based on application requests from a client. The state elements may be enqueued in a state queue associated with an application session for an application requests and the application requests may be forwarded to the application. Application responses from the application may be employed to perform further actions, including: generating message elements based on the application responses such that the message elements may be enqueued in a message queue associated in the application session; determining a portion of the state elements in the state queue that may be associated the message elements; updating the portion of the state elements to advance a protocol state based on the message elements such that the application responses may be communicated to the client.

The invention discloses a logistics full-link risk control method and device based on an AI large model and a storage medium, and relates to the technical field of intelligent logistics, and the method comprises the steps: obtaining a logistics business risk control requirement inputted by a user through an interactive interface; analyzing logistics business risk control requirements, extracting logistics risk factors, and generating a structured risk control rule based on an industry knowledge base and business data in combination with a machine learning algorithm and a large model; identifying and solving rule conflicts of the structured risk control rule by using a collision detection algorithm; dynamically adjusting a rule threshold based on business volatility to obtain an optimized risk control rule; and pushing the risk control rule to a service supervision system based on the message queue or the API interface, and judging whether to trigger interception, alarm or transportation path correction operation based on a service node of the service system. The logistics risk control efficiency is improved, and the requirements of modern logistics for accuracy and real-time performance are met.

The invention provides a robot control strategy migration method and device, a medium and a program product, and the method comprises the steps: collecting a state-action sequence, generated by a pre-training strategy model, of a robot in a real operation environment, and carrying out the preprocessing, and obtaining structured training data; judging whether a preset updating condition is met or not based on the running state, and when the preset updating condition is met, performing fine adjustment on the strategy model by utilizing the training data to generate an updated strategy model; thermally deploying the updated strategy model to a real operation environment of the robot, so that the robot operates based on the updated strategy model and generates new operation data under the condition of not interrupting task execution; the three stages of data collection, preprocessing, strategy fine adjustment and model deployment are executed circularly, data interaction and flow decoupling between the stages are achieved through a message queue, and continuous optimization based on real operation feedback is achieved. According to the method, the stability and generalization ability of strategy migration are improved through condition-triggered cyclic fine tuning and a hot deployment mechanism of uninterrupted operation.

The invention relates to the field of power equipment data processing, in particular to a multi-target dynamic optimization unit load intelligent distribution method, which adapts a micro-service cluster through a containerization protocol, adopts an OPC UA protocol and an MQTT protocol to respectively collect thermal power and new energy data at different frequencies, dynamically analyzes an IEC 61850 power grid instruction, converts and stores the IEC 61850 power grid instruction into a Kafka message queue, and sends the IEC 61850 power grid instruction to a network server. The problem of communication protocol heterogeneity is solved. Based on an IEEE 1588 protocol, sub-millisecond clock synchronization of edge nodes is realized, a cubic spline interpolation algorithm is adopted to align multi-frequency data, an interpolation window is dynamically adjusted in combination with a genetic algorithm, and a spatial-temporal characteristic matrix is generated and stored in a Redis database. And performing multi-objective optimization solution through an NSGA-II algorithm, and dynamically adjusting the weight of an objective function to adapt to the frequency modulation requirement of the power grid. A closed-loop feedback mechanism corrects model parameters through Kalman filtering, actual data are written back to a feature matrix, the robustness of the system is improved in combination with a hierarchical fault-tolerant strategy, the problem of space-time mismatch of data is effectively solved, and the economical efficiency and safety of load distribution are improved.

The invention relates to a construction method and system of an intelligent question-answering system based on a planning knowledge graph library. The method comprises the following steps: acquiring multi-source space planning data in real time; carrying out fusion on the planning data; vectorizing the fused planning data; a plurality of preset retrieval enhancement models are modularized, and interaction of vectorized planning data among the modules is realized through a message queue; through a natural language processing method and a message queue method, constructing a plurality of knowledge maps which can be accessed by the preset retrieval enhancement model; training a pre-training model through the knowledge graph; in response to a user question, retrieving an enhancement model through dynamic routing selection, and retrieving entities and relationships in the knowledge graph; and based on the retrieval result, generating a questioning result through a pre-training model. According to the method, intelligent question answering, scheme generation and evaluation of space planning are realized through the knowledge graph and the pre-training model constructed by the multi-source data, the working efficiency of the space planning is improved, and the manual workload and the error rate are reduced.

The invention relates to the technical field of big data and the field of financial science and technology, and discloses a database table synchronization method based on log data, which comprises the following steps: recording operation information of a business database table to log data, writing the log data into a message queue, obtaining the log data through a streaming data processing framework, and deserializing the log data into structured data; and extracting operation records, classifying the operation records according to operation type fields, distributing the operation records to processing channels corresponding to the table names, and updating or adjusting the table structure of the target table according to classification, or executing data change operation. Through the log data-based synchronization method, quasi-real-time synchronization from the business database table to the operation data storage module is realized, and the table structure is adaptively adjusted when the business table structure is changed, so that the data consistency problem and the system interruption risk are reduced, the data synchronization efficiency and stability are effectively improved, and the user experience is improved. And the efficient and stable data synchronization requirements of the service are met.

The invention relates to a production abnormality automatic identification and recovery process control method, which comprises the following steps of S1, realizing second-level synchronization of multi-source heterogeneous data, constructing a real-time data flow pipeline and generating a total-factor production situation data flow through a distributed message queue by adopting a Modbus / TCP protocol analysis algorithm based on industrial Internet of Things edge calculation; through the cooperative effect of industrial protocol analysis and distributed message queues, second-level synchronization of multi-source heterogeneous data is realized, a real-time data flow pipeline is constructed, data acquisition delay is effectively eliminated, the timeliness of anomaly detection is ensured, a dynamic weight distribution mechanism of a rule engine and a long and short-term memory network prediction model is adopted, and the real-time performance of the system is improved. By combining sliding window threshold detection, the accuracy and coverage of anomaly recognition are improved, false alarm and missing alarm caused by a single detection mechanism are reduced, and multi-dimensional root cause tracing is performed by combining a fault mode knowledge base through combined application of time sequence correlation analysis and a causal diagram inference engine.

The invention discloses a data processing method based on combination of data driving and a continuous casting production process, and belongs to the technical field of industrial data management. The method comprises the following steps: extracting multi-source heterogeneous data in the continuous casting production process according to an IT class and an OT class, and mapping a physical model of a physical sensor in a digital space; the Internet of Things equipment accesses collected data to an industrial gateway, an industrial grid carries out analysis, format conversion and downsampling on the data and then reports the data to a data engine, and the data engine stores multi-source heterogeneous data into a business database of a corresponding object model in a message queue publishing / subscribing mode; performing real-time online data management including abnormal state detection, data completion and the like; the production process data and the production materials are aligned in time and space; and a metallurgical affair map and a digital casting blank expansion map are constructed. According to the invention, online data management is realized, the data reliability is improved, and a data management scheme is provided.