125 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 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 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 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 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 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 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 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.

The invention relates to the technical field of artificial intelligence and machine learning, in particular to a deep neural network continuous learning method and system based on task-level attention, and the method comprises the steps of global experience pool construction, hierarchical feature enhancement network construction, index moving average model construction, task flow receiving, task sequential training, dynamic reasoning and the like. Core mechanisms such as a task attention module group and experience playback are fused in the sub-task sequential training and dynamic reasoning step, and efficient screening, storage and utilization of different task features are achieved. And by combining the optimized hierarchical feature enhancement network and the dynamic feature fusion design, the continuous learning comprehensive performance of the model is remarkably improved. According to the method, the efficient learning ability of the new task and the memory retention effect of the old task are both considered, and a brand new solution is provided for application of the deep neural network in a multi-task continuous learning environment.

The invention relates to the technical field of fault diagnosis of intelligent electronic equipment, and particularly discloses a fault diagnosis method and system for intelligent electronic equipment, and the method comprises the steps: collecting temperature data and operation response time in real time through a high-precision temperature sensor disposed on the surface of the equipment and an external high-precision timer; a temperature anomaly feature value and an operation response time anomaly feature value are respectively calculated by adopting fast Fourier transform and an autoregression integral moving average model, and are constructed into comprehensive feature vectors, and based on the feature vectors, whether the intelligent electronic equipment has a fault state is judged by using a random forest model. And the probability that the equipment breaks down in a period of time in the future is predicted through the multiple linear regression model, and for non-fault intelligent electronic equipment, the system formulates a personalized dynamic maintenance plan according to a prediction result, so that the accuracy and high efficiency of maintenance work are ensured.

The invention relates to the technical field of traffic services, in particular to a traffic service transport capacity demand prediction method and system, and the method comprises the steps: collecting historical operation data, track data, passenger flow counting data and external impact factor data for prediction, carrying out the preprocessing of the data, and dividing the data into a plurality of space attributions; constructing a time sequence feature, a periodic feature, a neighbor space feature and an instant available transport capacity feature set external factor feature based on the preprocessed data; generating short-term baseline prediction based on a seasonal autoregressive moving average model, constructing and training a space-time hybrid neural network model to extract spatial correlation and time dynamics of space attribution, outputting a space-time depth prediction result, and integrating the space-time depth prediction result with the short-term baseline prediction to obtain a demand prediction value of a future multi-step time period; and establishing an integer programming model comprising vehicle availability, shift constraints and transfer time constraints and solving the integer programming model to generate vehicle allocation or shift suggestions by taking the transport capacity level as the target and minimizing the scheduling cost.

The invention relates to the technical field of e-commerce, in particular to an e-commerce intelligent prediction management method and system based on big data, and the method comprises the following steps: obtaining a real-time message in a buffer area, generating a throughput rate sequence based on a sliding window, carrying out the difference and mapping of throughput rates of adjacent periods, and constructing a gradient slope sequence; and extracting a transient abrupt change ratio by means of a moving average model to generate a pre-trigger level identifier, extracting a set boundary extreme value, carrying out quantitative evaluation to generate discrete parameters, correcting a transfer matrix, and executing steady-state iterative solution to obtain prediction confidence to construct an elastic control instruction. According to the method, the sudden change trend of the business time domain dimension is accurately captured and static reference deviation interference is eliminated, so that deep analysis and real-time correction of the dynamic transfer characteristics of the core transaction link are realized, and the dilemma of research and judgment distortion and scheduling lag existing in a traditional rule when facing burst traffic is thoroughly avoided; and the full-link prediction precision and the elastic resource management and control efficiency in a complex transaction scene are remarkably improved.

The invention relates to the technical field of digital data processing, in particular to a parking lot parking space guiding system, device and method based on a vehicle AI platform, and the method specifically comprises the steps: carrying out the grid division of a parking lot, obtaining the parking space related data of each grid, and obtaining the urban weather and road condition data around the parking lot; obtaining unit occupation influence of each grid in each time interval according to correlation among various types of data in the parking space related data and a local data change trend, and constructing a parking space idle index of each grid in each time interval in combination with a discrete condition between urban weather and road condition data; training an autoregressive moving average model according to the parking space idle index of each historical time interval; and according to the trained autoregressive moving average model, predicting the parking space vacancy index of each grid in the next time interval, and recommending the grid with the maximum predicted value to a vehicle owner. Therefore, parking space guidance is realized, the accuracy of parking space vacancy probability evaluation is improved, and the parking experience of customers is optimized.

The invention relates to the field of petroleum and natural gas exploitation engineering, and discloses a multi-layer gas injection working condition intelligent early warning method and system based on real-time data, a medium and equipment. The method comprises the steps that a corresponding dynamic attenuation or dynamic increasing dynamic reference model is built for each gas injection layer based on historical real-time gas injection amount data, obtaining the predicted gas injection amount of the layer so as to determine the real-time flow interval of each gas injection layer; a moving average model of wellhead gas injection pressure is established for each gas injection position, a pressure moving average value changing along with time is obtained so as to predict a wellhead gas injection pressure value at the current moment, and a real-time pressure interval of each gas injection position is determined; according to the real-time flow interval and the real-time pressure interval, whether the real-time flow and the real-time pressure collected at the current moment exceed the corresponding interval ranges or not is determined, if the real-time flow and the real-time pressure exceed the ranges and set time, early warning is carried out, the early warning process of each layer is achieved through one thread, and multi-layer synchronous early warning is achieved through multiple threads.

The invention discloses a method and a system for analyzing an enterprise production state based on an electric power index, and relates to the field of electric power indexes. The method comprises the following steps: constructing an evaluation index system comprising two first-level indexes, namely production state data and export trade state data; an autoregressive moving average model and a Kendall rank correlation coefficient algorithm are used to construct a prediction model of the power consumption index to the export amount index; the enterprise production state is analyzed based on a panel space model, and an enterprise production state early warning index is obtained; setting a weight Wi of each evaluation index according to the enterprise production state early warning index, and constructing a fuzzy relation matrix R according to the evaluation index system and the weight Wi of each evaluation index; constructing a fuzzy evaluation matrix B according to the weight Wi and the matrix R; according to the matrix B, the maximum membership degree principle is adopted to calculate the production state and export trade state evaluation grade of the target enterprise; aiming at low analysis precision caused by neglecting correlation among indexes in foreign trade enterprise analysis in the prior art, the analysis precision is improved.

The invention relates to a battery replacement station battery mutual aid allocation method based on a battery demand prediction model and a DQN. The method comprises the following steps: acquiring a multi-dimensional battery data set of each battery replacement station; a deep neural network model and an autoregressive integral moving average model are adopted to predict the battery swap demand of each battery swap station; constructing a reinforcement learning environment which comprises a state space, an action space and a constraint condition; a deep Q network is adopted to construct a reinforcement learning model, a reward function is designed based on the battery demand satisfaction degree, the deployment cost and the battery health state, and an optimal deployment strategy is obtained through an intelligent agent and environment interaction training model; and formulating a battery allocation scheme according to an optimal allocation strategy output by the trained reinforcement learning model, and transmitting an allocation instruction to each battery swap station through a communication network to realize mutual aid allocation of batteries of the battery swap stations. The multi-objective optimization of mutual aid allocation of the batteries of the battery swap station is realized, the battery swap demand satisfaction rate of the battery swap station is improved, and the battery allocation cost is reduced.

The invention relates to the technical field of industrial control and equipment fault prediction, in particular to a tunnel furnace combustion array fault positioning method based on time sequence temperature data, which comprises the following steps: acquiring temperature data of each temperature zone, calculating a control loop deviation, determining neighborhood thermal compensation potential energy based on the control loop deviation and a preset asymmetric coupling coefficient, and determining the fault of a combustion array of a tunnel furnace according to the neighborhood thermal compensation potential energy. The convection covering strength is determined by combining the temperature change rate of the temperature zone and the noise reference, stagnation characteristics covered by heat flow are identified, and the performance degradation index of the combustion head corresponding to the temperature zone is determined based on the change characteristics of the convection covering strength along with time; and carrying out dynamic correction on the prediction residual error of the autoregression integral moving average model by using the performance degradation index, and carrying out fault positioning according to the autoregression integral moving average model after dynamic correction. According to the method, thermal compensation of the adjacent temperature zones can be stripped from the strong coupling thermal field, and accurate positioning of the early-stage micro-blockage fault of the combustion head covered by the heat flow is achieved.

The application provides a subjective question intelligent marking and learning situation analysis system based on writing characteristics, relates to the technical field of education, and the system architecture comprises a writing characteristic acquisition and analysis subsystem, an intelligent marking subsystem and a learning situation analysis and prediction subsystem. With the aid of quantum technology, all-round innovation and improvement are realized. The model constructed by quantum sensors and calculation makes micro dynamic data accurate and observable, time sequence characteristics deep and analyzable, and the quantum algorithm is remarkably effective in constructing the font aesthetics and multi-factor correlation model on a macro level. Quantum natural language processing and neural network cooperation, semantic style fusion innovation, knowledge graph reasoning with the aid of quantum graph algorithm, etc. can accurately judge and scientifically guide various types of questions. Quantum hashing and federal learning help to integrate multi-source data, quantum random walk and autoregressive moving average model realize psychological correlation prediction, accurately understand learning situation, effectively intervene to help students' physical and mental and academic progress, and greatly enhance the efficiency and value of the system in the field of education.

The invention provides a cutterhead load multi-gait prediction method and system for assisting TBM intelligent tunneling, and the method comprises the steps: carrying out the processing of real-time monitoring data through grading preprocessing, and obtaining standardized time sequence tunneling data; tunneling control parameters strongly related to cutterhead loads are screened based on grey relational analysis to serve as key tunneling features, time synchronization and vector splicing are conducted on the key tunneling features and geological indexes, and multi-source information state vectors are constructed; establishing a multivariable fractional order autoregression fractal integral moving average model; the method comprises the following steps: constructing a BiGRU-Seqseq-TAM network; inputting the multi-source information state vector into a BiGRU-Seqseq-TAM network, extracting time sequence features, and mapping and outputting mechanism parameters to be identified; and substituting into the model to reckon the predicted value of the cutterhead load in a plurality of time steps in the future. The invention also discloses a corresponding prediction system. According to the method, the capturing capability of the model on the dynamic evolution rule of the multi-gait load of the cutterhead is enhanced, and the transparency and interpretability of the model are remarkably improved.

The invention belongs to the technical field of lithium ion battery parameter identification, and particularly relates to an embedded optical fiber sensing lithium battery multi-physics field coupling parameter identification method and system. Comprising the following steps: acquiring temperature and strain signals in the lithium ion battery; performing periodic correlation analysis on the strain signal in the lithium ion battery, decomposing the strain signal into a periodic strain subitem and an irreversible aging strain subitem, and establishing a thermal-mechanical-aging multi-physics field coupling model of the lithium ion battery in combination with the temperature signal in the battery and the periodic strain subitem; and iteratively solving parameters of the thermal-mechanical-aging multi-physics field coupling model of the lithium ion battery based on an improved cuckoo search algorithm dynamically adjusted by a neural network. According to the lithium dendrite growth-related irreversible aging strain subitem extraction method based on the seasonal autoregressive moving average model and the improved cuckoo search algorithm based on dynamic adjustment of the neural network, effective identification of parameters is realized.

This invention belongs to the field of data caching and batch synchronization technology, and discloses a method and system for data caching and batch synchronization of a management system based on multi-level caching. The method includes: caching pre-acquired data using a caching strategy based on a pre-built caching architecture; extracting operation data from the cached data; and performing local operation processing and operation type judgment by combining a transaction engine and an operation classifier; acquiring the storage status data of the caching architecture in real time; optimizing the synchronization strategy using a decision tree algorithm; and performing batch synchronization processing by combining a chained version number mechanism; acquiring the running status data of the caching architecture; constructing a caching performance index system using the running status data; and performing data storage alarm processing by combining an autoregressive integral moving average model and association rules. This invention significantly optimizes the response speed of high-frequency operations and effectively reduces the load on backend servers, achieving efficient utilization of hardware resources.