282802 results about "Systems engineering" patented technology

The invention discloses a power transmission and distribution production task cooperation system and method based on an intelligent agent, and relates to the technical field of power distribution production task scheduling, the system comprises six modules: a natural language input interaction module processes a user instruction and multi-modal information, and generates structured data; the electric power field knowledge enhancement analysis module establishes mapping from a natural language to business data; the dynamic interaction context memory module stores historical interaction data and generates a context feature vector through a bidirectional LSTM and an attention mechanism; the intelligent task scheduling and conflict resolution module is used for disassembling instructions into sub-tasks, dynamically evaluating priorities in combination with three-dimensional indexes and resolving resource conflicts; the agent task execution and cooperation module drives agents to execute tasks according to priorities and synchronize states in real time; the system closed-loop feedback optimization module analyzes the execution log and automatically updates model parameters; according to the system, the problems of term analysis deviation, strategy staticization and insufficient self-optimization capability of a traditional scheduling system are solved.

The embodiment of the invention relates to the technical field of ship scheduling, in particular to a multi-source data-based intelligent auxiliary decision-making method for ship entering and leaving a port and berthing and leaving the port, and the real-time performance and accuracy of port scheduling decision-making can be improved by fusing multi-source heterogeneous data to construct a ship navigation situation awareness system. The space-time alignment technology effectively eliminates space-time dislocation of environment interference factors and enhances the characterization capability of the ship motion state and environment coupling relation. Quantitative evaluation of multi-dimensional navigation risks is realized based on a ship behavior prediction model, and potential risks such as channel deviation and berthing conflicts can be identified. A collaborative decision-making scheme considering safety and efficiency is generated through a collaborative optimization mechanism of the dynamic scheduling rule base and the risk indexes, and ship trajectory control precision is optimized through dynamic planning of navigational speed adjustment and steering time sequence. Closed-loop linkage of port resource allocation and ship navigation control is achieved through automatic instruction generation and execution, and the reliability and the response speed of the port operation process can be ensured.

The invention relates to the technical field of power equipment, and discloses a digital twin operation monitoring system for power equipment, which comprises a data sensing and acquisition system for acquiring key operation parameters of temperature, current, voltage, partial discharge, vibration and humidity of the power equipment in real time, and performing multi-dimensional data acquisition through a sensor and a data transmission module; the state evaluation and prediction system is used for performing equipment health evaluation and residual life prediction by using a prediction model LSTM based on the collected data, and updating a prediction result in real time; provided is a digital twin modeling system. Through the combination of edge calculation, an LSTM model and a digital twinning technology, the precision and real-time performance of health management of power equipment are improved, data quality is optimized through edge calculation, the LSTM model captures an equipment degradation trend, virtual-real fusion is realized through digital twinning, and accurate monitoring and early warning of the health state of the equipment are ensured, so that intelligent operation and maintenance decisions are optimized, the failure rate is reduced, and the safety of power equipment health management is improved. The equipment life is prolonged.

The invention discloses an equipment anomaly detection method and system based on multi-source heterogeneous data, and the method comprises the steps: obtaining a real-time multi-source monitoring data flow of production equipment in a smart park, carrying out the cross-modal data alignment processing of the real-time multi-source monitoring data flow, and generating a target monitoring data set, and executing a dynamic feature extraction operation in each edge computing node, generating a multi-dimensional equipment state feature set based on the target monitoring data set, inputting the multi-dimensional equipment state feature set into the trained anomaly detection integrated model, generating equipment anomaly probability distribution through a multi-level feature fusion strategy, and outputting the equipment anomaly probability distribution. Determining the abnormity type and abnormity confidence of the target equipment according to the equipment abnormity probability distribution, generating an equipment maintenance instruction set based on the abnormity type and the abnormity confidence, and sending the equipment maintenance instruction set to an equipment management terminal of the smart park to trigger abnormity processing operation; therefore, the automation degree and decision reliability of the equipment maintenance response of the smart park are improved.

The invention provides an intelligent power distribution load prediction and adaptive scheduling method, and aims to improve the response capability and operation efficiency of a power distribution system to load fluctuation. According to the method, historical load data, real-time meteorological information and user power consumption behavior data of a target power distribution area are collected, a multi-dimensional input data set is established, a short-term load prediction model is constructed based on the data set, and prediction of future load levels of multiple feeder lines and key nodes is achieved. According to a prediction result, potential voltage deviation, load imbalance and equipment overload risks are identified, and a dynamic scheduling control strategy is generated by adopting a rolling horizon optimization method in combination with a power grid topological structure and operation constraints; the strategy comprises control measures such as transformer tap position adjustment and capacitor bank switching; through issuing a scheduling strategy to an equipment control unit, active adjustment is completed before load change, voltage stabilization, load balancing and energy optimization are realized, and intelligent and adaptive operation guarantee is provided for a power distribution system.

The invention provides an AI-based big data distributed computing task automatic optimization method and system, and relates to the technical field of AI. The method comprises the following steps: constructing a multi-modal spatial-temporal feature sensing network to extract spatial-temporal features of tasks and resources, and training a hierarchical hybrid decision network to perform global task scheduling and local resource allocation. The global strategy network adopts a layered deep reinforcement learning architecture, and generates a decision by combining Monte Carlo tree search and priority experience playback; and the local execution network optimizes local deployment based on the graph neural network and multi-agent collaborative learning. In addition, a distributed anomaly detection network is deployed to monitor the performance in real time, self-adaptive adjustment and optimization are achieved through enhanced transfer learning, and finally the perception network is updated through knowledge distillation to achieve model evolution. According to the method, the execution efficiency and the resource utilization rate of the big data distributed computing task can be effectively improved, and the system operation cost is reduced.

The invention provides an intelligent subway equipment fault diagnosis method and system assisted by a large language model, and relates to the technical field of data processing, and the method comprises the steps: extracting key information through a large language model, constructing a multi-dimensional equipment fault knowledge graph, obtaining a historical fault data set, and extracting key fault monitoring parameters related to the fault, obtaining a sensing state data set, and judging whether the sensor state data is abnormal or not; and calling a pre-constructed sensing distortion correction algorithm, generating a fault monitoring correction parameter and executing parameter correction, inputting multi-dimensional monitoring data into a diagnosis engine driven by a large language model, and outputting a most possible fault type, cause analysis and recommendation processing strategy and a fault identification report. The technical problems that in the prior art, due to the lack of fusion modeling capacity for the unstructured fault text and the structured monitoring data, the intelligent degree of fault diagnosis is low, and accurate recognition and causal analysis are difficult to achieve are solved, and the fault recognition response speed and accuracy are improved.

A scalable platform for orchestrating networks of specialized AI multi-agent networks that enables secure collaboration through token-based protocols and real-time result streaming with advanced dynamic chain-of-thought pruning. The central orchestration engine manages domain-specific agents, implementing sophisticated multi-branch reasoning with contribution-estimation layers that evaluate each agent's utility using Shapley value-inspired metrics. The system employs information-theoretic and gradient-based surprise metric to guide memory updates and dynamic reasoning expansion, preventing local minima stagnation while preserving valuable insights through adaptive forgetting mechanisms. The platform unifies Monte Carlo tree search with contribution-aware estimation to detect high-synergy expert combinations while maintaining privacy through partial data approaches. It scales across distributed computing environments, enabling complex collaborative tasks like materials discovery, product engineering and manufacturing process design, biomedical research, and drug development. The system supports multi-party economic rewards through systematic contribution effort, cost and importance tracking, while standardized interfaces manage security, privacy, and policy constraints across heterogeneous agents.

The invention relates to the technical field of industrial automation control, and discloses a multi-machine collaborative industrial robot intelligent scheduling system and an application method.The system comprises a data acquisition module for acquiring real-time motion data through a multi-modal sensor; the feature fusion module is used for generating environment dynamic feature data based on graph neural network fusion data; the strategy generation module is used for generating collaborative strategy parameters by utilizing a collaborative reinforcement learning model; the trajectory optimization module is used for constructing a multi-target trajectory optimization model to plan an optimal synchronous trajectory; the motion control module is used for establishing a layered motion control model to realize cooperative motion control; a dynamic resource allocation module can also be included to optimize resource allocation. The application method sequentially executes corresponding steps according to the modules of the system. According to the method, the environment can be accurately sensed, the cooperation strategy can be optimized, the efficient track can be planned, accurate motion control and reasonable resource allocation can be realized, and the cooperative motion control capability and the production efficiency of the industrial robot in the complex environment can be effectively improved.

The present application relates to an on-line evaluation layered model including a monitoring data layer, a diagnosis method layer, an evaluation indicator layer, a monitored item layer, a key part layer, and an overall project layer. The method further includes collecting same-type multi-measuring-point monitoring data and multi-type multi-measuring-point monitoring data of a concrete dam, determining various indicator analysis results of the concrete dam based on the same-type multi-measuring-point monitoring data, determining various indicator operation scoring results of the concrete dam based on the multi-type multi-measuring-point monitoring data. In some embodiments, the method includes performing, based on the various indicator analysis results and the various indicator operation scoring results, analysis of an impact effect of external special loads on the dam caused by one or more risk events, and giving operation decision support suggestions of the dam in combination with overall performance graded evaluation of the dam.

The invention discloses an ancient building risk prediction management and control method and system based on a large model, and relates to the technical field of ancient building protection. An ancient building risk prediction management and control system based on a large model comprises a multi-source data acquisition module, a multi-modal data fusion module, a large model risk prediction module, a risk assessment and early warning module, a digital twinborn dynamic management and control module and a repair decision module. According to the method, the structure state data, the visual image data and the environment data are fused, so that the state of the historic building is comprehensively perceived; the self-supervised learning pre-trained ViT-GPT model is combined with the LSTM time sequence model, so that the time sequence analysis capability and the multi-modal data processing capability of risk prediction are remarkably improved, the future damage probability and the risk trend of the historic building can be accurately predicted, and the defects of the prior art in long-time span risk prediction are overcome.

The invention discloses an intelligent management system for energy consumption optimization and fault self-diagnosis of cleaning equipment, and relates to the field of intelligent maintenance of the cleaning equipment, and the system comprises the steps: obtaining three groups of core parameters, i.e., a historical vibration spectrum, a motor current harmonic component and a bearing temperature gradient, constructing a dynamic failure mode knowledge graph, performing time sequence correlation analysis on the historical fault data to obtain failure mode analysis data; establishing a multi-dimensional analysis platform, identifying a high-risk component, and updating a fault threshold value; introducing a service time attenuation factor and a working condition correction coefficient, establishing an aging degree quantitative model, and calculating an aging coefficient; and constructing and developing an energy consumption-reliability joint optimization module, and adjusting equipment operation parameters. The method has the advantages that the dynamic knowledge graph and the time sequence analysis model are constructed by integrating multi-source sensor data, precise diagnosis and self-adaptive threshold adjustment of the coupling fault of the cleaning equipment are achieved, aging evaluation and task scheduling optimization are combined, the energy consumption efficiency is improved, and the maintenance cost is reduced.

The invention discloses a construction site safety risk intelligent assessment method and system, and belongs to the technical field of engineering supervision. The method comprises the following steps: S100, collecting multi-source data including an equipment state, an image video, a personnel state, an equipment distance, an environment parameter and an engineering text in real time; s200, performing space-time alignment, preprocessing and cross-modal feature fusion on the multi-source data; s300, outputting a risk score and a risk level label based on the dynamic risk knowledge graph and a multi-model fusion algorithm; s400, generating a hierarchical disposal strategy according to the risk scoring hierarchy, and realizing risk closed-loop management and control through rectification verification and cooperation of multiple parties; and S500, outputting a three-dimensional visual risk distribution map and a compliance report. Through technologies of multi-source data fusion, dynamic risk mapping knowledge, intelligent closed-loop management and the like, comprehensive perception, accurate evaluation, efficient management and control and compliance landing of engineering supervision safety risks are realized, the occurrence rate of safety accidents is remarkably reduced, and reliable technical support is provided for intelligent construction site construction.

The invention relates to the field of security surveying and mapping, in particular to a network security space surveying and mapping method, system and device based on multi-source data fusion, and the method comprises the steps: obtaining network security data in real time, and constructing a dynamic network topological graph; calculating a time-varying vulnerability score based on the topological graph and a historical attack log, and predicting an attack path and a propagation probability through a Bayesian network; performing cross-domain fusion on equipment, service and user behavior characteristics by adopting a federated learning framework to generate a dynamic asset portrait; generating a risk thermodynamic diagram in combination with spatial autocorrelation analysis and a multi-index fusion algorithm; a defense strategy effect is simulated based on an attack graph reconstruction engine, a Pareto optimal strategy combination is generated through an NSGA-II algorithm, and closed-loop verification and dynamic parameter correction are realized by utilizing honeypot deployment and flow traction. Therefore, the problems of topology update lag, single risk assessment dimension, cross-domain threat association fracture, defense strategy static stiffness, non-closed loop of a verification system and the like in the traditional technology are solved.

The invention provides an HBase client main-standby switching method and system based on fault perception, and relates to the technical field of database management, and the method comprises the steps: collecting system operation data through a distributed fault monitoring module, building an anomaly detection baseline model, carrying out fault diagnosis based on a system health assessment distribution matrix, and generating a fault characteristic spectrum; and switching is triggered according to a risk level evaluation result, and the optimal standby node is selected for sequential switching by using a deep strategy learning algorithm, so that rapid and accurate fault detection, diagnosis and intelligent switching can be realized, and the reliability and performance of the system are improved.

The invention provides an equipment fault early warning method based on a neural network, a medium and equipment, and the method comprises the steps: collecting equipment state information at regular time, recognizing abnormal equipment, obtaining operation parameters and structure parameters of the abnormal equipment and a layout schematic diagram of a system where the abnormal equipment is located, constructing a dynamic coupling topology link, and extracting associated parameters; verifying and generating a first error weight in combination with a physical kinematics equation, generating a second error weight by matching historical fault data, and comprehensively generating fault state information; a detection mode is switched based on risk level evaluation, a system dynamic coupling topology link, associated parameters, abnormal equipment information and fault state information are input into a neural network model, fault link information is output, and finally first early warning information and second early warning information are generated according to a dynamic detection mode to realize two-stage early warning. According to the method, the fault conduction relation between equipment is modeled through the graph neural network, and fault path prediction and systematic risk early warning are realized.

The invention provides a procurement supply chain collaborative intelligent management method and a procurement supply chain collaborative intelligent management system. Belongs to the technical field of supply chain management. Constructing a purchase demand prediction model; dynamically adjusting the purchasing strategy according to the prediction result; the record and the evaluation result are stored on the block chain; the system automatically sends order information to a supplier; the supplier confirms the order through the block chain platform and updates the production progress and the logistics information; the system adjusts an inventory strategy in real time; the logistics state is monitored in real time; the system analyzes data of each link of the supply chain in real time and identifies potential risks. Through dynamic path planning and real-time transportation optimization, the logistics path can be adjusted in real time according to traffic conditions, environmental factors and emergencies, the transportation time is shortened, the transportation cost is reduced, and therefore the overall efficiency of a supply chain is remarkably improved.

An Internet-of-Things-based intelligent safety monitoring method and system for power plant operation. The method comprises: deploying sensors and collecting monitoring data in real time; transmitting the collected data to a central server, performing data processing, integrating the monitoring data, calculating a comprehensive environmental index, and preliminarily assessing an environmental monitoring condition; and comprehensively determining a device fault condition, remotely monitoring device status, starting a device emergency response mechanism, and performing intelligent safety monitoring of power plant operation. Power plant devices and environment parameters are monitored in real time, so that device anomalies and potential safety hazards are promptly captured to achieve rapid response. The stability of the power plant is improved, and a video monitoring system is integrated in combination with image recognition technology to monitor anomalous events in the power plant area. The level of safety management is enhanced, and decisions can be quickly made in emergency situations, improving flexibility and efficiency and enhancing management effectiveness and the accuracy and stability of the monitoring system.

The invention discloses a territorial space planning environment influence monitoring method and device. The method comprises the following steps: collecting and preprocessing multi-source environment monitoring data; performing spatio-temporal feature extraction on the preprocessed multi-source environment monitoring data by using a self-supervised spatio-temporal diagram mask transfer attention network to obtain initial spatio-temporal features; performing fusion processing on the initial spatial-temporal characteristics based on a multi-mode diffusion converter to obtain fusion characteristics; calculating an environmental impact evaluation index based on the fusion feature; and based on a preset visualization template and an early warning rule, generating a dynamic monitoring result including a space-time evolution diagram, a trend analysis diagram and early warning information according to the environmental influence evaluation index. Through deep learning and multi-modal data fusion technologies, the precision and efficiency of environmental impact monitoring are improved, and accurate evaluation and timely early warning of environmental impact in the land space planning implementation process are realized.

The invention discloses a dam safety monitoring system and method based on digital twinning, and particularly relates to the technical field of digital twinning. The method comprises the following steps: acquiring stress, deformation, displacement, inclination and environmental change data of a dam structure in real time, regularly carrying out high-precision calibration on a sensor, recording a time drift error, analyzing nonlinear sensitivity of a digital twin model under an extreme working condition, simulating the influence of the sensor error and the model sensitivity on dam evaluation, predicting a deviation degree, and calculating the evaluation accuracy of the dam. Based on a prediction result, a dynamic drift compensation algorithm is adopted to correct sensor data in real time, the sensitivity of the model is dynamically adjusted, the optimized data and model are integrated, the structural state and potential risk of the dam under normal and extreme working conditions are accurately evaluated, accurate early warning information and decision scheme are generated, and the accuracy of early warning is improved. The accuracy and reliability of dam safety monitoring and risk assessment are remarkably improved, decision delay or resource waste caused by misjudgment or missed judgment is reduced, and the dam safety guarantee capacity is improved.

The invention provides a power distribution network communication network fault diagnosis and self-adaptive reconstruction method, which comprises the steps of formulating corresponding layering and partitioning strategies aiming at different network scales and service types, including modular management of a large-scale multi-layer network and centralized response of a small-scale network; the faults of various networks can be effectively positioned and isolated; recognizing a reconstruction object after the fault positioning is completed, and formulating a network reconstruction scheme according to the recognized reconstruction object and the current state of the network, including but not limited to replacing a fault node, reconstructing a connection or modifying a route by starting a standby resource; after the reconstruction scheme is executed, the data collection and analysis period is adjusted according to the fault frequency, and the reconstruction interval is controlled to reduce redundant network structure adjustment and potential network fluctuation, including active detection and passive monitoring of the power distribution network and bidirectional monitoring of the network condition.

A system for multi-stage planning of construction processes and resource allocation, consisting of: a central planning engine configured to receive input data, including architectural design models, structural constraints, procurement schedules, and historical performance indicators; a task decomposition processor that is operationally connected to the central planning engine and configured to generate a hierarchical construction task graph by decomposing macro-level construction milestones into mid-level and micro-level subtasks, with each subtask having time estimates, location identifiers, resource requirements, and mutual dependencies; a hybrid planning processing unit configured to resolve time and resource constraints across the entire task diagram; a resource coordination controller that is operationally connected to the central planning engine, wherein the resource coordination controller includes a real-time database of work units, machines and material stocks, each resource being tagged with attributes such as availability, usage history, operating status and spatial location; a multitude of distributed execution units distributed across the construction zones, each distributed execution unit comprising an embedded controller, sensor interfaces, task status processing logic, and communication circuitry, each distributed execution unit being configured to receive planning instructions from the central planning machine, execute localized control logic for task confirmation and resource activation, and transmit task execution data back to the central planning machine; an adaptive conflict resolution processing unit that is operationally connected to the central planning engine and configured to detect conflicts in task execution or resource conflicts, simulate alternative task-resource allocation scenarios using a real-time multi-agent model, and autonomously update the task graph with revised task sequences and resource allocations; and A dashboard for the construction process, configured to visualize task progress, deviations from the planned schedule, and resource efficiency metrics, with the dashboard also being able to receive manual override inputs or approve automated conflict resolution proposals generated by the adaptive conflict resolution module.

The invention belongs to the technical field of equipment management, and discloses an industrial equipment fault prediction and health management method based on multi-sensor fusion, and the method comprises the steps: obtaining multi-source sensing data of industrial equipment, carrying out the signal decoupling analysis, and obtaining a decoupling characteristic spectrum; performing frequency domain conversion and modulation analysis to form a multi-dimensional characteristic spectrum system; analyzing the modal correlation of the multi-dimensional feature pedigree to obtain a fault feature mapping network; a mixed time sequence prediction model is constructed, residual life prediction and degradation trend evaluation are carried out, and an equipment health trend graph is obtained; establishing a health state evaluation index system, and performing reliability evaluation to obtain an equipment health state report; and generating a maintenance decision suggestion, and realizing real-time anomaly detection and maintenance suggestion pushing through edge calculation. Through multi-sensor data fusion and advanced analysis technologies, early warning and accurate prediction of industrial equipment faults are realized, and the operation reliability and production efficiency of the industrial equipment are remarkably improved.

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

The invention discloses a data center intelligent operation and maintenance method and system based on an industrial internet of things, belongs to the field of image processing calculation, and aims to solve the problems of privacy protection deficiency, serious manual dependence and weak energy efficiency and safety. Full-link dynamic optimization is realized through an edge calculation layer and an adaptive clustering network, edge nodes are integrated with a wavelet transform noise filtering technology, dynamic denoising is performed on multi-band sensor signals of temperature, vibration, current and the like, heterogeneous device time sequence data are aligned in combination with a dynamic time warping algorithm, sampling frequency differences of multiple devices are eliminated, and the multi-band sensor signals are obtained. A weight formula is put forward, a cluster head is dynamically elected based on node residual energy, data similarity and a load coefficient, a transmission path of a high-delay area is optimized in combination with a multi-hop routing protocol, the packet loss rate and the signal strength are evaluated in real time, redundant coding is started for a high-packet-loss link or the high-packet-loss link is switched to a low-power-consumption mode. And synchronizing the dynamic topology state with the digital twin platform through an open platform communication unified architecture protocol.

Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and / or user defined policies and rules to extract actionable information to help the customer optimize the network resources.

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

The invention provides a multi-source data fusion diagnosis method and system for new energy station equipment, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: collecting equipment operation data, and carrying out the preprocessing to obtain standardized data; transient features and drift features are extracted, a transient fault mode and a degradation fault mode are identified, and a dual-frequency-domain fault feature library is established; calculating a power transmission fluctuation value between devices, analyzing energy transmission efficiency, positioning a fault source device and identifying a fault propagation path; establishing a fault diagnosis evaluation index for grading diagnosis, and evaluating a fault development trend; and generating and executing a maintenance decision scheme. According to the invention, through multi-source data fusion analysis, accurate diagnosis and early warning of equipment faults of the new energy station are realized, and the equipment operation and maintenance efficiency is improved.

The invention discloses a network equipment integrated monitoring method and system. The method comprises the following steps: collecting multi-source heterogeneous data, constructing a protocol compatible layer, and supporting multi-protocol adaptation; data fusion and intelligent analysis: constructing a dynamic topology, analyzing an equipment configuration file, and generating a network topological graph; performing time sequence prediction according to a root cause analysis model, and predicting an abnormal trend; mining association rules, analyzing historical data, and extracting fault association rules; constructing an equipment fault knowledge base under the assistance of a knowledge graph, and accelerating root cause positioning; self-adapting an alarm threshold, analyzing historical data distribution, and dynamically adjusting the threshold; visual decision making and automatic processing are carried out, a 3D topological map is provided, and layered display is supported; and performing fault grading processing, comprehensively calculating a fault influence degree score, mapping to a fault grade and a work order type according to an influence degree score interval, and formulating a dynamic work order generation rule. A protocol compatible layer is constructed by deploying a lightweight agent program, multi-protocol adaptation is supported, and various network devices can be fully covered.

The invention discloses a smart park full life cycle management system and method based on digital twinning and Internet of Things, and relates to the technical field of smart park management, and the system comprises a sensing edge module, a data governance module, an intelligent analysis module, a life cycle module and a twinning modeling module. According to the invention, multi-protocol access and edge computing capability are supported, and the data transmission efficiency and stability are greatly improved; the intelligent analysis module outputs an accurate analysis result by constructing a multi-class feature matrix and deep multi-task joint modeling mechanism, and provides data support and model guidance for dynamic management and intelligent decision making of the park; the life cycle module integrates a Kepler optimization algorithm and a multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes combination of global search and local fine tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks; and the twin modeling module constructs a park three-dimensional model, so that the interactivity and operability of the system are improved.