1145 results about "Collaborative processing" patented technology

The invention belongs to the technical field of multi-agent collaboration, and provides an electric power cross-modal knowledge fusion multi-agent collaboration processing method and system, and the technical scheme is as follows: analyzing a task execution command instruction input by a user; key elements of the task are extracted, the task is decomposed into a plurality of sub-tasks according to the extracted key elements, and logic association and priorities among the sub-tasks are identified; according to the logical association and priority among the subtasks, allocating the subtasks to the corresponding agents; each agent executes the corresponding subtask according to a set execution path, the executed task result is shared to other agents to cooperatively complete the complex task, and finally a cooperative processing result is obtained through integration. Efficient execution of complex tasks such as document generation, review and management is realized, and smooth completion of the tasks is ensured.

The invention discloses a multi-door-lock cooperative processing method and system, and the method comprises the steps: generating an assistance request IGMP multicast message according to a preset triggering operation detected by a first door lock B and read RFID identification information, and transmitting the assistance request IGMP multicast message to a router; according to the assistance request IGMP multicast message, recording interface information and validity period associated with the RFID identification information in a multicast forwarding table entry, and caching the multicast message; according to the preset triggering operation detected by the second door lock A and the same read RFID identification information, an assisted request multicast message containing the RFID identification information is generated, and the assisted request multicast message is sent to the router; according to the RFID identification information and the multicast forwarding table item, after the validity period is verified to be not expired, the message is directionally forwarded to the first door lock B, the first door lock B sends an assistance confirmation message to the second door lock A, and a robot calling instruction is triggered. According to the embodiment of the invention, low-delay and high-reliability door lock cooperative control can be realized.

The method is suitable for the technical field of automatic driving, an automatic driving large model framework based on 3D space-time perception and human-like decision reasoning is provided, through deep integration of cross-modal dynamic semantic patch embedding and multi-scale chain reasoning, the three-dimensional space-time semantic reasoning ability of an automatic driving system in a complex scene is effectively improved, and the automatic driving efficiency is improved. The efficient cooperative processing of the full-view scene video stream and the global aerial view is realized; meanwhile, through a multi-level space-time semantic self-adaptive data extraction normal form and semantic action representation generation based on rule driving and semantic action interaction, and in combination with a cross-modal task-oriented multi-level training matrix architecture, the model shows excellent performance in the aspects of scene understanding, decision transparency and end-to-end driving task optimization. The accuracy of the system in dynamic behavior deduction is effectively improved, the decision interpretability is improved, and powerful technical support is provided for global path planning and human-like decision making of automatic driving.

The invention relates to the technical field of Internet of Things management, in particular to a lightweight Internet of Things management system based on a star flash protocol stack, which comprises a plurality of modules such as a star flash protocol communication module, a dynamic topology management module and a resource virtualization module. The satellite flash protocol communication module realizes low-power-consumption efficient connection of equipment; the dynamic topology management module optimizes the network topology by using an improved multi-agent Q learning algorithm; the resource virtualization module realizes accurate resource allocation through a weighted bipartite graph matching model; the safety protection module adopts an attention mechanism to detect abnormities and guarantee data safety; the edge co-processing module realizes intelligent task unloading by means of deep reinforcement learning; all the modules cooperatively work under the overall planning of the cross-module coordination controller, and the information barrier is broken. According to the invention, the problems of high communication energy consumption, poor resource allocation, weak security protection, low task processing efficiency, insufficient module collaboration and the like of a traditional Internet of Things system are effectively solved, the system communication efficiency, the resource utilization rate and the security are remarkably improved, and the task processing capability is enhanced.

The invention discloses a fusion method for automatic coprocessing of multi-source heterogeneous data, which comprises the following steps of: analyzing the format and semanteme of the multi-source heterogeneous data to obtain metadata features of each data source, the metadata features comprising data types, coding modes and semantic tags, and generating a standardized metadata description set; according to the metadata description set, a pre-established protocol template library is adopted, an applicable template is matched, an initial unified exchange protocol is generated, and the initial unified exchange protocol comprises a format conversion rule and a semantic mapping relation; and if the format conversion rule of the initial unified exchange protocol cannot adapt to the metadata features of the newly added data source, dynamically updating the protocol template through a machine learning algorithm to obtain an exchange protocol adaptive to the new data source.

The invention relates to the technical field of battery management, in particular to a battery module early abnormality early warning method and system fused with a time sequence knowledge graph, and the method comprises the steps: obtaining static parameters and dynamic sensor data of each battery node in a battery module, and constructing the time sequence knowledge graph; performing node-relation importance evaluation of topology embedding, and screening out key nodes and relations; constructing heterogeneous knowledge unified mapping of multi-manifold representation, and projecting battery voltage, temperature and state manifolds to a unified representation space; performing spectrogram-driven adaptive Transform structure optimization on the data of the uniform representation space, and performing spectrogram-driven adaptive Transform structure optimization on the data of the uniform representation space; bidirectional coupling graph-sequence information flow cooperative processing is realized, graph structure information is converted into sequence representation, and a graph structure is fed back and updated; carrying out multi-scale feature hierarchy fusion and integration, and integrating hierarchy feature information of a battery monomer, a module and a system; and evaluating the health state of the battery module based on the multi-scale fusion feature information, and generating early warning information in an abnormal state or an abnormal state.

The invention discloses a multi-modal data real-time identification and cooperative processing system based on edge computing and federated learning. The multi-modal data real-time identification and cooperative processing system comprises a cloud center coordination node, a plurality of edge computing nodes, a cross-modal encryption engine, a federated learning controller and a model updating verification module. The cloud center coordination node executes federated learning model aggregation and dynamic task allocation, and generates a cross-modal encryption strategy; and the edge computing node is configured with a multi-modal data acquisition module, a local model training unit and a co-processing gateway to realize multi-modal data acquisition and local processing. The system encrypts vision, acoustics and text data by using differentiated algorithms such as spatial confusion, frequency domain permutation and homomorphic encryption; the federated learning controller carries out multi-modal feature fusion, hierarchical encryption and dynamic networking at the edge node; and the model updating verification module performs aggregation updating after ensuring parameter consistency by using secure multi-party calculation. According to the method, real-time processing and privacy protection of multi-modal data are realized, and the data co-processing efficiency is improved.

The present application relates to the field of information collaborative processing. Provided are a task collaborative scheduling method and apparatus, a device and a medium. The task collaborative scheduling method is applied to a collaborative computing system comprising a plurality of nodes, and the method comprises: first, on the basis of original task description information of a target task, performing splitting and orchestration on the target task to obtain a plurality of sub-tasks and a task logic topological relationship between the sub-tasks; then allocating, on the basis of node information of the nodes, from the collaborative computing system a corresponding execution node for each sub-task, and generating sub-task description information; and finally, issuing the sub-task description information to the execution nodes, such that all the execution nodes can complete all the sub-tasks according to the orchestrated logic topological relationship, thereby obtaining an output result of the target task. The present application can cover diversified task collaborative scheduling scenarios and enables compatibility with access and scheduling of devices having different capabilities, thereby meeting the collaborative processing requirements for diverse service types and scales.

The embodiment of the invention discloses a task cooperative processing method and device based on multiple agents. The method comprises the steps that a to-be-processed task is disassembled into a plurality of sub-tasks based on a large model; determining a candidate agent corresponding to each sub-task of the plurality of sub-tasks from a preset agent library; evaluating the candidate agent corresponding to each sub-task of the plurality of sub-tasks, and determining a comprehensive score of the candidate agent corresponding to each sub-task of the plurality of sub-tasks; based on the comprehensive score of the candidate agent corresponding to each sub-task of the plurality of sub-tasks and the real-time state of the candidate agent corresponding to each sub-task of the plurality of sub-tasks, determining the agent corresponding to each sub-task of the plurality of sub-tasks by adopting a greedy algorithm; and processing the plurality of sub-tasks based on the intelligent agent corresponding to each of the plurality of sub-tasks to obtain a processing result of the to-be-processed task.

The invention discloses a computing power resource collaborative scheduling method and device in a multi-cloud scene and a storage medium, and belongs to the field of computing power resource management. Constructing a virtual computing power resource pool based on the multi-cloud cluster; receiving a to-be-processed task, and obtaining a target server identifier and target computing power resource state information; performing task processing capability evaluation on the target cloud processing server, and judging whether the computing power resource requirement of the to-be-processed task is met or not; when the computing power resource requirement of the to-be-processed task is not met, dividing the to-be-processed task into a plurality of sub-tasks; and performing cross-cloud cooperative distribution on the plurality of sub-tasks, so that the loads of the plurality of cloud processing servers tend to be balanced, generating a target scheduling scheme, and executing cross-cloud cooperative processing of the plurality of sub-tasks. The technical problems of low computing power resource scheduling efficiency and unbalanced load in the multi-cloud environment in the prior art are solved, and the technical effects of improving the computing power resource utilization efficiency in the multi-cloud environment, realizing cross-cloud cooperative processing and optimizing load balancing are achieved.

The invention relates to the technical field of artificial intelligence, can be applied to service scenes of pension service, financial science and technology, medical health and the like, and discloses an instruction understanding and task execution method, device, equipment and medium, and the method comprises the steps: receiving a voice instruction and a text instruction, and carrying out the cooperative processing through an instruction understanding model, and generating a structured task description; collecting environment data to construct a real-time environment model; generating a task execution strategy by utilizing a task execution model based on the structured task description and the real-time environment model; controlling the intelligent agent to execute the task according to the task execution strategy, and dynamically adjusting the action in combination with real-time sensor information; task execution data and user feedback information are collected, and the instruction understanding model and the task execution model are updated. According to the method, multi-modal information is fused through structural description, an execution strategy is generated in combination with real-time environment perception, actions are dynamically adjusted, model self-optimization is further achieved through execution data and feedback, and the understanding, decision-making and adaptive capacity of an intelligent agent is improved.

A submerged arc furnace intelligent inspection system based on cloud side-end cooperation belongs to the technical field of submerged arc furnace intelligent robot inspection, and comprises an end side device, an edge side platform, a cloud platform and a cross-layer cooperative processing module, the end side device is in communication connection with the edge side platform, and the edge side platform is in communication connection with the cloud platform; and the cross-layer cooperative processing module is used for intelligently deploying global resources of the end-side equipment, the edge-side platform and the cloud platform, predicting a future load trend by adopting a dynamic sparse LSTM model algorithm, inputting a prediction result into a security constraint PPO reinforcement learning algorithm to adaptively adjust a task and a computing power allocation proportion, and carrying out dynamic resource allocation according to an output result. According to the invention, the cross-layer cooperative processing module interacts and communicates with the cloud side, the edge side and the end side, so that a dynamic allocation strategy of resources is realized, and the problems of low resource utilization rate and poor real-time performance caused by a rigid resource allocation strategy during routing inspection are avoided.

The invention discloses an intelligent traffic data analysis and decision generation system based on multi-model dynamic collaboration, and the system comprises a multi-mode collection module which collects voice, text, video and structured data and loads a traffic knowledge graph; the feature processing module is used for extracting semantic, time sequence and spatial features and generating a fusion vector; the semantic modeling module is used for generating an intention vector in combination with the knowledge graph and the interaction record; the dynamic routing module selects an edge or cloud model according to the intention vector to generate a query statement; the Prompt memory module fuses the historical template and the alignment parameters to generate a query draft; the query verification module is used for executing semantic and structure verification and outputting a correction statement; the query execution module generates a response vector; the causal interpretation module generates an interpretation vector; and the response generation module outputs a multi-granularity result according to the user role. According to the method, the cooperative processing capability and semantic analysis precision of complex traffic query are improved.

The invention relates to a cloud side-end cooperative processing system based on a network computing technology. A cloud computing platform is used for storage of large-scale data and training of a deep learning model; the edge computing node module is used for receiving the data stream from the end side equipment and processing the data stream in real time; the end side equipment is used for collecting environmental data and primarily processing the environmental data, and the end side equipment comprises one or more of an unmanned aerial vehicle, an unmanned vehicle and an unmanned ship; the cloud edge-end cooperation module is used for efficient communication and calculation cooperation among the cloud, the edge and the end-side equipment; and the task scheduling and resource allocation module is used for scheduling and allocating calculation tasks in real time according to current task requirements, resource states and network conditions. According to the invention, the overall operation efficiency and real-time performance of the cloud side-end cooperative processing system can be improved, and the defects of the existing system in the aspects of processing capability and task scheduling are overcome.

The invention provides a multi-core heterogeneous system-oriented startup deployment and cooperative processing method, which comprises the following steps of: when the system runs, obtaining startup codes after a master core and a slave core are electrified, respectively obtaining mirror image files of Linux and RTOS (Real Time Operating System) on the basis of synchronous running and storage partition, and running the mirror image files on the master core and the slave core after moving the mirror image files; and after the heterogeneous operating system is loaded, generating an interrupt signal based on inter-core interrupt and a corresponding register through a CLINT module in the main core, and realizing data communication between the main core and each slave core based on the interrupt signal and the shared memory. By means of the method, after the system is powered on, the multiple cores can synchronously run the starting codes, and time delay and uncertainty possibly caused by one-by-one starting of the single cores are avoided; communication and data transmission among multiple cores are realized through interruption and memory sharing, the synchronism and consistency of data are ensured, and misjudgment and decision errors caused by data delay and inconsistency are avoided, so that the real-time performance of the system is remarkably improved.

The invention discloses a converter station intelligent gateway image recognition system based on a YOLOv3 target detection algorithm, and the system employs a three-stage cooperative processing architecture design, and builds seamless connection of a multispectral image collection layer, an edge calculation gateway layer, and a cloud operation and maintenance management platform layer. The invention further provides an equipment defect detection method based on the system, bimodal image data are collected through the visible light camera and the thermal infrared imager, preprocessing operation is carried out, equipment positioning and defect classification are synchronously executed by utilizing the improved YOLOv3 network, a structured detection result is output, temperature field analysis is carried out on an infrared thermal image, and the equipment defect detection result is obtained. And an abnormal heating area is identified, when defects are detected, multi-level risk response early warning is generated, and a defect diagnosis report is pushed to the cloud operation and maintenance management platform layer. Real-time image analysis of converter station equipment can be realized, the method is suitable for automatic detection of typical fault defects of the equipment, and the operation and maintenance efficiency of a power grid is improved.

The invention provides a service scene collaboration-oriented cross-service system micro-service processing method and system, and the method comprises the steps: firstly obtaining an initial interaction data set with a service scene label of a cross-power service system in a preset time period, and then carrying out the scene feature analysis of the initial interaction data set; obtaining electric power business scene features and interactive behavior features corresponding to each interactive data unit, then calling a pre-constructed business collaborative analysis model to perform collaborative correlation analysis on the features, and generating a collaborative processing strategy containing micro-service calling priorities and data interactive format specifications; and determining a business process adjustment scheme of each business system in the target power business scene according to the cooperative processing strategy, and finally generating a cooperative execution instruction containing a micro-service calling instruction sequence based on the business process adjustment scheme, and sending the cooperative execution instruction to the target business system to trigger a cross-system cooperative processing operation. Therefore, barriers among power business systems can be effectively broken, the cooperative processing efficiency and flexibility are improved, and the reliability of power supply is guaranteed.

The invention discloses an information resource efficient collaborative optimization method for a distributed computer system, which belongs to the technical field of information resource optimization control and comprises the following steps: executing a transaction according to a target lock element information set, monitoring popularity information of each resource segment, generating self-adaptive lock granularity decision information of each resource segment, and executing dynamic adjustment of lock granularity. And obtaining load sensing optimization parameters to complete lock competition optimization, and completing real-time iterative updating of lock element information and strategies. According to the method, the problems of resource competition and deadlock in the distributed system can be effectively solved, and the system performance and the resource utilization efficiency are improved. Transaction pre-allocation resources are reasonably allocated, the deadlock risk is reduced, lock granularity is dynamically adjusted according to resource segmentation popularity, lock competition optimization is completed based on load awareness optimization parameters, an adjustment result and the optimization parameters are synchronized to a distributed lock management library for real-time iteration updating, and it is ensured that a system always operates with an optimal strategy; and the distributed computer system efficiently and stably processes information resources in a cooperative manner.

The invention relates to the technical field of energy management and carbon accounting, in particular to an intelligent carbon emission monitoring system and method based on the Internet of Things, and the system comprises a distributed sensing network module which collects the energy consumption and emission data of an independent accounting unit in real time; the edge computing module is deployed at a local edge computing node to preprocess the data acquired by the distributed sensing network module; the cloud co-processing module receives the preprocessed data and carries out dynamic accounting and multi-source data fusion; the carbon accounting model module automatically matches accounting requirements according to the industry to which the enterprise belongs and generates a monitoring point layout scheme; and the intelligent visualization module is used for generating a multi-dimensional carbon emission data analysis chart and a compliance report meeting MRV requirements based on data of the edge calculation module, the cloud co-processing module and the carbon accounting model module. Full-process automatic management from data acquisition, real-time monitoring to verification is realized, and the accuracy, integrity and traceability of carbon emission data are ensured.

The invention provides a multi-agent cooperative processing system and method for multi-scene legal complaint consultation, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving a legal or complaint consultation text inputted by a user, and generating structured consultation request data; automatically generating an evidence obtaining template, and forming a standardized evidence obtaining data packet; constructing a demonstration tree and outputting a multi-scheme decision matrix; performing multi-objective optimization by combining the success rate, the timeliness, the cost and the regional qualification index, and outputting a comprehensive recommendation result; generating a knowledge unit set, and updating a regulation index and agent prompt template library; predicting a re-complaint risk and generating a remedy and optimization suggestion; and outputting review result data, and reversely updating the review result data to the agent configuration strategy in the step of the knowledge construction module. According to the method and the device, the technical targets of multi-agent cooperative processing and dynamic optimization management in the whole legal complaint consultation process can be realized, and the technical effects of improving consultation processing efficiency, enhancing scheme recommendation accuracy, reducing manual participation cost and ensuring data compliance and risk controllability are achieved.

The invention relates to the technical field of data management, and provides a cross-enterprise closed-loop order transfer and data cooperative processing method, which comprises the steps of collecting multi-modal data, generating a unique distributed hash identifier for the multi-modal data based on a block chain technology, and performing intelligent association on the multi-modal data in combination with transfer node information; constructing a cross-enterprise order flow graph model by using the multi-modal data fusion; detecting an abnormal mode in real time by combining a graph embedding algorithm with a deep neural network; on the basis of the order flow graph model, a dynamic responsibility allocation algorithm is utilized, and in combination with real-time environmental factors when an anomaly occurs, a specific responsibility node where the anomaly occurs is positioned, and a corresponding responsibility weight is allocated; and generating an adaptive repair strategy based on a multi-objective optimization algorithm according to the responsibility weight. According to the invention, the transparency and cooperation efficiency of cross-enterprise order transfer are significantly improved, and the stability and efficiency of a supply chain system are ensured.

The invention relates to the technical field of computer vision and image recognition, in particular to an image recognition method based on edge computing, which comprises the following steps: dynamically capturing an original image through a plurality of edge nodes, rejecting redundant regions through a multi-modal perception triggering mechanism, and establishing a cooperative processing group. Illumination equalization, noise filtering and resolution self-adaptive compression tasks are distributed according to dynamic role election, a standardized preprocessed image is generated, a lightweight convolutional neural network is operated in parallel to extract a dual-channel feature vector, and after entropy coding lossless compression and equipment identity tag and time sequence stamp attachment, the dual-channel feature vector is transmitted to a cloud end by adopting a lightweight encryption protocol. The cloud end analyzes the data packet, reconstructs a feature topological graph based on space-time relevance, loads a depth residual error recognition model to execute feature fusion and classification decision, feeds back and updates the weight of an edge node model, solves the problems of low collaborative efficiency and feature distortion, and improves the efficiency and precision of image recognition.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health, lease and the like, and discloses a distributed multi-terminal data cooperative processing method, which comprises the following steps: receiving a structured data unit containing business logic generated by an initiating terminal, and transmitting the structured data unit to a business verification server, receiving verification result data returned by the service verification server, and performing security encapsulation processing on the verification result data to generate security encapsulation data; calling a distributed communication service interface of the distributed operating system, and detecting a connectable device set in a network domain where the initiating terminal is located; and establishing a multi-path transmission link based on the transmission capability parameters of the terminal devices in the connectable device set, and distributing the security encapsulation data to the target terminal device group through the multi-path transmission link. According to the invention, efficient distribution and synchronization of data are realized through a multipath transmission link, the dependence on a background system is reduced, the timeliness and consistency of data synchronization are improved, and the maintenance cost of the system is reduced.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a target event processing method, device, equipment and medium based on language model collaboration.The method comprises the steps that a target event is monitored and recognized, multi-source data associated with the target event are collected, the multi-source data are fused to generate fused data, and the fused data are sent to a server; and inputting the fusion data into a pre-training language model to generate a decision scheme, performing decision coordination and matching based on the decision scheme, distributing a processing agent, executing a matched decision task by using the processing agent, and generating a decision result. According to the invention, through fusion of multi-source data and pre-training language model deep reasoning and combination of an intelligent agent dynamic cooperation mechanism, intelligent perception, rapid response and cooperative processing of complex events are realized, and the adaptive ability and processing efficiency of the system in response to unknown faults and complex tasks are improved.

The invention discloses an intelligent archive opening and identifying method based on a large model, particularly relates to the technical field of archive data auditing, and is used for solving the problems of insufficient cross-modal data analysis capability, lagging rule updating and low man-machine cooperation efficiency in the prior art. Fusing cross-modal features of texts, images and metadata through a hybrid expert model to generate multi-modal feature vectors, and dynamically allocating the multi-modal feature vectors to a rule network, a semantic network and a domain network for cooperative processing based on attention weights; the rule network parameters are optimized through gradient projection constraint, and regulation-driven real-time adaptation is achieved; matching sensitive data in combination with a multi-dimensional feature matrix of auditing personnel, and optimizing task allocation accuracy; removing redundant links by utilizing value flow analysis to generate a lightweight process, and recording as a tamper-proof evidence chain through a block chain evidence storage solidification operation; the auditing efficiency and accuracy are improved, and the compliance traceability is guaranteed.

The invention relates to the technical field of data sharing security, in particular to a government affair data security sharing method and system based on a block chain technology. The method comprises the following steps: acquiring user service handling information, analyzing user service requirements according to the user service handling information, and determining target multi-level department chain information; according to the target multi-level department chain information, performing multi-level encryption anchoring uplink storage on the user service handling information, and determining service multi-level department chain information; and based on an intelligent contract driving strategy, according to the user service demand and the service multi-level department chain information, performing multi-department chain type cooperative processing on the user service demand, and determining and outputting a government affair processing result. According to the application, the data sharing security among different hierarchy departments in the government affair processing process is improved, good balance between data security and government affair processing is effectively guaranteed, the working efficiency and experience of users are improved, and the government affair electronization process and efficient configuration of social resources are promoted.

The invention provides a real-time intelligent agent dynamic scheduling method and system composed of multiple large models, and is applied to the technical field of data processing. The method comprises the following steps: performing dynamic scheduling expansion around a plurality of large-model real-time intelligent agents, firstly receiving task request data of a real-time service scene, performing grouping according to task types, priorities and resource requirements, pre-configuring a scheduling strategy, and generating a pre-scheduling queue and resource pre-planning information; and in combination with the target configuration information, scheduling task information after secondary optimization is obtained through multi-dimensional optimization such as task splitting and priority rearrangement. Dynamically adjusting and generating a target scheduling instruction based on real-time system resources and a model operation state, and cooperatively processing the target scheduling instruction with the large model capability adaptation parameters to form scheduling execution information; and finally, issuing to model nodes according to preset service quality requirements and resource constraints, generating information such as adaptive parameters and priority rules through feature extraction, association processing and the like, and supporting multi-model cooperation to efficiently complete real-time service tasks.

The invention belongs to the field of customs declaration distribution, and particularly relates to a customs declaration intelligent dispatching and cooperative processing system, which carries out compliance auditing on declaration information through a business declaration module in combination with a user historical credit score and an initial auditing model, and marks processing difficulty and danger level on the information passing the auditing; the intelligent generation module generates a compliance declaration form based on the pre-dispatch label and a compliance rule base and performs encryption locking; and the collaborative dispatching module accurately matches and dispatches the customs declarations to auditing personnel by using a distributed enhanced dispatching model according to information such as customs declaration processing difficulty, comprehensive risk assessment scores and employee proficiency. The auditing feedback module monitors auditing progress and abnormity in real time and realizes dynamic closed-loop feedback through a declaration feedback adjustment chain; according to the invention, intelligent evaluation, precise dispatching and closed-loop collaborative management of customs declaration processing are realized.

The invention discloses an efficient processing method and system for online troubleshooting of the information department, and relates to the technical field of automatic operation and maintenance, and the system comprises a multi-modal data acquisition module, a dynamic knowledge graph construction module, a reinforcement learning driven self-healing module, an edge co-processing module and a cross-domain privacy calculation module. According to the method, the limitation of traditional single-dimensional monitoring is broken through, multi-source heterogeneous data such as server performance, container state and network flow are fused, a high-timeliness and high-consistency decision information base is constructed through unified time sequence alignment and semantic standardization, and a panoramic view is provided for dynamic analysis; based on an incremental learning mechanism and a causal inference engine, a service topology dependency relationship is updated in real time, a deep root cause is accurately positioned, and a cross-system and cross-level complex fault combination is solved.

The invention discloses a virtual restoration system for damaged cultural relics, relates to the field of virtual restoration of the damaged cultural relics, and solves the problems that the restoration effects of textures and geometric shapes of damaged areas are difficult to be completely consistent, the recognition capability for complex damage modes and disease types is weak, and the restoration effect is poor. The technical problems of difficulty in realizing sharing and cooperative processing of cultural relic data among different mechanisms and lack of digital display and propagation of models in the prior art are solved in the invention. The method comprises the following modules: a cultural relic fragment data import and export module, which is used for obtaining three-dimensional information of cultural relic fragments through a three-dimensional scanning device and then carrying out preprocessing operation; the cultural relic fragment data feature enhancement and classification module is used for extracting geometric features and texture features of cultural relic fragments by using corresponding technologies, and constructing a three-dimensional digital model after classification and splicing by combining with a machine algorithm, and the cultural relic fragment interactive splicing module is used for developing a data sharing platform by using a cloud computing technology. And constructing a virtual cultural relic restoration environment by using a virtual reality technology.