2107 results about "Multi parameter" patented technology

To significantly improve the prediction accuracy, response speed and management efficiency of large river bank slope disasters, an integrated bank slope monitoring method based on multi-parameter collaborative recognition is proposed. The solution includes step S1 of synchronously collecting data on bank slope displacement, pore water pressure, inclination angle, vibration frequency and environmental temperature and humidity to form an original monitoring dataset and construct a multi-parameter collaborative recognition network; step S2 of using a multi-modal data fusion algorithm to generate a fusion data matrix including spatiotemporal correlation features and perform spatiotemporal data alignment and outlier cleansing; step S3 of combining a geomechanical parameter library and a past disaster case library to output a risk level map and perform dynamic risk assessment model analysis; and step S4 of triggering a multi-level early warning mechanism and generating linked control commands including treatment suggestions to perform multi-level early warning and linked control.

The invention discloses a high-precision voltage measurement method and system based on multi-parameter adaptive compensation, and relates to the technical field of intelligent voltage measurement. The high-precision voltage measurement method and system based on multi-parameter adaptive compensation comprises the steps that S1, voltage signal data and disturbance source characteristic data are collected, preprocessing is completed, and an analysis basis is constructed; s2, analyzing voltage excursion in combination with disturbance current characteristics, and dynamically adjusting a compensation path; s3, evaluating a compensation correction effect, and driving a compensation output strategy in real time; and S4, comprehensively analyzing the offset characteristics and the correction result, judging an abnormal section and triggering adaptive adjustment. The problems that disturbance components cannot be comprehensively extracted from multiple parameters such as PWM modulation frequency, wire harness impedance and working condition load change and a compensation function cannot be reversely constructed in an existing method, and a measured value does not have stability are solved.

The invention specifically relates to the technical field of big data analysis, and discloses a water conservancy gate multi-parameter cooperative intelligent monitoring system, which comprises a multi-parameter acquisition module, a multi-parameter processing module, a comprehensive analysis module, an intelligent decision module, an operation and maintenance early warning module and a man-machine interaction module, the multi-parameter processing module is used for calculating flood control and discharge indexes, structure safety indexes and equipment health indexes; the comprehensive analysis module is used for judging gate risk levels; the intelligent decision-making module is used for generating an optimal gate scheduling scheme; the operation and maintenance early warning module is used for constructing a multi-level early warning mechanism; according to the method, parameter coverage is comprehensive, a gate digital twinborn model and a gate opening comprehensive evaluation model are constructed, the gate risk level is evaluated, an optimization strategy is dynamically adjusted through an intelligent decision module, the accuracy of gate risk judgment is improved, and the self-adaptive capacity of the system is improved.

The invention relates to the technical field of industrial safety, and discloses an electrolytic aluminum short circuit port operation safety early warning system based on multi-parameter collaborative awareness and intelligent diagnosis, and the system comprises a parameter collaborative awareness module, a dynamic diagnosis module, an early warning decision module, and an execution feedback module. By constructing a multi-dimensional parameter collaborative sensing mechanism, fusing temperature field distribution, current balance degree and insulation state multi-source data in real time and dynamically capturing early abnormal symptoms of a short circuit port, the hysteresis problem of traditional single-parameter threshold monitoring is solved, conversion from passive response to active defense is achieved, and the comprehensiveness and timeliness of operation state monitoring are improved; and meanwhile, based on a historical fault database and a real-time evolution model, a health index is generated and a fault path is predicted, so that maintenance personnel can pre-judge a development trend and a time window of potential risks in advance, and sudden equipment accidents are avoided.

The invention discloses a radioactive measurement data processing method for improving uranium exploration efficiency. The method comprises the following steps: standardized data acquisition: integrating a multi-parameter module, carrying out time-space synchronous acquisition of geological geophysical environment parameters, standardizing protocol alignment data, and supporting three-dimensional modeling; a three-dimensional coupling model is used for processing interference in a sub-module mode, LiDAR-DEM is used for correcting gamma attenuation in the terrain, an optical fiber thermopermeability instrument is used for correcting daughter errors in the hydrology, signal attenuation of a borehole is compensated through a drilling fluid chart, and a space-time continuous interference field is formed; performing dynamic equilibrium coefficient inversion: constructing equilibrium coefficient isoparametric mapping, and inputting rock core, logging and geochemical data into a random forest model; the transfer learning is trained by using historical data, and dynamic inversion and updating of a new area are carried out; intelligent data processing three-dimensional visualization: self-adaptive denoising and spectral shape matching reinforcement abnormity are carried out; constructing a three-dimensional model, and carrying out transfer learning to optimize the precision; the WebGL platform integrates multi-parameter display, virtual drilling functions and high-dimensional data intuitive interpretation.

The invention provides a sewage treatment carbon source intelligent adding method, which comprises the following steps: collecting multi-parameter feed-forward and feedback signals of water inlet and an anoxic tank, constructing a dynamic model containing feed-forward compensation, model prediction control and feedback compensation, calculating the theoretical adding amount of a carbon source, inputting a predicted value and feedback parameters into an LSTM network for correction, and optimizing a network structure by a genetic algorithm. The adding amount is controlled in a closed-loop mode through a variable frequency pump, the LSTM weight is updated on the basis that the error is larger than 5%, and finally a control strategy is optimized by using an NSGA-II algorithm and integrating carbon source consumption, effluent total nitrogen and energy consumption. The dynamic self-adaptive carbon source adding method is constructed by fusing feedforward perception, LSTM prediction, feedback regulation and multi-objective optimization, so that quick response and accurate control on water quality fluctuation are realized, the denitrification efficiency and the carbon source utilization rate are improved, and the method has excellent engineering adaptability and popularization value.

The invention relates to the technical field of bushing detection, and discloses a bushing insulation state detection method based on multi-parameter real-time monitoring. The method comprises the following steps: acquiring multi-dimensional monitoring data such as a partial discharge pulse sequence, dielectric loss angle tangent value fluctuation data and surface leakage current density distribution in a bushing operation environment in real time; carrying out insulation defect feature extraction on the data set, and generating an insulation defect distribution map containing a creeping discharge track, an internal air gap position mark and a carbonization channel density index; high-risk and potential defect areas are divided by adopting a dynamic threshold segmentation algorithm, boundary coordinates are determined, and an insulation state evaluation vector containing parameters such as a surface discharge intensity index and the like is constructed according to the boundary coordinates; and inputting the vector into a pre-trained insulation aging prediction model, and outputting a bushing residual insulation life estimated value and a defect development priority sequence. According to the method, all-around accurate monitoring and pre-judgment of the insulation state of the sleeve are achieved, and scientific support is provided for operation and maintenance decision making of the sleeve.

The invention discloses an energy storage power station fire early warning method and system based on multi-parameter fusion, and the method comprises the following steps: collecting the temperature, characteristic gas concentration, cell expansion force, voltage fluctuation and environment temperature and humidity data of a lithium battery of an energy storage power station in real time through a distributed sensor, and carrying out the cleaning, denoising and standardization processing of the collected parameters, temperature and gas concentration monitoring values are corrected through an environment temperature and humidity compensation algorithm, abnormal data caused by environment interference are eliminated, the preprocessed data are input into a preset intelligent early warning module, and the model is based on a random forest algorithm. Through multi-parameter fusion and intelligent algorithm deep analysis, in combination with environment compensation, interference elimination, early warning accuracy improvement, graded early warning and linkage response, full-stage accurate disposal is achieved, timeliness is enhanced, sensor redundancy, multi-cabin cooperation and other mechanisms guarantee reliability, a closed loop from monitoring to disposal is formed, and the fire risk and loss are effectively reduced.

The invention discloses a flowmeter-oriented embedded multi-parameter fusion calibration control method and system, relates to the technical field of industrial measurement and embedded control, and is used for solving the problem of unstable calibration caused by misalignment of multi-link flow signals, compensation lag and fusion oscillation. The method relates to synchronous acquisition, pretreatment and calibration of differential pressure type, ultrasonic wave and thermal type mass measurement links and environment parameters such as temperature, pressure and density. All-channel unified time scales are established through ternary binding, filtering and denoising, zero snapshot and alignment rules, a stability scoring mechanism is constructed in combination with offset behavior recognition, event classification and feature aggregation, and dynamic switching of a compensation path and a fusion strategy is driven. In a double-path fusion structure, table look-up compensation, model estimation, multi-algorithm parallelism and fusion track recording are adopted, version traceability and calibration result closed-loop control are achieved, and the stability and rechecking performance of mass flow output under complex disturbance are effectively improved.

The invention relates to an infrared spectrum baseline drift dynamic compensation method and system based on multi-parameter fusion, and the method comprises the steps: continuously obtaining spectrum data, current environment parameters and light source state data of a target oil full wave band, carrying out the feature extraction, and obtaining a baseline drift sensitive feature set and a coupling feature set; constructing a prediction model taking the baseline drift amount as output, and taking the baseline drift sensitive feature set and the coupling feature set as input; the reverse baseline drift amount based on the baseline offset of each wave band is superposed in the spectral data as compensation; according to the invention, through multi-source data acquisition and analysis, monitoring of spectrum baseline drift influence factors is realized; accurate correction of different spectral characteristics is realized through a dynamic compensation strategy special for a wave band; and through a continuous self-adaptive updating mechanism, stable tracking of the long-term drift trend is realized, and the effect of prolonging the equipment maintenance period is achieved.

The invention relates to the technical field of mine safety early warning, and particularly provides a mine risk early warning method based on multi-parameter fusion and trend prediction, and the method comprises the steps: collecting multi-dimensional safety parameters including mine environment, equipment operation and production working condition parameters in real time; acquiring mine historical data, and generating a parameter association feature set through time sequence analysis in combination with real-time multi-dimensional parameters; inputting the multi-dimensional parameters and the feature set into a trained risk trend prediction model, and outputting a current risk level and an evolution trend; according to the risk level, the evolution trend and the real-time parameters, early warning information and a response strategy are dynamically generated; and the communication link is adapted based on the risk level, and the early warning information and the strategy are pushed to the target terminal. The method breaks through single parameter monitoring limitation, breaks through traditional alarm hysteresis through multi-parameter fusion and trend pre-judgment, constructs a perception-pre-judgment-response-transmission complete closed loop, solves the problems of early warning and disposal disjunction, transmission failure and the like, and improves the active prevention and control capability and management and control accuracy of mine risks.

The invention relates to an active splitting and isolated island operation method based on load importance under a disaster condition, and belongs to the technical field of power systems and automation thereof. On the basis of disaster types and power supply area characteristics, constructing a load importance dynamic grading system, optimizing the weight through an analytic hierarchy process-index weight optimization collaborative algorithm, and combining with real-time updating to generate a grading result; the method comprises the following steps: deploying wide-area and local monitoring devices to collect power grid data, setting a safety threshold, triggering a splitting decision when the safety threshold exceeds the limit and the trend deteriorates, calculating an instability rate by using a multi-parameter collaborative instability pre-judgment algorithm, and starting pre-splitting preparation; and the main control center calls a target splitting section optimization algorithm to determine a section in combination with the grading result and the power grid data, generates a splitting instruction after verifying the stability of the island through load flow calculation, and performs classified stability control on the split island. The method can realize accurate load grading, early instability pre-judgment, guarantee of important load power supply such as medical treatment and the like, reduces catastrophe loss, and is suitable for power grid management and control under various disasters.

The invention relates to the technical field of power grid monitoring, and discloses a voltage sensor multi-parameter real-time monitoring method and device in an Internet of Things environment. According to the method, real-time voltage fluctuation data of a target area is acquired by deploying a multi-channel voltage sensor array and is transmitted to an edge computing node; dynamic feature extraction is executed in the edge nodes, and a multi-dimensional feature matrix containing voltage fluctuation ratio, harmonic distortion and phase deviation features is generated; identifying voltage sag, overvoltage and harmonic resonance event characteristics based on an anomaly detection model; spatial correlation modeling is carried out in combination with power grid topological structure parameters, the coupling strength of adjacent sensor node parameters is calculated, and a multi-parameter correlation map containing event propagation paths and influence ranges is generated; inputting the voltage into a diagnosis model to generate a power grid state diagnosis report, and dynamically adjusting the sampling frequency and filtering parameters of the voltage sensor according to the power grid state diagnosis report. According to the method, real-time monitoring and analysis of multiple parameters of the power grid can be realized, and comprehensive support is provided for power grid state evaluation.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention belongs to the technical field of electrical safety, and particularly relates to a human body electric shock protection method and system based on multi-parameter fusion, multi-dimensional characteristic parameters are formed by extracting a high-frequency harmonic component, a voltage abrupt change slope and a magnetic field intensity fluctuation value of a residual current waveform, and the weight is dynamically adjusted according to the instantaneous change rate of each characteristic, so that the human body electric shock protection is realized. Generating a weighted feature vector; and a comprehensive criterion value is calculated in combination with a preset rule base and is compared with a dynamic threshold value, so that whether the circuit breaker is triggered to act or not is determined. According to the method, through fusion analysis of a plurality of physical characteristics, the identification capability of the system in a complex electromagnetic environment is improved, the probability of misoperation caused by external interference is effectively reduced, the action accuracy and stability of the circuit breaker are improved, and the power utilization safety and the power supply continuity are guaranteed.

The invention relates to the technical field of medical early warning, and discloses a graded early warning system based on multi-parameter vital sign detection. According to the system, real-time physiological parameters such as the heart rate, the blood pressure, the oxyhemoglobin saturation and the body temperature of a patient are collected through vital sign monitoring equipment; and inputting the parameters into a feature extraction network, generating a multi-dimensional physiological feature vector, and constructing a dynamic risk assessment matrix containing physiological state change trends of different time windows according to the multi-dimensional physiological feature vector. Dividing risk grade intervals according to a preset grading early warning threshold value, adjusting the intervals by adopting a self-adaptive weight distribution strategy, and generating a comprehensive risk score; and when the score exceeds the preset early warning trigger line, activating a corresponding early warning response mechanism. The system can realize comprehensive dynamic assessment of the physiological status of the patient, is suitable for emergency treatment, intensive care and chronic disease nursing scenes, and meets the clinical health risk monitoring and early warning requirements.

The invention discloses a tobacco growth environment multi-parameter intelligent regulation and control method driven by the Internet of Things, and relates to the technical field of agricultural Internet of Things and environment intelligent regulation and control, and the method comprises the steps: based on the environment parameters of a tobacco planting area collected in real time and crop stem flow data, combining the current growth stage and quality target of tobacco, and obtaining a tobacco growth environment multi-parameter intelligent regulation and control result; dynamically generating a target concentration change track of the key metabolite representing the internal physiological activity of the tobacco; taking the target concentration change track as an optimization target, carrying out reverse solution by using a crop physiological mechanism model for describing the relationship between the environment and the physiological process, and calculating a target track of environment parameter set values which are required for realizing the optimization target and change along with time; according to the target trajectory, combining with a pre-established equipment disturbance propagation model, and solving a field domain control inversion problem to generate a coordination action sequence of the environment regulation and control equipment; and the cooperative action sequence of the equipment is executed to control the corresponding environment regulation and control equipment to work.

The invention relates to the technical field of network management, and discloses an intent-driven network management method and system based on a large language model (LLM). The method comprises the following steps: receiving a natural language intention of a network administrator, analyzing the intention by utilizing a pre-trained large language model, extracting a key target and constraint, generating a configuration scheme of a TCP / IP or QUIC protocol based on an analysis result and a network protocol knowledge base, and ensuring the effectiveness and safety of configuration through a network constraint verification module. And the configuration passing the verification is applied to the network system. The system comprises an intention input module, an LLM analysis module, a configuration generation module, a verification module, an application module and the like. According to the invention, through conversion from automation intention to configuration, the network management process is simplified, the management efficiency and flexibility are improved, the method is especially suitable for multi-parameter optimization scenes of protocols such as TCP / IP and QUIC, and intelligent network management is realized.

The invention relates to the technical field of automatic control, in particular to a multi-parameter cooperative control method and device for a clamping system of a precision boring and milling machine. According to the method, an industrial camera is used for collecting and processing a workpiece image and extracting visual features, and the visual features are fused with process parameters of a workpiece and geometric distribution parameters of a workpiece key area obtained from a process database; generating a comprehensive state vector; based on the vector, outputting a target clamping force adjustment coefficient by a pre-trained MLP decision model, and calculating a final target clamping force in combination with a safe clamping force range; in the main clamping stage, vibration, temperature and pressure signals are synchronously collected, multi-modal disturbance characteristics are extracted, observation vectors are constructed, the observation vectors are input into a preset fuzzy rule base for reasoning, and compensation activation factors are obtained; and finally, a current correction instruction is dynamically generated according to the clamping force deviation and the compensation factor, and an electro-hydraulic proportional valve is driven to achieve accurate compensation. According to the invention, the stability and the machining reliability of the clamping system are obviously improved.

The invention relates to the technical field of agricultural environment monitoring, and discloses a cultivated land quality monitoring method. The method comprises the following steps: acquiring soil multi-parameter data, and carrying out sensor network deployment and anti-interference processing; performing exception filtering, standardization and missing value interpolation on the data; cloud storage data are generated through sliding window segmentation and encryption compression; generating a monitoring report based on the multi-dimensional analysis and the dynamic chart; adopting a plurality of intelligent algorithms to identify abnormal modes and constructing an early warning strategy library; and finally, real-time monitoring and early warning grade division are realized. According to the invention, full-process automatic monitoring and intelligent early warning of the cultivated land quality are realized, and the monitoring precision and the decision-making efficiency are obviously improved.

The invention provides an intelligent aeration system and a control method, and the system comprises a multi-parameter monitoring module which collects parameters such as water depth, pressure, temperature, dissolved oxygen, inlet water ammonia nitrogen, chemical oxygen demand, and mixed liquid suspended solid concentration in real time; the data analysis and preprocessing module adopts a sliding window algorithm to carry out data cleaning and anomaly detection; the oxygen supply demand prediction module establishes an oxygen demand prediction model based on machine learning, and combines theoretical calculation and deep learning prediction; the multi-target intelligent control module adopts a reinforcement learning algorithm, establishes a Markov decision process model, and optimizes the processing effect and energy consumption at the same time; the execution control module is used for accurately adjusting the rotating speed of a fan, the opening degree of a valve and the running state of an aerator; and the system collaborative optimization module realizes linkage control of the aeration system and the carbon source adding system. The system has the functions of self-adaptive parameter adjustment, fault diagnosis and self-recovery, the DO control precision reaches 95% or above, the daily average electricity is saved by 9.7%, the NH4-N of effluent is reduced by 25%, and the annual comprehensive benefit exceeds 500,000 yuan.

The invention relates to the field of industrial automation control, and discloses an intelligent control system and method based on multi-parameter coupling analysis and predictive maintenance. According to the method, flow, temperature and water quality parameters are collected in real time through a sensor network, and after data cleaning and standardization processing, a parameter coupling relation model is constructed; an LSTM neural network is adopted to predict a system energy consumption trend, and a time sequence regression model is combined to analyze equipment performance attenuation characteristics; and when the predicted value exceeds a threshold value, dynamically adjusting operation parameters and optimizing load distribution to form closed-loop control. Accurate energy-saving control under complex working conditions is achieved, energy consumption can be reduced by 8%-12% through tests, the equipment maintenance cost is reduced by 15%-20%, and the system operation efficiency is remarkably improved.

The invention relates to the technical field of health management of optical fiber communication equipment, and discloses an optical fiber connector life prediction system based on digital twinning and multi-parameter fusion, which comprises a data acquisition module, a data preprocessing module, a feature extraction module, a model construction module, a fusion analysis module and a life prediction module. According to the optical fiber connector service life prediction system based on digital twinborn and multi-parameter fusion, multi-dimensional data is formed through the data acquisition module, complete and associated data support is provided for subsequent feature extraction and service life analysis, and a digital twinborn model is constructed based on three-dimensional structure parameters and material attributes of the optical fiber connector, so that the service life of the optical fiber connector is predicted. The virtual model can accurately reflect the structural state change of a physical entity in real time, in addition, the fusion feature vector and the structural state parameter are subjected to joint analysis, a comprehensive evaluation index capable of reflecting the association of the fusion feature vector, the structural state parameter and the comprehensive evaluation index is output, an accurate multi-dimensional evaluation basis is provided for life prediction, and then the life condition of the optical fiber connector is accurately predicted.

The invention discloses a liquid cooling charging module health state prediction system, and relates to the technical field of electrochemical detection, and the system comprises a multi-parameter collection unit which collects the pressure and flow data of a cooling liquid, and is provided with an independent collection node at each branch of a double-gun charging system, and provides basic data for monitoring; the dynamic calibration unit communicates with the acquisition unit, compensates data drift based on a temperature-vibration interference model of machine learning training, executes baseline calibration by using an idle period to update a reference value, and guarantees data accuracy; the leakage detection unit is used for calculating a data change rate after compensation, outputting an early warning according to a preset condition and positioning a leakage branch; and the health state evaluation unit fuses the early warning signal and the historical operation data to generate a prediction result. The problems that tiny leakage detection is difficult and a sensor is prone to interference can be solved, manual inspection is reduced, the maintenance cost is reduced, and the requirement of a high-power charging scene is met.

The invention relates to a power distribution terminal storage battery health state evaluation method based on multi-parameter fusion, and belongs to the technical field of power equipment operation and maintenance. Comprising the following steps: acquiring discharge depth and discharge times of deep discharge by collecting discharge data of a power distribution terminal storage battery in a frequent power failure scene, and obtaining a discharge characteristic data set; analyzing the discharge characteristic data set to obtain a voltage recovery curve after deep discharge, and determining dynamic recovery characteristics according to the voltage recovery curve; if the voltage recovery time of the dynamic recovery feature exceeds a preset threshold value, extracting corresponding discharge depth and discharge times from the discharge feature data set, and obtaining an internal resistance parameter change trend; analyzing discharge data in a frequent power failure scene through the retrained health state evaluation model, determining an alternating priority, and generating a battery alternating plan for operation and maintenance management optimization; accurate evaluation of the health state of the storage battery and operation and maintenance strategy optimization are realized.

The invention discloses a river sediment multi-parameter sensing self-adaptive sampling method and system, and relates to the technical field of environment monitoring, and the method comprises the following steps: S1, synchronously collecting the pH value, electrochemical potential, heavy metal binding state proportion, water body flow velocity and hydrodynamic parameters of sediment through a sensing device, and establishing a multi-parameter response baseline; and S2, based on the multi-parameter response baseline, collecting monitoring data in real time and executing differential analysis, and identifying a high-risk time period when the pH value change rate exceeds a threshold value and the heavy metal binding state proportion fluctuates. By constructing a multi-parameter response baseline and a linkage recognition mechanism, early sensing and accurate sampling of heavy metal release are realized, a closed-loop response system is constructed by combining chemical form analysis, self-correction and data feedback optimization recognition model, and the accuracy and intelligent level of pollution monitoring are improved.

The invention discloses a postoperative drainage device for clinical nursing and a monitoring system, and belongs to the technical field of biomedical engineering. The problems that an existing device manually monitors drainage liquid data, consequently, the monitoring precision is insufficient, and the complication probability cannot be predicted are solved, drainage liquid and patient vital sign data are collected in real time through the micro spectrograph, the character multi-parameter sensor, the micro pressure sensor and the intelligent bracelet and transmitted to the remote monitoring platform, and the monitoring accuracy is improved. Medical staff can check real-time data of a patient at any time and adjust a treatment scheme in time, so that the nursing efficiency and quality are improved; multi-parameter data such as color, metering and property of drainage liquid and vital sign data are analyzed through a risk prediction model, intervention measures are dynamically adjusted according to different complication types and probabilities in combination with patient condition changes and real-time monitoring data, accurate intervention on the complication is achieved, and the occurrence rate and severity of the complication are reduced.

The invention relates to the technical field of performance testing, and discloses a multi-parameter insulating sleeve aging performance testing method and system, and the method comprises the steps: carrying out the multi-parameter detection signal collection of an insulating sleeve, and obtaining standardized signal data; inputting the standardized signal data into a coupling recognition network for feature analysis to obtain a classification recognition result; performing aging factor threshold adjustment on the rising edge slope of the discharge pulse of the insulating sleeve based on the classification identification result to obtain a dynamic separation parameter; performing feature space matching and differential identification on the partial discharge signal according to the dynamic separation parameter to obtain feature classification data; phase clustering analysis and step response characteristic analysis are carried out based on the characteristic classification data to obtain the aging state variable coefficient of the insulating sleeve, the problem that the recognition precision of a traditional fixed parameter method is reduced in different aging states is solved, and high recognition accuracy and anti-interference robustness can still be kept in a complex electromagnetic environment.

The invention relates to the technical field of new energy power generation monitoring, in particular to a new energy wind power system fault detection method, system and device based on multi-parameter fusion and a medium. The method comprises the following steps: acquiring various operating parameters such as wind speed, wind direction, generator speed, temperature, voltage and current of a wind power system in real time, and performing feature extraction on the operating parameters to obtain key features related to a fault; analyzing the key features based on a deep learning algorithm, and judging the fault type of the wind power system; and outputting a fault diagnosis result in a visual mode, and sending the fault diagnosis result to a control center for remote monitoring and fault early warning. The technical problems that fault detection of a traditional wind power system is poor in real-time performance, not high in accuracy, low in intelligent degree and the like are solved, and reliable guarantee is provided for safe and stable operation of the wind power system.

The invention discloses a 16-channel high-precision synchronous data acquisition system based on a multi-parameter model, which belongs to the technical field of data acquisition and comprises a multi-parameter model driving module, a self-adaptive channel scheduling module, a closed-loop calibration compensation module and a collaborative synchronous optimization module. The multi-parameter model driving module establishes a multi-dimensional optimization model fusing the sampling rate, the channel load and the error trend to output optimization parameters; the adaptive channel scheduling module performs real-time quality evaluation and dynamic resource allocation according to the optimization parameters to generate a scheduling instruction; the closed-loop calibration compensation module monitors a synchronization error in real time and calculates a compensation parameter; the collaborative synchronous optimization module realizes inter-channel parameter sharing and collaborative control to generate a synchronous control signal, and feeds back an optimization result to the multi-parameter model driving module to form a closed loop, and through deep coupling and closed loop feedback of the four modules, high-precision synchronization of multi-channel data acquisition is realized, and the synchronization precision reaches + / -35ns.