889 results about "Group based" patented technology

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

The invention provides a multi-mode environment cooperative regulation and control system for poultry egg preservation. The system comprises a sensor network and a server, wherein the sensor network is used for collecting poultry egg data; the server is used for constructing a digital twinborn model according to the poultry egg data; perceptual data output by the digital twin model are input into a quantum neural network model, and a predicted fresh-keeping result and an optimal environment regulation and control parameter combination are obtained; according to the optimal environment regulation and control parameter combination, driving a regulation and control actuator group and feeding back actually executed regulation and control parameter values to the digital twinborn model; comparing the difference between the actual fresh-keeping effect reflected by the digital twinborn model and the predicted fresh-keeping result, and optimizing a mapping strategy from the current poultry egg state to the optimal regulation and control parameter by taking the fresh-keeping period extension rate as a reward function; the change condition of biochemical indexes is monitored through a digital twinborn model, and a monitoring result serves as a feedback signal to be input into the reinforcement learning module for strategy adjustment. The fresh-keeping period can be prolonged, intelligent prediction is achieved, quality traceability is guaranteed, and the energy consumption cost is reduced.

The invention discloses a hydro-junction gate group intelligent cooperative control method based on digital twinning, and relates to the technical field of water conservancy control, and the method comprises the steps: taking a water conservancy dynamic digital twinning model as a reinforcement learning environment, setting gate group control parameters and performance indexes, carrying out the training under diversified hydrological conditions, and generating a preliminary control strategy; inputting the preliminary control strategy into a water conservancy dynamic digital twinborn model, executing rollback simulation covering multiple working conditions, and obtaining simulation responses of the corresponding working conditions; scheduling performance indexes of simulation response are analyzed, high-quality control strategies are screened, scheduling conflict identification and conflict resolution among gate groups are carried out, and an optimal control strategy set is output; according to the method, by executing rollback simulation covering multiple working conditions and automatically identifying and eliminating scheduling conflicts among gate groups, the adaptability and accuracy of a scheduling strategy are improved, conflicts and risks are reduced, and efficient and safe operation of the hydro-junction is ensured.

The invention relates to the technical field of video processing, in particular to a multi-video semantic collaborative analysis method and system based on federated learning. Obtaining task complexity reported by the plurality of end-side computing devices respectively; dividing the plurality of end-side computing devices into K device groups based on the plurality of task complexities; respectively initializing K semantic analysis models for the K equipment groups at a server side; based on a federated learning framework, cooperating with a plurality of end-side computing devices and a server side to carry out layered distillation training on the K semantic analysis models; and correspondingly issuing the trained K semantic analysis models to each end-side computing device in the K device groups for video semantic analysis. According to the method, the overall efficiency, accuracy and generalization ability of multi-video semantic collaborative analysis can be improved, and configuration of computing resources is optimized.

Methods, systems, and apparatuses may assist scheduling for broadcast and groupcast on new radio Uu interface. In an example, there may be broadcast and groupcast transmission scheduling mechanism with: group based scheduling, UE specific based scheduling, or sub-group based scheduling. In another example, there may be a serving cell configuration for broadcast and groupcast transmission with dedicated cell for broadcast and groupcast, or broadcast and groupcast cell which is shared with the cell for unicast. In another example, there may be downlink control related configuration for broadcast and groupcast transmission with details of the BWP configuration, the CORESET configuration, or the search space configuration.

A base station and a user equipment (UE) may exchange messages to perform a Random Access Channel (RACH) procedure. The UE performs measurements on a plurality of signal synchronization blocks (SSBs), selects M physical random access channel (PRACH) groups based on one or more of the measured SSBs satisfying a predetermined threshold, provides the measurements for the M PRACH groups to a base station in an initial access transmission for a RACH procedure and monitors for a random access response (RAR) message based on a repetition number for the RAR message indicated by the base station.

The invention discloses a network scanning task intelligent segmentation and load balancing method and system. The method comprises the following steps: identifying abnormal fragments by executing integrity verification on a scanning task, analyzing a task dependency relationship to determine a divisible position, and generating a segmentation scheme in combination with granularity constraint; node resource states are collected to calculate load indexes, and scheduling weights are hierarchically configured according to load levels to establish parallel scheduling channels; constructing a node cooperation group based on load difference, formulating an inter-group circulation rule to form a cooperation scheduling space, fragmenting tasks according to node capacity, and generating a parallel execution plan; monitoring the execution progress to identify overstocked nodes, diagnosing bottleneck types and planning a dredging path to execute task migration; the resource time sequence data is collected to identify the low-load time period, and the task is distributed to the optimal time slot to be executed, so that the task segmentation reasonability and the node load balancing degree are improved, and the distributed scanning execution efficiency is improved.

The invention discloses a security and protection monitoring group target association extraction and behavior analysis method based on a neural network, particularly relates to the field of security and protection video intelligence, is used for solving the problem of group cross-view association and behavior judgment, and comprises the following steps: projecting a multi-path camera detection and tracking result to a ground coordinate, generating group candidates according to track convergence and separation; forming group description by the track topology and the picture entering sequence; setting passing anchor points at adjacent view angle boundaries according to the group description, and splicing to form a group track; constructing a relation graph by using the group tracks, calculating relation scores according to relative positions and speeds, and continuously compensating shielding positions by using tracks; and inputting the relation graph and the group track into a behavior model to generate labels and roles, generating a deviation correction signal when the labels and the roles are inconsistent, triggering relation edge reconfiguration and anchor point remapping, and realizing stable output of cross-view group association and behavior judgment.

The invention discloses a data cleaning scheme generation method and device and a readable storage medium, and the method comprises the steps: carrying out the grouping of pre-training task data through employing a clustering mode, and obtaining a plurality of data groups; extracting data characteristics of each data packet based on a large model; generating a data cleaning scheme for the corresponding data groups by using the pre-training model in combination with data characteristics, and executing the data cleaning scheme; calculating a data cleaning index, and storing the data characteristics, the data cleaning scheme and the data cleaning index as short-term memory into a short-term memory module; evaluating the overall cleaning quality evaluation score of the data cleaning scheme by using a reflection module to obtain a reflection result; the data characteristics, the data cleaning scheme, the overall cleaning quality evaluation score, whether data cleaning succeeds or not and the reflection result serve as long-term memory and are stored in a long-term memory module, the data cleaning process can have traceability, and the data cleaning scheme adjusting efficiency is improved.

The invention relates to an information processing method and device, equipment, a storage medium and a product, and the information processing method comprises the steps: receiving input multi-source knowledge information which comprises knowledge information from at least two information sources; under the condition that the multi-source knowledge information contains a conflict information group, based on an information field to which the knowledge information in the conflict information group belongs, performing conflict resolution processing on the knowledge information in the conflict information group to obtain target knowledge information; different information fields correspond to different conflict resolution modes; the conflict information group comprises a plurality of pieces of knowledge information with different information sources and information conflicts; and outputting the target knowledge information for the large model to call and perform question and answer reply. Through the method, the limitation of a traditional single rule in processing multi-source heterogeneous knowledge is effectively overcome, and the processing accuracy of knowledge conflicts in different fields is remarkably improved.

The invention provides a production scheduling method and system for large-size heavy components of a fabricated bridge and a storage medium. The production scheduling method comprises the steps that a planning data foundation is established; performing size aggregation grouping based on the planning data; determining a template stage sequence; distributing beam type group storage areas; calculating a target stacking horizon; carrying out reverse-order production sequence derivation based on a stacking target; and generating a component-level production and stacking plan. According to the method, the construction sequence, the component size aggregation, the stacking layer position and the storage area are considered in a unified mode under the complex scene, collaborative optimization of the production sequence, the stacking sequence and the hoisting sequence is achieved, reshipment is reduced, the overall efficiency is improved, storage yard management is more standardized, the informatization degree is high, multi-system linkage of WMS, MES, BIM and the like is facilitated, and the construction efficiency is improved. The traceable management of the components in the whole life cycle from design, production to installation is realized.

The invention provides a supply and demand matching degree-based multi-target collaborative optimization scheduling system for a light storage building group, and the system comprises a sensing layer which is used for collecting the physical operation state data of the light storage building group in real time; the network layer is used for transmitting data to the control layer; the control layer is used for generating a global scheduling plan by taking maximization of the comprehensive supply and demand matching degree as an optimization target according to the supply and demand matching degree prediction information of the first scale and the time-of-use electricity price information of the power grid; according to the supply and demand matching degree prediction information of the second scale and the physical operation state data, performing rolling correction on the global scheduling plan to generate a correction instruction; according to the supply and demand matching degree prediction information of the third scale and the physical operation state data, performing local autonomous judgment on the correction instruction, and triggering a local response when supply and demand fluctuation is detected; and the application layer is used for displaying the data acquired by the sensing layer and / or the output data of the control layer, so that the self-balancing capability of the system is fundamentally improved.

The invention relates to an ultrasonic large model sparse tensor optimization training and pushing acceleration method based on new generation supercomputing, and the method comprises the steps: carrying out the fine tuning of an ultrasonic large model, enabling the weight tensor of each expert model in the large model to generate a structured block sparse mode, and obtaining an ultrasonic sparse large model; constructing a sparse block index table and an expert index table, and associating to form a joint coding table; deploying an ultrasonic sparse large model to each parallel group of the new-generation heterogeneous supercomputing cluster; performing multi-round iterative training on the large sparse model; in each round of iterative training, dividing the sample ultrasonic data of the current batch into each parallel group; in each parallel group, determining a target expert model needing to be activated based on the feature data of each sample ultrasonic data, and positioning a target computing node based on the joint coding table; and activating the target expert model on the target computing node and loading the corresponding non-zero weight block so as to compute the feature data, and updating the model weight parameter based on an output result obtained by computation. The method can accelerate the processing efficiency of the ultrasonic large model.

The invention discloses an intelligent illegal behavior identification method and system based on multi-algorithm fusion, and relates to the technical field of image analysis. The method comprises the steps of collecting a region image sequence, identifying real-time personnel density information, adjusting the scale of a laser curtain wall region based on the personnel density information, forming a self-adaptive monitoring boundary, when a laser sensor detects an intrusion event, recording the intrusion moment and intrusion characteristics, calling an intrusion region image set, and obtaining the intrusion region image sequence; and analyzing the intrusion area image set by adopting an ensemble learning-based violation behavior recognition network group to obtain an interactive violation behavior recognition result, fusing the interactive violation behavior recognition result with the intrusion characteristics, introducing personnel density information to carry out environment adaptive compensation calculation, and outputting a final violation behavior recognition result. According to the invention, through fusion of image analysis, laser triggering and environment perception, high-robustness and high-accuracy intelligent identification of illegal behaviors in scenes such as subway security check is realized.

The invention discloses a visual prosthesis control method and system and a storage medium, and relates to the field of medical instruments, and the method comprises the steps: extracting at least one initial target image based on a target object selection result fed back by a user, carrying out the region-of-interest extraction of the initial target image, and obtaining a target object contour image, grouping each contour pixel point based on the brightness values of the contour pixel points in the target object contour map to obtain a plurality of data groups and a stimulation sequence thereof, mapping a stimulation current matrix of each data group based on the stimulation current value and the space coordinate of each contour pixel point, and obtaining a stimulation current matrix of each data group based on the stimulation sequence; and controlling the release of the stimulation current in the electrode array based on each stimulation current matrix in sequence. Redundant information is reduced based on user feedback and region-of-interest extraction, the stimulation sequence based on the average brightness value of each data set is configured, the stimulation current is controlled to be released in the electrode array based on each stimulation current matrix in sequence, and the perception quality of the brain to the target object is improved.

The invention discloses a stacker fault diagnosis method based on lightweight MobileViT, and relates to the field of stacker fault diagnosis, and the method comprises the steps: S1, deploying a sensor group based on a stacker work site, and obtaining original sensing data of eight modes; s2, preprocessing the obtained original sensing data based on a multi-sensor data fusion preprocessing process of noise reduction reconstruction-fusion-two-dimensional coding to obtain corresponding two-dimensional image data; s3, dividing the obtained two-dimensional image data, and training a MobileViT model based on the training set and the verification set; s4, inputting the test set into the trained MobileViT model to perform fault diagnosis, and performing visual analysis on the result, the lightweight MobileViT model designed by the application can greatly reduce the parameter quantity of a traditional deep learning model; the method has good classification performance, and especially has good practicability in the field of fault diagnosis of the stacker.

The invention relates to an equipment main circuit parameter global optimization method based on a large model iterator, and belongs to the field of power electronic equipment design, and the method comprises the steps: generating an initial parameter set through a parameter generation LLM; performing feasibility grouping based on the feasibility flag bit; performing dominating relation judgment on the data in the feasible solution pool; carrying out frontier stability judgment; and the parameter optimization LLM executes a decision according to the termination flag bit and the maximum number of iterations to obtain a final main circuit parameter set. According to the method, systematic optimization meeting physical constraints can be realized through cooperation of multiple external tools when the calculation capability of a large model is limited. The method comprises the following steps: constructing a closed-loop feedback iteration mechanism, performing strict physical boundary verification on parameters by using a steady-state analysis tool, and grouping data according to Pareto dominating logic; and meanwhile, leading-edge stability judgment and differential optimization strategies are introduced, so that the physical feasibility of the design scheme is ensured, and global optimal design considering technical performance and economic cost is realized.

Embodiments provide a social networking platform offering various services, such as, facilitating aggregation and management of a user's interaction on one or more social networking platforms, offering enhanced control over the level of privacy associated with the flow of user data, offering tools to customize the user's exposure to advertisement-related content on the social networking platform(s), integrating features to control aspects of how data / content is presented to and visualized by the user, empowering the user to multicast direct messages to other users without the other users having to meet certain constraints, empowering the user to create and / or join a group based on messaging threads, and the like. One or more of these enhanced services / features are associated with a powerful framework of authentication / permission model for access control.

The invention discloses a set communication method and device, a chip product, equipment and a storage medium, and relates to the technical field of distributed training. The method is executed by an adaptation layer, and the adaptation layer reconstructs a working communication process group based on a currently surviving training process under the condition that a fault process exists; an actual communication interface corresponding to the working communication process group is called, set communication operation corresponding to set communication is executed, a set communication result is obtained, the set communication result comprises fragment data of all training processes in the working communication process group, and the fragment data are data used when the training processes execute the distributed training tasks; adjusting operation is executed on the set communication result to obtain an adjusted set communication result, and the adjusting operation is used for reducing precision loss generated by missing fragment data of the fault process; and sending the adjusted set communication result to the training process in the working communication process group. According to the method and the device, the fault-tolerant process of the set communication is not perceived by the training process in a data fragmentation scene.

An information processing apparatus includes, a setting unit that sets a condition for a user classified into a user group from input data; a selecting unit that selects a to-be-analyzed group based on a user attribute corresponding to each condition with respect to each the condition; a scoring unit that scores each user meeting the condition based on a behavior history of each user corresponding to each condition with respect to each the condition; an extractor that extracts a warm-prospect user having a higher score than other users in the to-be-analyzed group based on a result of scoring by the scoring unit; and a provision unit that selects a to-be-compared user, specifies information on a characteristic behavior of the warm-prospect user, transmits information on the specified characteristic behavior and attribute information indicating the attribute corresponding to the warm-prospect user to a terminal device, and provides the specified and attribute information.

The invention relates to the technical field of social network information propagation prediction, in particular to an event propagation prediction method based on group influence. The method comprises the following steps: constructing a social network graph of target event participating users, calculating an activation probability matrix among the users by using historical propagation data, and performing social graph structure enhancement based on a threshold value to obtain a de-noised social graph; applying a graph neural network on the de-noised social graph and introducing information bottleneck constrained self-supervised contrast learning optimization node embedding, and reserving features useful for group division and propagation prediction; dynamically dividing user groups related to propagation according to the similarity and propagation context of de-noising node embedding to form a group-level propagation sequence; based on the group representation, utilizing an attention mechanism and a group relation graph to carry out modeling on propagation influence among the groups to obtain global group representation of the event; global group representation is combined with candidate user embedding, propagation probability distribution is calculated, and future participating users of corresponding events are predicted.

The invention discloses an industrial and commercial time-of-use electricity price dynamic adjustment model considering an electric power spot market. The method comprises the steps of obtaining a historical load curve of a preset category; dividing the historical load curve into a first time period group based on a first preset quarter and a second time period group based on a second preset quarter based on a hierarchical clustering algorithm and by combining a load weighted Euclidean distance and a Ward connection method according to the historical load curve; obtaining a clustered load curve according to the first time period group and the second time period group; determining load characteristics according to the clustered load curve; adjusting a load elasticity coefficient according to the load characteristics and historical load data; constructing an optimized load model according to the load elastic coefficient; inputting the optimized load model into a single-target artificial hummingbird algorithm, and determining the electricity price of each time period; and determining a final electricity price scheme according to the electricity price of each time period. The invention solves the problem of insufficient seasonal load adaptability of the current power system.

The invention belongs to the technical field of water resource optimal scheduling, and discloses a reservoir optimal scheduling method based on hybrid swarm intelligent optimization, which comprises the following steps: S1, collecting data required by reservoir historical inflow, a reservoir capacity curve, a water supply demand and a scheduling constraint condition; s2, setting a constraint condition and a scheduling target, and establishing a reservoir scheduling optimization model; s3, constructing a candidate solution group on the basis of combining reservoir scheduling decision variables, performing initial fitness calculation by utilizing an objective function of the reservoir scheduling optimization model, introducing a hybrid group intelligent optimization algorithm, performing multi-objective optimization scheduling calculation on the reservoir scheduling optimization model, and performing iterative updating; s4, completing all set iterations, and outputting a reservoir optimization scheduling scheme meeting constraint conditions; according to the method, the premature convergence problem of a traditional swarm intelligence optimization algorithm is effectively avoided, the convergence speed and the solution stability are improved, and the method is suitable for optimization solution of single-library and multi-library systems in single-target or multi-target scheduling problems of water supply, flood control, power generation and the like.

A method, computer system, and a computer program product are provided for debugging and determining root cause analysis of a server error. Resource data is obtained as relating to a server with error or debugging needs and grouped based on learned and programmed context specific components to determine a root cause analysis for the error or debugging need. An overall solution is then provided to address the server error or debugging need by evaluation a root cause analysis. The overall solution analyzes a plurality of disparate reasons causing the server error or debugging need and providing a single coherent solution based on interconnectivity of the plurality of disparate reasons. The overall solution is updated by obtaining historical data. A recommendation is then provided for the server to resolve the error or the debugging need based on the updated overall solution.

This application provides a packet processing method, a device, a system, and a storage medium. A first network device receives an original packet, generates an IPv6 packet based on the original packet and endpoint group (EPG) information, where the IPv6 packet comprises an IPv6 extension header and the original packet, and the IPv6 extension header comprises the EPG information, and sends the IPv6 packet. A second network device receives the IPv6 packet;obtains the EPG information from the IPv6 extension header, and processes the IPv6 packet according to a group based policy corresponding to the EPG information.

The invention relates to the technical field of federated learning, and discloses an image omics-oriented cross-domain federated machine learning training method, which comprises the following steps that: a server distributes a global orthogonal semantic base to establish a shared gradient space recessive semantic coordinate system, and when the stability of a local training process meets a preset condition, a client sends the global orthogonal semantic base to the client; a local gradient is projected to a coordinate system and uploaded together with a digital fingerprint representing the physical characteristics of imaging equipment, and a server groups clients according to the digital fingerprint and performs weighted aggregation based on the information concentration degree on received projection vectors in each group so as to reconstruct a global gradient to update a model. According to the method, through an aggregation mode of firstly performing grouping according to physical causes and then performing weighting in groups according to information values, systematic deviation from equipment differences and real pathological semantics from data contents can be effectively stripped, and sensitivity to key clinical features is kept.

The invention relates to the technical field of electronic countermeasures, in particular to an unmanned aerial vehicle group dynamic interception method and system based on distributed interference array collaboration. According to the method, an interference node cluster with autonomous logic association is dynamically deployed in a defense domain, and an elastically reconfigurable distributed interference field domain is formed through a software defined radio architecture; time-varying spectrum features are extracted through an adversarial neural network, and a dynamic threat fingerprint database is constructed; generating an interference vector with frequency band following and power self-adaption based on the dynamic threat fingerprint database, and updating the feature weight of the dynamic threat fingerprint database in real time according to interference efficiency feedback; and according to the situation evolution trend of the dynamic threat fingerprint database, a hierarchical interference wave sequence is generated based on a multi-agent consensus protocol, and a space-time coupled interception chain is formed. Through the method, the whole-process dynamic confrontation capability of the unmanned aerial vehicle group is realized, the efficient interception performance is maintained in a complex electromagnetic environment, and meanwhile, the interference risk on legal communication is reduced.

The invention discloses a data center dynamic energy-saving scheduling method and system based on AI prediction, electronic equipment and a storage medium, and the method comprises the steps: collecting load data of a data center server and external service flow data in real time to construct an original data set, and processing the original data set through employing a long short-term memory network to obtain a load change trend; according to the load change trend, determining distribution characteristics of the thermal load trend in a future time window, extracting idle computing resource data from the server group based on the distribution characteristics, and computing a computing resource distribution proportion needing to be adjusted; carrying out iterative optimization on the calculation resource distribution proportion by adopting a genetic algorithm to obtain an optimized server load distribution scheme; through a server load distribution scheme, obtaining associated refrigeration system parameter data, determining an adjustment vector of a refrigeration strategy, fusing real-time energy consumption data of a power supply module, and judging a correction demand of power supply scheduling; and obtaining the operation configuration of the overall energy efficiency optimization according to the correction demand.

The invention discloses an unmanned aerial vehicle cluster system and a distributed task planning method based on real-time communication and collaborative decision, and the method carries out the grouping and network simplification of unmanned aerial vehicle cluster distributed task planning based on conflict resolution. Comprising task conflict resolution based on a pre-estimation strategy, grouping based on task selection tendency and distributed network simplification based on bidding. According to the invention, each unmanned aerial vehicle selectively receives messages according to a necessary network constructed by the unmanned aerial vehicle, and redundant communication in internal circulation is further reduced; by comparing compiler output results, inconsistency is detected to discover defect examples. By adopting the multi-unmanned-aerial-vehicle cooperative task allocation method designed by the invention, the task allocation efficiency of a large-scale multi-unmanned-aerial-vehicle system can be remarkably improved.

The invention discloses a visual asynchronous event triggering and calling method based on a medical information system, which comprises the following steps of: asynchronously acquiring multi-source data streams such as clinical events, equipment monitoring, doctor advice execution and patient states in the medical information system to form a multi-source asynchronous medical data stream set; generating a system event triggering association group based on the set and creating an asynchronous event scheduling strategy table; performing system event association degree calculation on the data stream to obtain a multi-dimensional asynchronous event association matrix; and carrying out system asynchronous time delay compensation and event chain visualization processing on the incidence matrix based on the scheduling strategy table, and finally generating a dynamic asynchronous event correlation topological graph. According to the invention, by constructing a system event detection operator set, establishing cross-flow system relevance calculation and establishing a priority-based asynchronous scheduling optimization mechanism, intelligent triggering, calling and early warning functions of medical system events are realized.