1472 results about "Device status" patented technology

The invention discloses a digital twin enabling intelligent pump station preventive operation and maintenance system. Comprising a dynamic twin construction module, a multi-source heterogeneous multi-modal data acquisition module, an edge computing and cloud collaboration module, an equipment health degree evaluation module, a predictive maintenance decision module, a cross-system data fusion module, a self-evolution knowledge graph module, an intelligent diagnosis and early warning module, a self-adaptive maintenance decision module and a man-machine collaboration interaction module. And the dynamic twin construction module comprises a physical-virtual synchronous calibration mechanism and an equipment degradation parameter dynamic updating mechanism. According to the method, the limitation problem of a traditional static model is solved, the method can adapt to nonlinear changes under complex working conditions, the comprehensive judgment and prediction capability of the system on the equipment state can be enhanced, the energy utilization efficiency is improved, the energy consumption is reduced, the decision and verification mechanism is perfected, and the data acquisition and processing problem is improved; the problems of timeliness and flexibility of the model are solved, and the computing architecture and the response capability are optimized.

The invention relates to the technical field of project management, in particular to an intelligent management method and system for a civil engineering project, and the method comprises the following steps: obtaining personnel distribution, equipment state and material inventory data, extracting a key task node and an execution state, recognizing a progress lagging task, screening a key path node, analyzing a time difference, and marking a progress trend. And identifying an equipment influence section, judging resource allocation imbalance, and generating a progress monitoring index. According to the invention, through multi-source data acquisition of personnel distribution, equipment state and material inventory, difference identification between a task state and a plan node is enhanced, progress lag early warning efficiency is improved, progress fluctuation dynamic classification and trend identification are realized, construction rhythm optimization is assisted, and through coincidence analysis of equipment operation and a task fluctuation area, the progress lag early warning efficiency is improved. The construction efficiency bottleneck is identified, the scheduling accuracy is improved, fine progress monitoring is realized through investment and period difference comparison, and the progress control capability and the collaboration of on-site resource allocation are comprehensively enhanced.

The invention relates to the technical field of communication, in particular to a micro-segmentation-based multi-tenant zero-trust security system, which comprises a tenant network topology sensing module for acquiring network structures and service dependencies of tenants and generating a tenant-level network topology graph and a communication graph; a strategy rule automatic generation module generates a micro-segment access control strategy; the context-aware risk assessment module collects a user identity, an equipment state and a behavior track, and generates an access risk score; the strategy dynamic adjustment and application module updates the access control strategy; and the tenant isolation and zero-trust interaction module deploys a security sandbox to complete trust verification and isolation protection of cross-tenant requests. According to the method, the micro-segmentation strategy is generated by automatically identifying the service dependency boundary, the access control rule is dynamically adjusted based on the context, and refined isolation and risk-driven defense in a multi-tenant environment are realized in combination with the security sandbox and the zero-trust mechanism, so that the security and controllability of the system are remarkably improved.

The invention relates to the technical field of audio and video analysis and data processing, in particular to an AI-based multimedia equipment operation and maintenance management system, which comprises a data acquisition module, an analysis module, an equipment state modeling module, a fault prediction module, a maintenance strategy optimization module, a resource allocation module and a visual platform. The data acquisition module acquires temperature, vibration, audio and video feature data of the equipment through the sensor and the video equipment, and the data are preprocessed and then transmitted to the cloud; the equipment state modeling module constructs a dynamic baseline model based on historical data, abnormal signals are generated through real-time comparison, and the visual platform realizes multi-dimensional state monitoring through 3D topology rendering, AR auxiliary diagnosis and early warning linkage. The system solves the problems of response lag and low resource utilization rate caused by dependence on manual operation and maintenance in the prior art through a closed-loop management mechanism driven by a data stream, and improves the fault diagnosis precision and the operation and maintenance efficiency.

The invention discloses a virtual-real combination-based power generation side industrial control system network security target construction method. The method comprises the following steps of: constructing a virtual-real combination target environment consisting of a physical equipment layer and a virtual model layer; the heterogeneous industrial control protocol between the physical equipment layer and the virtual model layer is analyzed, protocol semantic information is extracted, and a bidirectional dynamic mapping rule of a physical equipment state and a virtual model state is generated based on the protocol semantic information; based on a state change event of the physical equipment layer, according to a bidirectional dynamic mapping rule, synchronizing changed equipment state data to the virtual model layer in real time, and simulating protocol behavior logic corresponding to the equipment state data in the virtual model layer according to a security test requirement; and based on the attack instruction or the abnormal state signal generated by the virtual model layer, according to the protocol specification format of the target physical equipment, converting the instruction or the signal into an executable control command, and driving the physical equipment layer to execute an operation corresponding to the control command.

The invention relates to the technical field of operation and maintenance control, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent equipment configuration changing method and device, equipment and a medium. Generating a preliminary equipment configuration instruction sequence based on the change intention information; rechecking the configuration instruction through the intelligent checking component, and generating a rechecked configuration instruction sequence; sending the rechecked configuration instruction to the target equipment and executing the rechecked configuration instruction; and verifying whether the equipment state meets the expectation, and if not, executing a rollback operation. Configuration accuracy is ensured through knowledge representation, configuration errors are automatically detected and corrected through intelligent verification, configuration efficiency and safety are improved through automatic issuing and state verification, abnormity is quickly recovered through automatic rollback, and reliability and stability of configuration change are improved.

The invention discloses an MES work order dynamic scheduling and resource self-adaption system based on real-time event triggering, and relates to the technical field of intelligent manufacturing and industrial automation, equipment states and material events are monitored in real time through interfaces such as a PLC and an OPCUA, event types and validity are analyzed through hardware filtering, feature code recognition and double verification, and the real-time event triggering is realized. Event data, MES batch information and equipment load data are integrated to generate standardized scheduling input, a preset strategy is matched, an optimal dispatching scheme is generated through a priority algorithm, material, equipment and logistics resource allocation and dispatching operation are completed, synchronization and MES interaction ensure that page logic is consistent, full-process automation of dynamic work order scheduling is achieved, and work order scheduling efficiency is improved. The equipment state and the material event are responded in real time, the production cooperation efficiency is improved by more than 30%, the resource adaptation accuracy rate reaches 95% or above, manual intervention is reduced, and the consistency of data and MES is guaranteed.

The invention relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the steps that an original voice instruction stream of a user is collected, the instruction stream comprises a time domain audio signal sequence, an environment noise spectrum and user pronunciation characteristic parameters, and voice information can be comprehensively captured; multi-modal instruction analysis processing is carried out on the original voice instruction stream, a structured control instruction set containing acoustic control intention identification, semantic operation object description and context correlation parameters is generated, and the analysis precision is improved; then executing player state adaptation based on the set, generating a dynamic control response sequence containing an equipment state adjustment command, a media content positioning parameter and an interface interaction logic identifier, driving a player to execute a multi-dimensional control operation and generating real-time play control effect feedback data; and finally, multi-modal analysis parameters are optimized according to feedback data, a self-adaptive instruction analysis strategy is generated, and the control experience of a user on the audio and video player is optimized.

The invention relates to the technical field of artificial intelligence, in particular to a family service method and system based on artificial intelligence, and the method comprises the following steps: obtaining a statement analysis action sequence and a task direction, collecting the feedback of a device to generate a state structure, judging an instruction trend, recombining a statement to generate a behavior chain, and disassembling an action recognition conflict to extract a main control path. Analyzing a behavior time sequence generation prominent trend, and adjusting a display sequence to generate a function scheme. According to the method, through semantic component extraction and equipment state mapping, the accuracy of instruction target recognition is improved, word order rearrangement and logic connection enhance the continuity of a behavior chain, a control path is optimized and sorted according to verb density and object association, the task scheduling precision is improved, and behavior time sequence analysis recognizes a high-frequency operation forward trend; according to the method, priority dynamic adjustment and rearrangement of recommended content in combination with instruction frequency and time sequence characteristics are realized, the response initiative and the service matching degree are enhanced, and collaborative optimization of semantic understanding, behavior prediction and content recommendation is integrally realized.

The invention discloses a computer network equipment fault positioning method and system and a storage medium, and relates to the technical field of fault positioning. The method comprises the following steps: performing multi-source operation acquisition on computer network equipment to generate an equipment state data set; according to the equipment state data set, abnormity judgment is carried out according to the dynamic baseline, monitoring extraction is carried out according to a judgment result, and an abnormal event sequence is constructed; performing association mapping based on a computer network equipment topological graph, constructing a network equipment dependency graph, traversing the network equipment dependency graph according to an abnormal event sequence to perform traceability positioning, and determining a fault source equipment identifier set; and performing fault root cause analysis according to the fault source equipment identification set, determining fault generation root cause data, and feeding back the fault generation root cause data to the network operation and maintenance unit to execute automatic fault handling. The technical problems of low network equipment fault positioning efficiency and poor positioning accuracy in the prior art are solved, and the technical effect of accurately and efficiently positioning the fault source is achieved.

The invention relates to the technical field of automatic control, in particular to an intelligent equipment management method and system, and the method comprises the following steps: obtaining a control cabinet log, extracting an equipment temperature load pressure sequence, comparing a set offset to generate a deviation label, collecting an operation time length and load change mapping early warning table, and generating an abnormal record; and extracting task configuration and equipment state re-marking alternating points to generate load configuration, resetting an instruction sequence to generate a scheduling sequence, extracting an instruction time-consuming marking interference section, and switching a standby link to generate an evasion list. According to the invention, by collecting the temperature, the load rate and the pressure sequence in the operation cycle of the equipment, the dynamic state identification capability is improved, the scheduling path is optimized, the resource contention is relieved, the delay path is marked in combination with the original response data, and the standby link is switched to avoid interference, so that the flexibility and stability of linkage scheduling are improved, and the task execution beat accuracy is enhanced; the instruction blocking and interruption risks are effectively reduced, and the operation toughness and response efficiency of a production line are improved.

The invention discloses a service-based calculation task dynamic abstraction method and system, and relates to the technical field of calculation task scheduling. The service-based calculation task dynamic abstraction method comprises the following steps: receiving and analyzing a to-be-executed task, and dividing the to-be-executed task into a plurality of independent service units; constructing a directed acyclic graph representing the dependency relationship between the service units, and executing topological sorting based on the directed acyclic graph to determine an execution sequence; and according to the resource demand of each service unit and the current system equipment state. According to the method, the technical problems that a traditional task scheduling method comprises but is not limited to the following technical problems that resource allocation is rigid, a heterogeneous computing environment cannot be dynamically adapted, and the hardware utilization rate is low; parallel arrangement is low in efficiency, depends on manual definition of an execution sequence, lacks an automatic arrangement capability and is difficult to deal with a complex task process; the real-time performance is insufficient, the task execution process is solidified, and the resource allocation and execution path cannot be dynamically adjusted according to the runtime state.

The invention discloses a hierarchical traffic prediction-based multi-terminal video display adaptive method, which comprises the following steps of: acquiring network traffic, user behaviors and equipment state information in real time by constructing a multi-dimensional feature acquisition module, and generating a dynamic feature matrix containing traffic, interaction and performance features; processing the feature matrix by using a deep learning model, and generating and dynamically adjusting a network traffic grading prediction result; formulating an optimized video content distribution strategy based on a prediction result and equipment performance parameters, and coordinating multi-terminal resource distribution; through user behavior analysis and network state monitoring, a self-adaptive video display control strategy is generated, dynamic adaptation of video resolution, frame rate and playing logic is realized, a full-link collaborative optimization module is constructed, terminal, network and application layer resource allocation is coordinated, a video transmission and display strategy is optimized, and network flow prediction and multi-terminal interaction requirements are combined to realize multi-terminal interaction. And generating a dynamic video layout and content switching strategy.

The invention relates to the field of computer accessories, and discloses a computer accessory intelligent recommendation system and method based on user behavior analysis, and the method comprises the steps: collecting interaction data of a user in an e-commerce platform, and constructing a behavior feature data set of user preference and a current equipment state through a behavior semantic deconstruction algorithm and an equipment configuration recognition mechanism; performing multi-dimensional feature extraction on the behavior feature data set, constructing a user equipment ecological model based on an accessory dependence modeling method, and introducing a behavior context sensing mechanism to perform dynamic learning training on the model; judging whether the recommendation model reaches a stable state or not according to the change trend of the fitting dependence path in the model training process; based on the optimized equipment ecological model, adopting a heterogeneous relation fusion recommendation strategy; and generating a personalized accessory recommendation scheme according to a sorting result, and performing feedback verification on the scheme in combination with a preset recommendation rationality evaluation model. The method has the advantage of improving the precision and experience of accessory recommendation.

The invention discloses an equipment state monitoring and anomaly analysis strategy method and system based on a PMS, and belongs to the technical field of equipment management.The method comprises the steps that equipment basic information, real-time operation state data, historical maintenance records and risk level parameters in an equipment management system are obtained, a comprehensive equipment state data set is generated, and a dynamic health attention index is calculated; and generating a strategy chain instruction set to schedule the sensor group to execute data acquisition operation, generating a target monitoring data set, inputting the target monitoring data set into the multi-stage adaptive analysis tree in combination with an analysis strategy instruction, generating a structured analysis report, and matching maintenance decision suggestions from the maintenance knowledge base and outputting the maintenance decision suggestions when a preset condition of a decision triggering strategy instruction is met. According to the method, the dynamic health attention index is adopted to drive strategy chain instruction generation, and the multi-stage adaptive analysis tree hierarchical diagnosis and closed-loop parameter optimization mechanism is combined, so that accurate distribution of monitoring resources, accurate positioning of fault root causes, economic optimization of maintenance decision and adaptive continuous upgrading of the system can be realized.

The invention belongs to the technical field of intelligent hardware control methods, and discloses a low-power-consumption intelligent hardware control method which comprises the following steps: acquiring working state parameters and user interaction information of intelligent hardware, and generating an equipment state feature vector; judging whether a dormancy condition is met or not according to the equipment state feature vector; when the sleep condition is satisfied, sending a network communication agent instruction; the coprocessor configures an internal timer and awakens signal output after receiving the instruction; the master control stores a system operation state, configures a wake-up source and then enters a deep sleep state; the coprocessor cuts off the power supply of the inactive module; when a timer of the coprocessor reaches a preset network heartbeat cycle, a wake-up signal is sent to the master controller; and the master control recovers the system state, updates the feature vector and re-decides whether to sleep or not. And through multi-dimensional state perception and a master-slave cooperation architecture, and through refined power management and a dynamic reevaluation mechanism, the equipment power consumption is obviously reduced on the premise of ensuring the user experience.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

The invention relates to the technical field of Internet of Things equipment, in particular to an Internet of Things equipment management method, computing platform and system, and the method comprises the steps: obtaining the state data of the Internet of Things equipment, carrying out the health scoring of the equipment periodically through a neural network model, and detecting the potential fault risk; constructing a task allocation strategy based on the equipment health score, and dynamically transferring a task to health equipment when the equipment has a potential fault risk; detecting the communication state of the potential fault risk equipment; potential fault risk equipment in a normal communication state is degraded into temporary communication equipment, and the health equipment is assisted to complete communication; and optimizing network topology and communication load by using temporary communication equipment based on a dynamic topology optimization algorithm driven by a graph model. According to the method, the reliability, the task completion rate and the network communication performance of the Internet of Things equipment can be effectively improved.

The invention provides an equipment state data analysis method and system applied to automated electrical equipment, and the method comprises the steps: obtaining a historical operation data set of target equipment, and constructing a multi-level state analysis model based on the historical operation data set, the multi-level state analysis model comprises at least one first analysis level and at least one second analysis level, key operation parameters are extracted from the real-time operation data flow of the target equipment through an operation parameter screening strategy in the first analysis level, and a parameter screening result is generated according to the key operation parameters; inputting a parameter screening result into a state evaluation strategy in a second analysis hierarchy, and generating a state evaluation index of the target equipment in combination with a dynamic threshold range corresponding to the key operation parameter; and outputting a real-time operation state grade of the target equipment according to a comparison result of the state evaluation index and a preset state threshold value. According to the invention, efficient monitoring and accurate early warning of the equipment health state can be realized.

The invention belongs to the field of artificial intelligence, particularly relates to a medical resource scheduling optimal configuration method for data analysis, and aims to solve the problems of non-uniform resource allocation, response lag, difficulty in data integration decision and the like in existing medical resource scheduling. According to the method, multi-source heterogeneous medical data (such as patient diagnosis and treatment, medical care, equipment states and department parameters) are uniformly collected, preprocessed and deeply analyzed, and real-time resource state characterization is constructed; based on the characterization, the demand is accurately predicted, supply is evaluated, an optimization algorithm is used for dynamic scheduling configuration, and an optimal scheme is generated. Then, scheduling is executed according to the scheme, and iterative optimization is fed back in real time; the medical service efficiency and quality can be remarkably improved, the patient experience is optimized, the operation cost is reduced, emergencies are effectively dealt with, and the resource management toughness of medical institutions is enhanced.

The invention relates to the technical field of industrial park intelligent monitoring, and particularly discloses a data acquisition and monitoring control method based on an intelligent industrial park, which comprises the following steps: acquiring key state data of equipment in real time, including operation time, operation temperature and operation power; an advanced signal processing algorithm is used for calculating an operation temperature abnormal index and an operation power abnormal index, the system can comprehensively evaluate the state of equipment and accurately judge whether slight or serious abnormal conditions exist or not based on detailed data analysis results, and the system can not only give out instant early warning notifications but also give out alarm information according to the serious abnormal conditions. According to the method, a machine learning model is adopted to predict the future operation state of the equipment, the equipment state abnormal coefficient in each future monitoring period is predicted based on the equipment state data in the current period, and classification and dynamic regulation and control of the equipment state are carried out according to a preset threshold value.

The invention belongs to the technical field of cooperative control, and particularly relates to a development experiment box multi-device cooperative control method based on an open-source gap system. According to the method, access, identity authentication, automatic discovery and dynamic connection of experimental box equipment are realized through a distributed equipment management platform of an open-source Gao Mongolia system; a uniform resource scheduling mechanism is utilized to complete resource information collection, strategy matching and dynamic allocation; task allocation is carried out by adopting an intelligent task scheduling algorithm and combining equipment states and task requirements; real-time data transmission between devices is realized by means of a low-delay communication protocol; and finally, automatically summarizing and analyzing experimental data and generating a report. According to the method, the problems of low equipment identification efficiency, low resource utilization rate, low task allocation efficiency and the like in the existing development experiment box are solved, the intelligent level and the system stability of equipment cooperative control are improved, and the method is suitable for a multi-equipment cooperative control scene of the development experiment box.

The invention provides a data compression method and device, and relates to the technical field of data processing and data compression. The method comprises the following steps: acquiring to-be-compressed data, and acquiring a data category of the to-be-compressed data; determining a plurality of candidate compression strategies corresponding to the to-be-compressed data based on the data category; the importance degree of the to-be-compressed data is obtained, and the equipment state parameter of the current equipment is obtained; determining a target compression strategy and a compression level corresponding to the target compression strategy from the candidate compression strategies based on the equipment state parameters and the importance; and executing the target compression strategy on the to-be-compressed data according to the compression level to obtain compressed data. According to the method, the target compression strategy can be dynamically determined according to the data type, the data importance and the equipment state, high flexibility and adaptability are achieved, the data storage and processing efficiency is improved, good balance between the data quality and the storage space is ensured, and the method adapts to modern changeable equipment environments and data requirements.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

The invention relates to a multi-account security management method and system based on a mobile energy storage charging pile. According to the method, a multi-level user account system and a dynamic authority management mechanism are established, a dynamic token carrying authority attributes is generated through encryption verification to achieve access control, user operation is captured in real time to generate log data with an identity label, the log data are synchronized with a cloud end, and an equipment control instruction is generated in combination with Internet of Things data collection and authority verification. Meanwhile, authority rules are dynamically updated, whole-course tracing of operation behaviors of the charging pile system, real-time effectiveness of authority change and linkage management and control of equipment states and user authorities are achieved, and the problem that potential safety hazards are caused by extensive authority control, difficult operation tracing and authority strategy lagging of a traditional charging pile management system is effectively solved.

The invention relates to an intelligent equipment function reliability verification method and device, equipment and a storage medium, and the method comprises the steps: obtaining operation environment parameters and equipment state data of intelligent equipment, and carrying out the construction evaluation of a redundancy verification architecture, and obtaining an initial verification reference; acquiring real-time operation data of the intelligent equipment, and performing function association analysis on the real-time operation data and the initial verification reference to obtain function abnormality features; acquiring state information of associated equipment of the intelligent equipment, performing cross validation on the state information of the associated equipment according to the function abnormality characteristics, and performing fault analysis on the state information of the associated equipment and the function abnormality characteristics to obtain an evolution fault injection scheme; and carrying out abnormal behavior identification on the real-time operation data to obtain an equipment repair requirement, and carrying out feasibility evaluation on the equipment repair requirement based on the function abnormal characteristics to obtain a dynamic repair strategy. According to the invention, a comprehensive guarantee mechanism can be provided for reliable operation of the intelligent equipment.

The invention discloses a campus intelligent terminal equipment management method and system based on multi-source state synchronization, and relates to the technical field of intelligent terminals. The campus intelligent terminal equipment management method and system based on multi-source state synchronization comprises the following steps: S1, a terminal cooperatively collects a context state data set, and normalization processing is carried out; s2, constructing a migration mirror image synchronization state data set, and carrying out migration handover of stage switching and playback; s3, constructing a pulse interference detection model, and performing interference window suppression and online correction; and S4, constructing a dual-time reference transaction consistency verification and migration submission determination closed loop, and carrying out linkage rollback and governance. According to the method, the state consistency and the operation stability in the cross-node migration process of the campus intelligent terminal are effectively improved, and the problem of pulse interference in equipment state migration is solved.

The invention relates to the technical field of engineering machinery control, and discloses a carry-scraper electrical control system and method based on intelligent control, and the system comprises a sensor module, a man-machine interaction module, a control module, an execution module, and a communication module. The method comprises the following steps: collecting real-time operation data and an operator instruction, generating a predictive deterministic map by a control module, and outputting a final control instruction for energy consumption optimization according to the predictive deterministic map to drive the carry-scraper. And meanwhile, the system continuously monitors the state of the equipment to realize fault early warning, calculates an energy consumption optimization index, and finally graphically displays the operation data, the fault early warning and the energy consumption index through the man-machine interaction module. According to the invention, an intelligent algorithm is integrated, so that accurate control and early warning of faults can be realized, energy consumption is optimized, and safety and economy are improved; and through simplified man-machine interaction, the defects of poor adaptability, complex operation and high energy consumption of a traditional system are overcome.

The invention discloses an intelligent operation and maintenance monitoring alarm method and system, and belongs to the technical field of monitoring alarm, and the method specifically comprises the steps: collecting the real-time communication data and a multi-dimensional dynamic label of each equipment node, the multi-dimensional dynamic label comprises a dynamically generated equipment state label, automatically updating label attributes and life cycles according to real-time communication data, generating self-adaptive alarm thresholds based on label matching dynamic threshold templates and historical data, constructing an equipment node network diagram, performing state evaluation based on the equipment node network diagram, and comparing, analyzing and judging whether the real-time state of each node is abnormal or not. Generating alarm information for the detected abnormal behavior, wherein the alarm information comprises a specific abnormal node, an abnormal data index and an abnormal reason prompt; according to the method, the dynamic threshold template is matched by using the historical data and the dynamic label, the adaptive alarm threshold is automatically generated, the fault node and the affected link are quickly positioned, the abnormal level is judged through real-time comparison and analysis, and detailed alarm information is generated.

The invention provides a network operation and maintenance instruction security decision-making method, system and device based on topology constraint and multi-agent arbitration, and belongs to the technical field of artificial intelligence and network operation and maintenance crossing. The method comprises the following steps: constructing a network topology attribute graph, and collecting and updating network equipment states and connection relationships in real time; generating a physical rule constraint and a logic rule constraint based on the topological attribute graph; creating a corresponding device agent for each network device, and distributing the constraint rule to the corresponding device agent; receiving an operation and maintenance instruction, and carrying out cooperative verification on the instruction through the multi-intelligent experience certificate chain; performing weighted decision on the verification result based on a dynamic weight decision mechanism to generate a final execution decision; and analyzing the high-risk instruction scene through a rule evolution engine, and updating the topology constraint library. According to the invention, the problem that the traditional operation and maintenance system is insufficient in intelligence is effectively solved, and high-reliability intelligent operation and maintenance support is provided for key scenes such as industrial internet.