793 results about "Data allocation" patented technology

The invention relates to a matrix storage operator optimization method and device, computer equipment and a readable storage medium. The method comprises the following steps: allocating memory resources for target data; determining a logic structure corresponding to the source data, establishing a first mapping relation between each logic index in the logic structure and a register address of the source data, and establishing a second mapping relation between each logic index in the logic structure and a memory address of the target data; and determining a corresponding relationship between a memory address of the target data and a register address of the source data based on the first mapping relationship and the second mapping relationship, and storing the source data stored in a register into a memory resource corresponding to the target data based on the corresponding relationship. By adopting the method, the generalization ability of the matrix storage operator can be improved.

The invention relates to the technical field of inventory management, in particular to an intelligent warehouse management system for main shaft parts based on digital twinning, which comprises a main shaft mapping module for acquiring a main shaft shipping space and an allocation path, comparing shipping space nodes with a moving timestamp difference to generate an identification result, the fluctuation identification module extracts channel nodes to analyze fluctuation threshold deviation and generate a report, the allocation control module generates a scheduling list based on queuing duration and shipping space data distribution weight, and the abnormity positioning module extracts unallocated parts to analyze interface difference and mark abnormal links. And the state early warning module integrates four indexes of abnormal data identification risk paths to generate a management scheme. According to the method, bin position nodes and main shaft movement timestamps are synchronously compared to identify difference nodes, a dynamic weight distribution mechanism is established in combination with channel node fluctuation analysis, an interruption link difference trend and interface delay data are integrated to construct a risk assessment model, and abnormal conduction path positioning and early warning are realized. And scheduling conflicts and resource waste caused by data deviation are reduced.

The invention relates to the technical field of data processing and communication, in particular to a multi-source heterogeneous data compression and transmission method and system for an intelligent fusion terminal, and the method comprises the steps: collecting multi-source heterogeneous data from different data sources through the intelligent fusion terminal, carrying out the data preprocessing of the collected multi-source heterogeneous data, and obtaining a data preprocessing result; comprising format unification and noise filtering; establishing a data feature analysis model, and performing feature extraction and classification on the preprocessed data; according to data feature extraction and classification results, dynamic weights are allocated to different types of data, and the data are divided into data blocks of different sizes according to the allocated dynamic weights and data features; for different types of data blocks, the priority of the data blocks is set, a proper compression algorithm is selected for compression, and the compressed data is transmitted through a network. The multi-source heterogeneous data is subjected to feature analysis and targeted compression processing, so that network bandwidth occupation and equipment energy consumption are reduced, and data transmission efficiency is improved.

The invention discloses an inference service deployment method, electronic equipment and a storage medium, and the method comprises the steps: responding to the creation of an inference service, reading a model information configuration table and an engine information configuration table, and carrying out the matching processing of the model information configuration table and the engine information table according to a pre-configured model name during the creation of the inference service, processing resources, inference engine mirror image identifiers and inference engine starting parameters which can be used for loading inference services on the container scheduling platform are obtained, and workload metadata used for bearing the inference services are created based on the processing resources, the inference engine mirror image identifiers and the inference engine starting parameters; the deployment of the inference service on the container scheduling platform is realized by distributing the workload metadata to the target cluster node. Through the method, the technical problem of relatively low deployment efficiency caused by manually configuring the parameters in the workload metadata depending on manpower in related technologies is solved, and the technical effects of simplifying the deployment process of the model reasoning service and improving the deployment efficiency are achieved.

The invention relates to the technical field of direct current motor servo drive control, and provides a Zynq AXI4 bus-based real-time transmission system and a control method thereof, and the system comprises a Zynq system on chip, a DDR3 common data area, an AXI4 bus interconnection architecture and a dynamic partition manager. By implementing the scheme, firstly, the multi-channel characteristic of the AXI4 bus is utilized, special transmission channels are distributed for different types of data, and parallel data interaction is realized; secondly, a dynamic partition manager is deployed at a PL end, a DDR3 public data area is dynamically divided into a plurality of partitions in real time according to needs, multi-task parallel is achieved, and the real-time performance of the system is improved; thirdly, the read-write operation on the DDR3 in each control period is split into a plurality of state machines through precise scheduling of the state machines, the state machines are combined into a state loop to be mounted to an AXI4 bus, it is ensured that key tasks are completed on time, and the certainty of the system is improved; fourthly, the size and the number of partitions in the DDR3 are dynamically adjusted through a dynamic partition manager, and the storage efficiency is improved.

The invention discloses a cross-chain meteorological and hydrological data consistency verification method oriented to multi-block-chain collaboration, which effectively solves the problem of data alignment caused by inconsistent time standards in a multi-chain environment by collecting multi-source data and generating a data tuple with a geographic identifier and distributing a unified reference timestamp for the data in combination with a physical clock synchronization mechanism. And furthermore, the time deviation is extracted through a layered verification structure and inter-chain cross check, and the timestamp weight is dynamically adjusted, so that the time synchronization precision of the cross-chain data is improved. Finally, a standardized time sequence is generated through segmented aggregation of the data, cross-chain storage is completed, regular backtracking verification is performed in combination with a supervision chain, time consistency and content credibility of the meteorological and hydrological data in the cross-chain circulation process are guaranteed, and efficient collaboration and traceability between heterogeneous block chain systems are achieved.

The invention relates to an optimization method of a data type conversion operator, computer equipment, a readable storage medium and a computer program product. The method comprises the following steps: allocating memory resources for target data; establishing a read mapping relationship between each logic index in the logic structure of the source data and the memory address of the source data, and establishing a write mapping relationship between each logic index in the logic structure and the memory address of the target data; traversing the logic structure, aiming at any traversed logic index, determining a read address corresponding to the logic index based on the read mapping relationship, performing data reading from the memory resource corresponding to the source data based on the read address, converting the read data element from the first data type to the second data type, and storing the converted data element in the memory resource. And writing the data elements subjected to data type conversion into memory resources corresponding to the target data according to the write mapping relationship. By adopting the method, the calculation capability of the data type conversion operator can be improved.

The invention provides a wireless sensing platform with an edge computing function, and relates to the technical field of wireless sensors. In order to solve the problems that in the prior art, a wireless sensor is large in data redundancy, high in transmission delay and weak in emergency recognition and response capacity in a complex scene, a joint processing mechanism combining a causal event recognition assembly, a causal data fusion assembly, an event separation assembly and a data distribution assembly is adopted. By detecting a causal change event in an environment and dynamically extracting and fusing sensor data with a strong causal relationship, redundant data transmission caused by emergencies is reduced, transmission delay is reduced, the robustness of causal data fusion in a concurrent event scene is improved, and the reliability of data fusion is improved. According to the invention, efficient and accurate sensing and response to emergencies in a complex environment are realized.

The invention discloses a load balancing method and a related device, and relates to the field of computers, and the method comprises the following steps: obtaining hash ring data; wherein the Hash ring data reflect the mapping relation between the server and the virtual nodes located on the Hash ring; adjusting the number of virtual nodes of the server based on the current load value of the server and a weight coefficient corresponding to a service scene to which the current service request processed by the server belongs; wherein the current service request is distributed to the server based on the Hash ring data; on the basis of the adjusted number of virtual nodes, Hash ring data are updated; and based on the updated Hash ring data, performing server allocation on the new service request, and sending the new service request to the allocated server for processing. According to the invention, the operation response is more timely while the load balancing is realized.

The invention relates to the technical field of digital communication, in particular to an intelligent electric meter dual-mode communication self-adaptive switching method, and provides the following scheme that operation data are divided into a plurality of data units at a sending end, priority evaluation is carried out, and the time delay, the packet loss, the energy consumption and the bandwidth state of two links are combined, so that the intelligent electric meter dual-mode communication self-adaptive switching method is realized. Positioning clearing cut points in the priority sequence so as to realize differentiated distribution of data between a high-speed link and a low-speed link; when the data is distributed to a low-speed link, sparse coding is carried out by adopting a preset dictionary, and differential updating is triggered when an error exceeds a limit, so that decoding consistency is ensured; a receiving end realizes unified analysis of the original message and the sparse message through cross-link association, dictionary updating and residual synthesis; according to the invention, the timeliness of the key message can be guaranteed, the communication energy consumption and cost can be reduced, and the cooperation efficiency of the dual-mode link can be improved.

The invention discloses a computing power server load prediction method and system, and belongs to the field of data processing, the computing power server load prediction method comprises the following steps: collecting historical load data of a computing power server, and preprocessing the historical load data, the preprocessing comprising data cleaning, time window construction, and data set division; constructing an LSTM (Long Short Term Memory) model, and training the LSTM model; predicting a future computing power server load through an LSTM model; compared with the prior art, the method has the beneficial effects that the overall computing resource utilization rate is effectively improved by collecting the historical load data of the computing power server, training the LSTM model, finally predicting the future computing power server load through the LSTM model and allocating the computing resources according to the predicted data; meanwhile, abnormal tasks are recognized, and the abnormal tasks are processed in time when appearing, so that other tasks are prevented from being affected.

The invention discloses a method for aggregating ocean buoy data, which comprises the following steps of: establishing a space-time correlation model according to the spatial distribution density of buoy clusters and the dynamic characteristics of an ocean environment, and adaptively dividing buoy groups with a data compression cooperative relationship; performing a data-related compression operation within the buoy group; constructing a transmission priority dynamic sorting engine, and calculating a dynamic weight coefficient; performing hierarchical packaging processing on the differential coding data, distributing enhanced forward error correction coding resources for high-priority data, and reserving redundant transmission time slots; the method comprises the following steps: receiving a hierarchical data packet through a low-orbit satellite and executing on-satellite preprocessing, reconstructing an original data stream by using a distributed decoder, and meanwhile, fusing multi-dimensional space-time related data to generate an aggregated data product; and finally, the aggregated data product is transmitted to a ground data center through a satellite downlink. According to the invention, uplink data volume is reduced, disaster monitoring timeliness is improved, ground station processing load is reduced, and system environment adaptability is enhanced.

The invention relates to a map visualization rendering method and system based on a WebGPU engine and computer equipment, and the method comprises the steps: obtaining map data in a unified format; inputting the map data into a rendering layer, and performing layer division on the map data according to spatial levels and element types to obtain multiple pieces of layer data; distributing an independent rendering thread for each layer data; performing block processing on the map data to obtain picture blocks with pyramid structures; and calling a corresponding rendering thread in the current view range and the scaling to load the corresponding picture block for rendering display. By means of the method and device, the problem that the map visualization rendering performance is poor is solved.

Disclosed herein are methods and systems that use artificial intelligence techniques for determining an allocation of an asset based, at least in part, on analyzing electronic images of the asset. In an embodiment, an AI model can be executed on a received data packet comprising a set of electronic images to generate a set of attributes. In an embodiment, the AI model can identify a segment of the asset associated with the electronic image, generate an attribute comprising a description of quality of the segment of the asset, and determine whether the electronic image is associated a subset of the electronic images. In an embodiment, the set of attributes and an indication of the subset with user input about the asset can be aggregated to generate evaluation data. In an embodiment, a computer model can be executed to determine an allocation of the asset to a class.

The invention provides a data distribution method, electronic equipment, a storage medium and a program product. The method comprises the following steps: for each service node cluster in a plurality of service node clusters, performing state detection on each service node in the service node cluster to obtain a state parameter of each service node; determining a weight corresponding to each service node cluster based on the state parameters of the service nodes in the plurality of service node clusters; and distributing the service data in the plurality of service node clusters based on the weight corresponding to each service node cluster. According to the invention, the accuracy of data distribution can be effectively improved.

Queries may be resolved against large quantities of collected data (such as traces) by dividing collected data into multiple time intervals and incrementally assigning multiple workers the query and the collected data over the multiple time intervals. For each time interval, these workers may conditionally update one or more summary data structures within the worker based on the query and the portion of collected data assigned to the worker. The summary data structures for each time interval may then be incrementally returned and merged with results from earlier time intervals to create a final merged query result.

Apparatuses, methods, and systems for determining winning arms of electronic testing. One method includes obtaining historical data related to the testing, creating a histogram based on the historical data, the histogram including bins and weights, creating a distribution by computing concentration parameters of the distribution from the weights of the histogram, executing the testing, receiving new data collected based on the execution of the test, allocating the new data into same bins as the bins of the histogram of the historical data yielding a new data bin count, computing a posterior distribution comprising updating the distribution using the same bins and the new data bin counts and the concentration parameters of the distribution, inferring corresponding central tendencies of samplings of a metric distribution, constructing an overall utility distribution for each arms of the test, and determining a winning arm of the testing.

The invention belongs to the technical field of video secure transmission, and particularly relates to an ultra-high-definition video secure transmission method and system, and the method comprises the steps: carrying out the dynamic partitioning of a video frame based on a CNN, and marking a security level; the key region adopts a quantum key to distribute a dynamic key to carry out AES-256 encryption, and the non-key region implements chaotic stream encryption; distributing a low-delay dedicated channel for the key data through an SDN reinforcement learning model; the receiving end performs layered decryption on the recombined video after verifying the validity of the quantum key; the system correspondingly comprises an intelligent blocking module, a quantum encryption gateway, a self-adaptive transmission controller and a credible decryption terminal, the 8K video encryption delay is smaller than or equal to 9.8 ms, the key data packet loss rate is smaller than or equal to 0.3%, and quantum computing attack is resisted.

The invention relates to the technical field of computer hardware acceleration and data processing, and discloses a data transmission method and device based on an accelerator card, and the method comprises the steps: collecting an original data stream of external equipment, carrying out the real-time feature analysis of the original data stream through a mode recognition engine, obtaining space-time related features, and carrying out the real-time feature analysis; performing binary coding on the space-time correlation features to obtain feature mark data; according to the instantaneous bandwidth of the M.2 interface and the load state of the AI chip, compressing the feature mark data into optimized compressed data by using a dynamic compression engine; inputting the optimized compressed data into a heterogeneous computing distributor so as to distribute a path selection instruction for the optimized compressed data through the heterogeneous computing distributor according to the feature marking type of the optimized compressed data; and according to the path selection instruction, completing data transmission of the optimized compressed data through the heterogeneous computing distributor to obtain a data transmission result. According to the method, the final data transmission result can meet the space-time consistency requirement of high-precision AI reasoning.

The invention relates to the technical field of data encryption transmission, in particular to an electric power communication resource data encryption method, system and equipment based on multi-algorithm fusion, and the method comprises the steps: dividing data into a core control class, an equipment state class and a resource information class according to the purpose of electric power communication resources, a security label containing data sensitivity, transmission timeliness and storage cycle information is distributed to each type of data; an initial quantum key is generated by adopting a quantum key distribution protocol, an asymmetric key pair is generated in combination with an SM2 algorithm, and the quantum key is signed by using an SM2 private key and then transmitted; the dynamic secret key update is triggered based on the timestamp or the data volume threshold value; encrypting core control type data, dynamically generating confusion layer parameters, and meanwhile, generating a message digest in combination with an SM3 hash algorithm; and the receiver decrypts the confusion layer parameter, decrypts to obtain the plaintext, and verifies the SM3 hash value to confirm the data integrity. And the limitations of a single algorithm on efficiency, strength and aggressiveness resistance are made up.

The invention discloses an energy storage power station operation and maintenance management system based on big data, and relates to the technical field of power station operation and maintenance management, and the system comprises an operation and maintenance platform which is in communication connection with a multi-source data processing module, a health state evaluation module, a fault analysis module and an optimization module. The multi-source data processing module is used for collecting energy storage associated data of a plurality of dimension types in the energy storage power station, allocating a time sequence operation window for the energy storage associated data of each dimension type for time sequence allocation association, performing feature quantization on the energy storage associated data under each time sequence to obtain a time sequence feature coefficient under the corresponding time sequence, and outputting the time sequence feature coefficient to the energy storage power station; summarizing into a time sequence feature set; the health state evaluation module is used for evaluating the energy storage state of the energy storage power station according to the time sequence feature set; the fault analysis module is used for carrying out fault analysis on the energy storage power stations of which the energy storage states do not reach the standard to obtain corresponding energy storage fault detail information; and the optimization module is used for establishing a multi-target constraint optimization model and completing energy storage optimization of the energy storage power station.

The invention discloses a data backup method and device, equipment, a medium and a computer program product, and relates to the technical field of data processing.The method comprises the steps that when a continuous protection snapshot is created for a source volume, the current backup generation of the source volume before the snapshot is created is set as a snapshot generation identifier; and synchronizing data by relying on mapping relation metadata including the current backup generation of the source volume, the last backup generation of the source volume, the storage backup data generation of the target volume and the water level field. Reading corresponding generation data of the source volume according to the water level mark in the first backup, writing into the target volume, and increasing the water level to the total number of source volume data blocks after completion; during incremental backup, a new snapshot is created, the water level is reset, only adjacent generations of difference data are synchronized, and a corresponding generation identifier is distributed for target volume data in each backup. In this way, no extra bitmap is needed to participate in difference marking, the synchronization efficiency is improved, the target volume keeps historical backup data of all generations in the synchronization period, and the problem that the target volume cannot effectively protect data in the traditional incremental backup synchronization stage is solved.

The invention provides a security optimization encryption method, system and device for data in a facility agriculture scene and a medium, and belongs to the technical field of agricultural informatization security, and the method comprises the steps: carrying out the dynamic division of security levels of data collected in a facility agriculture environment according to the data source device type, data content sensitivity and real-time requirements; distributing an encryption algorithm for the collected data according to a mapping relation between the security level and the encryption algorithm; secret keys are generated and managed based on an identity encryption mechanism, and then secret key fragmentation storage is carried out in combination with a trusted execution environment; when encryption is needed, whether the current trusted execution environment is safe or not is judged, and when the current trusted execution environment is safe, secret keys of the fragments are recombined, the multi-source heterogeneous data are encrypted through the distributed encryption algorithm, and encrypted data are generated; and carrying out secure transmission on an adaptive communication protocol after packaging the encrypted data. According to the method, the data security level is dynamically divided, the encryption algorithm is flexibly selected, the resource consumption is reduced, and the real-time performance and security are improved.

The invention relates to the technical field of data security protection, in particular to an energy internet data security transmission and authentication system, and provides a scheme that the storage efficiency is improved by adjusting the capacity of a self-adaptive block and dynamically adjusting the size of the block according to the energy data flow. And a credibility rating mechanism is adopted to allocate the data to the main block chain or the auxiliary block chain, so that storage resources are optimized and the data credibility is improved. In a cross-chain interaction process, technologies such as identity verification, zero-knowledge proof and time lock are combined, so that the security and tamper-proof capability of data transmission are improved. Block storage is optimized based on the energy supply and demand balance state, the write-in load is reduced, and efficient storage of data in different load environments is ensured. The method improves the storage flexibility, safety and credibility of energy data, and can be widely applied to the fields of smart power grids, distributed energy transactions, carbon emission monitoring and the like.

The invention relates to the technical field of storage system IO performance optimization, and provides a redirection data aggregation method and equipment, and the method comprises the steps: recognizing random small IO data from a written IO request, aggregating and writing back-end storage objects, and constructing a storage mapping relation in an index table; traversing the rear-end storage object in which the random small IO data is stored, and distributing data from the same logic address space to point to the same target rear-end storage object through a hash algorithm; writing the data distributed to point to the same target back-end storage object into the continuous physical space of the target back-end storage object in sequence, and updating the mapping relation in the index table; the step S2 and the step S3 are repeated before a preset termination triggering condition is met, and a hash algorithm different from all previous times is adopted when the step S2 is repeatedly executed each time; when the data are read, the storage mapping relation is obtained from the index table, and the read requests corresponding to the data located in the same back-end storage object are aggregated and then read at a time.

The invention provides a data transaction method and device, equipment, a medium and a program product, relates to the technical field of block chains, and aims to solve the problem of relatively poor efficiency of data transaction in related technologies. The method comprises the following steps: based on data demand information, creating a demand task pool by using an intelligent contract, and broadcasting the data demand information to each node in a block chain system; receiving at least one data abstract and guarantee fund submitted by the at least one data owner node through the smart contract, and writing the at least one data abstract into a demand task pool; under the condition that the first data demander node determines m first data owner nodes, price negotiation is carried out based on m price parameters submitted by the m first data owner nodes and the smart contract, and a transaction result is determined; and under the condition that the first data demander node pays the resource corresponding to the transaction price, distributing the first target data to the first data demander node. According to the invention, the data transaction efficiency can be improved.

Provided are systems, methods, and apparatuses for assisted cache data placement. In one or more examples, systems, methods, and apparatuses include assigning a first identifier to first data based on an aspect of the first data, the first data being data moved from a cache and assigning a second identifier to second data based on an aspect of the second data, the second data being moved to a storage device based on a policy of the cache. In one or more examples, systems, methods, and apparatuses include storing the first data in a first storage location of the storage device based on the first identifier and storing the second data in a second storage location of the storage device based on the second identifier.

The invention discloses a power time sequence prediction data set construction method and system, and the method comprises the steps: obtaining power load and weather data, forming a power data set, and carrying out the cleaning and alignment; meanwhile, multi-source public time series data such as energy and weather are obtained; uniformly formatting and normalizing the two types of data, extracting time-frequency features, calculating similarity between sequences, and screening out public data significantly related to the power load; a dynamic sampling weighting method is adopted to allocate a weight for each piece of data, a sampling frequency is set, and finally a heterogeneous power time sequence data set used for pre-training and fine tuning of a large model is constructed; according to the method, by integrating the multi-source heterogeneous data, constructing a unified standardized data format and fusing the multi-source data based on timestamp alignment, the consistency and availability of the data are remarkably improved, and the data fusion efficiency and the model generalization ability are improved.

This disclosure relates to data association, attribution, annotation, and interpretation systems and related methods of efficiently organizing heterogeneous data at a massive scale. Incoming data is received and extracted for identifying information (“information”). Multiple dimensionality reducing functions are applied to the information, and based on the function results, the information are grouped into sets of similar information. Filtering rules are applied to the sets to exclude non-matching information in the sets. The sets are then merged into groups of information based on whether the sets contain at least one common information. A common link may be associated with information in a group. If the incoming data includes the identifying information associated with to the common link, the incoming data is assigned the common link. In some embodiments, incoming data are not altered but assigned into domains.