258 results about "Linear prediction" patented technology

The invention relates to the technical field of temperature equalization control, and discloses a temperature equalization control method and device for a liquid cooling over-charging system. The method comprises the following steps: carrying out grid division on the liquid cooling overcharge system, inputting thermophysical parameters, and obtaining a temperature field calculation matrix through finite difference time domain analysis; establishing a two-layer distributed control network of a station-level control layer and a pile-level control layer based on the temperature field calculation matrix; acquiring real-time temperature data of the two layers of distributed control networks, and performing temperature field mapping to obtain temperature field distribution data; performing linear prediction and nonlinear prediction calculation on the temperature field distribution data to obtain a temperature change trend and a temperature extreme point; the target temperature is calculated according to the temperature change trend and the temperature extreme point, the cascade PID controller is adopted to adjust the rotating speed of the cooling liquid pump, and the temperature balance control parameters are obtained, station-level and pile-level cooperative control is achieved, cooling resources among the multiple charging piles are reasonably distributed, and the overall cooling efficiency of the system is improved.

The invention discloses an electromechanical equipment noise reduction method based on machine learning, and relates to the field of electromechanical equipment noise reduction, and the method comprises the steps: collecting sound pressure and vibration signals during the operation of electromechanical equipment, and collecting the operation state data of the electromechanical equipment; sparse reconstruction is carried out on the noise features; carrying out sound source localization on the reconstructed noise features by using a beam forming algorithm to obtain a noise distribution diagram; constructing a noise reduction model based on deep learning to obtain noise signals after noise reduction; extracting the frequency of the noise signal in a preset frequency range through a peak search algorithm; analyzing the acquired running state data by using a time-varying linear predictive coding algorithm TVLPC to obtain a noise frequency offset caused by the abnormal running state of the electromechanical equipment; generating an optimal solution by using a particle swarm algorithm; and according to the optimal solution of noise control, noise dominant frequency components are extracted for separation, and a plurality of phase-reversal noise reduction control signals are obtained. Aiming at low noise reduction precision of electromechanical equipment in the prior art, the noise reduction control precision is improved.

The invention relates to a place recommendation method based on a hypergraph neural network and a diffusion model, and the method comprises the steps: constructing an interest point recommendation model; the interest point recommendation model sequentially comprises a local trajectory flow hypergraph module, spatial-temporal feature coding, a multi-dimensional feature fusion network, a global hypergraph representation learning module, a feature optimization module, an aggregation layer, a frequency domain learning layer and a linear prediction layer. A long track of a user is analyzed and divided into space-time regions, three global hypergraphs are constructed, and the purpose is to comprehensively capture an overall behavior pattern of the user. In order to better optimize trajectory intention characterization, a feature optimization module based on an improved diffusion model is provided. A more stable and controllable reverse process is ensured by introducing multi-dimensional global representation, and an enhanced diffusion model is more suitable for a recommendation system by using a feature normalization and improved Transform network. And designing a loss function to train an interest point recommendation model, wherein the trained interest point recommendation model is used for recommending a next interest point to a new user.

The invention discloses a light-weight high-robustness transmission line high-risk bird voiceprint recognition method, which comprises the following steps: collecting birdsong audio in real time, obtaining an audio sample containing birdsong, uniformly preprocessing the obtained birdsong audio sample, and obtaining a bird voiceprint recognition result; then, Mel-frequency cepstrum coefficients, dynamic information changing along with time and linear prediction cepstrum coefficients corresponding to the audio signals are obtained through MFCC, Delta and LPCC methods respectively, and mixed features are obtained after splicing is carried out on the Mel-frequency cepstrum coefficients, the dynamic information changing along with time and the linear prediction cepstrum coefficients; and finally, inputting the mixed features into the constructed lightweight voiceprint classification network model, and outputting a classification result. The method has the advantages of being high in classification precision, wide in application range, low in occupied resource and high in environmental noise robustness, the network model is designed in a lightweight mode, the calculation speed is high, and the requirement for lightweight high-robustness twitter audio classification is met. According to the method, the automation level of bird audio recognition can be remarkably improved, manual intervention is reduced, and the method has wide practical application value in the field of bird voiceprint recognition.

The invention discloses a photovoltaic prediction method and system based on a bidirectional Mama structure and combined with a sky image, and the method comprises the steps: obtaining a continuous sky image sequence, extracting dynamic visual features and static visual features, and obtaining image sequence features through the integration of a convolution layer, space adaptive adjustment and global average pooling; performing time coding processing and linear mapping on the historical photovoltaic power sequence to obtain photovoltaic sequence features consistent with image sequence feature dimensions; a cross-source alignment integration mechanism is adopted for processing, a bidirectional Mamba structure is constructed, and a final image data source hiding state and a final photovoltaic data source hiding state after long and short time sequence dependency analysis are output; a cross-source depth feature correlation enhanced gating integration mechanism is adopted for processing, and final integration features are obtained through self-adaptive gating integration; and outputting a future photovoltaic power prediction value through the linear prediction layer. According to the method, the accuracy, the robustness and the calculation efficiency of ultra-short-term photovoltaic prediction can be remarkably improved.

The invention belongs to the technical field of deep learning and time sequence analysis, and particularly relates to a multivariable time sequence prediction method and device based on spatial-temporal feature fusion. The method comprises the following steps: acquiring multivariable traffic time series data, and processing the traffic time series data; dividing the processed traffic time sequence data into overlappable patches, and generating a patch embedding sequence through linear mapping; applying bimodal time attention to the patch embedding sequence to obtain fused attention features; based on the learnable node embedding matrix, generating a time-varying adjacency matrix through dynamic graph construction, executing graph convolution to obtain a time domain graph propagation result, executing fast Fourier transform, multiplying by a learnable scaling factor and then performing inverse transformation to obtain an inverse transformation time frequency result; adding the time domain graph propagation result and the inverse transformation time frequency result to obtain a final spatio-temporal characteristic; and flattening the final spatio-temporal characteristics, and generating predicted values of the variables in a future prediction window through a linear prediction head.

The invention relates to the field of biological medicine, and discloses a method and system for analyzing cough sound by using disease characteristics to diagnose respiratory diseases, and the method comprises the steps: deploying a six-microphone annular array to achieve the precise positioning and triggering of a sound source; self-adaptive spectral subtraction and Wiener filtering cascade are adopted to enhance the audio; segmenting a cough segment based on energy envelope; fusing the Mel-cepstrum, the linear prediction residual error, the harmonic energy ratio and the transient zero-crossing rate to construct a pathological feature matrix; extracting local, medium-range and global time sequence features through a three-branch parallel convolutional network; inputting a disease specific classifier to discriminate asthma, pneumonia and laryngitis respectively, and applying a feature decoupling regular term to improve interpretability. The system correspondingly realizes the modularized processing flow. According to the method, the cough sound collection quality and the disease subtype recognition accuracy in a complex environment are improved, meanwhile, the thermodynamic diagram is output to assist clinical decision making, and the diagnosis credibility and practicability are enhanced.

The invention discloses an indoor temperature and humidity spatial data prediction method fusing spatial interpolation and a graph convolutional network, and relates to the field of smart city and environment monitoring, and the method comprises the steps: obtaining indoor multi-point temperature and humidity data actually measured by a sensor, and the spatial position information of a monitoring mark point corresponding to the indoor multi-point temperature and humidity data; the temperature and humidity data, the spatial position information and the time dimension features of the marked points are used as input of Kriging interpolation, and temperature and humidity linear predicted values of part of unmarked points are generated; constructing a multiple linear regression model taking the Kriging prediction result, the measured data of the mark points, the time dimension characteristics, the spatial characteristics and the external meteorological data as independent variables and the target temperature and humidity values as dependent variables, and performing multi-dimensional fitting on the Kriging interpolation result; and constructing a feature matrix by using a linear predicted value output by the regression model and other features, and taking the feature matrix and an adjacent matrix as input of a graph convolutional network to generate indoor temperature and humidity three-dimensional space distribution. According to the invention, high-precision indoor space data prediction can be realized.

An intra prediction method by using cross component liner prediction mode (CCLM), includes: determining a luma block corresponding to a current chroma block; obtaining luma reference samples of the luma block based on determining L available chroma reference samples of the current chroma block, wherein the obtained luma reference samples of the luma block are down-sampled luma reference samples; calculating linear model coefficients based on the luma reference samples and chroma reference samples that correspond to the luma reference samples; and obtaining a prediction for the current chroma block based on the linear model coefficients and values of a down-sampled luma block of the luma block.

A path planning method oriented to various parking scenarios, said method relating to the technical field of unmanned vehicles. The method comprises: building a vehicle kinematic model, and designing a multi-stage non-linear prediction control model; designing a multi-stage non-linear prediction controller, and designing a constraint function which takes as basic constraints the pose of a vehicle at a starting point, the pose of the vehicle at a target point, a parking speed, a parking steering angle, a non-holonomic kinematic constraint, and the minimum safe obstacle avoidance distance; integrating constraints of a parking system into an optimization control problem that satisfies the multi-stage non-linear prediction control model and the constraints of the parking system; and using an interior point method to solve the optimization control problem, so as to obtain an optimal control sequence, and applying elements in a first column in the sequence to vehicle low-level control. The present invention can ensure the controllability and accuracy of parking and can also improve the flexibility and efficiency of a parking process, thereby greatly optimizing the architecture of an automatic parking control system.

Disclosed in the present invention are a data-driven linear MPC-based assessment method and apparatus for a frequency regulation capability of a wind farm, and a control method and apparatus. The assessment method comprises the steps of: offline acquiring historical operation data sets of wind turbines, and training a linear predictive control model, wherein the linear predictive control model is obtained by applying a dimensionality-increasing transformation process to a wind farm frequency modulation dynamic nonlinear model on the basis of a Koopman operator theory; acquiring real-time operation data of the wind turbines, and using the acquired real-time operation data to solve for a linear MPC optimization model, to obtain droop coefficients of the wind farm, wherein the linear MPC optimization model is constructed on the basis of the trained linear predictive control model and by taking the maximum droop coefficient of the wind farm as an objective and setting a safety rotational speed constraint condition; and assessing a frequency regulation capability of the wind farm on the basis of the obtained droop coefficients of all the wind turbines in the wind farm. The present invention has the advantages of ease of implementation, high assessment efficiency and accuracy, and strong scalability, and the like.

The invention provides a solid state disk data reading method and device, equipment and a storage medium. Relates to the technical field of storage. The method comprises the steps of obtaining a historical read operation data set of the solid state disk; the method comprises the following steps: dividing a historical read operation data set according to a set time window to obtain a plurality of samples, and performing embedding processing on the samples to obtain a corresponding embedded time sequence; performing stacking processing on the embedded time sequence through N stacking layers to obtain a stacking enhanced time sequence; linear prediction is carried out based on the stacking strengthening time sequences corresponding to all the samples, and a pre-reading operation sequence is obtained; according to the pre-reading operation information, reading corresponding data from a flash memory of the solid state disk and storing the data in a memory buffer area of a controller of the solid state disk; and receiving a data reading command, determining that the data reading command hits the data in the memory buffer area, and reading the hit data from the memory buffer area through the PCIe bus. Therefore, low-computing-power model prediction is realized and resource consumption and access delay are reduced.

The invention discloses a semi-supervised segmentation method combining double segmentation head frequency decoupling learning and entropy change pseudo label screening, is applied to the field of image processing, and aims to solve the problems that in an existing semi-supervised semantic segmentation method, pseudo label generation quality is difficult to guarantee, a model is single in enhanced disturbance response, and the efficiency is low. The problems of high false label error and unstable training caused by difficulty in distinguishing global structure change and local detail change are solved; the student model adopts a double-segmentation-head structure and is composed of a prototype prediction head and a linear prediction head, and function division is realized through a frequency domain decomposition mechanism: the prototype head receives low-frequency component characteristics of an encoder, the linear head receives high-frequency component characteristics of the encoder, and for a label-free sample, after random enhancement is applied, the original prediction head and the linear prediction head are divided into two segments; and respectively predicting category distribution of samples before and after enhancement by two segmentation heads of the teacher model, and calculating a change ratio of average prediction entropy. And based on the entropy change ratio before and after enhancement, synthesizing the reaction of the prototype head and the linear head, and jointly evaluating the prediction stability after enhancement.

The invention relates to the technical field of bridge safety monitoring, and particularly discloses a cable-stayed bridge cable force unbalance vortex vibration early warning system and method based on an intelligent calculation model, and the system comprises a multi-source data collection module, a phase-space reconstruction module, a vortex vibration mode decoupling module, a dynamic threshold setting module, a nonlinear prediction module and an early warning decision module. The vibration signals are mapped to a multi-dimensional phase space according to the phase space reconstruction theory, and multi-order frequency band vortex vibration modal decoupling is achieved in combination with density clustering analysis; establishing a multi-dimensional threshold hypersurface by adopting a support vector machine, and dynamically adjusting a judgment threshold according to the environmental parameters and the structural state; short-term high-precision prediction and medium-and-long-term trend prediction are realized by using a local linearization prediction model; a wind-vortex vibration mapping relation is established through the generative adversarial network; the self-adaptive filtering technology is adopted to eliminate traffic load interference, and the problems that in the prior art, cable force unbalance vortex vibration cannot be accurately recognized, early warning is not timely, and the false alarm rate is high are effectively solved.

A technique uses linear prediction to determine the location of spurious content in a digital phase-locked loop and suppresses the spurious content from propagating to the clock output. In at least one embodiment, the technique implements an iterative (e.g., recursive) computation.

The invention relates to the technical field of particle size measurement in the ore grinding production process. The ore grinding granularity soft measurement method comprises the steps of analyzing a time sequence characteristic matrix based on a step-by-step regularization characteristic sorting method, forming an optimized input characteristic matrix, obtaining a linear prediction value based on the optimized input characteristic matrix, and calculating the ore grinding granularity according to the linear prediction value. Performing error calculation on the linear predicted value and the actual measurement value to obtain a nonlinear predicted value, combining the linear predicted value and the nonlinear predicted value according to the weight to generate a preliminary soft measurement model, performing global optimization on the combined weight of the preliminary soft measurement model through a multi-objective optimization algorithm to generate an optimized soft measurement model, and performing soft measurement on the optimal soft measurement model. When the statistical property exceeds a set threshold value, a dynamic correction mechanism is triggered, and an updated soft measurement model is obtained according to the dynamic correction mechanism. The method has the effect of meeting the high-precision prediction requirement under the dynamic working condition.

The invention discloses a photovoltaic power generation time sequence prediction method based on periodic modeling and channel interaction, and the method comprises the steps: collecting and processing the historical data of photovoltaic power generation; then, extracting a learnable periodic mode in the time sequence, and removing a periodic component to obtain a residual component; then, through reversible instance normalization, sequence embedding, a channel interaction module and a linear predictor, modeling and prediction of a residual component are completed; and finally, adding the predicted residual component and the periodic component to generate a final prediction result. Through an aggregated channel interaction strategy, the calculation complexity is reduced while the channel correlation is captured, the dependence on an abnormal channel is reduced, and the robustness and the expansion capability of the model are improved; meanwhile, by directly modeling a periodic mode in the time sequence, the capability of extracting inherent periodicity in the photovoltaic power generation time sequence data is improved, so that future power generation data is predicted more accurately; the method can be widely applied to optimal scheduling and management of a photovoltaic power generation system.

An image blur correction device acquires a blur detection signal, and separates it into a high frequency band signal component and a low frequency band signal component using an HPF and an LPF. A high frequency side prediction processing unit acquires an output of the HPF, and a low frequency side prediction processing unit acquires an output of the LPF via a down-sampler. A prediction processing unit updates a filter coefficient of a prediction filter using an adaptive algorithm, and performs linear prediction on the blur detection signal. An adder adds an output of the high frequency side prediction processing unit to an output of the low frequency side prediction processing unit via a up-sampler, and outputs a superimposed output signal. Image blur correction control is performed on the basis of a superimposed output signal.

The invention discloses a resource quota adjustment method, device and equipment and a readable storage medium, and is applied to the technical field of distributed storage, and the method comprises the steps: determining current traffic data corresponding to a current service request; performing linear prediction based on the current traffic data to obtain initial predicted traffic, and performing nonlinear residual correction on the predicted traffic to obtain target predicted traffic; and configuring the resource quota based on the target predicted traffic to obtain a target pre-application resource quota. According to the invention, prediction is carried out by using the prediction model based on the current traffic data to obtain the initial predicted traffic, nonlinear residual correction is carried out on the predicted traffic to obtain the target predicted traffic, and the target predicted traffic is obtained through prediction and error compensation, so that the predicted traffic is more accurate, and the prediction efficiency is improved. Therefore, the target pre-applied resource quota obtained by performing pre-application on the resource quota based on the target predicted traffic subsequently is more accurate, the burst traffic resistance of the distributed storage system is improved, and the subsequent service quality is improved.

The present invention relates to the technical field of quality management, and specifically provides a quality management method based on standard data. Specifically, it includes: constructing a dynamic standard database; collecting production data in real time; adopting a hybrid anomaly detection and dynamic interpolation strategy to handle data missing and noise, generating a standardized matching code based on hash and polynomial ring operations to ensure data consistency; determining quality deviations based on a dynamic threshold model and fuzzy logic, quantifying the deviation amount by combining the weighted Euclidean distance, realizing hierarchical early warning through a membership function, and triggering response measures according to the early warning level; using a scoring model and non-linear prediction to evaluate the effectiveness of measures and screening optimization plans; a closed-loop feedback mechanism continuously updates model parameters and the knowledge base. Through dynamic data integration, hybrid detection algorithms and matching code design, the present invention effectively solves the problems of inflexible static thresholds, single anomaly detection, and poor data consistency in traditional quality management, and significantly improves the detection accuracy and system robustness.

The invention provides a voice de-reverberation method, device and equipment and a medium, and relates to the technical field of voice signal processing, and the method comprises the steps: obtaining a reverberation signal through a microphone array, and carrying out the short-time Fourier transform of the reverberation signal, and obtaining a time-frequency domain signal; respectively processing the time-frequency domain signal by using a plurality of high-directivity beam formers to obtain a time-frequency signal; designing a dereverberation filter based on a weighted linear prediction algorithm according to the plurality of time-frequency signals; performing dereverberation filtering on the time-frequency signal output by one of the high-directivity beam formers by using a dereverberation filter to obtain an expected time-frequency domain signal after dereverberation; and performing inverse short-time Fourier transform on the expected time-frequency domain signal to obtain a reverberation-removed time-domain signal. According to the method, the performance of an existing WPE algorithm can be improved, the reverberation removing performance of the WPE algorithm in a noise environment is improved while the calculation complexity is reduced, voice reverberation can be removed, and the voice quality is improved.

The present invention discloses a continuous speech formant extraction method based on peak selection, comprising: performing a preprocessing operation on a single frame of input speech; using a linear prediction method to preliminarily estimate the peak value in the spectral envelope of the speech frame; establishing a reference point and a formant trough, and then using a peak selection method to establish a mapping relationship between the peak value and the reference point; using the mapping relationship between the peak value and the reference point and the formant trough to determine the formant of the speech frame; and performing formant estimation on the continuous speech: dividing the continuous speech into frames according to different frame numbers, using the above algorithm to loop 100 times to obtain the formant parameters under different frame number tests, averaging the results after 100 loops, and obtaining the final result after smoothing. The method of the present invention can eliminate the influence of merged peaks and false peaks, and has a fast convergence speed and strong robustness.

The invention discloses a time-frequency autocorrelation comparison solar wind speed time sequence prediction method fused with frequency domain consistency, and the method mainly comprises the steps: carrying out the preprocessing of solar wind speed time sequence data, and dividing a data set; sample features are extracted through a Transform model; frequency domain context autocorrelation contrast loss is obtained through frequency domain context autocorrelation contrast learning in frequency domain analysis; time domain label cross-correlation comparison loss is obtained through time domain label cross-correlation comparison learning in time domain analysis; a linear predictor of the model is used for calculating sample features to obtain a prediction sequence, and the mean square error of the prediction sequence and a real sequence is used as prediction loss; based on the frequency domain context self-correlation comparison loss, the time domain label cross-correlation comparison loss and the prediction loss, obtaining training loss for model optimization through weight summation; and performing solar wind speed time sequence prediction by using the optimized model. According to the method, the extraction capability of the time sequence prediction model on the dependence between input and output is improved, and the solar wind speed time sequence prediction performance is effectively improved.

The invention provides a cloud sensing shared information fusion method for a vehicle and road cloud cooperative system. The method comprises the steps that the vehicle and road cloud cooperative system comprises a cloud, a vehicle end and a road end; the cloud end obtains environment sensing information from other terminals through network communication; a space-time two-dimensional calibration system is constructed, and the space-time two-dimensional calibration system is adopted to calibrate the environmental perception information; dividing a unified global ID for each target by adopting a global target feature matching method, and performing redundancy removal processing on the calibrated environmental perception information by adopting a confidence evaluation comprehensive redundancy removal mechanism; performing pre-estimation compensation on the environment perception information after redundancy removal by adopting a model based on state estimation and kinematics; transmitting the compensated information to a corresponding terminal by adopting a sensing information routing sharing strategy based on the global ID to complete information sharing; according to the method, a resampling optimization strategy and a collaborative prediction correction algorithm are adopted, compared with a traditional linear prediction compensation method, situation information real-time performance and prediction accuracy are remarkably improved, and timeliness and reliability of follow-up decisions are effectively guaranteed.

The invention belongs to the field of artificial intelligence and non-contact measurement, and discloses a lung function measurement and evaluation method based on a smartphone microphone. The lung function measurement based on the smartphone microphone comprises the following two parts. An expiration audio signal is collected through a microphone of the smart phone under the condition that the smart phone does not need to wear any external equipment. A CNN-LSTM cascade structure is constructed to convert an audio signal into approximate flow through a nonlinear relation between a deep learning analog audio signal and a flow volume curve, and a linear predictive coding (LPC) feature is used for improving an index effect. The invention provides a deep learning model for generating a flow volume curve by combining an audio signal with a signal linear predictive coding feature. According to the method, the comprehensiveness of lung function measurement based on the smart phone is improved, and lung function measurement can be conveniently and effectively carried out through the smart phone in a family environment.

This disclosure provides a signal processing method and apparatus. The method includes: obtaining Nr1×M1 signals, where the Nr1×M1 signals are echo signals of M1 signals that are sent by a radar to a target in a SIMO mode; obtaining Nt×Nr2×M2 signals, where the Nt×Nr2×M2 signals are echo signals of M2 signals that are sent by the radar to the target in a MIMO mode; performing first signal processing on the Nr1×M1 signals to obtain first processing data, where the first signal processing includes sequentially performing range FFT analysis, linear prediction, and Doppler FFT analysis; performing second signal processing on the Nt×Nr2×M2 signals to obtain second processing data, where the second signal processing includes range FFT analysis and Doppler FFT analysis; and performing velocity matching ambiguity resolution processing based on the first processing data and the second processing data.

The invention discloses a wind speed prediction method, system and device and a medium, and the method comprises the following steps: carrying out the preprocessing of historical wind speed data, and obtaining a historical time sequence; partitioning the historical time sequence to obtain multivariate time sequence blocks; performing feature conversion on the multivariate time sequence block to obtain a first sequence coding feature; performing space-time attention coding on the first sequence coding feature to obtain a target sequence coding feature; and performing linear prediction on the coding features of the target sequence to obtain a predicted wind speed value time sequence. According to the method, the time dependency relationship of each meteorological acquisition station can be effectively captured, and the prediction accuracy is remarkably improved; the correlation between different meteorological acquisition stations can be modeled, and the prediction performance is further improved; meanwhile, the system can be widely applied to the fields of wind energy production, weather forecast, climate research and the like, and the safety and reliability of a wind energy system can be improved.

The invention provides a power transaction service security risk prediction method and device based on time sequence detection. The method comprises the following steps: preliminarily dividing linear time sequence data components from time sequence data; evaluating the linear fitting significance of the divided linear time sequence data components by using an autoregression integral moving average model, and further generating a linear service security risk prediction result; performing residual processing on the to-be-observed time series data and time series data of which the linear fitting significance exceeds a preset threshold in the linear time series data component to obtain a nonlinear time series data component; extracting features from the non-linear time sequence data components by using a multi-layer stacked long-short-term memory network, and performing non-linear prediction to generate a non-linear service security risk prediction result; and integrating the linear service security risk prediction result and the nonlinear service security risk prediction result, and obtaining a final service security risk prediction result through an activation function.

The present application relates to a kind of place recommendation method based on hypergraph neural network and diffusion model, interest point recommendation model is constructed, interest point recommendation model successively includes local trajectory flow hypergraph module, space-time feature coding, multi-dimensional feature fusion network, global hypergraph representation learning module, feature optimization module, aggregation layer, frequency domain learning layer and linear prediction layer.Analysis of the long trajectory of user, and it is divided into space-time region, constructs three global hypergraphs, aims at comprehensively capturing the overall behavior pattern of user.In order to better optimize trajectory intention representation, propose feature optimization module based on improved diffusion model.Introduce multi-dimensional global representation to ensure a more stable and controllable reverse process, and use feature normalization and improved Transform network, enhanced diffusion model is more suitable for recommendation system.Loss function is designed to train interest point recommendation model, and the interest point recommendation model trained is used to recommend next interest point for new user.

The invention discloses an energy storage frequency modulation instruction prediction method and system based on a radius method, and belongs to the technical field of power system scheduling and optimization, and the method comprises the steps: evaluating a prediction difficulty value of a frequency modulation instruction sequence through the radius method; an ANFIS prediction network structure is improved according to the prediction difficulty value; and predicting a value of a frequency modulation instruction through the improved ANFIS network and outputting a result. The frequency modulation instruction prediction method solves the problems that an existing frequency modulation instruction prediction method lacks non-linear prediction difficulty evaluation of original data, personalized model adjustment cannot be carried out for different sequence complexity, response time is delayed, and prediction precision is improved.