1234 results about "Time resolution" patented technology

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice feature processing method, device, equipment and medium. Performing time resolution analysis based on the fused Mel band energy to generate a multi-scale Mel spectrum amplitude value, and performing nonlinear transformation on the multi-scale Mel spectrum amplitude value according to the noise intensity parameter to generate a noise suppression Mel component; and generating a perception weighting coefficient according to an auditory perception model, and executing frequency domain energy adjustment on the noise suppression Mel component to generate Mel spectrum representation. On the basis of frequency resolution self-adaption, time resolution dynamic adjustment and auditory perception modeling, nonlinear transformation and perception weighting processing are applied to the multi-scale Mel spectrum amplitude value, the influence of noise interference on voice features can be effectively reduced, and the key information retention capacity of voice signals is enhanced.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention provides an optical complex water body suspended sediment monitoring method based on man-machine cooperation, and belongs to the technical field of remote sensing water environment monitoring. The method comprises the following steps: acquiring a multispectral remote sensing image of a target area, preprocessing the multispectral remote sensing image, and performing space-time fusion to obtain a spectral data set with high space-time resolution; carrying out manual labeling on the spectrum data set, constructing a suspended sediment concentration classification data set, and carrying out synthesis expansion on a minority class of samples to generate an equalization training set; training a suspended sediment concentration inversion model by using the equalized training set to obtain an initial suspended sediment concentration predicted value, and dynamically optimizing the spectral feature weight and the classification boundary of the suspended sediment concentration inversion model based on expert knowledge; and locally correcting the initial suspended sediment concentration predicted value and diffusing the initial suspended sediment concentration predicted value to a global range based on a hydrodynamic law and a space consistency constraint to obtain a global suspended sediment concentration predicted value. The method combines remote sensing image analysis, machine learning and expert knowledge to realize high-precision inversion of SSC.

The present invention relates to the technical field of very-short-term photovoltaic power forecasting, and in particular to a very-short-term photovoltaic power forecasting method and system for real-time control, which intend to improve precision and real-time performance in photovoltaic power forecasting. The method comprises the following steps: performing normalization processing on meteorological data, and performing a feature correlation analysis; using a BP neural network to perform short-term photovoltaic power forecasting, inputting the meteorological data and historical output data, and outputting a short-term forecasting value with a resolution of 15 minutes; and performing spline interpolation and outlier removal on an upper-layer result of the BP neural network, and using same as a long short-term memory recurrent neural network input, so as to improve a temporal resolution of forecast data and obtain very-short-term photovoltaic power forecast data with a resolution of 1 minute. The method comprehensively considers meteorological factors and uses advanced neural network models and data processing techniques to achieve photovoltaic power forecasting on a very short temporal scale while ensuring forecasting precision, making the method suitable for the real-time control and optimized operation of photovoltaic power stations.

The invention relates to the technical field of territorial space planning, and discloses a monitoring and evaluation method based on multi-source remote sensing images and geographic information vector data. Comprising the following steps: acquiring a multi-source remote sensing image and geographic information vector data of a target area; performing preprocessing and fusion analysis on the multi-source remote sensing image, generating a dynamic earth surface change detection model with high time resolution, and constructing an initial monitoring baseline in combination with geographic information vector data; according to the method, a high-frequency change detection condition and a semantic segmentation condition are loaded on the basis of an initial monitoring baseline to form a comprehensive interpretation model, a real-time early warning evaluation index is output, and a dynamic earth surface change detection model is generated by preprocessing a remote sensing image. And generating an illegal behavior distribution result by using the key identification parameters, establishing a task distribution model to optimize a supervision scheduling scheme, and finally realizing accurate management and control. According to the invention, the efficiency and accuracy of territorial space planning monitoring are improved, and technical support is provided for intelligent supervision.

The invention discloses a satellite clock error forecasting method based on a Transform architecture deep learning model, and the method comprises the steps: carrying out the primary difference of historical satellite clock error data, obtaining a clock error difference time sequence, processing the clock error difference time sequence, and constructing a data set; a time sequence prediction model is constructed, a data set is used for training, in the time sequence prediction model, through sampling S, convolution C and interaction I operations of an SCI module, features of multiple time resolutions of the satellite clock error are extracted, and the satellite clock error prediction precision is improved; and performing prediction by using the trained time sequence prediction model to obtain satellite clock error prediction data. According to the method, the satellite prediction precision and stability are improved by utilizing the extraction capability of the Transform architecture on long-distance dependence; and the model is trained by using historical satellite clock error data, so that high-precision satellite clock error prediction is realized.

The invention discloses a method for testing dynamic comprehensive data of an electric cylinder in real time, and relates to the technical field of mechatronics test.The method comprises the steps that vibration signals, temperature data and electromagnetic interference data of the electric cylinder are collected and preprocessed, and a multi-modal data stream is generated; defining a spline basis function based on the vibration signal, and extracting a transient feature tensor of the vibration signal by using the spline basis function; constructing a transient environment state transition model based on the multi-modal data stream, inputting the transient characteristic tensor, the temperature data and the electromagnetic interference data into the transient environment state transition model, and generating a displacement compensation amount and a hidden state sequence; and constructing a digital simulation model, inputting the displacement compensation amount into the digital simulation model, and generating multi-physics field coupling simulation displacement. By constructing the spline basis function and the transient environment state transition model, the problem of vibration signal feature distortion is solved, and the extraction precision and the time resolution of the transient impact features are remarkably improved.

The invention discloses an LCZ temporal-spatial resolution control method based on multi-source remote sensing information, and relates to the technical field of image processing. The LCZ temporal-spatial resolution control method based on the multi-source remote sensing information comprises the following steps of scene weight evaluation, environment weight evaluation and temporal-spatial fusion feedback evaluation. According to the method, the initial weight is obtained through the multi-source remote sensing information identification scene, the remote sensing data and the scene characteristic data are obtained to obtain the scene weight evaluation coefficient, and the geographical environment data are obtained and combined with the remote sensing data to obtain the environment weight evaluation coefficient; then, whether dynamic weight redistribution is carried out or not is judged according to the scene weight evaluation coefficient and the environment weight evaluation coefficient, finally, a weight regulation and control evaluation coefficient is obtained according to the obtained space-time fusion feedback data to judge whether weight secondary distribution is carried out or not, and the effect of improving the space-time resolution control accuracy is achieved. The problem of inaccurate temporal-spatial resolution control in the prior art is solved.

The present invention relates to the technical field of prevention and control of geological disasters in mountainous coal mining areas. Disclosed are an InSAR-based measurement method for the angle of critical deformation of a coal mining area, and a system. The method comprises: acquiring a mining-induced surface deformation field image by means of small baseline subset-interferometric synthetic aperture radar; in the mining-induced surface deformation field image, separately drawing profile lines along a coal seam strike and a coal seam dip, and selecting target deformation points on the profile lines as points to be monitored; according to the incident angle, the flight direction and the azimuth angle of a satellite, obtaining vertical and horizontal deformation values; using a probability integral method to calculate parameters such as inclination, curvature and horizontal deformation; and drawing a curve graph on the basis of the vertical deformation of a target region and the above parameters, so as to obtain a corresponding angle of critical deformation. The InSAR-based measurement method for angle of critical deformation of a coal mining area provided by the present invention has the advantages of improved temporal resolution and reduced phase noises and the like, can achieve high precision and high efficiency, and can generate clear and visible Excel tables for surface deformation data of mining areas and corresponding profile data.

The invention discloses an ultra-weak light detection system and method based on an SPAD array and time correlation photon counting, and belongs to the technical field of ultra-weak light detection. A multi-channel SPAD array detection unit; a time-to-digital conversion module; the photon arrival time analysis unit comprises a TCSPC model, a noise modeling and suppression module and a self-adaptive convolution filter; a data compression and fitting module; a spatial denoising filter; an output unit; and a master control processing unit. Based on a multi-channel SPAD array detection unit, a time-to-digital conversion module, a TCSPC model, a noise modeling and suppression module, a self-adaptive convolution filter and the like, high-sensitivity imaging and quantitative detection of materials (such as black silicon) with extremely low reflectivity are realized. The system has the advantages of being good in real-time performance, high in time resolution, high in signal-to-noise ratio, high in on-chip processing capacity, high in integration and the like, and meanwhile the bottleneck of a traditional TCSPC method for large-scale photon data processing is solved.

The invention discloses a super capacitor energy scheduling method and system based on multi-time scale collaboration, and relates to the technical field of capacitor energy scheduling, and the method comprises the steps: calling a super capacitor energy scheduling log, and carrying out the division according to a time resolution hierarchy, and obtaining a plurality of time hierarchies; receiving a power grid dispatching instruction, performing power analysis on the load prediction data, and drawing a power reference curve of the super capacitor; dynamically correcting the power reference curve to generate a fluctuation stabilizing instruction; obtaining instantaneous charging and discharging control parameters of the super capacitor; and carrying out multi-time scale collaborative fusion, generating an energy scheduling instruction, carrying out scheduling verification, and determining an energy scheduling optimization instruction. According to the method, the technical problems of low scheduling efficiency and poor power grid stability caused by lack of multi-time scale collaborative analysis and difficulty in dynamically adapting to power grid fluctuation of super-capacitor energy scheduling in the prior art are solved, and the technical effects of improving the super-capacitor energy scheduling efficiency and the power grid operation stability are achieved.

The invention discloses a carbon sink dynamic prediction method, device and equipment based on multi-source remote sensing space-time fusion and three-dimensional point cloud deep learning and a storage medium, and relates to the technical field of remote sensing information processing and ecological environment monitoring, and the method comprises the steps: obtaining space-time fusion data, a vegetation point cloud inversion algorithm and a carbon sink prediction hybrid model; extracting three-dimensional point cloud biomass based on the vegetation point cloud inversion algorithm and the space-time fusion data, and determining vegetation parameter information; and predicting a carbon sink change trend based on the vegetation parameter information and the carbon sink prediction hybrid model, determining a carbon sink prediction result, and controlling a system to complete disaster assessment and risk early warning based on the carbon sink prediction result. According to the method, multi-source remote sensing space-time fusion is carried out to eliminate the space-time resolution difference, the three-dimensional point cloud biomass is extracted, the carbon sink change trend is predicted to realize disaster assessment and risk early warning, the multi-source data fusion precision and the vegetation parameter inversion accuracy are effectively improved, and the method has remarkable environmental benefits and social values.

The invention discloses a desertification monitoring method and system based on multi-source remote sensing data fusion, and the method comprises the steps: carrying out the weighted scoring of multi-source satellite remote sensing data according to the spectrum, cloud coverage and time resolution, and automatically switching to a data source with a higher priority when the score is lower than a threshold value. Thirdly, performing initial segmentation on the image by using a multi-scale segmentation algorithm in combination with spectrum and texture features, optimizing boundaries through a region growing algorithm, and merging regions with small spectrum differences into desertification plaques; and extracting construction land expansion and agricultural activity intensity indexes from the time series data, and establishing a quantitative relation model with desertification plaque change. And adopting a Kriging space-time interpolation algorithm for missing data to generate a continuous curved surface. And finally, fusing a multi-period segmentation result and human factor data, and outputting a desertification boundary dynamic change diagram and a human influence weight distribution diagram. According to the invention, accurate monitoring of the desertification process and quantitative evaluation of human influence are realized, and a scientific basis is provided for desertification control.

The invention discloses a sound velocity distribution forecasting method based on a Mama neural network, and belongs to the technical field of ocean observation. According to the method, the historical profile data of the forecasting task area is used as a reference, the historical profile data are arranged according to the time dimension, and then the sound velocity distribution of the forecasting area at the future moment is forecasted. According to the method, the problem of prediction of underwater sound velocity profile distribution in the time dimension is solved, sound velocity distribution at future moments can be accurately predicted in real time, and sound velocity distribution at specified time resolution intervals in the future is estimated; the problems of insufficient time resolution of ocean sound velocity distribution estimation and lack of future sound velocity forecasting function are solved, and important guiding significance is provided for ocean scientific research.

This invention provides a method and system for hierarchical audio classification, aimed at achieving high accuracy in prediction with enhanced decision time resolution. The disclosed method uses a perfectly tagged database which utilizes innovative techniques of labelling. Further data augmentation is also done using signal processing techniques like audio mixing and blending of different types of data. The disclosed method applies short term audio normalization on database for normalized training and prediction using AI based Long Short-Term Memory (LSTM) networks. The method and system employ the LSTM networks in a hierarchical structure to classify audio into desired 3 or more audio classes which include at least a background noise audio class. Decision time accuracy is improved by running the LSTM predictors over time overlapped slices and by using a separate transition detection neural network.

The invention discloses a remote sensing data normalization method and system based on geographically weighted nonparametric regression. The method comprises the following steps: establishing a geographically weighted nonparametric regression estimation model; obtaining a target image and a reference remote sensing image set, and forming a remote sensing image pair based on the target image and the corresponding reference remote sensing image; preprocessing a target image in the remote sensing image pair to obtain a resampled target image consistent with the reference remote sensing image in spatial resolution; judging a variable coefficient in each local window in the resampled target image and the reference remote sensing image, and screening out homogeneous pixels meeting conditions based on the variable coefficient and a preset threshold to form a homogeneous pixel set; and based on the geographically weighted nonparametric regression estimation model and the homogeneous pixel set, performing pixel-by-pixel calculation on a to-be-normalized image to obtain a normalization result. Remote sensing data with large breadth, high time resolution, consistent spectrum and consistent radiation can be used as a radiation reference, and normalization of any target image is realized.

The invention relates to the field of semiconductors, and discloses an FPGA-based time sequence generator, a time sequence generation method, terminal equipment and a readable storage medium, and the FPGA-based time sequence generator comprises a clock source which is used for providing a working frequency; the test period buffer is used for sequentially caching the data of each test period; the time sequence data cache is used for sequentially caching all time sequence data; the test period counter is used for starting counting every time one test period is entered; the time sequence edge generator is used for determining whether the time sequence clock is enabled or not according to the count of the test period data of the same test period and the integer part value of the time sequence data, and outputting the decimal part value of the time sequence clock which is not full of the integer multiple; the data generator is used for generating the received one-bit mode data into parallel data with a preset bit width when the time sequence clock is enabled; and the converter is used for converting the parallel data into serial data by using the preset frequency multiplication clock of the working frequency and then outputting the serial data. According to the scheme, the time resolution based on the time sequence generator can be improved.

The invention discloses a time division duplex multi-carrier synchronous transmission method based on 5G, which relates to the technical field of communication, and comprises the following steps of: establishing a carrier mapping table, respectively mapping a plurality of logic carriers to predefined physical resource blocks, and marking uplink and downlink switching time slots according to a time division mode; after a user terminal receives a synchronization signal, alignment is carried out through a high-precision local clock and the reference time of a main base station, and a synchronization reference point is recorded with a nanosecond-level time resolution. According to the invention, through the dynamic synchronization control and frequency domain interference suppression technology, the synchronization stability and the data transmission reliability in a high-speed scene are significantly improved; a synchronous load scoring mechanism is introduced to realize efficient scheduling and conflict avoidance of synchronous resources in a multi-user concurrent environment; and in combination with interference weight estimation and an adaptive filtering strategy, the anti-interference capability and environment adaptability of the system are effectively enhanced, and the requirements of key services such as the Internet of Vehicles and industrial control on low-delay and high-reliability communication are comprehensively met.

The invention relates to an abnormal battery positioning method and system, equipment, a medium and a product. The abnormal battery positioning method comprises the steps of determining operation data of a battery pack; performing abnormal value detection and normalization processing on the operation data to obtain target operation data; performing time sequence analysis on the target operation data, determining time characteristics, and obtaining spatial characteristics according to the position information of each battery and the target operation data; inputting the spatial-temporal characteristics into a thermal imaging model to generate a thermal image of the battery pack according to the spatial-temporal characteristics; and determining that the battery corresponding to the target area is abnormal in response to the fact that the target characteristic value of the target area in the thermal image exceeds a preset threshold value. By adopting the method, the problems that the traditional infrared thermal imaging technology is greatly influenced by the environment, the installation space is limited, the cost is high and the weak signal space-time resolution capability is insufficient can be solved, the thermal state of the battery pack can be accurately reflected, and the abnormal positioning and accurate early warning of the thermal runaway early stage can be realized.

The invention relates to an event-based lip reading method and device, equipment and a storage medium. The method comprises the following steps: collecting an original event stream of a lip sequence image through an event camera; therefore, the brightness change of each pixel can be asynchronously recorded with microsecond-level time resolution, and ultra-low delay, high dynamic range and sparse data representation are realized. Converting the original event stream into a frame-shaped event tensor based on a voxel representation method to obtain a voxelized event body; performing spatial feature extraction on the voxelized event body through a front-end network in an event-based lip reading model to obtain multi-scale spatial features; performing time-dependent modeling on the multi-scale spatial features through a rear-end sequence model in an event-based lip reading model to obtain a sequence code; therefore, space and time features can be fused, and the accuracy and stability of sequence coding are improved. And determining the lip reading recognition content according to the sequence code. Therefore, stable recognition of lip movement can be realized, and lip reading accuracy is improved.

The present invention relates to the field of pipeline leakage monitoring. Disclosed is an oil and gas pipeline leakage wave identification and monitoring system. In the present invention, an mCNN is combined with LFLBs for performing feature extraction on an acoustic wave signal collected by a DFB, and the collected data improves information completeness; a three-way parallel one-dimensional CNN used in the present invention exhibits good temporal resolution and sensitivity to high-frequency feature transformations in signals; and the present invention integrates advantages of different scales, enabling the algorithm to learn more features, and incorporating the LFLBs to further extract high-level local features. An mCNN-LFLBs network model of the present invention exhibits significant innovation and advancement on the technical level, and also demonstrates extremely high value in actual application. The network model not only provides a novel and efficient technical means for critical fields such as natural gas pipeline inspection, but also introduces new ideas and methods to research fields related to deep learning and signal processing.

The invention discloses a high-efficiency time sequence optical flow method, system and device for fusing event and image information and a medium. The method comprises the following steps: constructing a time sequence feature extraction module; constructing a double-branch collaborative iteration optical flow module of the event and the image; constructing a fusion modeling and iterative optimization optical flow module based on cross attention; training a dual-branch collaborative iteration optical flow module of the event and the image and a fusion modeling and iterative optimization optical flow module based on cross attention on a training set of an MVSEC public data set to obtain weights, and testing on a test set of the MVSEC public data set to obtain an optical flow calculation result; the system, the equipment and the medium are used for implementing the method. The method has the advantages of high time resolution, high dynamic modeling capability, fine multi-modal fusion, high estimation precision and the like, the motion sensing performance in a complex scene is remarkably improved, and powerful technical support is provided for an intelligent visual system.

The invention discloses a large-scale regional sea wave rapid forecasting method and device based on data driving, and the method comprises the steps: obtaining multi-source marine physical environment data of a target sea area, and generating a standard physical field data flow with unified temporal-spatial resolution; constructing a multi-channel space-time input tensor containing wind field driving information, terrain boundary information and historical wave state information; the difference between the predicted wave height and the real wave height is minimized through a back propagation mechanism, so that a trained wave height prediction model is obtained; receiving latest wind speed field data output by a real-time observation or numerical forecasting mode, and outputting a sea wave significant wave height prediction field at a future target moment; the device is used for implementing the method. According to the technical scheme of the method provided by the invention, through a physical lag alignment mechanism, the time delay of transmitting wind energy to wave energy is accurately captured, and the modeling precision is improved; and meanwhile, rapid deduction of a large-scale sea wave field is realized based on deep learning, and high accuracy and high timeliness are achieved.

The invention relates to the technical field of optical elements, systems or instruments, in particular to a temperature field testing method based on combination of colorimetric temperature measurement and multispectral imaging, and solves the technical problems of low temperature field temperature measurement reconstruction precision and low temporal-spatial resolution of an existing temperature measurement method. The method comprises the following steps: determining a spectral range and a temperature measurement range of a temperature field of a solid engine combustion chamber by utilizing an LIBS (Laser-induced Breakdown Spectroscopy) technology, coupling and packaging a multispectral optical filter and a CMOS (Complementary Metal Oxide Semiconductor) sensor, calibrating a multispectral camera by utilizing a blackbody furnace, and acquiring a spectral image of the temperature field by utilizing the multispectral camera. And obtaining a two-dimensional temperature field of the combustion chamber of the solid engine by using a colorimetric temperature measurement method, and performing comprehensive evaluation. According to the invention, multispectral information of the solid propellant combustion chamber flame can be obtained, and high space-time resolution detection of the two-dimensional temperature field of the solid engine combustion chamber is realized.

The invention discloses a Chinese electronic medical record data processing method and system based on a large language model. The method comprises the following steps: acquiring a Chinese electronic medical record, and performing data preprocessing to obtain a preprocessed Chinese electronic medical record; inputting the preprocessed Chinese electronic medical record into a multi-agent based on the fine-tuned large language model, and combining the multi-agent with the input data according to different processing tasks of the multi-agent to obtain a first cue word; retrieving a medical extraction knowledge base according to the first cue word to obtain corresponding medical knowledge, and combining the first cue word to obtain a second cue word; each agent calls the fine-tuned large language model according to the corresponding second cue word, information extraction and standardization processing are carried out, and extracted medical information is output. According to the method, the accuracy of medical entity and attribute extraction can be improved, combined extraction of complex medical entities and attributes thereof is achieved, standardized mapping of medical terms and time expressions is completed, and the term ambiguity and fuzzy time analysis problems are solved.

The invention discloses a thunderstorm and gale dynamic extrapolation forecasting method based on multi-task MSTA-ConvLSTM, and relates to the technical field of atmospheric sciences, and the method comprises the steps: constructing a multi-source fusion spatio-temporal data set and a gale process record file, and obtaining a data set; a thunderstorm gale nowcasting model based on an MSTA-ConvLSTM model is constructed; according to the method, a dynamic gating mechanism of lightning data is introduced, whether a lightning probability prediction task is started or not is intelligently judged according to the lightning activity intensity, thunderstorm, gale and co-evolution characteristics of the lightning process are considered, 0-3-hour high-temporal-spatial-resolution nowcasting is achieved, the limitation of traditional single-task forecasting is broken through, a multi-task learning structure is adopted, and the prediction efficiency is improved. The model is guided to recognize the lightning occurrence probability during main task gale prediction, and the overall perception capability of a severe convection system is improved; extreme sample learning is enhanced through a weighted loss function, and output is optimized in combination with a gust coefficient model, so that the forecasting precision and practicability are effectively improved.

The invention discloses a carbon sink calculation method based on a space-time fusion technology and flux data, and the method comprises the steps: carrying out the space-time fusion synthesis of a remote sensing image, calculating a leaf area index and chlorophyll concentration data with high space-time resolution, and improving the time and space resolution of carbon sink data; calculating daily net primary productivity data by using a light energy utilization rate model based on the leaf area index data and the meteorological data; correcting parameters of the light energy utilization rate model based on the daily net primary productivity data and the daily flux data; migrating the corrected parameters of the light energy utilization rate model to a flux-free site area based on the ratio of the chlorophyll concentrations to obtain the migrated parameters of the light energy utilization rate model; the migrated parameters of the light energy utilization rate model are transmitted back to the light energy utilization rate model, the net primary productivity is calculated, and the carbon sink data are calculated based on the net primary productivity, so that the carbon sink calculation method is higher in applicability, and reliable technical support is provided for carbon cycle research of an ecological system.

The invention discloses a total primary productivity estimation method and system based on multi-model coupling deep learning, and the method comprises the steps: obtaining the multi-source data of meteorological data, remote sensing images, latent heat flux, sensible heat flux and solar radiation, carrying out the quality control, missing value processing and nearest neighbor interpolation of different data sources, and carrying out the prediction of the total primary productivity. Unifying to a target spatial resolution and a time resolution; in a light energy utilization rate (LUE) model family, a solar radiation phase factor is introduced into a photosynthetically active radiation absorption ratio (FPAR) to obtain a phase modulation type FPAR, drought duration is introduced into a water stress function f (W) to obtain an exponential decay type f (W), and a GPP time sequence of a plurality of improved mechanism models is calculated according to the exponential decay type f (W); extracting spatial texture features from a remote sensing image stack by using a convolutional neural network (CNN), and performing cross-modal fusion on the spatial features and the GPP estimated by the plurality of improved mechanism models in a gating mode to form fusion representation; carrying out learning and collaborative optimization on the fusion representation of the GPP and CNN spatial features estimated by the plurality of improved mechanism models by adopting a gradient lifting tree model; and high-precision estimation of the GPP is realized through multi-model collaborative optimization. The method aims at solving the problem that a traditional light energy utilization rate model is insufficient in response under the extreme environment conditions of drought and intense radiation, the adaptability limitation of a traditional single model under the complex environment is broken through, and therefore high-precision GPP estimation under the complex environment is achieved.

The invention discloses a high-time-resolution flow field testing method and system based on sparse moment measured values and an intelligent power system, and belongs to the field of flow field testing and data reconstruction in bridge wind engineering. According to the method, overall low-frequency and local high-frequency sparse flow field data are obtained through a four-pulse fixed-frequency variable-frequency laser system and a four-exposure high-speed imaging PIV sampling system; a 32-dimensional nonlinear modal coefficient is extracted through a multi-scale convolution flow field sparse feature extraction model, then an unsampled time step coefficient is predicted through an LSTM intelligent power system model, and finally the unsampled time step coefficient is input into a multi-scale convolution auto-encoder to reconstruct a high-time-resolution flow field. The method does not need to depend on a pressure sequence, reduces the hardware and data processing cost through sparse sampling, accurately captures the flow field dynamics characteristics through intelligent modeling, solves the problems of expensive high-frequency hardware, loss of low-frequency reconstruction data and difficulty in model training in a traditional PIV test, and is suitable for flow field dynamics research and engineering optimization.

The invention discloses a lithium metal battery in-situ time space thermal monitoring system and method based on a distributed optical fiber, and the system employs a distributed optical fiber sensing technology (OFDR) to combine with a spiral arrangement structure of a single mode fiber (SMF), converts one-dimensional temperature data into two-dimensional plane distribution, and achieves the precise monitoring of an internal temperature field of a battery. Besides, by introducing a super-resolution algorithm based on an SRGAN model, a low-resolution temperature image is converted into a high-resolution image, details are reconstructed, and the spatial resolution and the time resolution are improved. The method integrating physical sensing and the super-resolution algorithm can reduce the thermal runaway risk, prolong the service life of the battery, realize more uniform lithium deposition and prolong the cycle life of the battery, thereby providing powerful technical support for safe operation and performance optimization of the lithium metal battery. In addition, a hot spot generation mechanism can be explored, and the effectiveness of a battery protection strategy is verified. According to the invention, real-time, lossless and high-precision large-measurement-area super-resolution monitoring can be provided for the lithium metal battery.