1009 results about "Data allocation" patented technology

The invention relates to the field of business data distribution, financial science and technology and medical health, and discloses a multi-agent cooperative task execution method, which comprises the following steps: receiving a task instruction input by a user, and carrying out task step configuration on the task instruction to obtain a sub-task sequence; judging whether the task information of each subtask meets a preset execution logic standard, and if yes, sending the subtask to an intelligent agent according to an identifier of the intelligent agent corresponding to the subtask; and recording task data generated when each agent executes the subtask, extracting a second keyword in the task data to construct a decision-making dependency relationship graph among the agents, extracting collaborative data of each agent collaborative completion task instruction from the decision-making dependency relationship graph, and optimizing subsequent task allocation by utilizing the collaborative data. According to the method, the cooperation process among the agents is optimized, and continuous learning and improvement are carried out, so that the overall efficiency and reliability of the multiple agents are improved.

The invention belongs to the field of image data processing, and relates to an improved YOLOv10-based PCB surface defect detection system and method, the system is obtained by improving a YOLOv10 network model, a C2f module in a backbone network of the model is replaced by a multi-scale cross-stage fusion module, and the multi-scale cross-stage fusion module is used for extracting deep features of an image and capturing multi-scale information in input data; a dynamic perception feature information enhancement module is introduced into the neck network and applied to processing of P4 and P5 feature maps; a detection head of a YOLOv10 network model is replaced by a Lag module, the Lag module carries out channel adjustment through a grouping normalization convolution module and then passes through a shared convolution group, and data output by the shared convolution group is distributed to a positioning convolution module and a classification convolution module. The system can realize the detection of small defects under the interference of complex background noise, and has the advantages of high detection precision and efficiency and the like.

The invention discloses a virtual power plant energy scheduling method and system under a local data abnormal condition, and the method comprises the steps: recognizing a data type and an incidence relation related to abnormal data through an incidence matrix after the abnormal data of a virtual power plant are collected and recognized; and calculating an abnormal data correction value based on the association relationship function and the real-time association data, and performing weighted average on the abnormal data correction value and the original abnormal data to obtain correction data. And then calculating an abnormal data time difference and a correlation deviation degree, and distributing a credibility weight for the corrected data. And finally, calculating the correlation degree between the scheduling parameter of the preset scheduling scheme and the weighted correction value, and selecting the scheme with the highest correlation degree as a virtual power plant resource scheduling scheme. By implementing the technical scheme provided by the invention, the scheduling accuracy in a data exception state is improved.

The invention provides an anomaly detection method and device based on adaptive multi-scale feature modeling, and relates to the technical field of data anomaly detection. The method combines time component decomposition and a gating network, and allocates suitable expert networks for data on different time scales. In an expert layer, a deep convolutional network and a cross-time-slice attention mechanism are utilized to mine mutual relations in different time periods and among the different time periods in a time sequence. Then, combining a graph structure, and modeling spatial interaction characteristics among variables; and finally, learning the potential representation of the data to generate reconstruction data, and calculating a reconstruction error and the KL divergence of a potential space to carry out model training, thereby carrying out efficient anomaly detection under an unsupervised condition. According to the method, the precision of anomaly detection in the industrial control system is improved, a more flexible and accurate anomaly detection tool is provided for practical application, and the method adapts to complex dynamic changes of time sequence data in the industrial control system under the condition that no label data exists.

The invention provides an open set cross-domain hyperspectral classification method and system based on multi-mode and optimal transmission, and relates to the technical field of hyperspectral image classification. The method specifically comprises the following steps: acquiring and utilizing an original hyperspectral image to construct a labeled source domain data set and an unlabeled target domain data set; utilizing a spatial-spectral feature extractor to respectively obtain spatial-spectral features of the source domain data and spatial-spectral features of the target domain data; and determining a source domain prototype, calculating a similarity matrix between the target domain data and the source domain prototype, solving an optimal transmission matching matrix, distributing a pseudo tag for each target domain data, and performing text correction on all the source domain data and all the target domain data by using a contrast learning loss function to obtain the target domain data. And the source domain prototype and the spatial spectrum feature extractor are optimized by constructing the total loss of minimizing hyperspectral image classification, so that classification prediction can be carried out on the hyperspectral image to be classified, and target domain data can be classified more accurately.

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 block chains, and discloses a block chain node fault recovery method and a block chain system.The block chain node fault recovery method comprises the following steps that S1, the fault state of a block chain node is detected through a fault detection and notification module, whether a target node needs to be recovered or not is judged, and a recovery request is broadcasted; s2, through a recovery strategy generation module, generating initial probability distribution based on the recovery demand of the target node and the bandwidth and load state of the normal node, and determining a data distribution strategy of the normal node; and S3, according to the response state of the normal node, the probability distribution is adjusted through a dynamic adjustment and optimization module. According to the method, the problems of low node fault recovery efficiency, non-uniform task distribution, poor transmission reliability and long verification time consumption in the prior art are solved by dynamically adjusting probability distribution optimization, data blocking and multi-path parallel transmission, redundant coding combined consistency verification and a hierarchical priority strategy; and the efficiency and adaptability of data recovery are greatly improved.

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 relates to a data processing method and system of an NAND FLASH storage array, computer equipment and a storage medium. The method comprises the following steps: receiving a data writing or reading instruction; and analyzing the instruction through the system-on-chip, and distributing the data to a target module in the plurality of NAND FLASH modules connected in parallel according to a preset strategy. And writing the data into the target module or reading the data from the target module through the control and communication line. And executing data management operation through the system-on-chip, specifically, writing data into different NAND FLASH modules in a dispersed manner by adopting a dynamic wear leveling algorithm so as to balance the erasing times of each module. And detecting and marking bad blocks in the NAND FLASH module in real time, and avoiding the bad blocks in calling operation after data storage. By the adoption of the method, the storage performance, reliability and expandability of satellite-borne storage can be comprehensively improved, and the method is suitable for task scenes with high reliability requirements.

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 provides a self-service printing regulation and control method and system for multiple printers, and the method comprises the steps: obtaining the configuration data of each printer when an edge computing device is successfully connected with the multiple printers; obtaining the printer state of the corresponding printer according to the configuration data of each printer; acquiring a printing order sent by a user, and extracting printing file data and printing requirements of the printing order; based on the printing requirement, the configuration data of each printer and the state of the printer, a target printer is allocated for the printing file data, and the printing file data and the printing requirement are sent to the target printer for printing through the edge computing device; and after the printing file data is printed, a printing success message is output, so that multiple printers are reasonably scheduled, the overall failure rate is reduced, and the success rate, the processing efficiency and the resource utilization rate of the printing task are improved.

The invention relates to a super-resolution fluorescence lifetime microscopic imaging method and system based on pixel relocation. The method comprises the following steps: S1, carrying out multi-focus parallel scanning on a sample by using an acousto-optic deflector; s2, synchronously collecting a fluorescence intensity image and fluorescence lifetime data through an area array detector and a point detector; and S3, carrying out super-resolution reconstruction on the fluorescence intensity image based on a pixel relocation algorithm, and mapping fluorescence lifetime data to a reconstructed image to generate a super-resolution fluorescence lifetime microscopic image. According to the invention, an AOD is adopted to replace a scanning galvanometer in a traditional TCSPC-FLIM imaging system as a light beam scanner; addressing scanning of AOD, image acquisition of EMCCD and life data acquisition of PMT and TCSPC cards are closely linked by combining well-designed multiple paths of synchronizing signals, and fluorescence life data are distributed to each dot matrix through a later data processing method, so that organic combination of super-resolution and FLIM can be realized without complicated equipment, and SR-FLIM images of samples are obtained.

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 relates to the technical field of electronic digital data processing, in particular to an operation state monitoring method and system for injection molding equipment, and the method comprises the steps: collecting a multi-dimensional operation parameter time sequence of the injection molding equipment at different moments, collecting parameters of the same dimension into data sets, and constructing an isolated tree based on each data set; after sorting tree nodes of the isolated tree according to a data average value, distributing a tree node sequence number for each item of data; the combination of the dimension parameters at the same moment is regarded as a data point, the distribution relation of any two data points on the isolated tree is compared, the same-group degree of the data points is calculated, and the data points with the same-group degree higher than a set threshold value are classified into the same group; calculating the abnormal degree of the data points; and if the abnormal degree exceeds a set threshold value, determining that the data point is an abnormal data point and giving an alarm at the same time. The problem that the accuracy and reliability of state detection are not high is solved.

The invention discloses a data processing system and a data processing method of a hybrid cloud in a wireless communication system. The method comprises the steps that wireless communication equipment receives and sends data through a multi-mode communication interface, and a hybrid cloud platform receives the data and distributes the data to a public cloud or a private cloud for processing and storage according to a preset strategy. And then, the data processing module performs real-time cleaning, decoding and analysis to generate a processing result. Based on a deep reinforcement learning algorithm, the network optimization unit optimizes and adjusts communication parameters. And the processing result and the optimization instruction are fed back to the wireless communication equipment through the feedback channel, so that the performance and efficiency of the equipment in the network are improved. The invention further shows the characteristics of guaranteeing data security, a data cleaning mechanism, optimizing strategies, improving the resource utilization rate and the like, and the system efficiency and the data processing efficiency are enhanced.

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 an airport geographic data dynamic updating method and system based on a digital twin technology. The method comprises the following steps: defining a multi-dimensional label of airport geographic data and a data acquisition device deployment strategy matched with the multi-dimensional label; completing collection of airport geographic data corresponding to different label combinations according to a deployment strategy; transmitting the collected airport geographic data in real time through the constructed communication network architecture; defining QoS levels matched with different label combinations, and allocating network resources to airport geographic data corresponding to different label combinations in real time; for the airport geographic data transmitted to the cloud, real-time airport geographic data fusion is completed through a built-in three-engine data fusion architecture of the cloud; and setting a multi-strategy collaborative updating mechanism, completing real-time updating of the airport geographic data, generating updated airport geographic data, and feeding back the updated airport geographic data to the digital twin system. According to the invention, real-time and comprehensive data acquisition can be realized, and real-time dynamic updating of airport geographic data is realized.

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.

Systems and methods for migrating and integrating data from one or more external locations to an internal location are disclosed. The system comprises multiple temporary locations to sort, transform, or store the data in the migration process. During the process, the data could be assigned various attributes, which are determined based on metadata associated with the original data. The system will track these attributes as the data moves through the various temporary locations.

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 discloses a data annotation method and device, electronic equipment and a computer readable storage medium. The method comprises the steps that multiple pieces of data to be annotated are acquired, and data of a preset proportion is extracted from the data to be annotated to serve as annotation quality inspection data; the multiple pieces of data to be labeled are divided into a first number of data task packages, the data task packages are distributed to a second number of labeling personnel for data labeling tasks, and each data task package comprises at least one piece of labeled quality inspection data; in the data labeling process of a first labeling person, a labeling result, submitted by the first labeling person, of any labeled quality inspection data is obtained, and the first labeling person is any labeling person; and when the labeling result indicates that the number of labeling errors of the first labeling person reaches a preset number threshold value, distributing the remaining unlabeled data in the data task packet which is currently subjected to the data labeling operation by the first labeling person to at least one second labeling person. According to the invention, the data annotation quality of the annotator can be monitored in real time, and the data annotation quality and efficiency are improved.

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.

The invention relates to a log alarm data processing method and device, a storage medium and electronic equipment, and relates to the technical field of big data processing.The method comprises the steps that the data source category of original log alarm data is determined, and a key information extraction model corresponding to the data source category is distributed to the original log alarm data; performing semantic analysis on the original log alarm data based on the key information extraction model to obtain alarm key information in the original log alarm data corresponding to the data source category; performing multi-dimensional correlation analysis on the alarm key information of the different data source categories based on a preset correlation analysis model to obtain a correlation relationship between the alarm key information of the different data source categories; and performing alarm root cause reasoning on the association relationship between the alarm key information based on a preset fault reasoning model to obtain an alarm association analysis result. According to the invention, the accuracy of the alarm association analysis result is improved.