868 results about "Data dependence" patented technology

The invention relates to the field of power plant control, discloses a virtual power plant control method, system and equipment based on a neural network, and is used for solving the core problems of high data dependence, low topology safety and difficulty in multi-scale collaboration in traditional virtual power plant control. According to the virtual power plant control method based on the neural network, a correction instruction set, a joint estimation value and a topology constraint matrix are input into a neural network controller, and a cooperative control signal is output through singular perturbation decoupling of a fast-varying subsystem and a slow-varying subsystem. And the cooperative control signal is issued to the distributed power supply inverter, the energy storage converter and the intelligent switch, and meanwhile, an execution result is monitored in real time and fed back to the phase space reconstruction module, so that closed-loop control is formed. By constructing the Lyapunov candidate function and calculating the virtual damping coefficient, the transient stability, real-time persistent homologous analysis and topology self-healing instruction generation of the system are enhanced, and the self-healing capability and the fault-resistant capability of the system are improved.

The invention belongs to the technical field of target detection and recognition, and particularly relates to a thinking chain-based large and small model collaborative target detection and recognition method. According to the method, the small model is responsible for most of easy-to-detect targets, the calculation pressure of the large model is reduced, the large model is responsible for suspected samples, vision and language multi-mode reasoning is combined, the overall false detection rate and the omission ratio are both reduced, confidence evaluation is conducted through the joint probability, automatic screening and manual rechecking of uncertain results are achieved, the reliability of key results is guaranteed, and the method is suitable for large-scale popularization and application. According to the'pseudo thinking chain + pseudo label 'method, by means of reasoning and labels generated by the model, data dependence on manual labeling is reduced, only low-confidence samples are manually confirmed, the human intervention range is narrowed, the human cost is remarkably saved, and semantic information with finer granularity is provided for the model by introducing phrase-level feature descriptors. And the identification capability of complex target attributes and states is improved.

The invention discloses a bearing fault diagnosis method based on fusion of an improved capsule network and zero sample learning, and relates to the technical field of state monitoring and fault diagnosis of electromechanical equipment, and the method comprises the following steps: collecting a multi-mode signal during the operation of a bearing, employing an improved wavelet threshold denoising algorithm for the multi-mode signal to eliminate environmental noise, and then employing a zero sample learning algorithm for the multi-mode signal; according to the method, the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm are adopted to extract the time-frequency domain mixed features as sample data, and the GAN is combined to expand the bearing sample data, so that the data dependence of traditional deep learning is broken through, the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm, and the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm. Small sample data learning is realized, and by training a pyramid capsule network and optimizing a cross entropy loss function and combining cross-modal joint optimization and a zero sample inference engine, the diagnosis accuracy of known faults is greatly improved, and unknown fault types can be effectively inferred.

The invention discloses an unmanned aerial vehicle image small target detection method based on dynamic filtering and an adaptive sparse Transform. According to the method, an end-to-end target detection framework is adopted, a dynamic filtering module is introduced into a backbone network, global feature interaction is achieved through data-dependent frequency domain operation, and linear calculation complexity is maintained. For feature interaction in a scale, an adaptive sparse Transform module is introduced to enhance the capability of focusing key information on high semantic hierarchy features of a model, and noise interference and feature redundancy are effectively suppressed at the same time. Through the combination of dynamic filtering and adaptive sparse Transform, the model can extract image foreground information more effectively on the premise of not significantly increasing the calculation burden, and the problem that a traditional target detection model is susceptible to complex background interference is significantly relieved.

The invention provides a GPU heterogeneous computing resource allocation method and system based on task scheduling, and the method comprises the steps: carrying out the feature analysis of a to-be-scheduled task, so as to extract the calculation intensity, video memory demands, inter-task data dependence and historical execution data of the to-be-scheduled task, and obtaining a task feature vector; constructing a performance model of heterogeneous GPU resources to predict execution efficiency and resource consumption of the GPU under the target task based on hardware performance parameters and running states of the GPU; and matching the to-be-scheduled task with the GPU resources according to the task feature vector and the performance model of the heterogeneous GPU resources. Through task feature analysis and GPU resource performance modeling, the adaptive relation between tasks and GPU resources can be deeply mined, to-be-scheduled tasks are accurately allocated to the most appropriate GPU, the resource utilization rate is greatly increased, the task execution sequence and resource allocation are dynamically planned, and the task execution time is effectively shortened.

The invention provides a resource scheduling control method and system for a big data server, and the method comprises the steps: constructing a multi-dimensional resource portrait module, collecting the CPU, memory, network, storage I / O load and task queue length of each node in real time, and predicting a resource demand trend through a time sequence algorithm; extracting characteristics such as calculation intensity, data dependence, memory requirements, network transmission quantity and the like; adjusting the weight coefficients of the resource utilization rate, the task completion time and the energy consumption efficiency according to the system load and the historical effect; establishing a bipartite graph model by taking a resource trend as a node feature and a task vector as an edge feature, and calculating a matching score through graph convolution and a multi-objective optimization function; the scheduling scheme is synchronized by adopting a consistency algorithm; automatic rollback and reallocation are carried out when resources are detected to be insufficient; and optimizing a weight coefficient and a network parameter through reinforcement learning. Through the method, the system resource utilization rate can be improved, the task execution efficiency is improved, the overall scheduling effect stability is improved, and the system fault recovery time is shortened.

The invention discloses an instruction execution method and device for an artificial intelligence chip, and relates to the technical field of artificial intelligence chips, and the method comprises the steps: carrying out the deep learning driven feature recognition and resource demand prediction of an input task, and generating a demand prediction report of the task for computing resources through the analysis of a computational graph and a data dependency relationship of the task; a computing unit and memory resources are intelligently scheduled, an optimal instruction execution path is dynamically selected, and meanwhile a caching strategy is optimized; automatically generating a micro instruction set corresponding to the task according to the computing resource demand, the computing characteristic and the intelligent scheduling result of the task; when multiple tasks are executed in parallel, the execution sequence of the multiple tasks is dynamically adjusted according to the calculation load and the resource sharing condition of the tasks, and resource allocation is optimized. According to the method, the computing resources and the memory bandwidth required by each task can be accurately predicted through the deep learning driving analysis of the task computing graph, so that the allocation of the computing resources is optimized.

The invention provides an industrial data online migration method, medium and system of a productivity middle platform, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, historical access records of an industrial data source are collected, and a deep neural network model comprising a multi-head attention layer, a time sequence feature extraction layer and a residual connection layer is constructed; and the prediction of the data change rate is realized. And based on a prediction result, dynamically calculating a data change rate demarcation point by utilizing an optimization equation set, and dividing a data table into different change frequency categories. And then constructing a data dependency relationship matrix, and determining a migration sequence by adopting an improved topological sorting algorithm. Batch migration, incremental migration and real-time synchronization mechanisms are adopted for different types of data, and model updating is triggered through real-time monitoring, so that dynamic optimization of a migration strategy is ensured. Finally, data consistency verification is carried out, migration accuracy is guaranteed, and the technical problem that the online migration efficiency of industrial data is low due to real-time dynamic change of the data change rate is solved.

The invention discloses a compliance verification method and device for medical process data, and relates to the field of medical data, and the method comprises the steps: constructing a medical process knowledge graph, and defining the data dependence relation of clinical test, production and declaration stages; dynamically activating the rule subsets based on the current process stage, and calculating and loading association rules through dependency to form a composite rule set; converting the structured data, the medical image and the handwritten text into a unified vector space, calculating a cross-modal semantic consistency score, and resolving conflicts according to a preset priority or a cross-modal attention fusion mechanism; and when the flow stage is switched or the historical data is modified, full-link backtracking verification is triggered. The device comprises a process modeling module, a rule dynamic generation module, a multi-modal alignment module and a visual proof module. According to the invention, the compliance verification efficiency is greatly improved through the dynamic rule engine, and the supervision risk and operation cost caused by data defects of pharmaceutical enterprises are effectively reduced.

Provided in the present application are a training / application method for a representation learning model, and a device and a medium. The training method comprises: extracting a plurality of source codes from an open-source repository, and on the basis of the plurality of source codes and a large language model, generating a training sample set, wherein each sample in the training sample set comprises assembly codes and natural language text; on the basis of an assembly code data set, performing pre-training to generate an assembly coder, and on the basis of a natural language data set, performing pre-training to generate a text coder; on the basis of the training sample set and a contrastive learning algorithm, performing alignment training on the assembly coder and the text coder, so as to obtain a code representation learning model and a natural language representation learning model which are semantically aligned; and on the basis of the code representation learning model and the natural language representation learning model, which are semantically aligned, constructing a representation learning model. The method of the present application significantly improves the generalization ability and accuracy of a representation learning model, and greatly reduces dependence of the model on samples and a large volume of tagged data.

The invention discloses a data cable self-adaptive production method and system based on image analysis, and the method specifically comprises the steps: synchronously collecting three-mode image data containing visible light, infrared light and polarization at a gas injection section, an extrusion section and a molding section of a data cable through a multispectral imaging unit; performing spatial alignment on the three-mode image data by adopting a sub-pixel registration algorithm to obtain standard image data; based on the standard image data, combined diagnosis is carried out on the cable gas injection structure, the insulation layer quality and the surface defect through a multi-task analysis engine, and a defect diagnosis result is obtained; and based on a defect diagnosis result, dynamically adjusting the traction speed, the extrusion temperature and the gas injection pressure by utilizing a fuzzy PID controller optimized by reinforcement learning to form online process parameter closed-loop control. The defects of single function, static detection, high data dependence and the like of a traditional data cable production detection method are effectively overcome, and a more efficient and intelligent solution is provided for data cable production.

The invention discloses a road-oriented full-process digital collaborative management system and method, particularly relates to the technical field of road data management, and is used for solving the problem of abnormal multi-role collaborative conflict recognition. According to the method, dynamic linkage management of task states and role behaviors in the whole highway design process is achieved by building the task-driven atlas and the multi-role scheduling model, and the dynamic linkage management of the task states and the role behaviors in the whole highway design process is achieved by extracting access behaviors, task records and data version states, calculating role collaborative offset and recognizing and controlling the write-in permission of task conflict nodes. A priority factor is constructed based on a stage index, data dependence and a time urgency degree, a nonlinear model is adopted to generate scores, automatic reconstruction of a scheduling sequence and data ownership is driven, role permission and a collaborative view are synchronously updated, a data access control closed loop based on task state evolution is formed, and a three-dimensional responsibility chain is constructed by whole-process behavior traces. The traceable management of collaborative operation is realized, and the intelligence of collaborative scheduling and the accuracy of data management are improved.

The invention discloses a lithium battery thermal runaway control and safety response strategy system, which realizes the real-time monitoring and prediction of the internal temperature and voltage state of a battery through thermal-electric coupling modeling and multi-sensing data fusion, and realizes the real-time monitoring and prediction of the internal temperature and voltage state of the battery based on a model prediction control and threshold enhancement strategy. And triggering graded safety response in the early stage of thermal runaway. The system adopts a multi-stage response mechanism including measures of primary early warning, active cooling intervention, emergency power-off fire extinguishing and the like, a gas suppression path and cooling mode switching function is designed, and heat diffusion and spreading and toxic gas harm are effectively suppressed. Compared with a method depending on complex digital twinning or deep learning, the method is simple in structure, rapid in response and low in data dependence, has good system stability and engineering applicability, and can be widely applied to the field of lithium battery safety management of electric vehicle battery packs, energy storage power stations and the like.

The invention relates to a wind power equipment blade fault detection technology, discloses an unsupervised wind power equipment blade fault detection method based on a phase perception parallel attention mechanism, and solves the problems that an existing wind power equipment blade fault detection method is high in dependence on labeled data, insufficient in generalization ability under strong noise and variable working conditions and high in fault detection efficiency. And a weak transient fault signal and a dynamic change characteristic are difficult to capture robustly. According to the scheme of the invention, the method comprises the steps: collecting a blade operation audio signal, and extracting a dual-channel time-frequency feature containing an amplitude spectrum and a phase spectrum through improved short-time Fourier transform; a deep adversarial auto-encoder is constructed by using an encoder containing a phase perception parallel attention module, a decoder and an auxiliary encoder, and normal working condition feature distribution is learned by reconstructing an error loss, potential representation consistency loss, adversarial loss and phase consistency loss optimization model during off-line training; in the reasoning stage, the fault is judged based on the feature distance score and the reconstruction error score.

The invention belongs to the technical field of computer vision and natural language processing, and discloses a small sample city scene image analysis method based on BERT and self-supervised learning. Potential information of unlabeled urban environment image data is fully mined in combination with self-supervised learning, and multi-modal features of urban environment images and description texts are integrated by using a cross-modal semantic enhancement mechanism, so that accurate diagnosis of small sample urban environment images is realized. According to the method, the generalization ability of the model in a small sample scene is improved, the diagnosis efficiency and accuracy of an existing method in a complex analysis scene are remarkably improved, and the defects that in the prior art, data dependence is high, the requirement for hardware resources is high, and cross-modal information utilization is insufficient are overcome.

The invention relates to the technical field of wind power generation. The invention provides a yaw static and dynamic error adaptive method based on big data backtesting, which comprises the following steps of: constructing a multi-dimensional feature vector through multi-source data fusion data, and establishing a dynamic feature data set; based on the dynamic characteristic data set, a space-time diagram convolutional network is adopted to establish a wind power plant dynamic error prediction model, and space-time evolution rules of wind shear, turbulent flow and wake flow effects are captured; constructing a variational self-coding reference model based on historical full wind speed section data, calculating a residual error between a current working condition and the variational self-coding reference model in real time, and taking the residual error as a static error prediction value; performing adaptive weight fusion on the static error prediction value and the dynamic error prediction value to obtain a fusion error; and the fusion error is converted into the yaw angle correction amount, and the wind facing action is executed. The problems that an existing yaw error recognition technology is high in data dependence, lack of dynamic analysis, poor in model adaptability and difficult in complex wind field processing are solved.

The invention discloses an unsupervised semi-pairing cross-modal retrieval method and system based on deep learning, relates to the field of artificial intelligence, and is used for solving the problems of annotation data dependence, asymmetric semantic association and high-dimensional storage efficiency. According to the method, a double-branch visual encoder and a dynamic prompt text encoder are combined, dynamic weighting of visual-text features is achieved through gating cross attention, and modal redundancy interference is restrained. An enhancement strategy is generated through low-frequency semantic guidance, and the long-tail word coverage rate is increased; a dual-stage quantitative hierarchical index is constructed, coarse-grained clustering and fine-grained product quantitative compression feature storage is adopted, and million-level data real-time retrieval is supported. A degradation aware increment maintenance mechanism monitors data distribution offset through a KL divergence threshold, and triggers index reconstruction to maintain long-term update precision. According to the method, limitation of a traditional strong pairing model is broken through, cross-modal sensitive content second-level positioning is achieved, asymmetric semantic alignment is effectively solved, and retrieval efficiency is improved.

The invention relates to the technical field of databases, and discloses a multi-type database performance optimization and operation and maintenance management method and system.The multi-type database performance optimization and operation and maintenance management method comprises the steps that real-time performance indexes of a heterogeneous database cluster are collected, and a noise reduction data set is obtained; generating a cross-library performance coupling degree matrix through the resource competition coupling degree and the data dependence coupling degree; constructing a coupling relation graph; identifying a bottleneck node set based on the coupling relation graph and the abnormal level; generating an optimization strategy; and performing strategy conflict identification according to the optimization strategy, generating a processing strategy, and executing the processing strategy optimization strategy. Through the improved weighting centrality algorithm, the core bottleneck node which has the greatest influence on the whole cluster is accurately identified, a differential optimization strategy is adopted according to the database type, a perfect strategy conflict detection and avoidance mechanism is established, mutual interference in the optimization process is effectively prevented, the optimization success rate is improved, and the optimization efficiency is improved. And the optimization time is shortened.

The invention provides a steel structure welding quality detection method and a storage medium, and the method comprises the steps: generating a composite image # imgabs1 # marked with the type and position of a welding defect based on an unmarked real welding region image # imgabs0 # of a steel structure; the real steel structure welding image # imgabs2 # and the composite image # imgabs3 # marked with the welding defect type and position are combined into a steel structure welding image set # imgabs4 #; performing datamation processing on the steel structure welding image set # imgabs5 # to generate a deformable grid # imgabs6 # matched with the type of the welding defect and a preprocessing image set # imgabs7 #; and inputting the preprocessed image set # imgabs8 # into a pre-trained building engineering steel structure welding defect detection model so as to output welding defect characteristics of a welding seam area. The steel structure welding quality detection method is small in data dependence, high in welding defect recognition robustness and high in intelligent degree, and the intelligent level of constructional engineering steel structure welding quality detection can be improved.

The invention relates to the technical field of chip design, and discloses a chip dynamic power consumption scheduling method and system based on an intelligent algorithm. The method comprises the following steps of: firstly, acquiring instruction stream data operated by a chip in real time, extracting a feature vector comprising an instruction dynamic change vector and context associated data, and determining a power consumption prediction mapping parameter according to the feature vector; and when the parameter exceeds a preset threshold value, an accurate power consumption prediction result is generated by adjusting the weight of the convolutional neural network. Subsequently, a synchronous timing demand is calculated based on the instruction switching frequency and the data dependency, and an initial power supply configuration is determined. By monitoring task load classification signals, the power consumption distribution proportion is adjusted when the signals are lower than a threshold value, the optimized power supply configuration is obtained, and the improvement index of the resource distribution efficiency is calculated according to the optimized power supply configuration. And finally, according to the index, dynamically adjusting a limiting condition of a scheduling period, and forming a self-adaptive optimization framework, thereby realizing accurate prediction and dynamic optimization scheduling of the chip power consumption.

The invention belongs to the technical field of track detection, and more specifically relates to a track scene thunder-vision fusion anti-intrusion monitoring method and system based on sparse feature fusion. The method comprises the steps that a laser radar obtains three-dimensional point cloud data, a high-definition camera obtains visible light image data, and data preprocessing is carried out; performing external parameter automatic calibration optimization and time alignment compensation on the laser radar and the high-definition camera to obtain time-space aligned multi-modal data; fusing the camera semantic features and the laser radar geometric features based on multi-modal data of space-time alignment to obtain fused features; and after multi-modal feature fusion is completed, a target heat map is generated through a sparse detection head, and graded early warning is realized in combination with a dynamic safety distance model. The method solves the problems that an existing recognition algorithm is high in false alarm rate, and the false alarm rate is higher under low-light conditions such as rainy days or dusk; the data dependence is strong, and the ability to detect unlabeled novel foreign matters is almost zero.

The invention relates to the technical field of engineering process control, and discloses a ball mill granularity soft measurement method based on a time sequence large model. Constructing a multi-feature fusion module for extracting multi-scale features based on Convld K3, Convld K5 and Convld K1, and constructing a soft measurement model in combination with multi-head attention and a large language module with a fixed weight; the soft measurement model generates a first feature and a query matrix, generates a key matrix and a value matrix according to a fixed weight of the large language module, generates a second feature based on the query matrix, the key matrix and the value matrix, and fuses the second feature with the first feature to obtain a fused feature; and then a granularity prediction result corresponding to the field data is obtained through a large language module, so that the problems that an existing soft measurement model is too high in dependence on large-scale sample data, insufficient in modeling capability for complex nonlinear process parameters and low in prediction precision are solved.

The invention relates to the technical field of cloud edge collaborative computing power scheduling, and discloses a heterogeneous computing power scheduling optimization method based on a cloud edge collaborative architecture. The method comprises the following steps: acquiring real-time computing power state data of all available computing nodes in the cloud edge collaborative architecture; performing heterogeneous type division on the computing nodes according to the real-time computing power state data to generate a three-layer computing power resource pool containing cloud computing nodes, edge computing nodes and terminal computing nodes; extracting task calculation features for the current to-be-scheduled task set, wherein the features comprise calculation intensity, data dependence and real-time requirements; constructing an initial task allocation scheme based on the matching relationship between the task calculation features and the three-layer computing power resource pool; iteratively optimizing the initial scheme by adopting a dynamic load balancing strategy to generate a final task scheduling instruction; the instructions are distributed to the corresponding computing nodes to be executed, and computing power state changes in the task execution process are continuously monitored.

The invention discloses a consumer feedback analysis method based on a multi-agent model, and the method comprises the steps: firstly sampling and generating consumer multi-dimensional feature data, and expanding the data into a semantic portrait through a large model; therefore, an intelligent agent containing a memory module is constructed, and part of the intelligent agent is set as an opinion leader. Commodity attributes, propaganda and competitive products are input into each agent, and initial evaluation is independently output; and then multiple rounds of viewpoint interaction are carried out in the same social evaluation area, the opinion leader has a higher sampled weight, and memory and comments are updated. And after simulation is finished, updating evaluation is generated again and compared with the initial evaluation, attitude changes are analyzed, all data are summarized, and a visual comprehensive report is output. According to the method, accurate simulation and feedback analysis of consumer behaviors are realized by combining a large language model and a multi-agent simulation test technology, and the defects of a traditional method in the aspects of consumer portrait dynamics, agent adaptability, interaction complexity, data dependence and the like are overcome.

The invention discloses a heterogeneous computing power cooperative scheduling system and method for mixed precision training, and belongs to the technical field of artificial intelligence computing. The system comprises a computational graph analysis and operator portrait module which is used for analyzing and dividing a model computational graph and extracting operator features; the heterogeneous hardware capability sensing and matching module is used for managing performance files and real-time states of heterogeneous hardware in the cluster and matching optimal execution hardware for each calculation partition; and the data flow coordination and pipeline parallel controller is used for generating a global execution plan, managing cross-device data dependence and communication and calculating overlapping optimization execution efficiency through communication. According to the method, the problem of low scheduling efficiency of mixed precision training in a heterogeneous environment is solved, automatic and accurate mapping from a calculation task to heterogeneous hardware is realized, the training speed is remarkably improved, the training cost is reduced, and the overall resource utilization rate of a cluster is improved.

The invention relates to the technical field of function verification, in particular to a processor loading storage unit function verification method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out formalized verification on a processor loading storage unit through a formalized verification tool, and extracting feature data which does not cover a critical path; wherein the feature data at least comprises a time sequence feature, an address feature and a data dependence feature; converting the feature data into constraint rules available for the dynamic simulation platform, and generating test excitation; and executing dynamic simulation verification based on the test excitation to obtain a coverage rate result. According to the processor loading storage unit function verification method provided by the invention, through formalized verification and dynamic simulation closed-loop cooperation, the directional constraint is generated by extracting the key path features, the verification period is greatly shortened, and the coverage rate convergence efficiency is improved.

The invention relates to the technical field of artificial intelligence and data analysis, in particular to an unsupervised anomaly detection method and system based on comparative potential fusion. The method aims at solving the problems that in the prior art, an unsupervised anomaly detection method is limited in feature expression ability, sensitive in noise, insufficient in potential feature discrimination and lack of statistical interpretability in detection results. According to the method, the global potential features generated by comparison learning and the self-encoder reconstruction residual error are fused, the statistical model is combined for self-adaptive threshold judgment, the problems of insufficient feature expression and high noise sensitivity in multi-source heterogeneous time series data anomaly detection are effectively solved, and the method has the advantages that the detection precision and robustness are improved, and the dependence on labeled data is reduced.

The invention provides a data synchronization method and device for a distributed computer storage system. The data synchronization method comprises the following steps: determining a data concentration ratio of execution data in each data synchronization transaction in a database through a data range and a data increment of each data synchronization transaction; obtaining all synchronous storage nodes of a database in the computer storage system, and determining a node allocation strategy of the database during data synchronization according to the load characteristics of the synchronous storage nodes in the database and the data dependency relationship between the data synchronization transactions; performing dependency quantification on execution conditions of the transactions in the database according to a node distribution strategy and each centralized feature to obtain execution dependency of the transactions in the database; and performing balanced allocation on the synchronous storage nodes of the data synchronization transactions in the database based on the execution dependency. Based on the scheme, the balanced distribution of the synchronous storage nodes in the database can be realized, so that the data delay of the computer storage system in data synchronization can be reduced.

The invention relates to the technical field of multi-peak spectrum demodulation, and particularly provides a fiber bragg grating multi-peak spectrum demodulation method and system. The method comprises the following steps: extracting local spectral features based on an experimental reference spectrum to form initial atoms, and performing translation offset and normalization processing to obtain an over-complete spectral atom dictionary; performing global offset preliminary estimation based on the dictionary, and obtaining preliminary estimation values of peak sites of the measurement spectrum and the reference spectrum through cross-correlation calculation; executing constraint orthogonal matching pursuit sparse recovery based on the estimated value, and recovering atomic displacement from the measurement spectrum by using block sparsity, translation consistency and non-negative constraint; and performing wind speed inversion and calibration based on the atomic displacement, and converting the wind speed into a wind speed estimated value through a nonlinear calibration model to obtain a final result. According to the method, a dictionary based on experimental data is constructed, dependence on large-scale labeled data is reduced, a physical mechanism and sparsity prior are fused, and the problems that a traditional method is insufficient in precision and weak in generalization ability in a complex environment are solved.

The invention relates to the field of cold and hot data storage, in particular to a cold and hot data storage optimization method based on a large model, which comprises a business integration module, a data dependence module, a mode updating module, a cold and hot partitioning module and a file merging module, and is characterized in that the business integration module is used for collecting business data to form a training data set; the data dependency module is used for fitting business data to obtain a data consanguinity model, the mode updating module is used for predicting an access mode, the cold and hot partitioning module is used for calculating the heat of the data and performing dynamic scheduling, and the file merging module is used for automatically merging small files. A data storage structure is standardized, storage space is reduced, more efficient and more accurate cold and hot data classification can be achieved, the resource utilization rate of data storage hardware is improved, hot data response delay is reduced, the system storage utilization rate is remarkably improved, and the storage efficiency is improved.