179 results about "Moving-average model" patented technology

The invention provides a sewage treatment plant terminal effluent carbon source quality detection and evaluation method, which realizes full-flow dynamic evaluation and regulation of carbon source quality through on-line monitoring and intelligent algorithm fusion. According to the method, an online water quality full-spectrum detector is used for collecting original spectrum data flow, and after preprocessing such as variational mode decomposition denoising and mutual information feature selection, organic matter content quantification, variation trend analysis and anomaly detection are completed in combination with algorithms such as a support vector machine and an autoregressive moving average model. An entropy weight method is introduced to dynamically adjust the weight of the evaluation model, model parameters are optimized based on a gradient descent algorithm, a process adjustment instruction is generated through reinforcement learning and fuzzy logic, and an automatic system is linked to execute regulation and control. According to the method, the problems of hysteresis and singleness of traditional offline analysis are solved, multi-dimensional real-time evaluation, abnormal quick response and process dynamic optimization of the quality of the carbon source are realized, the sewage treatment efficiency and the effluent quality stability are improved, and a technical support is provided for continuous standard reaching of the quality of the carbon source.

According to the mechanical arm control method based on multi-scale dynamic state estimation and error compensation, high-frequency and high-precision estimation of the state of a mechanical arm system is achieved through multi-sensor data fusion and an extended Kalman filtering algorithm, and the accuracy and reliability of state estimation are remarkably improved; on the basis, a multi-scale error analysis method combining a time domain and a frequency domain is provided, and an autoregressive moving average model is utilized to dynamically predict system errors, so that the sensing and pre-judging capability of the system on the errors is enhanced; a comprehensive control strategy including multiple links of feedforward, self-adaption and feedback is designed, a dynamic compensation and adjustment mechanism of errors is introduced, meanwhile, a real-time execution framework based on three-layer task scheduling is constructed, cooperative operation of all functional modules is coordinated, and the real-time performance and high efficiency of a control system are ensured. The method is suitable for multiple fields of industrial manufacturing, intelligent robots and the like, and provides technical support for precise control of mechanical arms in complex scenes.

The invention provides a photovoltaic wind power energy storage intelligent management method and system for a building. The method comprises the following steps: acquiring operation parameters, environmental conditions and historical fault data records of system equipment, and preprocessing the acquired data; establishing health assessment indexes by using the comprehensive wind and light energy storage equipment performance model, and determining the health degrees of different equipment; the fault abnormity level is judged according to the fault alarm signal sending time and frequency of the system equipment; through combination of an autoregressive differential moving average model and a long-short-term memory model, the photovoltaic and wind power comprehensive electric quantity is predicted, and the loss abnormity level is judged; and in combination with the health degree, the fault abnormity level and the loss abnormity level of the equipment, making a patrol inspection and daily maintenance work plan, and sending out reminding and early warning. By analyzing and diagnosing key equipment and core performance of the system and early warning key events, energy waste and equipment damage caused by improper maintenance can be effectively avoided.

The invention relates to a task simulation method and device of a virtual simulation platform and computer equipment. The method comprises the following steps: a data synchronization management module performs data synchronization of each heterogeneous virtual simulation sub-platform according to a pre-controlled data synchronization frequency, and further needs to perform online evaluation and offline evaluation so as to optimize an autoregressive moving average model and a reinforcement learning model to perform simulation at the next moment. By adopting the method, the efficiency of data synchronization between heterogeneous simulation platforms can be improved.

The invention discloses a power consumption control method and device for a power distribution network monitoring control chip and a storage medium, and the method comprises the steps: carrying out the clock domain division of the power distribution network monitoring control chip, and obtaining a target clock domain; obtaining current load data and historical load data of the target clock domain; according to the historical load data, trend prediction processing is carried out by using an autoregressive moving average model to obtain predicted load data; according to the predicted load data and the power consumption state of the control chip, performing action evaluation processing by using preset reinforcement learning to obtain a state action function value; calculating a to-be-adjusted clock frequency and a to-be-adjusted voltage according to the current load data; determining a predicted clock frequency and a predicted voltage according to the state action function value; and performing power consumption control on the target clock domain according to the target clock frequency and the target voltage to obtain a power consumption control result. The power consumption control is realized, and the resource utilization rate and the control efficiency are improved. The method can be widely applied to the technical field of electric power engineering.

The invention relates to the technical field of information security, in particular to a key management system and method based on a medical block chain, and the system comprises a state evaluation module, a reverse tracking module, a foresight analysis module, an intelligent alarm threshold adjustment module, a resource priority scheduling module, a reliability analysis and fault tolerance module, and a process optimization and decision support module. According to the method, risk assessment and classification are effectively carried out and a key access control strategy is adjusted by adopting a support vector machine, a decision tree algorithm and a deep learning algorithm, a key use trend is predicted by utilizing an autoregression integral moving average model and a long-short-term memory network, and the key access control strategy is optimized through an isolated forest and a deep learning network. An alarm threshold value is dynamically adjusted, the early warning capability of the system for potential security threats is improved, a key management process is optimized through a genetic algorithm and a particle swarm optimization technology, a security decision and a response strategy are executed in combination with a rule engine and a machine learning model, and the response efficiency and the decision accuracy are improved.

The invention discloses a lithium battery residual life prediction method, and belongs to the technical field of lithium battery life prediction, and the method comprises the steps: constructing a capacity degradation data sequence according to the capacity degradation data of a lithium battery; decomposing the capacity degradation data sequence through fast ensemble empirical mode decomposition to obtain a fluctuation item and a trend item; predicting the trend term by adopting particle filtering to obtain a trend term prediction result; predicting the fluctuation item through a fractional autoregression moving average model to obtain a fluctuation item prediction result; and combining the trend item prediction result with the fluctuation item prediction result, constructing a prediction curve, predicting the failure cycle of the lithium battery, and realizing prediction of the residual life of the lithium battery. The nonlinear characteristic and the long-term correlation characteristic in the degradation process of the lithium battery can be considered at the same time, and the accuracy of battery life prediction is improved.

The invention provides a high-power charging pile self-adjusting method, medium and system for a high-temperature environment, and belongs to the technical field of electrical variable measurement, and the method comprises the steps: extracting time domain features and frequency domain features of an environment parameter, a charging temperature, an input parameter and an output parameter; a correlation matrix of each parameter is calculated through a Pearson's correlation coefficient and principal component analysis, and a stability matrix and a variability matrix are obtained through singular value decomposition. Based on the two matrixes, an autoregressive integrated moving average model and a conditional autoregressive model are respectively constructed, and the long-term trend and short-term fluctuation of each parameter are predicted. And then establishing a game model, and solving to obtain an optimal output parameter by taking the long-term stability and the short-term stability of the output parameter as a target, and taking the optimal output parameter as a charging pile output parameter in a next time period. The charging process is continuously optimized with the preset duration as the cycle period, and the technical problem that real-time optimization of charging adjustment is difficult to achieve due to the fact that an existing charging control technology is mainly based on a single temperature threshold value is solved.

The embodiment of the invention discloses a dynamic collaborative optimal selection control method for denitration of a garbage incinerator, and relates to the technical field of garbage incineration treatment. The method comprises the steps that operation parameters, collected in real time, of the garbage incinerator are obtained and preprocessed; based on the mechanism of waste incineration and denitration reaction and in combination with historical operation data, a dynamic model of the denitration process of the waste incinerator is established, and the denitration process is described by adopting an autoregressive moving average model; according to the preprocessed operation parameters and the established dynamic model, the working conditions of the garbage incinerator are recognized and classified in real time; multivariable model predictive control is adopted to make a cooperative control strategy; and outputting the formulated cooperative control strategy to execution mechanisms of the denitration system and the incineration system. According to the method, the denitration efficiency can be improved, nitrogen oxide emission can be accurately controlled, and the ammonia escape rate and the denitration cost can be reduced.

The invention discloses a non-abandoned inheritance display method and system based on artificial intelligence, and particularly relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring dynamic detail time sequence data from a capture system in real time, analyzing track smoothness by using Fourier transform, generating a lossless reference model by using ultrahigh-resolution 3D scanning and image equipment, and performing texture consistency analysis on a difference salient region by combining point cloud registration and a difference heat map to evaluate spatial deviation of a track. Judging the fidelity of dynamic details, comprehensively evaluating the capture accuracy in combination with the smoothness and the fidelity, dividing capture results into accurate capture and inaccurate capture, and archiving the accurately captured data to realize efficient reuse; for data which are not accurately captured, track dynamic complementation is carried out through an autoregression integral moving average model, the accuracy and integrity of non-abandoned dynamic detail capturing are remarkably improved, it is ensured that the display effect is real and coherent, and accurate inheritance and wide propagation of non-abandoned cultural connotation are facilitated.

The invention discloses an industrial chain situation prediction analysis system and method based on AIGC, and belongs to the field of data processing systems or methods specially suitable for the purposes of commerce, finance, management, supervision or prediction. An AIGC model based on the combination of a generative adversarial network (GAN) and a variational auto-encoder (VAE) is constructed; through adversarial training, KL divergence optimization and adaptive learning rate adjustment, a data rule is accurately captured, a moving average method, an autoregressive integral moving average model (ARIMA) and a random forest algorithm in situation prediction are cooperated, prediction stability and accuracy are enhanced, traditional defects are improved, in the aspect of analysis and management, polynomial fitting interpretation trend is deeper, and the prediction efficiency is improved. Risks and opportunity points are accurately identified through a comprehensive index system and a scientific method, decision making is assisted, on the whole, the industrial chain situation prediction analysis process is comprehensively innovated, scientific industrial development decision making is powerfully promoted, and enterprise competitiveness and industrial resource allocation efficiency are improved.

The invention discloses an emotion regulation system based on multi-dimensional perception and an evaluation method thereof. In the invention, through the emotion classification and intensity evaluation module using the support vector machine algorithm, the system not only can accurately divide the emotion of the user into different categories, but also can evaluate the intensity of the emotion, so that a more personalized emotion regulation scheme is provided for the user. The accurate emotion recognition enables the system to provide corresponding intervention measures according to the real-time emotion state of the user, such as music relaxation, breathing guidance or emotion catharsis games, so that the user is effectively helped to relieve negative emotion, and the emotion self-management ability is improved. The emotion trend analysis module predicts the emotion change trend by using an autoregressive moving average (ARIMA) model, so that the system can have perspectiveness, identify possible emotion fluctuation in advance and take preventive measures. The method can quickly respond when the emotion of the user is abnormal, and timely assistance is provided, so that the influence of emotion problems on the daily life and work of the user is reduced.

The invention discloses heat supply equipment based on computing power and a control method, and belongs to the technical field of heat supply control. The method comprises the following steps: acquiring heat supply data, and performing anomaly detection on the heat supply data; when the heat supply data is not abnormal, the distribution condition of the hot water in the heat supply pipe network is analyzed, and a flow analysis result and a pressure analysis result are obtained; optimizing the operation parameters of the heat supply network, and calculating the heat recovery efficiency of the heat supply network; constructing and training an autoregressive integral moving average model, inputting current power utilization related data and heat recovery efficiency, and outputting a thermal load demand prediction value; influence weights of the indoor temperature and the outdoor temperature on the thermal load demand are determined, a temperature difference is calculated, a temperature correction coefficient is calculated according to the temperature difference, and a thermal load demand prediction value is adjusted by using the temperature correction coefficient; setting a power corresponding relation, and adjusting the power of the electric boiler by stages; the operation time and the stop time of the electric boiler are calculated according to the operation state data of the electric boiler, and the electric boiler is set according to the operation time and the stop time obtained through calculation.

The invention discloses a digital twinning-based clean room intelligent operation and maintenance system and method, and relates to the technical field of intelligent operation and maintenance, and the method comprises the steps: obtaining the state data of a clean room through arranging a sensor, obtaining the power data of clean room equipment through an external data source, and carrying out the preprocessing at an edge node, and generating a multi-source time series data set; constructing a multi-scale prediction model by integrating a long short-term memory network, an autoregressive integral moving average model and a graph neural network, and generating a multi-scale sequence data set based on the multi-source time sequence data set; performing analogue simulation on physical field coupling in the clean room through a quantum computing platform to generate a clean room digital twinborn simulation model; and loading the clean room digital twinborn simulation model to a cloud analysis platform, optimizing the running state of the clean room by using a gradient descent method, and generating an optimized operation and maintenance scheme. According to the invention, simulation is carried out on physical field coupling in the clean room through the quantum computing platform, and the authenticity and real-time performance of a simulation model are greatly improved.

The invention provides an energy scheduling optimization method for an energy storage tunnel, which comprises the following steps: collecting real-time operation data of all energy storage units in the energy storage tunnel, evaluating the health state of each energy storage unit based on a multi-dimensional health monitoring model, and identifying potential faults through a time sequence analysis and anomaly detection mechanism; constructing a multi-level distributed scheduling architecture; based on the historical load data, the external environment factor and the state of the energy storage unit, predicting a load demand through a weighted moving average model, and dynamically adjusting a prediction result of the load demand in combination with an adjustment factor; according to the prediction error of the load demand and the health state threshold value of the energy storage unit, designing an anomaly detection mechanism to judge whether to trigger fault detection, and after the fault detection is triggered, dynamically adjusting the electric quantity of the energy storage unit or switching a standby unit to realize automatic repair of the system; and integrating the health state, the electric quantity state and the load prediction result of the energy storage unit, and dynamically optimizing a global charging and discharging strategy by taking minimization of energy loss and health deterioration as a target.

The invention discloses a network bandwidth prediction method and system, and the method comprises the steps: collecting historical bandwidth data of a target network, carrying out the prediction through a deep reinforcement learning algorithm, and obtaining a reinforcement learning bandwidth prediction value at each moment; predicting by adopting a heuristic algorithm to obtain a heuristic bandwidth predicted value at each moment; carrying out weighted fusion on the reinforcement learning bandwidth prediction value and the heuristic bandwidth prediction value to obtain a fusion prediction bandwidth at each moment; carrying out serialization processing on the fusion prediction bandwidth at all moments to obtain a fusion prediction bandwidth sequence, and carrying out first-level moving smoothing processing on the fusion prediction bandwidth sequence by adopting an autoregression moving average model to obtain a prediction bandwidth first-level smoothing sequence; and performing secondary dynamic smoothing processing on the fused predicted bandwidth sequence by adopting a Kalman filtering algorithm to obtain a predicted bandwidth secondary smoothing sequence. According to the network bandwidth prediction method and system provided by the embodiment of the invention, the prediction accuracy of the network bandwidth is improved.

The invention discloses a sudden snow disaster risk degree probability prediction method oriented to insufficient historical time sequences. The method comprises the following steps: step 1), constructing a multi-index risk index based on related data such as historical snow disasters; 2) performing time sequence modeling on the violent snow disaster risk index by adopting an autoregressive integral moving average model, and capturing trend and periodic characteristics of the violent snow disaster risk index; 3) discretizing a continuous risk index of modeling production into a plurality of risk levels, and constructing a state transition probability matrix; step 4), introducing space neighborhood influence and carrier exposure constraint, and constructing a Markov space weighted state transition model; 5) coupling CMIP6 climate scene data, and dynamically adjusting future state transition probability, and 6) fusing ARIMA trend prediction and Markov state transition probability, and outputting future multi-period snowstorm disaster risk level probability distribution.According to the method, the accuracy of snowstorm disaster risk prediction and the modeling ability and prediction adaptability of future climate change scenes are improved.

The invention belongs to the field of data science and machine learning, and constructs a prediction system fusing a convolutional neural network (CNN), a long-short-term memory network (LSTM), a seasonal autoregressive integral moving average model (SARIMAX) and a data-driven modeling method in order to solve the problem of battery temperature prediction. The system focuses on analyzing transient temperature change during fast charging of the battery and a temperature evolution rule caused by aging, an exclusive data feature extraction and fusion strategy is formulated for different working conditions, and model parameters are optimized. Key performance indexes such as prediction accuracy, model robustness and working condition adaptability are remarkably improved, and the method has a wide application prospect and an important practical value in thermal management of the battery of the electric vehicle.

The invention discloses a direct-current power distribution network fault positioning system and method based on a neural network and an optimization algorithm, relates to the field of direct-current power distribution network fault positioning, and designs a fault location strategy in combination with a generalized regression neural network and a grey wolf optimization algorithm. The method comprises the following steps: performing sample expansion on original fault voltage data by adopting an autoregression moving average model to obtain a large number of data samples, performing time-frequency domain feature extraction on a fault transient voltage signal by utilizing S transformation and permutation entropy, constructing a fault feature quantity, inputting the fault feature quantity into a GRNN for training, and optimizing parameters of the GRNN by adopting a GWO of an improved iteration strategy to obtain a fault transient voltage signal; and finally obtaining a fault positioning result. The fault location method designed by the invention is excellent in performance and strong in robustness, and realizes accurate fault location of the MMC type flexible direct-current power distribution network, thereby ensuring safe operation of the flexible direct-current power distribution network.

The invention relates to the technical field of water quality analysis, in particular to a water quality evaluation method based on data analysis, which comprises the following steps: based on a satellite remote sensing technology, analyzing water spectral characteristics by adopting a deep neural network algorithm, and generating water characteristic data. According to the method, the satellite remote sensing technology and the deep neural network are combined, the accuracy of water body spectral feature analysis is improved, particulate matter in water is classified through image recognition, the analysis speed is increased, the result accuracy is improved, environmental DNA sampling is used for evaluating the biological diversity, and basic data are generated for microbial activity analysis; a micro-fluidic chip and a fluorescence labeling method are used for improving the activity analysis sensitivity of microorganisms and algae, a support vector machine algorithm is used for improving the water quality detection accuracy, an autoregression integral moving average model and a random forest algorithm are combined for deep water quality change analysis, and a comprehensive risk assessment model is used for comprehensively assessing the water quality risk. And a basis is provided for accurate water quality management.

The invention provides a photovoltaic typical scene generation method and device based on source-load time sequence decomposition, and the method comprises the steps: collecting historical photovoltaic output time sequence data of a region, and carrying out the decomposition based on an empirical mode; the method comprises the following steps: acquiring load time sequence data of a region, and decomposing the load time sequence data based on a simplified autoregressive moving average model; performing feature extraction on the photovoltaic output periodic component, the photovoltaic output random component and the photovoltaic output trend component to obtain photovoltaic periodic features, photovoltaic random features and photovoltaic trend features; performing feature extraction on the load periodic component, the load random component and the load trend component to obtain a load periodic feature, a load random feature and a load basic feature; carrying out clustering analysis according to the photovoltaic periodic characteristics, the photovoltaic random characteristics, the photovoltaic trend characteristics, the load basic characteristics, the load periodic characteristics and the load random characteristics to obtain a photovoltaic typical scene; according to the method, the description of the rural source load characteristics is more accurate.

The invention discloses a liquid oxygen methane rocket engine fault prediction method and system based on an ARMA model, and the method comprises the steps: constructing a modularized simulation model of a liquid oxygen methane rocket engine, injecting fault factors to simulate typical faults (such as methane pump cavitation, turbine blade ablation and the like), and obtaining normal and fault data containing Gaussian noise; carrying out time sequence modeling on the six key parameters by adopting an ARMA (Autoregressive Moving Average Model), and optimizing model parameters through ACF / PACF order determination and AIC criteria; establishing a self-adaptive residual threshold formula (threshold = residual mean + / -2 times of standard deviation), and realizing early warning of the fault by combining a judgment criterion that the threshold is continuously exceeded for three times and three parameters cooperatively give an alarm; experiments show that the alarm time is 0.1-0.13 seconds earlier than that of a traditional red line threshold method, the false alarm rate is reduced from 18.7% to 0.3%, and the spaceflight-level real-time performance and reliability requirements are met; the method does not need a large number of fault samples, is high in anti-interference capability, and is suitable for online health monitoring of reusable rocket engines.

The invention provides a power distribution room fault detection method and system combining edge calculation and machine learning. Voltage, current, temperature, humidity, equipment vibration signals and sound signals in the power distribution room are collected in real time through the sensor module; analyzing the collected real-time data through a pre-trained differential autoregressive moving average model to carry out future data prediction; performing denoising and normalization preprocessing on the real-time data and the prediction data; performing time-frequency domain analysis on the preprocessed data, and extracting time-frequency features for fault diagnosis and prediction after dimension reduction through principal component analysis; a pre-trained fault diagnosis model based on a support vector machine analyzes the dimensionality-reduced time-frequency characteristics to obtain the fault condition of the power distribution room; wherein the differential autoregressive moving average model and the fault diagnosis model are operated by a plurality of edge computing controllers built according to a parallel computing architecture.

The invention relates to the technical field of lubricating oil quality monitoring, in particular to a lubricating oil quality on-line monitoring system and method, and the system comprises a data collection module which collects lubricating oil parameters in real time through a sensor array; the communication transmission module is used for constructing a data flow network and supporting multi-level interaction; the data analysis and diagnosis module is used for realizing state evaluation and fault early warning; the user interaction and visualization module provides a human-computer interaction interface and decision support; and the intelligent decision module realizes monitoring-maintenance closed-loop control, gives an alarm in time and realizes full-link intelligent control triggered by a maintenance action. According to the method, the prediction precision and the early warning capability of the degradation trend of the oil system are effectively improved through the improved autoregression moving average model, the real-time state of the oil is accurately reflected by dynamically adjusting the alarm threshold value, and the fault positioning precision is greatly improved through the space matching algorithm.

The invention discloses a landslide disaster early warning method based on an LSTM-SARIMA mixed data driving model. Global and local noise elimination is performed on radar displacement monitoring data by adopting a two-stage noise reduction method combining moving average and wavelet transform, so that the signal-to-noise ratio is effectively improved; the method comprises the following steps: decomposing slope displacement data into a trend term dominated by gravity and a periodic term influenced by the environment by utilizing a Hodry-Precott (HP) filtering method, and realizing differentiated analysis of a multi-factor action mechanism; a mixed prediction model combining a long short-term memory network (LSTM) and a seasonal autoregressive integrated moving average model (SARIMA) is provided, modeling and prediction are carried out on decomposed displacement components respectively, and results are fused to obtain a high-precision total displacement prediction value; an improved T-t curve is constructed based on a predicted displacement-time curve, a tangent angle index for landslide early warning is proposed according to a Saito three-stage theory, and quantitative and interpretable early warning criterion setting is realized.

The invention belongs to the field of wind power, and discloses a wind power data anomaly monitoring method and a related device, and the method comprises the steps: obtaining to-be-monitored wind power data and historical wind power data; inputting the to-be-monitored wind power data and the historical wind power data into a preset wind power data anomaly monitoring model to obtain residual data of the to-be-monitored wind power data; obtaining an abnormal monitoring result of the to-be-monitored wind power data according to the residual data of the to-be-monitored wind power data and a preset residual threshold; wherein the wind power data anomaly monitoring model is obtained by pre-training a seasonal autoregressive moving average model. The characteristic that wind power data can be influenced by changes of factors such as seasonality is fully considered, a seasonal autoregressive moving average model is adopted for self-adaptive modeling, and the seasonality and tendency of the wind power data are automatically captured, so that a wind power data anomaly monitoring model can adapt to different data characteristics and change dynamics, and the monitoring accuracy is improved. The wind power data with time sequence, nonlinearity and complexity can be effectively processed, and the accuracy of wind power data anomaly monitoring is improved.

The invention relates to a multi-source meteorological data fusion technology, and discloses an air temperature reconstruction method fusing data of a micro-meteorological device, which improves the temporal-spatial resolution and precision of ground temperature under a complex terrain. The method comprises the following steps: densely deploying micrometeorological devices in a complex terrain area to obtain high-frequency observation data, carrying out quality control, and carrying out hierarchical processing in two dimensions of space and time by taking pattern forecast grid point data as an initial background field: in the spatial dimension, dynamically updating a fusion weight for grid points with observation stations by using geographical weighted regression, and carrying out data fusion; a residual machine learning model is combined with multi-topographic feature correction for grid points without observation stations, and a high-precision space fusion background field is generated; in the time dimension, errors after space fusion are decoupled into a trend term and a periodic term, an autoregressive integral moving average model is used for predicting a trend, a Fourier algorithm is used for correcting a periodic phase, and then time dimension machine learning correction is carried out on a grid point of an observation-free station. And finally, complete-process automatic, high-temporal-spatial-resolution and low-error complex terrain area air temperature reconstruction is realized.

The invention relates to the technical field of drilling, and provides a roofbolter feedforward-feedback model-free adaptive tracking control method and system based on rotation force. According to the method, a valve-controlled hydraulic propulsion control model for driving a hydraulic cylinder and a corresponding reversing valve in the working process of the hydraulic jumbolter is established, and a nonlinear autoregressive moving average model between the propulsion force in the working process of the hydraulic jumbolter and the output driving current of the reversing valve is obtained through the valve-controlled hydraulic propulsion control model; through a nonlinear autoregression moving average model of the hydraulic jumbolter, an output identification observer deployed with a data-driven adaptive control algorithm of the hydraulic jumbolter is established, and the output identification observer performs adaptive tracking control on the working state of the hydraulic jumbolter through the deployed data-driven adaptive control algorithm. The tracking control problem of the valve control electro-hydraulic servo unit under the complex working condition is effectively solved.

The invention discloses a digital asset management method and system based on multi-objective optimization, and the method comprises the steps: firstly constructing an enhanced fragmentation management network architecture, introducing an interaction frequency-based histocompatibility analysis method and a four-dimensional resource dynamic monitoring mechanism, and achieving the load prediction in combination with an autoregressive differential moving average model. Secondly, establishing a multi-objective optimization framework based on organizational affinity, load balancing and privacy protection, and adopting a differential privacy protection mechanism and dynamic privacy budget management to realize a Pareto optimal solution through a gradient descent method; and finally, designing an intelligent fragmentation decision algorithm and an enhanced load balancing strategy, constructing a layered traceability architecture based on a Merkle tree, and realizing full-life-cycle credible traceability in combination with zero-knowledge proof and a commitment mechanism. Therefore, inter-organization relation privacy and system load information security are protected, system resource configuration and cross-fragmentation cooperation efficiency are optimized, and the overall performance and availability of digital asset sharing service are maximized.

The invention discloses a distribution line tree line fault diagnosis method and a fault isolation integrated switch device, and relates to the technical field of distribution line fault diagnosis. The method comprises the steps of extracting a jump slope distribution sequence of a high-frequency traveling wave signal, performing feature extraction on the jump slope distribution sequence through an autoregression moving average model to obtain feature parameters of the autoregression moving average model, and learning the feature parameters through a support vector machine to obtain a fault type. According to the method, light-weight and high-precision tree line fault identification can be realized.