28results about How to "Efficient migration" patented technology

The invention relates to a server application architecture migration method and device and a storage medium, and the method comprises the steps that an automatic detection module analyzes and checks a to-be-migrated application under an X86 architecture, and the to-be-migrated application is subjected to static code analysis, dynamic performance analysis and system compatibility check; according to analysis and check results of the automatic detection module, the automatic adjustment module replaces an X86 specific instruction and a library function, performs memory allocation optimization and memory mapping adjustment, and generates an automatic adjustment report; and the automatic optimization module executes a performance benchmark test, and executes memory access optimization, algorithm optimization, task scheduling optimization and multi-core parallelization adjustment according to a test result. According to the server application architecture migration method and device and the storage medium, the architecture adaptation process can be simplified, performance optimization support and automatic dependency relationship management can be provided, the migration efficiency can be improved, and the cost can be reduced.

The invention relates to the technical field of natural language processing, in particular to a model distillation method and system based on a teacher model and situational reasoning. By using a multi-teacher learning framework, the student model can draw knowledge from a plurality of large teacher models, and diversified learning paths are provided for the student model; due to the introduction of a context awareness and example generation mechanism, the understanding and reasoning ability of a student model to a complex situation is enhanced by considering context information of input data and an input example; the reasoning chain strategy can ensure that the student model can generate an accurate and deep reasoning process, and the decision process of the teacher model is simulated; a parameter fine adjustment technology is adopted, only key parameters in the student model are adjusted, efficient migration of teacher model knowledge is achieved, and the number of parameters needing to be adjusted in the distillation process of the student model is reduced; the resource consumption in the model deployment and updating process is remarkably reduced, so that the trained model keeps the performance similar to that of the model.

The invention discloses a data migration method and device, a storage medium and a computer program product. The method comprises the following steps: acquiring an initial data set of a plurality of service modules in a service system to be migrated, the plurality of service modules being function modules obtained by splitting according to a plurality of functions provided by the service system; determining the similarity among the service modules according to the initial data set of each service module, and clustering the plurality of service modules according to the similarity to obtain a plurality of clusters; and for each clustering cluster, performing dimension reduction processing on the initial data set of each service module in the clustering cluster to obtain a target data set of each service module, and migrating the target data set of each service module in the clustering cluster. According to the method and the device, the technical problem of data missing and inconsistency of dimension-reduced data due to the fact that the dependency relationship between the business modules to which the data belong is not considered when dimension-reduced migration is carried out on a large-scale data set in the prior art is solved.

The present invention discloses a method and device for data migration, and relates to the field of computer technology. A specific implementation of the method includes: obtaining a data table to be migrated from a source database according to a data source name; extracting target data from the data table to be migrated based on table fields; mapping the table fields to target fields, and converting the type of the target data to the target type to generate migration data; and storing the migration data in the target database. This implementation can efficiently migrate massive data; and in the process of migrating massive data, the massive data is secondary processed to be processed into data that meets the needs of analysis or search.

The invention discloses a database migration backup method and system. The system comprises a parameter input module and a data export module, wherein the data export module is used for exporting data and comprises a table information export unit, a data export unit, a log recording unit, an error processing unit and a permission modification unit; the data transmission module is used for data transmission and comprises a script transmission unit, a data transmission unit, a log recording unit II and a file verification unit; and the data import module is used for importing data and comprises a data import unit, a log recording unit III, a data verification unit and a circulation unit. According to the method, the data migration efficiency is greatly improved, manual operation errors are effectively avoided, and the method has remarkable practical value and popularization potential. The method is suitable for data export and migration scripts of a GreenPlum database cluster in a Centos7 environment, and is particularly suitable for database backup and migration of database management in a multi-tenant and multi-GreenPlum cluster environment of a company.

The invention discloses a full-spectrum response BiOBr / Bi2O3-x / WO3-x double-S heterojunction material rich in oxygen vacancy as well as a preparation method and application of the full-spectrum response BiOBr / Bi2O3-x / WO3-x double-S heterojunction material. The heterojunction is prepared by taking bismuth nitrate pentahydrate, potassium bromide and WO3-x microspheres containing oxygen vacancies as main raw materials through a wet impregnation method, and the value range of x in Bi2O3-x and WO3-x is 0 < = xlt; 1 represents that the two have oxygen vacancies with certain concentration. Due to the introduction of the wide-spectrum response semiconductor materials WO3-x and Bi2O3-x, the double-S-type heterojunction shows a remarkable full-spectrum absorption capability. Meanwhile, oxygen vacancies in the material can serve as effective electron capture centers and transmission channels, and separation of photo-induced electron-hole pairs is effectively promoted. Based on the synergistic coupling effect among BiOBr, Bi2O3-x and WO3-x, the heterojunction can degrade ciprofloxacin (CIP) and reduce Cr (VI) (85% and 74% respectively) at the same time. The heterojunction material provided by the invention is simple in preparation process and good in repeatability, and has important application potential in the field of environmental governance.

This invention discloses a 3D seismic fault segmentation method based on 3D adaptive coding and cross-domain collaborative learning, which addresses the problems of scarce annotations, the inability of 2D models to effectively represent 3D structures, and unstable predictions across different work areas. The method first preprocesses the source domain labeled and the target domain unlabeled seismic data. A lightweight 3D adapter is inserted based on the frozen backbone parameters of the 2D visual model, and a dual-path encoder with spatial and depth perspectives is constructed, fusing features from both perspectives to form a joint 3D representation. Then, a lightweight 3D decoder is used to progressively restore resolution and generate a fault probability volume. The source domain uses binary cross-entropy and Dice loss for supervised learning; the target domain uses a heterogeneous bi-sub-network composed of a visual branch and a 3D convolutional branch. Pseudo-labels are adaptively generated through voxel confidence, and unsupervised training is achieved by combining consistency and difference regularization, thus enabling stable and reliable 3D fault segmentation across different work areas even without target domain labels.

The invention belongs to the technical field of solid-state batteries, and discloses an alloy ion / electron mixed conductor and application thereof. The mixed conductor is prepared by taking antimony powder, lithium powder or sodium powder and at least one metal powder of lanthanum, scandium, yttrium, aluminum, gallium and indium as raw materials through ball-milling mixing, tube sealing annealing and quenching treatment. When the mixed conductor is used as an interlayer of a lithium / sodium metal negative electrode and an electrolyte in the solid-state battery, the growth of lithium / sodium dendrites can be effectively inhibited through a specific nucleation mechanism, and the utilization rate of the metal negative electrode is improved; when the conductive additive is used as a conductive additive of a silicon, red phosphorus or graphite / hard carbon negative electrode material, by virtue of high ion / electron conductivity and good potential stability, the interface side reaction is reduced, and the rate capability and the cycling stability of the negative electrode material are remarkably improved.

The present application relates to the technical field of computer vision, and relates to a 3D Gaussian sputtering stylization method, system, device and storage medium; wherein the 3D Gaussian sputtering stylization method comprises the following steps: obtaining a style image and a three-dimensional Gaussian scene to be stylized; performing non-negative matrix decomposition on the style image to extract an initial color basis matrix and an initial coefficient matrix of the style image; constructing a color orthogonal decoding model according to the initial color basis matrix and the initial coefficient matrix, and recalculating color parameters of each Gaussian primitive of the three-dimensional Gaussian scene; performing style transfer optimization on the Gaussian primitive, updating trainable parameters until an iteration termination condition is reached; and determining a stylized three-dimensional Gaussian scene according to updated geometric attribute parameters and color attribute parameters. The present application can solve the problems of color incoordination and dirty color during 3D Gaussian sputtering stylization.

The invention relates to a preparation method of a polyurethane porous foam loaded CdS / MIL-101 (Fe) photosensitizer as well as a product and application of the photosensitizer, the structural advantages of an MOFs material are utilized to enhance photoinduced electrons generated by CdS to promote reduction of CO2, and the photoinduced electrons are immobilized on a porous carrier to realize rapid solid-liquid separation of a catalyst, so that the photosensitizer has a good application prospect. On the basis of the characteristics of large specific surface area, porosity, a large number of unsaturated active sites and the like of the MOFs material, CO2 can be directly captured by air. The constructed efficient photocatalytic CO2 reaction system can effectively promote charge transfer of a composite system interface, the CdS electron mass transfer process is improved, and the CO2 photoreduction capacity is further improved. Meanwhile, the active component CdS / MIL-101 (Fe) is loaded on the porous foam carrier through simple in-situ immobilization, so that the loading rate of the composite material can be further improved, and the photosynthetic carbon sequestration rate is accelerated.

The present invention relates to the technical field of multi-machine cloning cross-regional data transmission, specifically a method and system for multi-machine cloning cross-regional data transmission of a cloud phone; first, a user submits a cloning task request, the system generates a task list according to the request, calculates the required resources and allocates them to the target area, establishes a task framework diagram, then logically divides the instance data, marks the difference data blocks, and authenticates the link relationship between instances through a dual link protocol, then combines multi-threading technology to transmit the difference data blocks, and dynamically balances thread task allocation to optimize efficiency. During the transmission process, the system performs consistency verification on the data, detects and retransmits the erroneous data blocks, and uses a timeout and a maximum number of mechanisms to avoid infinite retransmissions. The final target instance completes data reconstruction by merging the difference data blocks, and verifies data consistency through a hash value. The present invention is based on task management, data sharding, link authentication, and verification and repair to ensure efficient and secure data cloning.

The application discloses a multi-modal low-rank adaptive cognitive function estimation method based on physiological-molecular phenotype, belongs to the technical field of cognitive function estimation, and first collects physiological-molecular phenotype, dynamic physiological signals and cognitive test time information of a subject in a plateau hypoxic environment, and constructs a 28-dimensional multi-modal input feature vector after preprocessing; then, a hybrid expert base model is pre-trained based on a source cognitive task with the largest data volume; finally, the base model parameters are frozen for a new target task, a LoRA low-rank adaptive technology is introduced, only a small number of parameters are trained to complete cross-task adaptation, and the cognitive task reaction time and accuracy are output to realize estimation. The application realizes unified estimation of eight types of core cognitive functions, has high migration efficiency, excellent estimation precision and stability, and can be accurately applied to real-time monitoring of cognitive functions of workers in a plateau hypoxic environment.

The invention discloses an intelligent grade classification method based on ore image features, and belongs to the technical field of machine vision, and the method comprises the steps: constructing an ore image data set, dividing the ore image data set into a training set, a verification set and a test set, and carrying out the preprocessing operation of the ore image data in each subset; constructing a residual neural network model of a multi-branch structure as an ore grade detection model; training the ore grade detection model based on the ore image data set; and detecting the grade of the to-be-detected ore by using the trained ore grade detection model. According to the method, the ore grade classification model is constructed based on the multi-branch structure and the residual error structure, the intelligent degree of the mine in the mineral separation link can be improved, guidance is provided for production and ore blending, and the method has important practical application value and significance for improving the operation efficiency of the mine.

The application discloses a bacteriostatic sunscreen self-dehumidifying mask, and relates to the technical field of masks.The bacteriostatic sunscreen self-dehumidifying mask is characterized by a three-layer gradient structure of a hydrophobic layer, a hydrophilic layer and a photothermal layer, synergistic effect of the three layers, efficient directional migration of water under a one-way moisture guiding mechanism, rapid evaporation and self-dehumidification realized in combination with the light-heat conversion characteristics of the photothermal layer, significant reduction of moisture accumulation in the mask, avoidance of facial moisture and bacterial breeding risks during wearing, and improvement of overall comfort and hygiene level.The air vent is located at the lower side to guide the directional circulation of air flow, reduce facial discomfort, and the user can fix the mask through a detachable plastic frame, replace the composite moisture-absorbing gel module conveniently, and ensure long-term and efficient protection.

The invention relates to the technical field of membrane separation, in particular to a chitosan modified MXene electrodialysis membrane as well as a preparation method and application thereof. The invention provides a preparation method of a chitosan modified MXene electrodialysis membrane. The preparation method comprises the following steps: mixing an MXene nanosheet dispersion liquid and a chitosan solution, carrying out a reaction under inert gas protection, carrying out centrifugal washing on the reacted mixed solution, carrying out suction filtration on the obtained dispersion liquid to form a membrane, and carrying out drying treatment. According to the electrodialysis membrane prepared through the method, chitosan molecules are limited between MXene nanosheet layers, a two-dimensional confinement channel structure is formed, the channel regulates and controls the ion migration behavior through the space size and the chemical environment, migration of divalent magnesium ions under the effect of a direct-current electric field is blocked, meanwhile, monovalent lithium ions can migrate preferentially, and the effect of the monovalent lithium ions on the surface of the membrane is improved. Therefore, selective separation of Li < + > / Mg < 2 + > is realized.

The invention relates to a virtual machine migration method and system, and relates to the technical field of virtual machines. The virtual machine migration method is applied to a control node in a target cluster, a virtual machine processing component is deployed in the control node, and the method comprises the following steps: receiving a virtual machine migration request through the virtual machine processing component; respectively determining a source node and a target node of the to-be-migrated virtual machine according to the virtual machine migration request through the virtual machine processing component; wherein the source node is deployed with a first virtual machine management component, and the target node is deployed with a second virtual machine management component; and controlling the migration task state of the to-be-migrated virtual machine according to the source node and the target node through the virtual machine processing component so as to indicate the first virtual machine management component and the second virtual machine management component, and migrating the to-be-migrated virtual machine from the source node to the target node according to the migration task state. By adopting the method, the virtual machine migration function based on the Kubernetes platform can be realized.

This invention discloses a video prediction model compression method and system based on optical flow-guided knowledge distillation, belonging to the field of computer vision technology. The method includes: generating optical flow distillation loss by constraining the optical flow prediction distribution of the student network to be consistent with that of the teacher network; generating channel alignment loss by aligning the channel attention distributions of the teacher and student networks using divergence; generating pixel-level reconstruction loss based on the difference in pixel values ​​between the generated image predicted by the student network and the real image; generating perceptual loss based on the difference in high-level semantic feature space between the generated image predicted by the student network and the real image; and obtaining the trained student network based on the optical flow distillation loss, channel alignment loss, pixel-level reconstruction loss, and perceptual loss. This invention can solve the performance degradation problem caused by neglecting spatiotemporal characteristics in existing compression methods for video prediction tasks.

The invention provides an active migration type unloading method and system for an AGV dependent computing task, and the method comprises the steps: building an AGV task unloading model based on a VEC system; the AGV divides the received tasks into sub-tasks and obtains a task sequential unloading process; dividing a state space and an action space of the AGV task unloading model based on a task sequence unloading process; calculating time delay to obtain total energy consumption of task unloading; establishing an active migration type unloading scheme, and performing unloading tasks of the current time slot and the next time slot and state updating of an obtained result; defining total energy consumption of task unloading as an optimization problem, and converting the optimization problem into a finite time domain time-varying Markov decision problem; and solving to obtain the minimum total energy consumption required by task unloading completion, and completing active migration type unloading oriented to the AGV dependent computing task. According to the invention, efficient and energy-saving service migration and computing unloading in the vehicle-mounted edge computing network can be realized.

The invention discloses a gallium nitride device and a preparation method thereof, and relates to the technical field of semiconductor devices. The gallium nitride device comprises a sapphire substrate, an AlN nucleating layer, an N-type AlGaN buffer layer, an intrinsic AlGaN active layer, an electron blocking layer and a P-type AlGaN layer which are sequentially stacked from bottom to top, a gradient doping edge stop layer is arranged on the edge of the P-type AlGaN layer, and meanwhile a bottom electrode in ohmic contact with the N-type AlGaN buffer layer and a top electrode in ohmic contact with the P-type AlGaN layer are formed respectively. According to the invention, by optimizing the structural design of the substrate, the nucleating layer, the buffer layer, the active layer, the electron blocking layer, the edge termination layer and the electrode, and combining with the seagull optimization algorithm to accurately regulate and control the preparation process parameters, the comprehensive improvement of the light extraction efficiency, the carrier recombination efficiency, the breakdown voltage and the long-term working stability is realized; and the photoelectric property and the reliability of the gallium nitride device are obviously enhanced.

The present invention provides cell-loaded microfibers, preparation methods, root canal implants and applications. The embodiments of the present invention determine a suitable concentration of GelMA hydrogel, which can promote the survival and proliferation of DPSCs and human umbilical vein endothelial cells (HUVECs), and has good biomechanical properties to produce microfibers. Two GelMA microfibers of DPSCs and HUVECs were prepared by a silicon tube-based coagulant-free bath method. The results of live / dead fluorescence staining and Ki-67 immunofluorescence staining showed that these two cells maintained a high survival rate and proliferation ability in GelMA microfibers. Immunofluorescence staining confirmed that DPSCs were fully diffused in the microfibers, and CD90 and laminin were highly expressed. In the embodiments of the present invention, the nude mouse ectopic dental pulp regeneration experiment was used to verify the regeneration of DPSC-microfiber and HUVEC-microfiber mixed aggregates in vivo. Compared with the traditional mixture of DPSCs and HUVECs in the GelMA hydrogel block, the cell-loaded microfiber aggregation produced more pulp-like tissue, blood vessels and odontoblast-like cells positively expressing DMP-1 and DSPP.

The present application relates to the field of environmental monitoring and remote sensing information processing technology, and discloses a method and system for predicting the concentration of nutrient salts in a breeding area coupled with remote sensing data. The method comprises: constructing a physically constrained ecological hydrodynamic model to generate synthetic remote sensing-nutrient salt pairing data; fusing multi-source remote sensing and measured data to form a high-dimensional feature tensor; separating breeding sensitive features and environmental interference features through an attention-guided domain-invariant feature decoupling network; transferring prior knowledge of the source domain large-scale breeding area using a dynamic weight cross-domain knowledge distillation architecture; and finally outputting real-time high-precision nutrient salt concentration distribution through a lightweight regression head and time series smoothing. The system includes corresponding functional units. The present application improves the prediction accuracy and stability in small sample scenarios, suppresses negative transfer, and provides support for precision breeding and eutrophication early warning.

The invention belongs to the technical field of negative electrode materials, and relates to a preparation method and application of an amorphous metal oxide nanorod negative electrode material. The preparation method of the amorphous metal oxide nanorod negative electrode material comprises the following steps: uniformly mixing an organic ferric salt and an organic manganese salt, adding an organic ligand, stirring until precipitate appears, cleaning the precipitate, performing freeze drying, and then performing heat treatment to obtain the amorphous metal oxide nanorod negative electrode material. By adopting the nanorod structure, more reaction sites can be provided so as to promote the intercalation and deintercalation rates of lithium ions; due to the one-dimensional characteristic of the nanorod, the diffusion path of lithium ions in the material is shortened, and the rapid charging and discharging capacity is enhanced. In addition, the amorphous structure is adopted, so that the mechanical stress of the material can be reduced, and the cycling stability is improved; the electronic conductivity is superior to the performance of the same crystal state, and the overall conductivity of the battery can be effectively improved.

The application relates to the technical field of cloud computing, and particularly discloses a method for cross-cluster migration of a virtual machine, which comprises the following steps: connecting a source cluster where a to-be-migrated virtual machine exists and a target cluster where the to-be-migrated virtual machine is to be migrated to a same backup storage device, generating backup data of the source cluster in the backup storage device, suspending the service of the to-be-migrated virtual machine when the to-be-migrated virtual machine reaches a migration condition, calling a backup recovery interface of the target cluster to recover the backup data to a second back-end storage device mounted by the target cluster, and configuring the to-be-migrated virtual machine in the target cluster, so that the cross-cluster migration of the virtual machine is realized in the form of backup data import and export, the storage structure of the source cluster and the storage structure of the target cluster do not need to be changed, an agent virtual machine for the cross-cluster migration of the virtual machine does not need to be created, and the efficient cross-cluster migration of the virtual machine can be realized. The application also discloses a device and equipment for cross-cluster migration of a virtual machine and a storage medium, which have the above beneficial effects.

The application discloses an illumination self-enhancing photocatalytic material and a preparation method and application thereof, and belongs to the technical field of photocatalytic materials. The material is obtained through cross-linking reaction of a composite photocatalyst, thioctic acid, polyethylene glycol and polyacrylamide, wherein the composite photocatalyst comprises the following raw materials in parts by mass: 1.2-2.4 parts of ammonium molybdate tetrahydrate, 0-5 parts of thiocarbamide and 0.006-0.018 parts of fullerene. The application can realize efficient removal of tetracycline by introducing the composite photocatalyst with high specific surface area and combining with the hydrophilic hydrogel matrix. Meanwhile, the material can enhance the hydrogel strength under illumination, and further improve the mechanical strength and durability of the material.

The invention discloses a pulse reversal-electroosmosis synergistic dechlorination system and method for bridge reinforced concrete, and relates to the technical field of corrosion prevention of reinforced concrete structures. The invention aims to solve the problems of large anode polarization, high steel bar secondary corrosion risk, difficulty in quantification of dechlorination efficiency, large anode rigidity, incapability of fitting a complex structure, no quantitative standard of regeneration efficiency, high anode cost, difficulty in replacement and no derivation basis of service life in the existing constant direct-current electric field dechlorination technology. Through optimization of pulse inversion parameters and electro-osmosis collaborative design, a chloride ion migration amount formula is established, efficient migration of chloride ions is achieved, and the stability of steel bars is guaranteed; the resource circulation value is quantitatively evaluated, and the cost and environment burden are reduced; a dissoluble chitosan frame is designed, an anode service life formula is deduced, automatic disintegration and overall replacement within 24 hours after the anode is used up are achieved, a universal and quantifiable dechlorination system is constructed, and the full demand from efficient dechlorination to low-cost maintenance is met through multi-technology cooperation and formulation evaluation.

The application provides a cross-well area drilling parameter migration method and system based on transfer learning, which first acquires a source well area drilling parameter historical data set, a scene data set, an operation effect data set, and a target well area initial drilling scene data set and operation demand data set, then constructs a cross-well area drilling parameter transfer learning model containing a source domain feature extraction layer, a domain adaptation layer, a knowledge transfer layer and a target domain parameter generation layer, then inputs the source well area data into the source domain feature extraction layer to extract a feature vector, inputs the target well area scene data into the domain adaptation layer to adjust a parameter mapping matrix, then inputs the domain adapted vector and the source well area operation effect data into the knowledge transfer layer to generate a target domain preliminary parameter feature vector, combines the target well area operation demand data to input the target domain parameter generation layer to generate an adapted parameter set, and finally transmits the parameter set to a target well area drilling control system to perform drilling operation, thereby improving the accuracy and efficiency of cross-well area drilling parameter migration.

The embodiment of the present invention discloses an operating system migration method, device, server and storage medium. The method includes: determining a target disk corresponding to a target machine; writing data to the target disk according to a source machine to be migrated corresponding to the target machine; and modifying the boot information of the target machine to the target disk to achieve operating system migration. The problem of complex operation during operating system migration is solved. Disk data replication is completed by writing data to the target disk of the target machine, and the boot information of the target machine is modified so that the boot information of the target machine points to the target disk, so that the target machine can boot and start the system according to the information of the target disk after startup, thereby achieving operating system migration. After creating a target machine on a cloud platform, it is only necessary to write data to the target disk and modify the boot information of the target machine to achieve operating system migration. The implementation process is simple and convenient, and there is no need to establish an additional transfer machine, so that the migration of the operating system can be completed efficiently.

The invention relates to the technical field of virtual machine migration optimization, and discloses a virtual machine migration optimization method under a hyper-converged architecture, and the method specifically comprises the following steps: 1, starting a migration sending request: starting a virtual machine migration process on a source host, and sending the migration request to a target host through a virtualization management layer; the service interruption time in the migration process of the virtual machine can be remarkably shortened by starting migration to send a request, pre-allocating resources, establishing a data transmission channel, copying a memory and a running state, simultaneously processing input and output requests, transferring a control right, taking over the running of the virtual machine, releasing the resources, monitoring the performance of the virtual machine and automatically processing exceptions; the migration time is effectively shortened, the migration efficiency is improved, and the service interruption time is reduced, so that the user experience and the service continuity are improved, the technical problems in the migration process of the virtual machine in the traditional virtualization architecture are solved, and a new thought and a new solution are provided for the application of the virtualization technology in a modern data center.