555 results about "Data reconstruction" patented technology

The invention discloses a river flow missing data reconstruction method, which comprises the following steps of S1, constructing a river network topological structure, and quantifying hydraulic correlation; s2, spatio-temporal feature fusion and multi-source information extraction; s3, performing multi-task cooperative flow reconstruction and confidence coefficient prediction; s4, dynamic weighting and result correction of meteorological factors; and S5, performing anomaly detection and model dynamic updating. According to the method, a river flow missing data reconstruction method is set, the steps are mutually fused and used, and topology-aware space-time diagram convolution is performed: a river network topology structure is encoded into a weighted adjacency matrix for the first time, space correlation features are extracted through a diagram convolution network, and the problem that a traditional method ignores hydraulic connection is solved; a multi-task collaborative learning mechanism: synchronously outputting a flow reconstruction value and confidence, combining topological smooth constraints, and realizing reconstruction reliability quantification while ensuring precision; and meteorological dynamic weighted correction: dynamically adjusting the node weight based on the real-time rainfall intensity, and accurately adapting to the nonlinear response of the water flow in the heavy rainfall period.

The invention relates to the technical field of ultrasonic detection, and discloses an ultrasonic scanning path optimization and three-dimensional reconstruction method, device, equipment and medium, and the method comprises the steps: positioning a key mark point of a target area, generating an initial scanning path, and controlling an ultrasonic probe to move along the initial scanning path to collect first ultrasonic image data, extracting target structure features to construct an initial three-dimensional point cloud model; establishing a surface projection model; fitting to generate a surface profile curve and calculating the tangential direction of a sampling point; optimizing the orientation of an ultrasonic probe based on the tangential direction to generate an optimized scanning path; and reconstructing a final three-dimensional model. According to the invention, through key mark point positioning and incident direction optimization based on the tangential direction, the scanning path of the probe is enabled to be adaptive to curvature change of the target area, and through combination of real-time contact force adjustment and multi-stage image data acquisition, three-dimensional point cloud space coverage and imaging consistency are improved, and high-precision three-dimensional reconstruction of a complex anatomical structure is realized.

The invention discloses a space-time adaptive power prediction system and method for distributed photovoltaic power generation in a mountainous area, and particularly relates to the technical field of fault prediction and health management. The method is used for solving the problem that the accuracy of power prediction and equipment health state evaluation is influenced by environment characteristic data reconstruction reference drift caused by equipment performance degradation in the prior art. A spatio-temporal data sequence is constructed by acquiring historical data of a photovoltaic unit, a spatio-temporal association network of generated power among equipment is constructed, performance stability of a reference equipment group is evaluated, and when the stability does not meet conditions, a performance attenuation spatio-temporal mode is analyzed to identify common features and personalized features. The propagation path of performance degradation on the space-time correlation network is analyzed based on the characteristics, a critical point is evaluated, reference correction is performed on the environment characteristic data by using the common characteristics and the critical point, and finally, the corrected environment characteristic data is used for executing power generation power prediction and equipment health state evaluation. Therefore, the prediction accuracy and the health management reliability are improved.

A correlation-aware adaptive codebook compaction system for multi-modal data compression that preserves cross-modal relationships while providing enhanced reconstruction quality. The system analyzes temporal and spatial relationships between different data modalities to generate correlation maps that guide compression decisions. A virtual management layer performs stream characterization and adaptive routing, while a processing pipeline implements primary codebook compression with mismatch handling for novel data blocks. High-entropy data segments receive pre-compression processing before codebook compression. Sequential registration data is processed through matrix factorization and dedicated matrix codebooks. The system continuously monitors data distribution characteristics and automatically retrains codebooks when drift thresholds are exceeded. A neural upsampling subsystem uses correlation information to guide cross-modal enhancement processes through modality-specific networks and attention mechanisms. The unified output includes compressed data streams, correlation maps, synchronization metadata, neural model parameters, and updated codebooks, enabling synchronized reconstruction with preserved cross-modal relationships and enhanced quality through correlation-guided neural upsampling.

A federated system and method for data compression optimization in distributed device networks. The system comprises multiple edge devices that analyze local data patterns to generate device characteristic profiles while performing local compression optimization and maintaining data privacy. Edge devices contribute to collaborative learning by generating privacy-preserved updates without transmitting raw data. A central coordination system aggregates encrypted contributions using secure multi-party computation protocols, identifies device groups based on data pattern similarities, and generates optimized compression parameters for each group. The system coordinates collaborative training of data reconstruction models across device groups and deploys group-optimized reconstruction capabilities. Device grouping is performed by calculating similarity scores between device characteristic profiles and clustering devices with scores above predetermined thresholds. The system dynamically adapts compression and reconstruction parameters through federated learning while preserving individual device data privacy, enabling efficient data compression and near-lossless recovery across heterogeneous Internet-of-Things networks.

The invention discloses a smart city planning three-dimensional scene reconstruction optimization system combined with semantic segmentation, and belongs to the technical field of three-dimensional scene reconstruction optimization. The system comprises a data sensing module which collects aerial images and laser point clouds, and complements a sheltered area to obtain city modeling data; the preprocessing module performs denoising, data registration and data fusion on the city modeling data to generate texture point cloud data; the semantic understanding module realizes multi-modal semantic segmentation of the texture point cloud data through a fine-tuned SAM network and an improved RandLA-Net, and semantic tag data is obtained through restoration and optimization; the reconstruction optimization module constructs an initial three-dimensional grid model based on the texture point cloud data, and optimizes a ground feature boundary and a missing region of the model in combination with a semantic tag; and the output application module converts the optimized model into a standard format and outputs the model. Through deep coupling of semantic segmentation and reconstruction optimization, the precision of the three-dimensional scene model is improved, and reliable support is provided for smart city planning.

The invention discloses a BIM (Building Information Modeling) lightweight and streaming rendering method and system, and the method comprises the steps: carrying out data separation, partitioning and simplification processing on an original BIM, and generating lightweight partitioned model data containing associated metadata; based on geometric features of each block in the lightweight block model data, adaptively determining a compression parameter and performing compression coding, and associatively storing the compressed block data and metadata; and according to a rendering request of a client, dynamically selecting to-be-transmitted block data based on the multi-dimensional evaluation model, transmitting the block data to the client in a streaming manner, and performing data reconstruction and rendering display by the client. Through collaborative optimization of fusion data separation, QEM simplification and Draco compression and in combination with intelligent streaming transmission based on a multi-dimensional evaluation model, high-fidelity and low-delay rendering of a large-scale BIM model at a Web end is realized.

The invention discloses a privacy protection evaluation method and a privacy protection evaluation device based on a diffusion model. The method comprises the following steps: acquiring a self gradient obtained by an attacker client in a federated learning system in a local training process and a global gradient after server aggregation; dynamically estimating gradient information of the damaged benign client based on gradient differences in historical communication rounds, and mapping the gradient differences into low-dimensional condition vectors through a condition encoder; injecting the low-dimensional condition vector into a noise prediction network of a diffusion model through a cross attention mechanism, driving the diffusion model to generate virtual data, and training in a local model by using the virtual data to obtain a forged gradient; and according to the similarity loss between the forged gradient and the estimated gradient, alternately optimizing the parameters of the diffusion model and the generated virtual data until the similarity loss converges. According to the method, the gradients of other clients can be fitted more accurately, and the accuracy and authenticity of data reconstruction are effectively enhanced by utilizing a multi-round gradient difference guide diffusion model.

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 relates to a model training method, a DEM data reconstruction method and device, a medium and equipment. The method comprises the steps that sample DEM data are obtained, the sample DEM data comprise first sample DEM data of first precision and target sample DEM data of second precision, the grid side length of the target sample DEM data is one Nth of the grid side length of the first sample DEM data, and N is a positive integer larger than 1; converting the first sample DEM data into second sample DEM data with second precision through a depth residual layer of the reconstruction model, and extracting a deep feature map of the second sample DEM data; extracting near-watershed region features and gradient features from the deep feature map through a feature extraction layer of the reconstruction model to obtain reconstruction DEM data including a near-watershed region and a gradient region; determining the total loss of the reconstructed DEM data and the target sample DEM data; and adjusting parameters of the reconstruction model according to the total loss so as to converge the reconstruction model. The technical problem of low DEM data reconstruction accuracy is solved.

The invention relates to the technical field of converters, in particular to an LLC perception type physical information nested neural network parameter estimation method suitable for an LLC resonant converter, and the method comprises the following steps: S1, constructing a continuous time state space model of the LLC resonant converter, and carrying out the discretization processing; s2, an LLC perception type physical information nested neural network is constructed, and the LLC perception type physical information nested neural network comprises a data reconstruction network and a physical information nested neural network which are connected and is used for online parameter identification of the LLC resonant converter; the data reconstruction network comprises a resonance state judgment layer, a K2 operation layer, a pseudo label generation layer, a constraint layer and a data reconstruction layer; the physical information nested neural network comprises a middle state mapping layer and a physical layer; defining LLC perception type physical information nested neural network input; s3, constructing a loss function; and S4, deploying and executing the LLC perception type physical information nested neural network model.

A power transmission tower grounding resistance intelligent measurement method based on ground potential field distributed sensing comprises the steps that a ground potential sensing network is arranged around a power transmission tower grounding device, and a ground potential field distributed measurement system with an iron tower as the center is established; a broadband characteristic coding current signal is injected into the grounding system through a weak current injection module; acquiring potential response data of each node of the ground potential sensing network excited by the feature coding current signal by adopting a distributed synchronous acquisition technology, and reconstructing complete ground potential field spatial distribution from sparse node observation data based on a compressed sensing theory through a signal reconstruction algorithm; and inputting the reconstructed complete ground potential field spatial distribution map into a pre-trained map neural network model, and outputting an intelligent identification result and an anomaly diagnosis conclusion of the ground resistance. According to the invention, non-contact, distributed and high-precision grounding resistance measurement under the condition that a grounding lead does not need to be disconnected is realized, and the measurement precision and reliability under a complex working condition are remarkably improved.

The invention discloses a simple layout method for distributed optical fiber deflection monitoring of a continuous beam bridge cluster, and the method comprises the steps: laying upper and lower edge strain sensing optical cables on a boundary beam of the continuous beam bridge cluster, only laying the lower edge strain sensing optical cable on a middle beam, and obtaining the deflection of the boundary beam through the upper and lower edge strain monitoring data of the boundary beam; and reconstructing the upper edge strain data of the middle beam by utilizing the upper edge strain data of the boundary beam, and realizing the accurate reconstruction of the deflection of the middle beam by combining the lower edge strain monitoring data of the middle beam through a continuous beam bridge deflection reconstruction method based on data-physical hybrid driving. The method not only solves the problems of large sensor consumption, complex arrangement, high construction cost, poor cluster monitoring economy and the like caused by simultaneous arrangement of optical fibers on the upper edge and the lower edge of each beam in the prior art, but also solves the problems of difficulty in arrangement of sensors and large construction damage caused by bridge deck pavement coverage on the upper edge of the middle beam; and the robustness and the data reconstruction capability of the monitoring system are effectively improved.

The invention relates to the field of seismic data reconstruction, in particular to a seismic data low-rank reconstruction method fusing double-domain transformation, and the method comprises the steps: obtaining original incomplete seismic data, and initializing to-be-reconstructed seismic data and other model variables and parameters under an alternating direction multiplier method frame; performing fractional order gradient transformation on seismic data to be reconstructed along different directions, and mapping to obtain fractional order gradient domain data; performing space-time Hankel tensor expansion transformation on each piece of fractional order gradient domain data, and converting the data into a space-time Hankel matrix; performing low-rank constraint on each space-time Hankel matrix by adopting a Schatten norm to obtain a seismic data low-rank reconstruction model fused with double-domain transformation; and carrying out iterative solution on the model by using an ADMM framework until the model is stable, and obtaining a final seismic data reconstruction result. The method provided by the invention can effectively reconstruct the seismic data with the missing trace, and has a reconstruction effect with strong robustness and relatively good precision for incomplete seismic data with different missing degrees.

The invention discloses a structural health monitoring abnormal data reconstruction method based on a submerged space diffusion model, and belongs to the technical field of structural health monitoring. The method comprises the following steps: firstly, converting one-dimensional structure monitoring time sequence data into a two-dimensional image, mapping the two-dimensional image to a low-dimensional potential space by using a pre-trained variational auto-encoder, and obtaining a potential variable containing semantic information of an original signal; then, defining a forward diffusion process in the submerged space, and establishing an evolution path from the structured features to Gaussian noise; constructing a conditional diffusion U-Net network containing time step embedding and frequency domain conditional coding, and training the network through a mask region selectivity mechanism to learn noise inversion distribution; in a reverse generation stage, a known area forced alignment strategy and a bidirectional resampling mechanism are introduced, and a soft mask smoothing technology is combined. The invention aims to obtain a high-quality time sequence signal which can meet the requirements of subsequent modal parameter identification and long-term performance evaluation by using a generative probability inversion mechanism.

The invention relates to the technical field of computer-aided modeling, in particular to a heating and ventilation equipment energy efficiency optimization method and system based on digital twinning, and the method comprises the steps: obtaining a real-time operation data set, and constructing a mechanism model; obtaining a model prediction data set by using the mechanism model, and training a data reconstruction model; inputting the real-time operation data set into the data reconstruction model to obtain a reconstruction error, and taking the reconstruction error as a state deviation data set; dynamically correcting an adjustable model parameter of the mechanism model based on the state deviation data set, and generating a dynamic calibration digital twinborn model; and finally, executing a multi-target collaborative optimization algorithm based on the dynamic calibration digital twin model, and generating an energy efficiency optimization control instruction. According to the method, the state deviation caused by the performance degradation of the physical equipment is quantified by utilizing the data reconstruction model, and the mechanism model is dynamically corrected by taking the state deviation as feedback, so that the problem that a static model is disjointed from a physical entity state is solved, and high-precision dynamic simulation and energy efficiency collaborative optimization are realized.

The invention relates to the field of mining laser gas detection, in particular to a modulation and demodulation method and system for carrier noise suppression, and the modulation and demodulation method comprises the following steps: S1, generating a driving current sequence through a laser gas sensor; s2, collecting a light intensity sequence to carry out data reconstruction; s3, extracting a harmonic signal, sending the reconstructed sequence into a lock-in amplifier, and respectively extracting a second harmonic signal and a first harmonic signal; and S4, detecting the gas concentration through the ratio by using the mapping relation between the gas concentration and the ratio of the second harmonic signal to the first harmonic signal. According to the method, a continuous data discrete reconstruction technology is introduced into a modulation and demodulation process, and a reference time sequence is generated by utilizing the characteristics of a laser output signal, so that self-matching and dynamic adjustment of modulation frequency are realized, external signal interference is effectively avoided, and the harmonic extraction stability is improved; meanwhile, the hardware structure is simplified, and the adaptability of the system to different gas absorption spectral lines is enhanced.

The invention relates to the technical field of intelligent irrigation, in particular to an intelligent watering control method and system for agricultural production, and the method comprises the steps: data collection, global data reconstruction, soil moisture content prediction, watering strategy optimization, sensor layout optimization, control execution and model updating. In the prior art, a pure mathematical method is generally adopted to calculate global humidity from sparse point data, and multi-dimensional environment information cannot be effectively fused, so that the reconstruction precision is limited and the physical credibility is insufficient; a physical information enhanced confrontation generation network is adopted to carry out global perception, sparse measurement values, geographic coordinates and real-time environment data are fused into conditional tensors to be input into a generator, and training is guided by forcing sensor data consistency in a loss function and introducing moisture diffusion physical constraints; the method can generate a high-resolution and physically reasonable global soil humidity distribution diagram, and significantly improves the reconstruction precision and reliability from sparse points to planar information in a complex farmland environment.

The invention discloses an end-to-end polarization hyperspectral image classification method and system, belongs to the technical field of deep learning and optical imaging, and solves the technical problems of low reconstruction process speed, limited precision, low information utilization rate in a classification process and weak feature extraction capability in the prior art. The method comprises the following steps: carrying out target shooting based on a snapshot type space coding hyperspectral polarization imaging system, and carrying out system coding on a shot image to obtain two-dimensional aliasing data; reconstructing the two-dimensional aliasing data into a polarization hyperspectral data cube by using the trained reconstruction network; training the classification network based on the polarization hyperspectral data cube to obtain a trained classification network; and performing joint fine tuning on the trained reconstruction network and the trained classification network to obtain a polarization hyperspectral image classification model, and performing polarization hyperspectral image classification by using the polarization hyperspectral image classification model. The method is used for realizing high-quality reconstruction and accurate classification of the polarization hyperspectral image.

The invention belongs to the technical field of magnetic field imaging and neural network image processing, and particularly relates to a high-resolution magnetic field imaging method and system based on a sparse data reconstruction network, and the method comprises the steps: inputting a to-be-reconstructed sparse magnetic field distribution image into a pre-constructed sparse data reconstruction network, and outputting a high-resolution complete magnetic field distribution image; the network comprises a generator and a discriminator and is optimized by performing adversarial training only by using sparse samples and masks thereof, and the training process does not need to depend on paired high-resolution and low-resolution complete images. The generator realizes image reconstruction through multi-scale information completion, depth feature extraction and up-sampling, ensures the fidelity of a sampling point by adopting pixel loss constrained by a mask, and improves the physical authenticity of an unknown region in combination with adversarial loss. The system integrates a data preprocessing module, a network reconstruction module, a loss optimization module and a result output module, can quickly generate a high-quality magnetic field image from low-efficiency sparse sampling data, and effectively solves the contradiction between the resolution and the acquisition efficiency in magnetic field imaging.

The invention discloses a power load time sequence anomaly detection method and system based on a Mama and LSTM hybrid network, and belongs to the technical field of power system data analysis and artificial intelligence, and the method comprises the steps: inputting the preprocessed power load and related factor time sequence data into a Mama-LSTM hybrid encoder; performing time sequence data reconstruction and anomaly probability prediction in parallel by using depth features output by an encoder, and performing model training by jointly optimizing reconstruction error loss and anomaly detection loss; calculating a comprehensive abnormal score based on the trained model, and judging an abnormal point by adopting a dynamic threshold value; and outputting an anomaly detection result and providing an analysis report containing an unsupervised evaluation index and multi-dimensional visualization. Through deep series fusion of Mama and LSTM, long-term dependence and complex modes in a power load sequence are effectively captured, and the accuracy, robustness and interpretability of anomaly detection are significantly improved in combination with a joint training strategy and an unsupervised evaluation system of the system.

Photoacoustic medical-device navigation systems and methods provide alternatives to those incorporating fluoroscopy for medical-device navigation in patient bodies. A medical-device navigation system can include an optical-fiber stylet, ultrasound transducers, and a console. The stylet can transmit light to an instant location of a distal tip of an elongate medical device in a patient's body and, thereby, irradiate endogenous chromophores to generate ultrasound-frequency photoacoustic pressure waves therefrom. The ultrasound transducers can detect ultrasound signals corresponding to the photoacoustic pressure waves. The console can instantiate medical-device navigation processes for navigating the elongate medical device via the stylet as the elongate medical device is advanced to the target location in the patient's body. The medical-device navigation processes can include acquiring ultrasound-signal data from the ultrasound transducers, reconstructing images from the ultrasound-signal data, and displaying reconstructed images on a display for navigating the elongate medical device to the target location in the patient's body.

The invention discloses a robot remote adaptive data acquisition control system and method based on dynamic value evaluation. According to the system, dynamic information value factors of data points are calculated in real time through a local processing unit of the robot, and the factors integrate the data change rate, the uncertainty, the task context and the abnormal probability. Based on the factor, the system dynamically adjusts the sampling frequency, the compression strategy and the transmission priority. The remote center is responsible for data reconstruction and fusion and utilizes machine learning closed-loop optimization to acquire parameters. According to the invention, the problems of resource waste and key information acquisition omission caused by fixed frequency sampling are solved, and the optimal balance of data acquisition efficiency and quality under a limited bandwidth is realized.

The invention relates to a three-component high-dimensional seismic data reconstruction method and device based on vector tensor reduced rank, a medium and equipment, and the method comprises the steps: firstly, placing three-component high-dimensional seismic data at three imaginary part positions of a quaternion array in a time domain through employing a quaternion tool, and setting a real part of the quaternion array to be 0 value; and realizing vector joint representation of the quaternion-based three-component seismic data. Quaternion representation can protect the nonlinear orthogonal relation of three component seismic data in the space direction, and vector field structural characteristics of seismic waves are kept. And secondly, transforming a time domain quaternion array into a frequency domain by using quaternion Fourier transform, extracting a spatial high-dimensional quaternion array corresponding to each frequency domain slice, and performing missing data restoration and reconstruction on the high-dimensional quaternion array by using a tensor reduced-rank reconstruction technology based on a quaternion parallel matrix decomposition algorithm. According to the method, the memory occupation amount is remarkably reduced, the calculation amount is small, the calculation cost is low, and the reconstruction calculation efficiency is high.

The invention discloses a marine geological environment multi-dimensional data reconstruction and dynamic visualization mapping method and system, and belongs to the technical field of marine information technology and data visualization. In order to solve the problems that in the prior art, standard data asset structure is isolated, heterogeneous data deep fusion is difficult, and mass data visualization performance is bottleneck, the method mainly adopts the following technical scheme that standardized multi-source data is received, a data volume mapping rule is established, discrete observation data is reconstructed and fused into a unified and continuous three-dimensional data volume, and the three-dimensional data volume is obtained; and executing a three-dimensional space pattern recognition algorithm on the three-dimensional data volume to recognize a feature entity, and providing on-demand query and multi-resolution access services through a visual mapping interface. According to the method, efficient automatic modeling and high-performance interactive analysis of mass and multi-dimensional marine geological environment data can be realized.

The invention relates to the technical field of power systems, and discloses a wind and light output scene-oriented power system power flow data reconstruction method, system and device and a medium, and the method comprises the steps of constructing structured boundary input data; generating a voltage amplitude and a phase angle estimation vector of each node through a deep neural network model, and taking the voltage amplitude and the phase angle estimation vector as a power flow initial solution; performing physical constraint check on the initial solution, identifying out-of-limit nodes and determining an adjustable point set with reactive power regulation capability; and in the range of the adjustable point set, constructing an optimization function, solving to obtain a local adjustment quantity, correcting the initial solution, and generating a final power flow solution meeting the operation constraint of the power system. According to the method, data-driven modeling and physical constraint checking are fused, overall mapping is performed on a high-dimensional scene formed by wind and light output and load by using the deep neural network, the problem that the convergence performance is sharply reduced when the dimension is increased and the number of scenes is increased in a traditional iteration method is avoided, and rapid, stable and engineering-available power flow data generation is realized.

The embodiment of the invention provides an anomaly detection method and device, electronic equipment, a storage medium and a computer program product, and is at least applied to the field of artificial intelligence, and the method comprises the steps: carrying out the standardization processing of an index data sequence of a to-be-detected object in a preset time window, and obtaining a standardized index sequence; performing data reconstruction on the standardized index sequence to obtain reconstructed index data; determining a reconstruction error of the to-be-detected object in a preset time window based on the index data sequence and the reconstruction index data; and performing anomaly detection on the to-be-detected object based on the reconstruction error. According to the invention, the detection efficiency of the anomaly detection process can be improved, and the detection quality of the anomaly detection process is ensured.

A system for generating semantically structured three-dimensional scene representations from unstructured multimedia input data, comprising: an input data receiver configured to receive unstructured, multimedia input; a two dimensional detector configured to receive the unstructured, multimedia input data and to execute computer vision logic on the received unstructured, multimedia input data to detect one or more building materials, one or more damage patterns, one or more architectural elements, and to extract dimensional information; a three-dimensional reconstruction engine configured to reconstruct a metrically accurate three-dimensional model of the physical interior space from the input data; a metadata generation engine configured to generate structured metadata from outputs of the two dimensional detector and the three-dimensional reconstruction engine; and a rule-based converter engine configured to process the structured metadata in accordance with a set of externally defined, carrier-specific rules to generate one or more claims estimate reports.

The invention discloses a photovoltaic output time sequence prediction method and system, and the method comprises the steps: obtaining historical photovoltaic operation data and future environment data, and collecting the inherent parameters of a photovoltaic power station; respectively extracting association among different types of data, performing data reconstruction, sequentially extracting a time sequence dependency relationship and causal association among the data, and generating multi-source mixed features; dynamic characteristic modulation and limitation are carried out on multi-source mixed characteristics through an irradiance gating activation unit and a physical constraint output unit, and photovoltaic prediction output is output through linear mapping; pre-training loss is calculated and training is carried out, photovoltaic prediction output at the next moment is predicted, combined fine tuning loss is calculated, and parameters are updated; and correcting the photovoltaic predicted output at the moment corresponding to the correction time step into the theoretical output of the mechanism model, and continuing to predict. According to the invention, by introducing the irradiance gating activation unit and the physical constraint output unit and combining a fine tuning and correction mechanism, the physical rationality, stability and high precision of photovoltaic output prediction are realized.

Embodiments of the present disclosure relate to systems and methods for reconstructing magnetic resonance imaging (MRI) images from under sampled k-space data. The method includes acquiring multi-coil under sampled k-space data with a fully-sampled calibration region, determining a k-space convolution kernel, and producing an initial multi-coil MRI image estimate via inverse Fourier transform. The estimate is regularized using a trained deep learning regularizer, transformed to synthetic fully-sampled k-space data, and convolved with the kernel to produce data-consistent synthetic k-space data. This is combined with the original under sampled data to produce an estimated fully-sampled k-space data, which is then inverse transformed to update the multi-coil MRI image estimate. The process iteratively improves image quality, reduces noise, and preserves features, enhancing MRI reconstruction from under sampled data.