3562 results about "Acquisition apparatus" patented technology

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 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 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 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 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 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 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.

The invention discloses a rapid calibration method and system for automatic testing equipment of a vehicle machine, and relates to the technical field of automatic calibration, and the method comprises the steps: inputting a preset standard calibration signal to the testing equipment, and collecting the response data of each testing channel of the equipment as calibration reference data; comparing the calibration reference data with a built-in standard reference value of the equipment, and calculating deviation data of each test channel; based on the deviation data, calibration parameters of each channel are calculated, and the calibration parameters comprise a gain compensation coefficient, a frequency compensation coefficient and a phase compensation coefficient; the calibration parameters are loaded to the corresponding channels of the test equipment, a preset standard calibration signal is input for verification test, and when errors of the channels are smaller than a preset residual error threshold value, calibration is completed, and the calibration parameters are stored. By establishing the gain-frequency-phase joint compensation model and combining the parallelization verification and increment optimization algorithm, the high-precision rapid calibration of the automatic test equipment of the vehicle machine is realized.

The invention relates to a substation operation risk identification method based on a multi-view video and high-precision positioning. Acquiring video data of a working site through a plurality of cameras with fixed visual angles and mobile video acquisition equipment; a high-precision positioning system is used for obtaining three-dimensional space coordinates of operators and equipment in real time; establishing a three-dimensional digital twinborn model of the substation equipment, and performing dynamic scene reconstruction based on the multi-view video stream to generate a real-time three-dimensional scene of the operation site; fusing the positioning data and the three-dimensional scene by adopting a space-time fusion algorithm to generate a dynamic digital portrait of the operator; and carrying out real-time analysis on behaviors and positions of operators by using a risk assessment algorithm based on a preset risk rule, calculating to obtain a risk assessment value, and setting a feedback mechanism to continuously optimize positioning and scene reconstruction precision. According to the invention, efficient, accurate and real-time identification and early warning of the operation risk of the transformer substation are realized, and the safety management level of an operation site is effectively improved.

The invention discloses an equipment fault early warning system and method based on the Internet of Things, and relates to the technical field of equipment fault early warning, and the method comprises the steps: collecting mechanical operation parameters and production process parameters of equipment, and setting each piece of equipment as an independent intelligent agent; establishing a three-level topological structure; based on the dual-threshold model, judging the running state of the equipment; when the equipment intelligent body detects that the equipment is abnormal, the equipment intelligent body immediately marks the state of the equipment intelligent body as abnormal and generates a feature code; the equipment intelligent agent receiving the feature code analyzes associated parameters in a process associated parameter atlas, and sends an analysis detection result to an adjacent equipment intelligent agent and a central management node; the central management node summarizes analysis results of all the equipment intelligent agents and judges the influence of faults on the fly ash recycling production process; and according to the severity and the influence range of the fault, performing graded early warning, and sending early warning information.

The invention provides an industrial equipment energy efficiency evaluation and maintenance decision-making method and system based on reinforcement learning, and relates to the technical field of reinforcement learning, and the method comprises the steps: collecting equipment operation data and extracting features, constructing a maintenance time sequence decision-making model based on deep reinforcement learning, and achieving the model optimization through the combination of transfer learning; and generating a maintenance decision scheme according to the energy efficiency index analysis and the time sequence causal relationship, executing maintenance, and recording process data for online learning and updating of the model. The energy efficiency management level of industrial equipment is improved, the service life of the equipment is prolonged, and the operation and maintenance cost is reduced.

The invention discloses a WMS warehouse task dynamic scheduling method and system based on multi-objective optimization, and the method comprises the steps: collecting scheduling parameters, such as task priority, equipment load and goods location distance, through a RidgeOS intelligent warehouse scheduling platform, and inputting a dynamic scheduling task uncertainty perception model to generate an uncertainty feature vector; constructing a scheduling decision space by adopting an elastic time slice-reinforcement learning hybrid algorithm, dividing an elastic time slice interval, and iteratively updating a scheduling strategy in the interval; sorting the to-be-executed tasks of the warehouse according to the updated strategy, wherein the uncertainty feature vectors are used as constraints during sorting; a scheduling instruction is sent to the equipment through the platform, and meanwhile parameter change data in the equipment execution process is collected and fed back to the sensing model. The system comprises six units which are connected. According to the method, the task execution uncertainty can be accurately perceived, the scheduling strategy flexibility and iteration efficiency are improved, and the intelligent storage multi-target optimization scheduling requirement is met.

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 relates to the technical field of deep learning and defect detection, and discloses a multi-modal fusion defect perception and identification method based on deep learning, and the method comprises the steps: 1, obtaining multi-modal data: obtaining the multi-modal data of a target object through a collection device, and obtaining the multi-modal data of the target object; comprising visual data and non-visual data, and the data format covers two-dimensional images, three-dimensional point clouds, time series data and the like. According to the multi-modal fusion defect perception and identification method based on deep learning, the advantages of visual data and non-visual data can be integrated through multi-modal data fusion, the characteristics of a target object are described more comprehensively, information loss caused by single-modal data is reduced, the accuracy of defect detection is improved, and in industrial part detection, the detection efficiency is improved. By fusing the image and the point cloud data, defects such as cracks and holes on the surface of the part can be identified more accurately, and the detection precision is obviously improved compared with that of a single-mode method.

The invention discloses a physical fitness evaluation method based on human skeleton trajectory tracking, which comprises the following steps: S1, acquiring whole-process video data of a physical fitness test through video acquisition equipment, and generating a video frame according to an acquisition frequency; s2, processing the video frame by using a skeleton dynamic analysis algorithm, and extracting human skeleton key points; s3, extracting identity features of the testee through a motion map identity recognition network, and tracking and confirming the identity features; s4, performing action recognition, fragment segmentation and compliance judgment on the skeleton key point time sequence track; s5, aiming at the abnormal skeleton key points, performing complementation and time sequence smoothing by adopting an inverse kinematics inference method; s6, inputting the complete skeleton key point time sequence data into the human body action evaluation model for processing, and quantizing and outputting a physical ability evaluation index; and S7, generating a physical ability evaluation report and giving action feedback and optimization suggestions. According to the invention, objectivity, accuracy and intelligent level of physical ability evaluation are effectively improved.

The invention discloses a real estate information verification method and system based on an image technology, and relates to the technical field of real estate information verification, and the method comprises the following steps: image collection: employing a collection device with multiple functions, and through the means of adjustable illumination, automatic paper flattening, multi-mode scanning, real-time quality monitoring, etc. Obtaining a high-quality and comprehensive paper house property certificate file image; acquiring a paper house property certificate file image by utilizing acquisition equipment which is provided with an adjustable lighting system and has an automatic paper flattening function; according to the method, comprehensive information is acquired through multi-mode scanning, the risk of information omission is effectively reduced, noise is accurately removed through image preprocessing, the contrast ratio is enhanced, information missing is repaired, a clear image is provided for follow-up processing, multiple kinds of accurate feature analysis are newly added in the feature extraction link, rich and accurate feature vectors are formed, and the image quality is improved. And the capability of judging the authenticity and integrity of the file is greatly enhanced, so that the accuracy and reliability of verification are remarkably improved.

The invention relates to a mechanical exoskeleton rehabilitation training system and method based on a brain-computer interface. The system comprises electroencephalogram acquisition equipment, a preprocessing unit, a feature extraction unit, a motion intention decoding unit, a mechanical exoskeleton control unit, a muscular tension state monitoring unit and a self-adaptive functional electrical stimulation feedback unit. The method comprises the following steps: preprocessing electroencephalogram and electromyographic signals; electroencephalogram and myoelectricity time-frequency features are obtained through feature extraction; an electroencephalogram decoding model is used for decoding to obtain the movement intention of the patient, and an exoskeleton control instruction is generated to control a mechanical exoskeleton control unit to drive the limb movement of the rehabilitation patient for rehabilitation training; meanwhile, the muscular tension state of the patient is analyzed according to the time-frequency characteristics of electroencephalogram and myoelectricity, functional electrical stimulation of different intensities is applied in a self-adaptive mode, stimulation feedback is enhanced, and the muscular tension state of the patient is adjusted. According to the invention, deep fusion of brain-controlled exoskeleton training and low-frequency nerve electrical stimulation adjustment can be realized, and the rehabilitation effect of a stroke patient is improved.

The invention discloses a hotspot acquisition equipment adaptive control system based on multi-modal data fusion, and relates to the technical field of intelligent sensing and adaptive control, the hotspot acquisition equipment adaptive control system comprises a heterogeneous sensor array and a space-time alignment engine, each sensor clock is synchronized through an event trigger mechanism, and asynchronous data is processed by adopting B-spline interpolation; the feature pyramid fusion network extracts multi-scale features, and key information is enhanced through a channel and a space attention mechanism; the self-adaptive hot spot tracking module predicts a trajectory by adopting volume Kalman filtering; the dynamic resource allocation module is used for optimizing equipment scheduling according to a hotspot priority evaluation result; the multi-agent cooperative control layer is used for realizing autonomous negotiation among equipment through reinforcement learning; according to the edge-cloud cooperative computing architecture, computing resources are dynamically allocated according to task complexity. According to the method, the omission ratio is reduced through multi-modal data fusion, space-time alignment, temperature measurement errors and a self-adaptive resource allocation mechanism, and the reliability and practicability of the system are remarkably improved.

The invention provides a thermal power generating unit intelligent start-stop process multi-level linkage cooperative control optimization method, which relates to the field of thermal power generating unit control, and comprises the steps of collecting equipment group operation parameters, establishing a multi-level dynamic control reference, constructing a cascade response mechanism to determine a target operation interval, subintervals are divided according to the parameter coupling degree, compensation adjustment coefficients are set for hierarchical adjustment, and a linkage control link is constructed to coordinate the adjustment process of each level of equipment group. According to the method, accurate cooperation among equipment in the starting and stopping process of the thermal power generating unit is achieved, the safety and efficiency of the starting and stopping process are improved, and energy consumption is reduced.

The invention belongs to the technical field of industrial equipment inspection, and particularly discloses and provides an industrial equipment-oriented multi-terminal dynamic task collaborative inspection system, which comprises the steps of collecting equipment real-time operation state data and inspection terminal space position information, generating a dynamic priority queue, and flexibly adjusting the priority of an inspection task; direct communication connection is established between inspection terminals, relay nodes are established by detecting adjacent terminals, and autonomous reconstruction of inspection network communication is realized; a to-be-detected device is split into physical detection units which can be operated independently, and a task fragment combination containing a time window and a space range is allocated, so that multi-terminal cooperative work is realized; the running state data and the historical normal value range are compared in real time, the adjacent inspection terminals are automatically triggered to cooperate with a reinspection instruction, and it is ensured that data are accurate and reliable; and updating the three-dimensional state atlas of the equipment according to the detection result and the load state of the communication network, and synchronizing to all the inspection terminals in the connection state to ensure the consistency of the state views of the equipment.

The invention relates to the technical field of power supply system management, and discloses a centralized management method and management system for a power supply system. The method comprises the following steps: monitoring a current fluctuation sequence and voltage offset data of each node of a power supply network in a continuous operation period, and collecting an equipment state log text and load change time sequence information; identifying a time point of abnormal sudden change of current fluctuation, and extracting a characteristic parameter set of a power supply quality reduction event in combination with a voltage offset data change amplitude; matching a historical abnormal event library, positioning key operation record fragments strongly related to load change in the log, and generating a mapping relation table of equipment operation behaviors and power supply quality fluctuation through semantic analysis and time sequence alignment; and correcting the parameters of the load prediction model, outputting and executing a power supply priority dynamic adjustment strategy, and triggering automatic calibration of equipment operation parameters. According to the method, comprehensive control of the power supply system is realized, abnormity can be identified and processed in time, association between operation and quality is clear, and stability and flexibility of operation of the power supply system are improved.