2110 results about "Long short term memory" patented technology

The invention relates to the technical field of robot control, and discloses a heavy-load robot motion trail method and system based on machine learning. The method comprises the steps that historical movement track data of the heavy-load robot in a working scene are collected, and the data comprise a joint position sequence, an end effector pose sequence and environment obstacle distribution information; the data is preprocessed, track features are extracted, a space-time correlation matrix is constructed, and the matrix is used for representing the dynamic coupling relation between joint movement and the tail end pose; training a trajectory prediction model containing a long and short-term memory network and an attention mechanism based on the matrix, and generating a collaborative mapping relation between a joint position and a tail end pose; obtaining a current task target pose sequence and an environment constraint condition in real time, and outputting a candidate track set meeting dynamic constraint through a model; and adopting a multi-objective optimization algorithm to screen candidate tracks, generating an optimal track instruction and issuing the optimal track instruction to an execution mechanism. The method adapts to the complex characteristics and variable working conditions of the heavy-load robot, and the track adaptability is improved.

The invention discloses an energy storage system real-time diagnosis and networking control method and system based on digital twinning and deep learning, and the method comprises the steps: collecting the electrical, thermal and aging state data of an energy storage battery cluster through a multi-mode sensor, constructing a multi-physics field coupled digital twinborn model by using a graph neural network and a long short-term memory network; performing synchronous mapping on battery cluster operation data acquired in real time and the digital twinborn model to generate a state evolution sequence in the battery cluster with advanced prediction capability; based on the state evolution sequence, predicting a dynamic stability boundary of the key node of the power grid and a possible instability risk time period in the future; and according to the dynamic stability boundary and the instability risk time period, generating a cooperative adjustment instruction of the output voltage amplitude, the phase and the virtual impedance of the network construction type energy storage equipment. According to the embodiment of the invention, the diagnosis reliability, the control foresight and the operation safety of the energy storage system in a complex power grid environment can be improved.

The invention discloses a water quality time sequence prediction method of an SSA-VMD-LSTM-XGBoost hybrid model, and belongs to the technical field of water quality monitoring and prediction. Comprising the following steps: (1) data preparation and preprocessing; (2) optimizing the water quality time sequence decomposition of the VMD based on SSA: optimizing a penalty factor and a modal number of the VMD by adopting a sparrow search algorithm (SSA), and decomposing the water quality time sequence into a plurality of sub-components with high stability and low complexity by utilizing the optimized VMD; (3) construction and training of an LSTM-XGBoost hybrid prediction model: constructing a hybrid prediction model fusing long-short term memory (LSTM) and extreme gradient boost (XGBoost), inputting a high-frequency component into the LSTM model, inputting a low-frequency component into the XGBoost model, and finally performing superposition and integration on prediction results of the models; and (4) multi-component prediction result integration and performance verification. According to the method, adaptive optimization of VMD parameters is realized through SSA, the feature extraction and time sequence modeling capability is improved by combining the advantages of LSTM and XGBoost, and the prediction precision and stability of the water quality time sequence are effectively improved.

The invention provides an ocean wind field prediction method based on a neural network, and belongs to the technical field of ocean wind field prediction.The method comprises the steps that sparse ocean observation data are collected, a spatial covariance matrix is established, the spatial covariance matrix is converted into a graph structure, and then multi-hop neighborhood feature aggregation is conducted through a graph convolutional network; a tensor decomposition algorithm is combined for modeling high-order feature interaction to generate a gridding wind field, a bidirectional long-short-term memory network encoder is used for extracting space-time invariant features, a multi-layer perceptron predictor is used for directly mapping a future multi-step wind field, and a course learning strategy and a Shenchang differential equation boundary layer are matched for correction. The technical problem that sparse ocean observation data are difficult to accurately reconstruct into a high-resolution gridding wind field is solved.

The invention provides a production energy efficiency optimization method and system based on industrial big data, and the method comprises the steps: generating an industrial production data set and creating an industrial knowledge graph according to multi-dimensional operation parameters, energy consumption state data and production line constraint information generated by a target factory, mining a causal association relationship among the multi-dimensional operation parameters through ontology reasoning analysis to generate a causal association path; executing a sequential association rule mining operation on the energy consumption state data to obtain association rule mining information of energy consumption fluctuation and operation parameter change, and matching and fusing the association rule mining information and a causal association path to generate a candidate root cause set of energy efficiency abnormality; inputting the candidate root cause set into a bidirectional long short-term memory network, positioning root cause information of energy efficiency abnormity through time dimension relevance modeling and spatial dimension feature reinforcement, and finally generating energy efficiency optimization guidance containing parameter adjustment priority and process optimization suggestions. The accuracy of energy efficiency anomaly root cause positioning and the pertinence of optimization measures are improved.

The invention discloses a carbon emission real-time regulation and control method and system based on multi-source data fusion and AI decision, and relates to the field of data processing systems or methods specially suitable for administrative, commercial, financial, management, supervision or prediction purposes, in the method, real-time production data is collected through Internet of Things equipment, and the real-time carbon emission intensity is calculated in combination with a carbon accounting engine. And constructing an AI optimization layer comprising a process knowledge graph, a long-short-term memory network and a reinforcement learning agent, converting process constraints into mathematical boundary conditions, predicting energy demands, carrying out iterative optimization by taking carbon emission intensity minimization as a target, generating a dynamic scheduling strategy, and realizing real-time adjustment of operation parameters of production equipment. The method and the device are used for improving the accuracy of carbon emission monitoring data on production scheduling and reducing the risk of high-carbon-intensity production caused by improper time period selection of enterprises.

The invention relates to the technical field of industrial platform data analysis, in particular to a renewable resource recovery data management system based on the Internet of Things, which comprises the steps of synchronously acquiring weight, spectrum and microwave characteristic data of a tested resource through a data acquisition module, executing moisture weight decoupling operation by utilizing a resource matching module, and performing data analysis; then, an inventory evolution module tracks performance loss of resources in real time by using an evolution model of an integrated long and short-term memory network, generates a resource attenuation weight, and constructs a two-dimensional decision matrix containing scheduling priority and preprocessing strength grade instructions in combination with real-time inventory saturation; and finally, the clearance scheduling module executes dynamic pruning and bidirectional optimization through a resource scheduling model, accurately allocates the loading share and the access sequence of each node, and generates a dynamic instruction set containing a delivery sequence. According to the method, feeding homogenization is realized through industrial data multi-dimensional collaborative analysis.

The invention discloses a power supply equipment fault prediction method and device based on deep learning, and relates to the technical field of power system equipment fault prediction and deep learning application. The method comprises the following steps: acquiring a power grid topological structure, an equipment operation state, a historical fault record, a real-time equipment load and environmental condition data; forming a space-time correlation basic diagram according to the power grid topology and the equipment operation state, and calculating the correlation strength by using a diagram neural network; calculating fault time delay and determining a transmission path set by using a long short-term memory network in combination with association strength and historical fault records; fusing multiple data to calculate a cross-regional fault propagation probability, and generating a predicted fault path list; and the fault prediction output of the long-short-term memory network input is updated, and the real-time operation data verification optimization of the power grid is combined, so that accurate cross-regional cascade fault prediction is realized, and safe and stable operation of the power grid is ensured.

The invention relates to a production abnormality automatic identification and recovery process control method, which comprises the following steps of S1, realizing second-level synchronization of multi-source heterogeneous data, constructing a real-time data flow pipeline and generating a total-factor production situation data flow through a distributed message queue by adopting a Modbus / TCP protocol analysis algorithm based on industrial Internet of Things edge calculation; through the cooperative effect of industrial protocol analysis and distributed message queues, second-level synchronization of multi-source heterogeneous data is realized, a real-time data flow pipeline is constructed, data acquisition delay is effectively eliminated, the timeliness of anomaly detection is ensured, a dynamic weight distribution mechanism of a rule engine and a long and short-term memory network prediction model is adopted, and the real-time performance of the system is improved. By combining sliding window threshold detection, the accuracy and coverage of anomaly recognition are improved, false alarm and missing alarm caused by a single detection mechanism are reduced, and multi-dimensional root cause tracing is performed by combining a fault mode knowledge base through combined application of time sequence correlation analysis and a causal diagram inference engine.

The invention discloses a directional drilling track accurate control technology based on machine learning, and relates to the technical field of drilling engineering. The directional drilling track accurate control technology comprises the following steps that underground parameters of a drilling tool during underground operation are obtained; establishing a dynamic model based on the drilling tool structure and the motion state; based on the dynamic model, introducing a long-short-term memory neural network, and constructing a hybrid prediction model; drilling parameters are input into the hybrid prediction model, and trend information of multiple tracks is output; based on current geological conditions, drilling tool configuration and operation safety constraints, performing simulation evaluation on the trend information of the plurality of candidate tracks, and screening out an optimal track meeting track precision and underground safety requirements from the candidate tracks; based on the optimal track, control strategy input is constructed, and a state space containing a tool face angle, target azimuth deviation and a drilling tool state is set; and through a deep reinforcement learning method, an advanced adjustment instruction for the guiding tool is generated, and drilling operation is executed according to the optimal track and the advanced adjustment instruction.

The invention discloses a music stave sentiment classification method and system based on multi-level distillation, and belongs to the technical field of music information processing. The method comprises the steps of firstly collecting music stave data and converting the data into stave data vectors, then performing feature extraction by using a long short-term memory network, then constructing a teacher network and a student network for knowledge distillation, and realizing multi-level knowledge transmission through temperature scaling, KL divergence loss and mask feature distillation. And finally, training a lightweight classification model to complete sentiment classification. The knowledge distillation technology is creatively applied to staff sentiment classification, the classification accuracy is effectively improved through an online multi-level distillation mode, and the technical problems that a traditional method lacks semantic information and a self-supervised model is not suitable for sentiment tasks are solved. The method has the main advantages of high classification precision, light model weight, capability of effectively capturing music emotion features and the like.

The invention relates to the field of power system automatic control and new energy grid-connected operation, and provides an energy storage-containing new energy field station frequency modulation optimization method based on deep reinforcement learning. The method comprises the following steps: firstly, establishing a frequency response model containing an energy storage new energy field station, constructing a frequency modulation problem into a Markov decision process, and defining a state space containing a charge state and frequency deviation, an action space taking a charge and discharge power instruction as an element, and a multi-target reward function fusing a frequency regulation effect and charge state management; and then designing an intelligent agent network based on a long short-term memory network and an improved soft actor-commentator algorithm, constructing an online training framework with priority experience playback and a sliding time window, and realizing adaptive optimization of the strategy. And finally, generating a robust frequency modulation instruction in real time based on the trained model. Simulation shows that the frequency long-term regulation and control capability and the intelligent level can be remarkably improved, and the frequency modulation performance and the frequency modulation margin are effectively coordinated.

The invention belongs to the technical field of urban rail vehicle detection and maintenance, and discloses a full-process optimization method and system for polygonal wear of a metro vehicle wheel. The method comprises the following steps: firstly, constructing a digital twin-driven train rigid-flexible coupling dynamic model, carrying out global sensitivity analysis, establishing a sensitivity index model, screening key dynamic performance indexes, and carrying out batch simulation to construct a dynamic response database; feature extraction and classification model training are carried out on the index data, multi-layer wavelet packet decomposition is carried out on the one-dimensional vibration signals, and a multi-channel feature vector is constructed and input into a one-dimensional residual network model; inputting actually acquired data into the trained model, calculating a relative close degree to generate a comprehensive index and a grading result, and generating turning repair suggestions based on grading; meanwhile, multi-source monitoring data are collected, a long-short-term memory network is used for predicting the abrasion evolution trend, finally, turning repair suggestions and trends are integrated, an accounting model and an evaluation system are constructed, and an optimal maintenance decision is generated through a multi-target optimization algorithm.

The invention relates to the technical field of direct-current power supply fault diagnosis, in particular to a fault self-diagnosis modular direct-current power supply system, which is characterized in that a failure mechanism dynamic modeling unit constructs a multi-stress coupling degradation mapping library, adopts a degradation mechanism decoupling algorithm to separate and superpose failure effects and generates a dynamic degradation model; the early degradation capture unit deploys a high-frequency wide-domain sampling circuit and a noise suppression signal chain, combines time-frequency domain joint feature extraction and separates degradation feature signals from background noise, and the self-adaptive life prediction unit adopts a long short-term memory network and random forest fusion algorithm to dynamically correct parameters and output residual life probability distribution. The intelligent fault judgment unit realizes progressive diagnosis through a hierarchical threshold decision mechanism, and the health management cooperative execution unit triggers a pre-maintenance strategy, executes power flexible derating, redundancy switching or directional fusing isolation, and improves the reliability of the system.

The invention provides a coal mine cable aging degree analysis and evaluation method and system, a medium and a product, and relates to the technical field of coal mine electrical equipment state monitoring and safety early warning, and the method comprises the steps: collecting cable aging data in real time through a distributed monitoring terminal arranged in an underground coal mine; inputting the aging data characteristics corresponding to the cable aging data into a dynamic weighted fusion evaluation model, and calculating to obtain a comprehensive aging index of the current cable; when the comprehensive aging index exceeds a preset early warning threshold value, inputting a comprehensive aging index sequence and an environmental load feature sequence in a preset historical time window into a pre-trained long-short-term memory network model, and outputting an aging index change curve in a preset future time period; and calculating the residual life of the cable according to the curve, and triggering cable replacement early warning when the residual life is smaller than a preset threshold value. According to the method, the accuracy of cable aging state evaluation under complex working conditions can be improved, and the misjudgment rate caused by fluctuation of a single parameter is reduced.

The invention discloses a short-term power load prediction method, system and device based on multi-intelligent-model fusion and a medium, and belongs to the technical field of short-term power load prediction, and the method comprises the steps: obtaining regional historical load data and meteorological data; performing data cleaning on the obtained load data and meteorological data; measuring linear and nonlinear correlation between the power load and the meteorological factors, and screening meteorological data with high load correlation; decomposing the load data into a time sequence by using an empirical mode decomposition method based on combination of multi-scale permutation entropy to obtain a multi-scale sub-data sequence; respectively predicting the multi-scale sub-data sequences to obtain prediction results; carrying out weighted fusion on the prediction result through a long short-term memory network model to obtain a load prediction result, and optimizing model parameters to obtain a trained multi-model prediction model; and predicting the test set data by using the trained model to obtain a final load prediction result. According to the invention, the precision and adaptability of load prediction are effectively improved.

The invention relates to the technical field of large model interaction, in particular to a long-term and short-term memory processing system based on a large model and a vector database, and provides a scheme that short-term context memory and long-term stable memory are subjected to hierarchical management and joint retrieval by performing semantic analysis and structured modeling on user interaction contents. Under the condition that historical interaction content exists, a theme segment division and intention stack mechanism is introduced, ordered organization and step-by-step alignment of intention states in multiple rounds of conversations are achieved, and matched memory entries are obtained from a cache database and a long-term database based on query semantic representation. Furthermore, by performing normalization, situational rewriting and conflict suppression on memory entries, controllable reasoning prompt information is constructed and input to a large model to generate target output, so that the consistency, stability and controllability of a generated result are improved in a multi-round interaction and cross-session scene.

The invention discloses an airport flight quantity and throughput prediction method and system, and relates to the technical field of intelligent airport management. The method comprises the following steps: firstly, determining a future target time period to be predicted, and obtaining related multi-source data according to the future target time period; preprocessing the data and extracting time sequence and non-time sequence features; processing time sequence features by using the trained long-short-term memory neural network model, processing non-time sequence features by using the extreme gradient boosting tree model, and respectively obtaining prediction results; and integrating the outputs of the two types of models through a weighted fusion strategy to obtain an accurate prediction value of the flight sortie and the single-machine passenger capacity, and further calculating to obtain a passenger throughput prediction result. According to the method and the system provided by the invention, through the architecture of multi-source data fusion and hybrid intelligent model cooperation, the prediction precision and reliability are effectively improved, and scientific decision support is provided for airport operation management.

The invention relates to the technical field of meteorological disaster early warning and emergency management, and discloses a meteorological disaster risk assessment and prevention method based on artificial intelligence, and the method comprises the steps: obtaining multi-source heterogeneous data, carrying out the cleaning, alignment and standardization processing of the multi-source heterogeneous data, and constructing a multi-dimensional feature data set; based on the multi-dimensional feature data set, outputting predicted meteorological elements of the target area in a future preset time period through a meteorological prediction model, extracting interaction features of the predicted meteorological elements and non-meteorological factors from the multi-dimensional feature data set, and inputting the predicted meteorological elements and the interaction features into a long and short term memory-convolutional neural network hybrid model to obtain a long and short term memory-convolutional neural network hybrid model; outputting the meteorological disaster risk probability and risk level of each grid unit in the target area; based on the meteorological disaster risk probability and the risk level, differential prevention instructions for different risk level areas are generated, and the technical problems that in an existing meteorological disaster risk assessment and prevention method, multi-source data integration is difficult, and meteorological prediction precision is insufficient are solved.

The invention discloses a multi-agent intelligent way-finding and real-time decision-making method based on MADDPG, and the method achieves the autonomous collaborative decision-making of multi-target optimization through the technical links of environment perception, dynamic obstacle intention prediction, single-agent pre-training, strategy distillation and multi-agent collaborative training. The method comprises the following core steps: predicting a dynamic obstacle intention based on a long short-term memory network of an attention mechanism; constructing a threat field model to quantify the environmental risk; forming basic obstacle avoidance and path optimization experience through single agent pre-training; empirical migration is realized by utilizing strategy distillation so as to shorten a multi-agent training period; and multi-agent cooperative training is completed based on a centralized training-distributed execution architecture and an MADDPG algorithm. According to the method, the path length, the energy consumption, the safety and the task completion rate can be considered, the survivability, the cooperation efficiency and the task adaptability of the unmanned aerial vehicle formation in the complex dynamic environment are remarkably improved, and the method can be widely applied to different-scale formation and multiple task scenes such as reconnaissance, communication relay and situation awareness.

The invention discloses a national secret algorithm data encryption transmission method and system. The method comprises the steps of obtaining to-be-encrypted data and network parameters, establishing a Bayesian network probability ablation model, performing Monte Carlo sampling ablation national secret encryption and evaluating attack risks, and generating a probability security encryption strategy; and extracting a Brinell feature set, constructing a long and short-term memory network time prediction model, and optimizing by using a simulated annealing algorithm to obtain an optimal encryption parameter configuration sequence. Generating a key pair according to the sequence and SM2, establishing a shared key by means of an elliptic curve Diffie-Hellman protocol, deriving an SM4 session key through SM3, and establishing a hybrid encryption key system; constructing a teacher and student network model, optimizing multi-thread scheduling through adversarial distillation training and a retrieval enhancement technology, and generating a multi-thread parallel encryption architecture; and network parameters are monitored in real time, a reinforcement learning adaptive decision engine is constructed, a strategy is dynamically adjusted, and adaptive encryption transmission is completed. According to the invention, the optimal balance between the security and the efficiency in the data encryption transmission process is realized.

The invention discloses a public building cold load short-time prediction method fusing physical information, and the method comprises the steps: collecting and preprocessing the historical cooling capacity, indoor environment, outdoor weather and equipment operation state data of a public building at a fixed time interval, and obtaining multi-dimensional input features; respectively establishing a workday sub-model and a holiday sub-model according to workday and holiday scene division; the workday sub-model and the holiday sub-model jointly form a cold load prediction model, the workday sub-model adopts a long short-term memory (LSTM) network, and the holiday sub-model adopts a light gradient elevator (Light GBM); a physical constraint loss function based on building energy balance and heat conduction residual error is introduced in the training process, and the physical constraint loss is fused into a total loss function according to a weighting coefficient so as to constrain that the output of each sub-model accords with the law of energy conservation and thermal inertia; monitoring the prediction error MAPE in real time and performing online calibration; and outputting a short-time cold load prediction result.

The invention discloses a power distribution network transient characteristic prediction method based on supervised learning, and relates to the technical field of power distribution network state prediction, and the method comprises the steps: collecting historical operation data through a power distribution network monitoring system, carrying out the data preprocessing, and obtaining standardized multi-dimensional time series data; carrying out transient feature extraction, constructing a high-dimensional feature set, and carrying out feature dimension reduction according to a transient event tag to generate a feature subset; inputting the feature subset into a mixed supervised learning model of a gradient boosting decision tree GBDT and a long short-term memory network LSTM for joint training to obtain a transient feature prediction result; and calculating a root-mean-square error according to the transient characteristic prediction result and the real-time monitoring observation value of the power distribution network, and dynamically adjusting hyper-parameters of the supervised learning model based on a Bayesian optimization algorithm. According to the method, the detection accuracy can be improved, the calculation complexity can be reduced, and the discrimination capability and the time sequence prediction capability of the model are considered.

The invention provides a self-supervised continuous 3D hand posture tracking method based on a lightweight inertial measurement unit, and the method specifically comprises the following steps: firstly, obtaining a hand motion signal by using a lightweight consumer-level IMU, and extracting features by using a multi-stage neural network containing a bidirectional long-short term memory (Bi-LSTM) module; the global displacement and orientation of the hand are processed through a wrist posture estimation module, and three-dimensional coordinates of hand skeleton points are solved and calculated by means of a finger motion chain; secondly, a self-supervised completion network is realized by combining a random mask and dual loss, and the features are processed to complete complete attitude reconstruction; and finally, combining network output with kinematics prior, generating a three-dimensional grid with reasonable anatomy, solving the problem that joint deformation is inconsistent with torque, and realizing accurate acquisition of a three-dimensional hand posture. For a dynamic hand motion tracking task, the method can adapt to a sparse IMU deployment scene and realize continuous high-precision tracking; the real-time performance demand of daily interaction can be met, and a low-cost solution can be provided for the fields such as medical rehabilitation and virtual interaction which have strict requirements on attitude precision.

The invention discloses a distribution-micro collaborative operation simulation optimization method, which belongs to the technical field of simulation optimization, and comprises the steps of preprocessing grid-connected point voltage data, tie line power data and communication time delay data, constructing a power distribution network power flow physical network following a Kirchhoff's law, outputting a source load power prediction curve by using a long short-term memory network algorithm, and calculating the distribution-micro collaborative operation according to the source load power prediction curve. And a distribution-micro collaborative simulation optimization model is obtained based on residual error rolling correction tie line impedance parameters, a delay penalty term is set in a target function in combination with the preprocessed communication delay data, a power regulation instruction is obtained by using a particle swarm optimization algorithm, and a dynamic simulation video stream is generated. According to the invention, through rolling correction of the tie line impedance parameters and setting of the delay penalty term positively correlated with the time delay, the problem of control failure caused by physical deviation caused by model parameter solidification and communication time delay accumulation is solved, and the defects of voltage deviation calculation distortion and inaccurate network loss evaluation are eliminated. And the simulation precision and the operation stability of distribution-micro cooperation are improved.

The invention relates to the technical field of plastic extrusion molding control, and particularly discloses a self-adaptive energy efficiency steady-state control method and system in a plastic extrusion molding process, and the method comprises the steps: collecting multi-dimensional data of the plastic extrusion molding process, the multi-dimensional data comprises process data representing the current production state, aging characteristic data representing the equipment health degree and auxiliary data; and performing preprocessing and feature fusion on the multi-dimensional data, calculating to obtain an equipment aging comprehensive index, constructing an aging trend prediction model based on a long-short-term memory network, and outputting an aging trend prediction value and aging rates of the screw and the machine barrel. According to the method, multi-dimensional data are collected in real time, the equipment aging comprehensive index and trend prediction model is constructed, the multivariable coupling coefficient and the multi-objective optimization function are dynamically corrected, and self-adaptive energy efficiency steady-state control under the equipment aging condition is achieved.

The invention provides a method for monitoring and controlling cable-stayed buckling construction of a concrete-filled steel tube arch bridge based on a digital twin system, and the method comprises the steps: collecting the monitoring data, such as wind speed and wind direction, arch rib stress displacement, tower bottom stress, tower deviation and cable force, through a sensor; establishing a concrete filled steel tube arch bridge construction digital twinning system based on a three-dimensional model, real-time monitoring, data analysis and a visual platform; time sequence data is decomposed into different frequency components through variational mode decomposition (VMD), a bridge construction stage prediction model is established through a long short-term memory network (LSTM), and real-time accurate prediction of the construction progress and state is achieved. And secondly, based on the prediction model and on-site real-time monitoring conditions, an intelligent load adjustment construction control method based on a digital twin system is provided, and high-precision control over automatic load adjustment of cable hoisting and real-time rectification of tower displacement is achieved. The problems that in a traditional monitoring and control method, early warning is not timely, the assembling precision is insufficient, and the arch axis shape control difficulty is large are solved.

The invention discloses an energy storage system intelligent energy management control method and system, and relates to the technical field of energy storage system intelligent energy management control, and the method comprises the steps: obtaining an energy storage unit operation state parameter and an environment disturbance factor matrix through a multi-source data collection device, and carrying out the data preprocessing; and constructing a long-short-term memory neural network model, dynamically adjusting a prediction time window of the model and optimizing a target function weight coefficient based on the load predicted by the model, and determining a charging and discharging control instruction. And updating and optimizing the target function weight coefficient and the charging and discharging strategy library through a reinforcement learning algorithm to realize self-adaptive optimization adjustment of the charging and discharging strategy. According to the method, high-precision energy management and intelligent optimization control of the energy storage system in a complex environment are realized. The load prediction accuracy and the energy utilization rate of the system are improved, the aging rate of the battery is reduced, the service life of the battery is prolonged, the adjusting capacity of the energy storage system is improved, and the overall stability of the energy storage system in dynamic change is enhanced.

The invention discloses a mechanical equipment residual service life prediction and uncertainty quantitative analysis method and device. The method comprises the following steps: acquiring multi-dimensional time sequence sensor data generated by mechanical equipment to be predicted in an operation process; inputting the multi-dimensional time sequence sensor data into a pre-trained physical constraint Bayesian neural network model; wherein the physical constraint Bayesian neural network model comprises a segmented bidirectional long short-term memory network feature extraction module, a hierarchical gating recursive regression network degradation dynamic modeling module and a Bayesian reasoning and physical constraint fusion regression module which are connected in sequence; the segmented bidirectional long-short-term memory network feature extraction module is used for processing input multi-dimensional time sequence sensor data and outputting hidden feature vectors representing local degradation dynamics of equipment; the hierarchical gating recursive regression network degradation dynamic modeling module is used for processing hidden feature vectors and capturing global degradation dynamic features through a complex numerical value hidden state updating mechanism; the Bayesian reasoning and physical constraint fusion regression module is used for carrying out Weibull distribution parameter regression based on the global degradation dynamic characteristics, introducing a deep implicit physical residual constraint and outputting a probability distribution parameter of the residual service life; and based on the probability distribution parameters, generating a residual service life prediction result and uncertainty quantitative information of the mechanical equipment.

The invention discloses a fused salt heat storage and Carnot cell combined coal-fired unit peak-load regulation operation method, device and equipment, a medium and a product, and relates to the field of unit peak-load regulation operation. Acquiring information data; based on a power regulation demand prediction model, performing peak regulation demand prediction according to the information data to obtain a prediction result; the power regulation demand prediction model is obtained by adopting a multi-objective optimization model and performing time sequence analysis on a long-short-term memory network model; determining an energy scheduling strategy based on the prediction result; the energy scheduling strategy comprises a heat storage power coordination instruction and a heat release power coordination instruction; and according to the energy scheduling strategy, operation adjustment is conducted on the coal-fired unit, the fused salt heat storage subsystem and the Carnot battery subsystem based on the control system. The invention aims to improve the peak regulation performance of the coal-fired unit.