4043 results about "Acquisition apparatus" patented technology

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 belongs to the technical field of electromechanical equipment operation and maintenance, and discloses an electromechanical equipment self-adaptive intelligent early warning system based on multi-source sensing data. The system is composed of a multi-source sensing module, an edge data acquisition and preprocessing module, a data cleaning and multi-dimensional feature extraction module, an equipment state dynamic modeling module, an intelligent fault prediction and trend analysis module, a self-adaptive early warning threshold generation and dynamic adjustment module, and an intelligent decision and remote cooperation module. The system is composed of a multi-source sensing module, an intelligent low-carbon operation and maintenance management and control module and a digital twin system integration and full-period mapping module, multiple sensors are deployed through the multi-source sensing module to acquire multi-dimensional data of equipment, cleaning and calibration are performed through the edge data acquisition and preprocessing module, deep processing is performed through the data cleaning and multi-dimensional feature extraction module, and multi-dimensional feature extraction is performed through the multi-source sensing module. The data integrity and accuracy are ensured; and data are quickly transmitted among the modules, so that the monitoring system can accurately present the running state of the equipment in real time.

The invention provides an equipment group collaborative fault prediction analysis method and system for a complex industrial process, and relates to the technical field of fault prediction, and the method comprises the steps: collecting equipment group data, and reconstructing a multi-scale time sequence feature; constructing a quantum walking enhanced dynamic association map; extracting spatial-temporal features and mapping the spatial-temporal features to a Riemannian manifold space; identifying an abnormal mode and generating hidden variable probability distribution; constructing a fault evolution model driven by a non-equilibrium state statistical fluctuation theory; and solving the cooperative field equation set to obtain a control parameter sequence. The method can effectively improve the accuracy and reliability of equipment group collaborative fault prediction and reduce the maintenance cost.

The invention provides a GPU computing power scheduling method based on a one-cloud multi-core heterogeneous computing power platform, and the method comprises the following steps: S1, carrying out heterogeneous resource registration and modeling, accessing hardware equipment containing multiple types of GPUs through a resource registration module, collecting the equipment model, the video memory capacity and performance index data, and carrying out heterogeneous resource modeling; constructing a resource feature database containing a topological relation, and supporting hybrid access of chips; s2, virtualized resource reconstruction: pooling a physical GPU into virtual GPU resources by adopting a hardware abstraction layer technology, realizing video memory isolation and calculation unit division through a containerization technology, and configuring each virtual GPU instance with an independent drive stack and a security sandbox; and S3, submitting a multi-modal task, receiving a CUDA / OpenCL calculation task submitted by a user, analyzing task demand parameters including a calculation core number, a video memory occupation amount and a data throughput threshold, and generating a task descriptor containing a priority label.

The invention provides an advertisement recommendation method and system fused with user dynamic behavior modeling. The method comprises the following steps: acquiring a cross-device operation record of a user on multiple terminal devices, and synchronously acquiring a device unique identification code, high-frequency click area data and an operation timestamp; and constructing a space-time relation graph based on combined analysis of the equipment switching time interval and the click behavior, and generating a feature set reflecting the behavior correlation degree. The trajectory similarity is calculated at the edge calculation node through hardware acceleration, and migration correlation parameters are generated. And inputting the space-time relation graph into a behavior analysis model to extract coherent behavior characteristics, and forming a multi-dimensional scene portrait in combination with geographic position change and interface element distribution. And finally, through identifying a user cross-device interest migration mode, dynamically screening advertisement contents matched with the current scene, and realizing accurate pushing strategy optimization based on behavior continuity. According to the technical scheme provided by the invention, the accuracy of cross-terminal advertisement recommendation and the scene response speed are remarkably improved.

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention provides a tunnel construction monitoring analysis method and system based on AI video monitoring, and relates to the technical field of artificial intelligence. A tunnel construction original video stream data set is acquired through a multi-view video acquisition device, and a spatio-temporal feature sequence is generated through spatio-temporal feature fusion processing; the spatiotemporal feature unit comprises a pixel region movement track, a personnel wearing identifier and a construction equipment contour feature, then extracting dynamic features of the spatiotemporal feature sequence to obtain a dynamic spatiotemporal feature set, and executing construction personnel statistical processing based on the dynamic spatiotemporal feature set; generating a hole entering personnel quantity statistical result, a personnel distribution statistical result of each construction part and a personnel identity matching result, performing work efficiency comparative analysis according to the personnel statistical result and a preset construction progress plan, and generating a construction progress deviation index and resource allocation optimization strategy; and when the progress deviation index exceeds the first threshold value, early warning is triggered, and a strategy is output to the management terminal, so that effective monitoring and management of tunnel construction are realized.

The invention relates to the technical field of ships, in particular to a fault monitoring system and method for a ship power system, and the system comprises a multi-source data collection module which carries out the real-time collection of the operation parameters, environment parameters and equipment state parameters of the ship power system through a collection device. The fault early warning module is used for predicting the development trend of the fault after the fault diagnosis is completed; the early warning decision module generates early warning information of different levels according to the state evaluation result, the fault diagnosis result and the RUL prediction result, gives targeted operation and maintenance decision suggestions, and pushes the suggestions to a ship cockpit, a shore-based operation and maintenance center and an operation and maintenance personnel mobile terminal; 24-hour uninterrupted multi-parameter acquisition of the ship power system is realized through the multi-source sensor network, three types of parameters of operation, environment and state are covered, acquisition delay is reduced, and the problems of poor timeliness and incomplete parameter coverage of traditional manual inspection are solved.

The invention provides a virtual power plant platform source network load storage equipment real-time monitoring and optimization method and system, and relates to the technical field of virtual power plant intelligent optimization scheduling, and the method comprises the steps: collecting equipment operation data, carrying out the noise reduction and feature compression to obtain feature data, and recognizing the equipment fault type based on the feature data; carrying out decoupling reconstruction on the feature data in time and space dimensions to obtain an equipment incidence matrix, constructing an equipment topological graph based on the equipment incidence matrix and a credible fault type, carrying out bidirectional tracking on the equipment topological graph according to a fault diffusion probability, and generating a fault propagation path; grading and sorting the equipment on the fault propagation path, and outputting a fault influence prediction result and an equipment priority list; and carrying out cooperative scheduling optimization on the equipment in the virtual power plant by adopting a hierarchical reinforcement learning algorithm to realize cooperative scheduling optimization of the equipment in the virtual power plant.

The invention discloses a GIS partial discharge intelligent diagnosis system and method based on one-dimensional ultrahigh frequency signal analysis, and relates to the technical field of power electrical equipment intelligent monitoring, and the system comprises a signal collection and preprocessing module which is used for collecting ultrahigh frequency signals of GIS equipment and obtaining preprocessed signal data through a dynamic threshold algorithm; the discharge initial judgment module is used for performing multi-dimensional sequential judgment to eliminate interference discharge data so as to obtain effective discharge signal data; the feature extraction module is used for performing time domain kurtosis and pulse width analysis, frequency domain energy distribution analysis and time-frequency domain wavelet entropy calculation based on the multi-dimensional features of GIS partial discharge, and generating an optimized feature subset; and the type identification module is used for identifying the partial discharge type by using the integrated learning model to obtain a diagnosis result. According to the invention, the problem of unstable recognition accuracy caused by insufficient signal preprocessing, single feature representation and single classification algorithm in the prior art is solved.

The invention relates to the technical field of concrete member detection, and discloses a concrete member defect identification and labeling system, which comprises an image acquisition equipment matching module, a defect feature analysis module and a real-time labeling regulation and control module, and a defect classification priority judgment module capable of being additionally arranged. The image acquisition equipment matching module calculates and matches the optimal equipment through the adaptive characteristic value based on the image resolution, the equipment acquisition precision, the working distance and the illumination compensation parameter; the defect feature analysis module performs quantitative analysis on features such as textures, crack forms and hole distribution of zoning images by using algorithms such as multi-scale image segmentation and frequency domain transformation; the real-time labeling regulation and control module dynamically adjusts the labeling position according to the defect position offset, the size change rate and the illumination fluctuation parameters; and the defect classification priority judgment module divides defect grades according to crack width, hole density and the like. The system improves the automation level and accuracy of concrete member defect detection, and is suitable for constructional engineering member quality detection.

The invention relates to the technical field of intelligent operation and maintenance and data processing of an electric power system, in particular to an electric power inspection system and method. The method comprises the following steps: collecting equipment state data, constructing a time sequence evolution characteristic of a three-order state transition tensor field modeling equipment space state, and identifying short-term high-risk equipment based on a weighted abnormal score and a dynamic threshold strategy; a historical state peak value is recorded through a state-time matrix fusion exponential decay model, a forgetting risk factor is calculated in combination with the current silencing degree, and equipment which is seriously abnormal but is silencing recently is actively rechecked; constructing a dynamic evolution diagram structure, optimizing an inspection path by adopting a class genetic perturbation mechanism, and scheduling multiple teams in parallel through task cluster division and queue matching; and establishing an equipment abnormity linkage propagation model to predict a cascade risk, and automatically matching an optimal disposal scheme in combination with a strategy template library. According to the method, closed-loop control from anomaly recognition, path optimization to strategy execution is formed, and the accuracy and timeliness of power operation and maintenance are improved.

The invention belongs to the technical field of computers, and particularly relates to an Internet of Things security data real-time monitoring and protection method and system, and the method comprises the steps: collecting equipment communication and state data through an edge agent, and analyzing and extracting standardized metadata; constructing an equipment behavior contour vector based on a sliding window, and dynamically maintaining a global equipment topological graph; triggering a primary alarm in combination with behavior deviation detection and topology abnormity; outputting a threat score and an attack intention through rule matching and Bayesian network double-engine collaborative reasoning; and executing automatic response according to grading, and feeding back and correcting a behavior baseline to realize closed-loop optimization. The system comprises a data acquisition module, a protocol analysis module, a behavior modeling module, a topology maintenance module, an anomaly detection module, a collaborative reasoning module, an automatic response module and a baseline correction module. Through full-link real-time modeling and cross-device collaborative analysis, the attack detection rate is significantly increased to 98% or above, the false alarm rate is lower than 2%, the response delay is controlled within 800 milliseconds, and the security and adaptive ability of the Internet of Things system are enhanced.

The invention relates to the technical field of intelligent monitoring, and discloses an equipment state intelligent monitoring platform based on data fusion and the Internet of Things technology, which is based on a scene feature quantitative capture module, uses a multi-scene adaptive sensor to collect the physical state and scene factors of equipment, constructs a two-dimensional feature vector through modal completion and space-time alignment, and carries out real-time monitoring on the two-dimensional feature vector. A scene label is generated in combination with dynamic threshold matching, a data fusion parameter dynamic adjustment module solves the problem of fusion layer dynamic adaptation deficiency by means of a structured collaborative weight algorithm and multi-modal hybrid filtering, and a model lightweight fine adjustment module optimizes parameters according to difference recognition, output layer fine adjustment and federal aggregation processes and performs incremental issuing. The scene constraint type decision module quantifies cost by means of labels and generates work orders by means of a multi-objective optimization algorithm, and the feedback optimization module adjusts and solidifies parameters through three-dimensional evaluation and reinforcement learning, and realizes cross-scene accurate monitoring of multi-field equipment in combination with unique binding of equipment identities and storage visualization of a quality supervision platform.

The invention relates to the technical field of electrical fault detection, in particular to an electromechanical equipment health state diagnosis method and device based on machine learning, and the method comprises the following steps: collecting the current, voltage and temperature parameters of equipment, calculating the real-time average value and standard deviation of each parameter through numerical processing, and obtaining a parameter monitoring result; and monitoring a result based on the parameters. The current, voltage and temperature parameters of the equipment are collected in real time, numerical processing is carried out on each parameter, the real-time average value and the standard deviation are calculated, and accurate basic data support is provided for state monitoring. Based on the monitoring data, parameter abnormity is judged through threshold analysis, abnormal changes can be dynamically recognized in the equipment operation process, and the timeliness and accuracy of fault discovery are improved. In combination with past performance data, the future change trend of parameters is predicted through a regression algorithm, early warning for equipment operation can be formed, and problem expansion or out-of-control is avoided.

The invention relates to the technical field of abnormal state prediction, provides a multi-dimensional time sequence equipment abnormal state prediction method and system, and solves the problems of high false alarm rate and risk prediction inaccuracy in the prior art. The method comprises the following steps: collecting a multi-node operation state data set and communication transmission layer time sequence offset information of an equipment distributed system; jitter and delay association processing is carried out on the data, and a communication stability feature set is generated by quantizing space-time correlation of a jitter extreme value and delay fluctuation; obtaining physical layer deformation and temperature drift data of the communication cable, analyzing and generating a physical layer disturbance feature sequence based on optical signal feature offset, and converting the physical layer disturbance feature sequence into channel abnormal strength features; and cross-level feature coupling is carried out on the communication stability feature and the channel abnormal strength feature, and a communication node failure or data transmission abnormal risk is predicted based on a coupling result. According to the invention, millisecond-level linkage prediction of equipment-level physical disturbance and system-level communication risks is realized, the fault false alarm rate is reduced, and the cascade failure risk is blocked.

The invention relates to the technical field of sound anomaly detection, in particular to a sound anomaly detection method and device based on a Transform model, equipment and a medium, and the method comprises the steps: collecting a sound signal during the operation of the equipment through a data collection interface, and obtaining an original sound signal; resampling is carried out on the collected sound signals, and normalization processing is carried out on the resampled data; mel-frequency cepstrum coefficient features are extracted from the sound signals after normalization processing, and the sound signals after normalization processing are input into a pre-training module to output high-dimensional features including time sequence and semantic information; splicing the Mel-frequency cepstrum coefficient features with the high-dimensional features to form a comprehensive feature vector; inputting the comprehensive feature vector into a support vector machine model, and performing abnormal sound recognition through a trained classification hyperplane; and detected abnormal information is fed back to the user in real time. Multi-feature fusion enables the model to identify abnormal sound more accurately.

The invention relates to the technical field of power equipment operation and maintenance management, in particular to a power equipment asset health management and predictive maintenance service system which comprises a data acquisition and integration module, a feature engineering module, a health assessment and prediction engine maintenance decision and early warning module and a service interface module. The data acquisition and integration module acquires equipment operation parameters through multiple types of sensors, and associates pre-stored equipment asset information to generate an equipment comprehensive data stream; the feature engineering module cleans and standardizes the equipment comprehensive data stream, and constructs a space-time correlation feature matrix; the health assessment and prediction engine comprises a health state assessment unit and a fault prediction unit, the health state assessment unit outputs a health index HI by using a gradient boosting decision tree, and the fault prediction unit outputs a fault probability and a remaining service life RUL in a future preset time period; and the maintenance decision and early warning module generates a grading early warning signal and a maintenance strategy scheme. The intelligent level of operation and maintenance of power equipment is improved, reliable operation of the equipment is guaranteed, and the operation and maintenance cost is reduced.

The invention relates to the technical field of brain-computer interfaces, in particular to an electroencephalogram signal amplification method, device and equipment based on topological gradient field coding. The method comprises the following steps: acquiring a first data set corresponding to a scalp electroencephalogram of a testee through electroencephalogram acquisition equipment, and performing Hilbert transform to extract phase information so as to calculate a topological gradient field; key phase features meeting the energy concentration ratio feature and the duration feature are extracted from the topological gradient field, so that the key phase features with the energy concentration ratio threshold value larger than a preset value and the duration exceeding a time threshold value are screened out; inputting the key phase features to a pre-constructed generative adversarial network, and generating an enhanced phase field by a generator of the generative adversarial network through adversarial training; and combining the enhanced phase field with the low-frequency amplitude component, subjected to wavelet decomposition, of the first data set to construct a complex field, simulating spatio-temporal evolution of the complex field based on a nonlinear dynamic model, and generating a finally enhanced and amplified electroencephalogram signal. And efficient characterization of electroencephalogram phase information is realized.

The invention discloses an interactive personal health management system based on artificial intelligence and an operation method. Different from a part of existing modes, limited health data and brief basic information which are acquired by a single acquisition device and serve as a unique health information source of a user are not limited, but only serve as health basic information, and a concept and an implementation scheme of'health supplement information 'are provided. On the basis of obtaining the health basic information, through alternate or simultaneous use of multiple types of collection devices and multiple collection ways and modes, more health supplement information of the user is obtained, and a comprehensive AI health information source of the user is jointly constructed. The AI health information source is analyzed and calculated through an artificial intelligence health algorithm, once the AI health information source changes, the AI health assessment result is adjusted accordingly, an infinite loop mechanism is formed, the user health assessment result is promoted to be infinitely close to precision, and health information is interacted with the user in real time through the AI health digital human in a natural language.

The invention relates to the technical field of production scheduling, and discloses a production scheduling visualization method based on big data. According to the method, multi-dimensional production data such as equipment real-time states, material inventory, personnel skills and order delivery periods are collected, and a four-dimensional dynamic constraint model is generated through a dynamic constraint modeling algorithm; and obtaining an initial scheduling scheme by using a mixed integer programming and improved genetic algorithm dual-drive optimization engine, inputting the initial scheduling scheme into a visual interaction module, displaying a result by using a drag-and-drop Gantt chart, and detecting resource conflicts in real time. The feasibility of the adjusted scheduling scheme is verified through a dynamic re-calculation algorithm, and then the model is updated. According to the method, multi-dimensional data are integrated, production scheduling optimization and visualization are realized, production resource conflicts can be effectively solved, the production efficiency is improved, the cost is reduced, and an efficient solution is provided for enterprise production management.

The invention provides a production data monitoring method and system based on artificial intelligence, and belongs to the technical field of data processing, and the method comprises the steps: generating an initial monitoring strategy according to a quality influence factor evaluation model; based on the multi-modal data acquisition parameters, controlling multi-modal data acquisition equipment to acquire multi-source data in the charging pile assembling process; performing fusion processing on the multi-source data to generate a quality feature vector representing an assembly state; comparing the quality feature vector with a quality judgment threshold value, and outputting a state monitoring result; inputting the quality feature vector into a preset quality prediction model to obtain an assembly quality prediction result; determining a quality defect type and a risk level of the charging pile through a predefined quality defect mapping rule based on the state monitoring result and the assembly quality prediction result; according to the quality defect type and the risk level, at least one of the initial monitoring strategies is adjusted, and an updated monitoring strategy is generated; and the production quality and the production efficiency of the charging pile are improved.

The invention discloses a video monitoring equipment fault real-time detection, root cause diagnosis and closed loop processing method and system. According to the method, by collecting multi-dimensional operation data of equipment, a dynamic weighted health degree model is constructed to realize early-stage accurate discovery of a fault; performing intelligent alarm grading by combining time sequence prediction and health degree change; realizing automatic root cause diagnosis by utilizing topological correlation analysis, log semantic analysis and case similarity matching; and closed loop processing and model self-optimization are realized through a work order system. According to the method, the problems of lagging fault discovery, difficulty in positioning, slow repair and disjunction in assessment in the prior art are effectively solved, and the operation and maintenance efficiency and the service quality are remarkably improved. The abstract drawing is Figure 1.

The invention provides an intelligent alarm system and method for monitoring power plant equipment, and the method comprises the steps: collecting the temperature, pressure and vibration data of the equipment through a sensor, and generating a holographic image through the scanning of a laser radar, so as to complete the data collection and environment modeling. A space-time atlas is constructed and analyzed by means of a space-time network, faults are analyzed and positioned by means of topological data, and data fusion and deep analysis are achieved. And constructing a Bayesian network by adopting intelligent learning and adaptive optimization of evolutionary strategy optimization and memory enhanced neural network prediction, and carrying out fault diagnosis and causal inference in combination with anti-factual reasoning. And the group intelligent collaborative diagnosis and emotion calculation aided decision is used for intelligent decision and collaborative operation, and finally, an operator is assisted to work based on a man-machine interaction function. The system and the method can comprehensively and accurately monitor equipment, efficiently diagnose faults and assist in decision making, and improve operation safety and reliability of a power plant.

The invention relates to the technical field of map data acquisition, in particular to a surveying and mapping geographic information map data acquisition method and system, and the method comprises the steps: building a space-time reference network: generating an underground-earth surface space-time reference network for connecting the underground with the earth surface through acousto-optic joint calibration; generating a multi-source correction parameter: fusing the underground inertial navigation data, the surface atmospheric refraction parameter and the equipment temperature drift coefficient, and calculating a multi-source correction parameter set for coordinate system conversion and error compensation; space-time evolution element collection: calibrating a mobile collection device in real time by using the multi-source correction parameter set, synchronously obtaining underground pipeline point cloud, an earth surface building image and a construction machinery track, and generating a space-time evolution element body with a space-time version label; and dynamic topology evolution modeling: analyzing an element change sequence in the spatio-temporal evolution element body, and constructing a dynamic evolution topology model. According to the method, differential analysis and a conflict backtracking strategy are combined, so that the adaptive updating and decision support capability of the map data in a construction disturbance scene is improved.

The invention relates to the technical field of state detection and fault diagnosis, in particular to an intelligent early warning and fault diagnosis system for a thermal power plant, which comprises a multi-source data acquisition module for acquiring data in real time; the edge computing node is used for performing noise filtering and abnormal value correction on the acquired data; the digital twin modeling unit is used for constructing a dynamic simulation model of the equipment based on a physical model and historical data; the hybrid analysis engine is used for positioning early abnormal detection and fault sources; the visual early warning interface is used for dynamically displaying the health state and the fault probability of the equipment and generating a graded alarm signal; according to the invention, the multi-source data acquisition module acquires equipment multi-dimensional signals in real time, after edge computing node filtering and denoising, a digital twin modeling unit constructs a precise simulation model, a hybrid analysis engine fuses LSTM and a Bayesian algorithm, fault features are deeply mined, data weights are optimized, and the fault detection accuracy is improved. According to the system, the accuracy and timeliness of diagnosis are remarkably improved.

The invention provides a substation self-adaptive inspection method based on equipment health degree dynamic evaluation. The substation self-adaptive inspection method based on equipment health degree dynamic evaluation comprises the steps of S1, collecting electrical parameters, mechanical vibration parameters and environmental parameters of substation equipment in real time through a multi-source sensor network, and S2, performing data cleaning and time synchronization on the electrical parameters, the mechanical vibration parameters and the environmental parameters, and performing subset dynamic normalization processing. According to the substation self-adaptive inspection method based on equipment health degree dynamic assessment, electrical, mechanical and environmental parameters of the equipment are acquired in real time through the multi-source sensor network, and the real-time performance and accuracy of equipment health degree assessment are remarkably improved by combining dynamic normalization processing and real-time anomaly detection. And the LSTM neural network is used for analyzing the change trend of the equipment health index, so that the residual life of a key component can be predicted, and differentiated inspection and resource optimization allocation can be realized.

The invention discloses a dynamic load balancing optimization method for AI intelligent computing system server equipment, which relates to the technical field of load balancing optimization, and comprises the following steps: acquiring equipment performance data, and preprocessing the acquired equipment performance data; based on the preprocessed equipment performance data, extracting equipment state features through time sequence analysis and periodic fluctuation detection; predicting the future load change of the equipment based on the state characteristics of the equipment, dividing the priority of the equipment according to the prediction result, and analyzing the resource demand; generating a resource allocation strategy by applying a resource matching algorithm according to the device priority and the resource demand; and executing the resource allocation strategy, receiving feedback information in real time in the execution process, and optimizing the resource allocation strategy according to the feedback information. According to the method, the response speed and the service quality of the server equipment are remarkably improved, and the resource utilization efficiency is maximized.

The invention discloses a multi-point distributed high-precision industrial equipment operation monitoring method and system, and relates to the technical field related to industrial equipment monitoring, and the method comprises the steps: independently collecting the operation parameter data of equipment through a plurality of sensor nodes; transmitting the operation parameter data to a central control unit in real time for fusion processing, and generating an equipment operation state feature set; performing fault prediction and health assessment on the equipment operation state feature set to obtain an equipment health score; and dynamically triggering an alarm signal according to the equipment health score, determining a target sensor node, and generating a target maintenance instruction. The technical problems that in the prior art, the equipment operation monitoring efficiency is low, the overall condition of the equipment is difficult to master comprehensively, a flexible monitoring and early warning mechanism is lacked, the fault position of the equipment cannot be accurately positioned and quickly responded and processed, and industrial equipment cannot efficiently and stably operate are solved. The technical effect of improving the accuracy and stability of equipment fault positioning is achieved.

The invention relates to the technical field of intelligent equipment autonomous control, and discloses an AIGA-driven intelligent equipment autonomous control and feedback adjustment method, which comprises the following steps of S1, collecting equipment body state parameters and external environment data in real time through a multi-mode sensor array; s2, combining the collected data with a preset user instruction and a real-time environment semantic analysis result; s3, disassembling the task target into a continuous action instruction set; s4, executing the action instruction set through the layered real-time control architecture; s5, monitoring an action execution effect based on the multi-modal sensor array; and S6, analyzing the relevance between the evaluation result and the action instruction through a causal inference engine. According to the method, an equipment action execution result is fed back to an intention generation layer in real time, a deviation source is positioned through a causal inference engine, a control strategy is dynamically adjusted, closed-loop iteration from one-way instruction execution to perception-decision-execution-verification is achieved, and the effect of self-adaptability in a dynamic scene is achieved.