306 results about "Recurrent neural network model" patented technology

The invention discloses an underground construction decision-making method based on three-dimensional geological modeling and risk hot area identification, and relates to the field of fusion of artificial intelligence and geological engineering. The method comprises the following steps: firstly, acquiring drilling data, geological radar images and seismic reflecting layer information, constructing a three-dimensional geological voxel model with spatial topology constraints, and accurately describing a geological unit structure by adopting an irregular grid mode; and then, extracting a time sequence characteristic index under construction disturbance, forming a continuous time sequence characteristic vector, inputting the continuous time sequence characteristic vector into a convolutional recurrent neural network model with a space attention aggregation mechanism and a deep memory unit, and predicting a risk heat value of each space position. And on the basis, through heat gradient clustering and neighborhood consistency analysis, a dynamic high-risk hot area is identified, and a risk hot area map is constructed. And finally, in combination with the construction stage, the equipment plan and the sensor feedback information, constructing a multi-target auxiliary decision function, and generating a construction decision result including operation path reconstruction, rhythm adjustment and power limit and control suggestions.

The invention discloses a nuclear radiation index early warning system based on multi-algorithm fusion, and the system comprises an environment data collection module which is used for collecting gamma-ray dose rate data, wind speed data, wind direction data and geographic position information, and constructing a nuclear radiation environment data set; the dose rate prediction module is used for predicting a future change trend of gamma ray dose rate data based on a sparse recurrent neural network model; the anomaly detection module is used for identifying an abnormal mutation point and positioning the position of a high-risk sensor; the diffusion region modeling module is used for calculating a nuclear radiation diffusion influence region; the grid risk evolution module is used for simulating time sequence evolution of a regular grid risk state based on the multi-state cellular automaton; and the fusion judgment module is used for fusing output results and generating a final early warning grade and a spatial risk distribution result. According to the invention, accurate prediction of the nuclear radiation risk and dynamic space early warning are realized, and the early warning accuracy and response efficiency are significantly improved.

The invention discloses a health risk assessment method and early warning system based on multi-source data analysis. The health risk assessment method comprises the following steps: S1, collecting and preprocessing multi-source health data through medical detection equipment; s2, extracting key health indexes, sequence features and statistical features based on the health data set; s3, adopting a recurrent neural network model to construct a health risk assessment model; s4, optimizing structural parameters of the health risk assessment model by adopting an improved dragonfly algorithm; s5, performing performance evaluation on the health risk evaluation model by using the optimal parameter set; s6, deploying the final health risk assessment model in a health risk assessment and early warning system; and S7, when the health risk assessment result exceeds a preset risk threshold, automatically generating early warning information. According to the method, the recurrent neural network model, the improved dragonfly algorithm and the multi-source health data fusion optimization technology are combined, and health risk assessment and early warning based on multi-source data analysis are realized.

The invention relates to the technical field of isolator service life prediction, and discloses an isolator remaining service life prediction method based on deep learning. The method comprises the following steps: acquiring a vibration signal and a temperature signal when the isolator operates, and constructing a multi-dimensional sensor sequence; dividing a plurality of sub-sequence units containing a fixed time window; extracting time domain and frequency domain features of each sub-sequence unit, and generating a fusion feature vector; calculating statistical distribution characteristics and variation trend characteristics of key indexes in the vector, and outputting a health state index; through a bidirectional recurrent neural network model containing an attention mechanism, taking the health state index and the historical degradation data as input, processing a time sequence dependency relationship through multi-layer residual error connection, and generating a residual service life prediction value; and updating the multi-dimensional sensor sequence according to the real-time sensor signal, and dynamically correcting the predicted value. According to the method, multi-dimensional data are integrated, degradation characteristics are deeply mined, and the accuracy and adaptability of prediction of the remaining service life of the isolator can be improved.

Provided in the present invention are a power-distribution-network self-healing method and system taking photovoltaic output into consideration. The method comprises: acquiring historical operation data of a photovoltaic power station and irradiance observation data from a meteorological station; on the basis of the historical operation data of the photovoltaic power station and the irradiance observation data from the meteorological station, predicting the generated power of the photovoltaic power station by using a convolutional long-short-term memory recurrent neural network model that takes sparrow search into consideration; on the basis of the generated power of the photovoltaic power station, a segment-switch state of a power distribution network and a network topology of the power distribution network, constructing an objective function and a constraint condition for a power-distribution-network self-healing model, and obtaining the power-distribution-network self-healing model; solving the power-distribution-network self-healing model by using a propagation search algorithm, so as to obtain an optimal recovery strategy; and executing the optimal recovery strategy by means of segmented switches and node loads. The present invention can realize self-healing of a power distribution network while ensuring the minimum power generation cost of a distributed power source, the minimum network loss and the minimum node voltage deviation.

The invention discloses a classroom environment intelligent supervision method and system based on data analysis, and the method comprises the steps: obtaining multi-source heterogeneous data in a classroom environment, eliminating the sensor noise of the multi-source heterogeneous data through employing an improved Kalman filtering algorithm, carrying out the time-space alignment of the data after noise elimination, and obtaining the aligned multi-source heterogeneous data; establishing a CRNN three-layer convolutional recurrent neural network model, and inputting the aligned multi-source heterogeneous data into the CRNN three-layer convolutional recurrent neural network model for recognition to obtain a classroom teaching environment state; and carrying out classroom risk early warning and self-adaptive classroom environment regulation and control according to the classroom teaching environment state. The classroom emergency risk can be actively prevented, automatic adjustment can be achieved through an intelligent air conditioner and an illumination system, a safe and comfortable teaching environment is created, and classroom efficiency improvement and teacher and student health guarantee are facilitated.

The invention belongs to the technical field of pollution source detection, and discloses a pollution source detection method and system for water environment treatment. Comprising the steps that acoustic disturbance data are collected through an underwater acoustic sensor, real-time edge preprocessing is carried out, water environment data of different depths are synchronously collected, water environment gradient data are calculated, and then data synchronization is achieved through a unified timestamp. Fourier transform is adopted to extract frequency domain characteristics of the sound disturbance signals, the main frequency, the energy difference and the frequency spectrum skewness are calculated, and disturbance sound wave indexes are generated according to the main frequency, the energy difference and the frequency spectrum skewness. In combination with disturbance sound wave indexes and water body environment gradient data, a sliding window is adopted to construct a time sequence sample set, a recurrent neural network model for predicting future pollution release risks is trained, differentiated pollution release threshold values are set according to different water body types, and when model output exceeds the threshold values, sediment pollution early warning instructions of corresponding levels are automatically generated. And efficient and graded pollution event intelligent identification and response are realized.

According to the hydrogen clock error forecasting method, system and device based on the improved recurrent neural network and the storage medium, the memory characteristic of the recurrent neural network is used for representing the time relation, so that hydrogen clock error forecasting is carried out, and the accuracy of hydrogen clock error forecasting is improved. The method comprises the following steps: acquiring original clock error data of a hydrogen clock, and preprocessing the original clock error data to obtain preprocessed clock error data; constructing a hybrid recurrent neural network model, and initializing the hybrid recurrent neural network model; dividing the preprocessed clock error data into a training set, a verification set and a test set; based on the training set, the verification set and the test set, using an RAdam optimizer and MAPE as loss functions, and optimizing the hybrid recurrent neural network model until a preset precision is reached; and preprocessing the clock error data of the to-be-predicted time period, and inputting the preprocessed clock error data of the to-be-predicted time period into the optimized hybrid recurrent neural network model to obtain a predicted clock error value.

The invention belongs to the field of distributed optical fiber sensing, and provides a Brillouin temperature extraction method and system based on a self-attention recurrent neural network, and the method comprises the steps: collecting real Brillouin gain spectrum data through a Brillouin optical time domain analysis system which is built in advance, and carrying out the preprocessing of the real Brillouin gain spectrum data; based on the preprocessed Brillouin gain spectrum data, utilizing the trained self-attention recurrent neural network model to obtain a corresponding Brillouin temperature; a meta-learning algorithm is introduced in the process of a self-attention recurrent neural network model, so that the network model can share initialization parameters among a plurality of tasks, the parameters are optimized through meta-training, the network model can quickly adapt to a new task, training from zero is not needed in the face of multi-task learning or the new task, and the training efficiency is improved. The model can be quickly adjusted through a small amount of gradient updating, the training time is shortened, the performance is improved, and the problems of long time, poor real-time performance, poor adaptability and the like of curve fitting are solved.

The invention discloses an investment risk early warning method and system for the whole life cycle of a power grid, and relates to the technical field of power system risk management, and the method comprises the steps: collecting historical operation data and real-time data, carrying out the preprocessing, constructing a project feature vector matrix P, inputting a recurrent neural network model for training, and obtaining a risk recognition reference model M; inputting the real-time data into the risk identification reference model M, and extracting risk feature values R of the power grid investment project in different stages; performing clustering analysis by using a fuzzy c-means clustering algorithm to obtain a risk grade division threshold T; and inputting the risk characteristic value R and the risk grade division threshold T into a constructed risk evolution model E, calculating to obtain a risk evolution trend index alpha and a risk mutation probability beta, inputting the risk evolution trend index alpha and the risk mutation probability beta into a Bayesian early warning network for early warning, and feeding back an early warning result to the risk identification reference model M for updating. According to the invention, through data driving and model adaptive improvement, the intelligence of risk early warning is realized.

The invention discloses a photovoltaic power generation power prediction method based on Bi-LSTM, and relates to the technical field of photovoltaic power generation power prediction methods. The method comprises the following steps: constructing a Bi-LSTM recurrent neural network model; historical data of photovoltaic power generation power and related meteorological data are obtained to serve as model data and are aligned into a time sequence; preprocessing the model data and training a Bi-LSTM recurrent neural network model; and optimizing hyper-parameters of the Bi-LSTM recurrent neural network model by using a genetic algorithm to obtain an optimal Bi-LSTM recurrent neural network model to predict photovoltaic power generation power. According to the method, the Bi-LSTM model is utilized, and past and future information in the time sequence data can be captured at the same time, so that the accuracy of photovoltaic power generation power prediction is improved; meanwhile, effective features can be screened and utilized more effectively, the defects that a traditional prediction model is poor in feature screening capacity and prone to noise interference are overcome, and then the prediction precision of the photovoltaic power generation power is remarkably improved.

The invention relates to the technical field of robot control, and discloses a mechanical arm trajectory planning control method and system based on a BAFARNN model. The method comprises the steps that a mechanical arm kinematics model is established, and a trajectory tracking problem is converted into a time-varying equation; designing a bounded adaptive function to activate a recurrent neural network model, defining an error function and constructing a dynamic equation; designing a piecewise adaptive coefficient function, and dynamically adjusting the gain according to an error norm and time; setting a Lissajous curve as an expected trajectory, and initializing a simulation environment; the joint speed is solved in real time through an ODE numerical method, and the mechanical arm is driven to move; actual motion data is collected and compared with an instruction, and closed-loop feedback control is triggered when the actual motion data exceed a threshold value. According to the method, rapid convergence is achieved through the piecewise adaptive coefficient function, the bounded activation function and the negative feedback mechanism are adopted to suppress noise, and high-precision and real-time trajectory tracking of the mechanical arm in the dynamic environment is achieved.

The invention relates to the technical field of resource scheduling, in particular to a digital-intelligent non-accompanying service management scheduling system, which comprises a service baseline construction module for instantiating a service item into a standard plan event flow; the state portrait generation module is used for collecting service object data flow, establishing a dynamic time sequence signal by applying a recurrent neural network model, and quantifying the occurrence probability of a future abnormal event through multi-step prediction; when the triggering is abnormal, executing causal two-way collision positioning reasons by applying the knowledge graph; the task load calculation module is used for integrating the plan event flow into a plan task component, and generating a worker resource list in combination with the predictive risk score and the worker real-time load and space reachability analysis containing the fatigue index; the system further comprises a task distribution module, the optimal staff is determined and a closed-loop scheduling instruction is generated by minimizing a composite cost function containing predictive load cost, and digital intelligent unattended service management from prediction to scheduling is realized.

The invention provides a map updating method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps of performing prediction processing based on historical change data of a global map and current environment data through a first convolutional neural network model to obtain a first prediction result of map elements in the global map; performing first fusion processing on the global map and the part of the local map at the plurality of collection times to obtain a fusion result of the plurality of collection times, and performing prediction processing based on the fusion result of the plurality of collection times through a recurrent neural network model to obtain a second prediction result of map elements in the global map; wherein the local map is determined according to the vehicle perception data of the vehicle driving on the global map; and performing map updating processing on the global map according to the first prediction result and the second prediction result of the map elements in the global map. According to the method, the timeliness and accuracy of map updating can be remarkably improved.

The invention relates to the technical field of non-ferrous metal metallurgy, and discloses a multi-gun top-blown furnace high-temperature infrared monitoring spray gun position intelligent regulation and control method and system, and the method comprises the steps: carrying out the dimension reduction processing of key data in a blowing process through principal component analysis; processing the output preprocessed key data by using a recurrent neural network model; the recurrent neural network model captures a long-term dependency relationship of temperature change, predicts dynamic parameter change in the smelting process, and outputs an adjustment demand of the height of the spray gun; adjusting the height of the spray gun in real time by adopting a closed-loop control system based on the prediction result of the adjustment demand; and the predicted adjustment requirement of the spray gun height is converted into a control signal, and the spray gun height of the multi-gun top-blowing converting furnace is dynamically adjusted through an execution mechanism. The system comprises a data processing module, a height output module and a feedback loop module. According to the method, the height of the spray gun is continuously corrected and adjusted according to actual conditions, so that the stability and optimization effect of the process are kept.

The invention discloses a household energy storage dynamic optimization method and system combined with load feature learning, which are used for reducing prediction errors caused by sudden loads and improving response efficiency and economical efficiency of a household energy storage system. The method comprises the following steps: calculating a prediction error between real-time total load data and a basic prediction result, identifying and locking one or more high-power electric appliances causing the prediction error as target electric appliances, and decomposing and extracting historical operation state data of the target electric appliances from the total load data; constructing a time sequence feature vector based on the historical operation state data, and training by using a recurrent neural network model to extract the sudden load feature of the target electric appliance; fusing the optimized sudden load characteristics with a basic prediction result to generate a total load prediction curve; and based on the total load prediction curve, combining the residual capacity and the operation health state of the household energy storage system, and adopting a multi-objective optimization method to dynamically generate a charging and discharging strategy.

The invention relates to the technical field of component strength detection, and discloses a big data-based component strength judgment system, which comprises a data acquisition module, a data preprocessing module, a single-point strength analysis module and a continuous strength evaluation module. The data acquisition module obtains multi-dimensional parameters of the component and performs standardization processing, and main body features and abnormal fluctuation areas are separated; the preprocessing module identifies the boundary and calibrates a parameter data space coordinate system; the single-point strength analysis module generates a topological network through key nodes, and judges the strength of a detection unit in combination with structural feature density and span; and the continuous strength evaluation module generates a continuous fluctuation curve based on the parameter sequence, and evaluates the strength continuity through the curvature change rate and the recurrent neural network model. The system realizes multi-dimensional data intelligent analysis, improves the comprehensiveness and accuracy of component strength detection, is suitable for complex structure strength evaluation, and provides technical support for engineering safety.

The invention discloses a low-sample neural network structure reliability evaluation system and evaluation method, and relates to the technical field of engineering structure safety monitoring, and the evaluation system comprises a cloud server which is used for constructing a recurrent neural network model containing a static variable embedding mechanism, completing model training and converting a model format; the edge calculation terminal is used for receiving and preprocessing real-time data of the sensor, executing multi-step prediction to output a future time period response sequence, and calculating a future failure probability through virtual Monte Carlo simulation; the sensor assembly is used for collecting structure state time sequence data; and the communication module is used for realizing data interaction and alarm signal transmission operation. According to the method, collaborative modeling of time-varying and static uncertainty is realized by adopting a static variable embedded recurrent neural network model, failure probability distribution is generated at an edge computing terminal in combination with a virtual Monte Carlo technology, failure risk prediction in a future time period is supported, and real-time and accurate reliability early warning can be realized in a resource limited scene.

The invention provides a method for determining the influence of a straw returning mode on tobacco-rice rotation soil carbon emission, and belongs to the technical field of soil carbon emission. The method comprises the steps that test plots are divided, and different straw returning treatment groups are arranged; installing a static gas tank measuring system and regularly collecting a gas sample; measuring the emission of CO2 and N2O and the concentration of CH4 by using a gas chromatographic method, and calculating the carbon emission flux of the soil; analyzing a straw decomposition process by applying a soil microorganism kinetic equation; analyzing the dynamic change of a soil carbon library based on a carbon recurrent neural network model; constructing a carbon flow network topological structure by using a minimum spanning tree algorithm and identifying key nodes; according to the method, comprehensive influences of environmental factors, crop growth parameters and microbial community structures on carbon emission are integrated through a multi-head cross attention mechanism, accurate quantification and prediction of straw returning carbon emission are achieved, and a scientific basis is provided for optimizing a farmland carbon management strategy and promoting conversion from a farmland carbon source to a carbon sink.

The invention provides a self-adaptive source-load-storage coordinated optimization control method and system, and the method comprises the steps: S1, collecting multi-element load data generated during the operation of a microgrid, and forming a time series data set; s2, key variable characteristics which have the largest influence on the micro-grid load operation state in the micro-grid load requirements are recognized; s3, determining and constructing a source-load-storage cooperative control model based on the key variable characteristics, and determining an optimal decision variable value meeting a real-time load demand; and S4, making a regulation and control strategy according to the optimal decision variable value, and realizing optimal operation of the micro-grid. According to the method, the load demand of the power system is predicted based on the trained recurrent neural network model, the most critical characteristic variable is identified, the time sequence data which can most represent the power grid load operation state during the microgrid operation period is extracted and analyzed, and the unnecessary calculation amount is reduced by focusing on the most critical characteristic variable, so that the power grid load operation state is optimized. Therefore, the whole analysis process is more efficient and accurate.

The invention discloses a power transmission corridor tree species growth prediction method, system, device and medium, and belongs to the technical field of tree growth prediction.The method comprises the steps that tree species data and environment data in a power transmission corridor area are obtained, the tree species data and the environment data are preprocessed, and a training data set is obtained; training a cascade recurrent neural network model based on the training data set to obtain a trained cascade recurrent neural network model; predicting the growth trend of the tree species according to the trained cascade recurrent neural network model to obtain a prediction result, performing error analysis on the prediction result, and optimizing the trained cascade recurrent neural network model according to an error analysis result; performing growth trend analysis according to the prediction result, and determining the growth trend of the tree species; and carrying out early warning on the growth trend of the tree species through the prediction result in combination with a preset safety threshold. According to the method, the tree growth prediction precision is improved through a cascade structure.

The invention provides an AI-based oil well yield prediction system and method, and relates to the technical field of artificial intelligence, and the system comprises a production data collection module which collects real-time data of oil well production and processes the real-time data to obtain processed data; the historical data collection module collects historical data of oil well production; a feature extraction module performs feature processing on the historical data to obtain feature data; and the prediction module constructs a recurrent neural network model, optimizes the recurrent neural network model by using an artificial mouse optimization algorithm to obtain an optimal model, and inputs the processed real-time data and the historical data into the optimal model to obtain a predicted yield. According to the method, the recurrent neural network model is constructed, the recurrent neural network model is optimized by using the artificial travel mouse optimization algorithm, the processing data and the feature data are input into the optimal model, the predicted yield is obtained, and the accuracy and efficiency of oil well yield prediction are improved.

The invention relates to the technical field of advertisement marketing, in particular to an advertisement marketing system for after-sales data statistics. The system comprises a data acquisition module, an intelligent analysis module, a precision marketing module and an evaluation feedback module. The customer basic information, purchase information, after-sales behavior data and customer feedback data are acquired through the data acquisition module, the intelligent analysis module establishes a recurrent neural network model for predicting the customer loss probability, and the precision marketing module performs precision marketing by taking the customer loss probability predicted by the intelligent analysis module as the horizontal axis and the customer value as the longitudinal axis. The method comprises the following steps: establishing a probability-divided two-dimensional matrix, formulating a corresponding advertisement marketing strategy, adopting different advertisement marketing strategies for different types of customers, improving pertinence, redeeming the customers to the greatest extent, reducing enterprise loss, establishing an evaluation system by an evaluation feedback module, evaluating the effect of advertisement marketing activities at regular intervals, and improving the efficiency of advertisement marketing. And the advertisement marketing strategy is continuously optimized.

The invention relates to the technical field of electric digital data processing, in particular to a shock absorber data processing method and system based on digital twinning, and the method comprises the steps: obtaining a multi-channel data signal of a shock absorber, and constructing a panoramic vibration data tensor; extracting a discrete digital sequence on a panoramic vibration data tensor time dimension, executing a variational mode decomposition algorithm, and separating and extracting a transient impact feature vector flow; constructing a double-time-axis sliding mapping window, and mapping the transient impact feature vector flow to a future node of a prediction response time axis based on a front and rear wheel preview mechanism of a vehicle wheelbase; operating the time sequence recurrent neural network model, and predicting and generating a target state data matrix of future nodes; and in the numerical constraint space, a prediction type damping adjustment instruction data packet is solved and generated through a rolling optimization algorithm. Through prospective state prediction and optimization solution, the problem of data timeliness caused by calculation delay in digital twinning is solved, and real-time, predictive and safe adjustment of the shock absorber is realized.

The invention discloses a license plate recognition method and system for automatic highway toll collection, and the method comprises the steps: collecting a vehicle image through a plurality of image sensors, sequentially carrying out the noise reduction, contrast enhancement and white balance adjustment of the vehicle image, and generating an initial vehicle image; synthesizing different light source images in the initial vehicle image by using an image fusion technology; performing coarse positioning on a license plate area in the synthesized vehicle image based on an improved YOLOv7 model in combination with a Canny edge detection algorithm; and segmenting license plate characters in the license plate region image by using a semantic segmentation model of U-Net, and performing end-to-end character recognition through a lightweight CRNN convolutional recurrent neural network model to obtain a license plate recognition result. And the recognition speed is greatly improved while the recognition accuracy is ensured.

The invention relates to the technical field of power system data processing, in particular to a power system data processing method and system based on dynamic period detection, and the method comprises the steps: collecting a historical query log of a power business system, and extracting time sequence data of query features through a sliding window; training a recurrent neural network model by using the time sequence data, and predicting a current optimal query period length; on the basis of the optimal query period length, predicting an index combination required by query through a classification model, and generating a candidate index set; optimizing an execution strategy of the candidate index set by taking the minimum index construction cost and the maximum query coverage rate as targets; and obtaining query data of the power business system in real time, and updating the candidate index set and the execution strategy when the change of the business period characteristic is detected. The objective of the invention is to match the periodic change of the power system data, improve the indexing efficiency and reduce the maintenance cost.

The invention discloses a music playing intelligent evaluation system and method based on multi-mode and man-machine cooperation, and relates to the technical field of music playing intelligent evaluation, and the method comprises the steps: collecting audio signals, video images and playing motion data in a music playing process, and obtaining a multi-mode information data set; performing feature extraction on the multi-mode information data set, and analyzing the audio signal, the video image and the playing action data by applying a convolutional neural network and a recurrent neural network model to generate a basic feature set; based on the basic feature set, utilizing a deep learning algorithm to identify and analyze emotion content conveyed in the playing process, and outputting an emotion intensity value and an emotion category; analyzing intonation error, rhythm deviation and strength matching degree according to comparison between the standard music score and the playing record, evaluating action normalization of the player in combination with action characteristics extracted from the playing action data, and calculating scores of all dimensions; and adjusting the weight between the objective score and the subjective score according to the application scene.

The invention relates to the technical field of post insulator detection, and discloses a post insulator crack detection method and system based on stress wave recursion characteristics, and the method comprises the steps: S1, generating an initial stress wave through a high-energy pulse exciter, and introducing the stress wave into a post insulator; s2, collecting a dynamic response signal of insulator stress wave propagation by using a distributed fiber bragg grating sensor network; s3, carrying out preprocessing on the dynamic response signal; s4, acquiring real-time environment parameters through an environment monitoring sensor; s5, combining material attributes and geometric structures of the post insulators; and S6, outputting recursive quantization features based on the recursive neural network model. According to the method, a stress wave propagation path is dynamically tracked by constructing a recurrent neural network model, multiple reflection characteristics of waves are quantified, abnormal phase deviation is accurately captured based on material attribute physical constraints, and the energy characteristic analysis capability of tiny defects is remarkably enhanced.

The invention belongs to the technical field of sensor measurement and recognition, and particularly relates to a human body motion recognition system based on LSTM (Long Short Term Memory), which comprises a data acquisition module, a data calculation module and a data application module, and utilizes a neural network model and a wearable electronic sensor to detect a human body walking state. A new long short term memory (LSTM) recurrent neural network model, namely an LSTM-STRM model, is established, the model combines multi-task learning, an attention mechanism and spatio-temporal feature fusion to accurately classify the walking state of the human body, and the walking state is compared with the original LSTM model. The result shows that the LSTM-STRM model can be used for classifying the time sequence data collected by the measuring unit, the states of the five targets can be classified with high precision, and the recognition accuracy is higher than that of an original LSTM model. The method improves the recognition precision, is high in adaptability, is suitable for the fields of medical treatment, human-computer interaction, exercise training and the like, and has a wide application prospect.

The invention discloses an air conditioner energy saving method and device based on a neural network and a genetic algorithm, and the air conditioner energy saving method based on the neural network and the genetic algorithm comprises the steps that S1, data information corresponding to the temperature in a building is obtained; s2, the obtained data information is input into a recurrent neural network model for prediction processing, and the recurrent neural network model outputs predicted indoor temperature and total active power; wherein the recurrent neural network model is obtained by training the historical data information of the building; s3, the predicted indoor temperature and total active power are input into a genetic algorithm module for optimization calculation, and an optimization result is obtained; and S4, whether the obtained optimization result reaches an optimization target or not is judged, and if yes, the optimized indoor set temperature and the optimized air supply temperature are output.