1560 results about "Group based" patented technology

The invention provides an intelligent storage robot group cooperative scheduling method based on deep reinforcement learning, and relates to the technical field of intelligent storage, and the method comprises the steps: constructing a group perception module through a hierarchical attention mechanism, and generating a dynamic cooperative perception matrix; establishing a deep reinforcement learning model for strategy learning; and designing a multi-level reward function and optimizing a training process through an adaptive weight adjustment mechanism. According to the invention, the cooperative efficiency of warehouse robot group scheduling is improved, the task conflict rate is reduced, and the adaptability of the system to a complex dynamic environment is enhanced.

The present disclosure discloses a non-motor vehicle recognition method and system based on a multi-sensor collaboration and relates to the technical field of intelligent transportation. The method includes: constructing a sensor group based on a plurality of sensors, and performing a data collection based on a target range through the sensor group to generate a multi-class regional dataset; transmitting the multi-class regional dataset to a data fusion channel to generate an initial fusion dataset; synchronizing the initial fusion dataset to a data preprocessing unit to perform preprocessing to generate a target fusion dataset; utilizing a feature extraction unit to traverse the target fusion dataset to perform a feature extraction of a target non-motor vehicle, and generating a target feature information set; and constructing a target recognition unit, and intelligently recognizing the target fusion dataset through the target recognition unit.

The invention discloses an abnormal detection method and system for a splayed anti-loosening line of a rail train, and relates to the technical field of rail traffic detection, and the method comprises the steps: image collection and processing: shooting an image of a fixed point region containing the anti-loosening line, and preprocessing the image to obtain a target image; line integrity judgment: extracting a bolt, a bolt marking line and an anti-loosening line through an analysis model to form a target area graph, and judging whether the anti-loosening line is complete or not according to line segment continuity and form; performing line deformation depth analysis, and synchronously obtaining the position of an anti-loosening line intersection point and deformation data through the model in combination with the loosening angle of the bolt marking line when the line deformation is complete; performing line deformation safety judgment, setting a safety threshold group based on historical data, and comparing deformation data to output a signal; after line deformation abnormity judgment and comparison signal receiving, actual data are compared with theoretical data output by the mechanical model, and abnormal or normal signals are output. The anti-loose line full-dimension high-precision detection is achieved, automation and real-time performance are improved, and train operation safety is guaranteed.

The invention discloses an electric power safety monitoring management method, and relates to the technical field of electric power monitoring, and the method comprises the steps: obtaining a basic data set, calculating the dependency weight between devices, obtaining an associated device set, and constructing an initial network diagram according to the associated device set; determining an abnormal device and a corresponding abnormal time node; obtaining a first association group and a second association group according to the association device set; overlapping the first association group and the second association group with the initial network diagram to obtain a main path and an auxiliary path; calculating the risk intensity value of each device in the main path, obtaining the disposal efficiency and evaluation indexes of different time nodes, and obtaining the optimal intervention time; calculating a risk delay growth curve of the secondary path, identifying a risk acceleration inflection point, and determining the optimal prevention time; by quantifying electrical dependence and physical distance, the limitation that a traditional topology network only pays attention to physical connection is broken through, a fault conduction key path is accurately locked, and cross-device chain reaction is quantified.

The invention discloses a processor, a graphics card, equipment and a resource allocation method and device, and belongs to the technical field of computer resource management. The processor comprises a thread group assembling unit, a thread group scheduling execution unit and at least two processing units, the thread group assembling unit is used for assembling the tasks into one or more thread groups based on the attribute information of the tasks in a task assembling stage; and allocating computing resources for one or more thread groups with the thread group granularity, wherein each thread group is allocated with a processing unit; the thread group scheduling execution unit is used for scheduling one or more thread groups to the processing unit corresponding to each thread group for execution according to a unified scheduling rule, and the unified scheduling rule is a scheduling rule shared by different types of tasks; and the at least two processing units are used for executing the scheduled thread groups. And the computing resources are allocated according to the thread group granularity, so that the execution efficiency of the processor is improved.

The invention discloses an intelligent management method and system for offshore low-broadband data transmission, and the method comprises the following steps: S1, dividing an offshore communication network into a plurality of bandwidth sensitive intervals, collecting dynamic environment data, and marking a network operation mode state; s2, associating and matching the dynamic environment data with a network operation mode state; s3, generating a data transmission strategy group based on the network state feature set; s4, simulating a path selection behavior and generating a path load characteristic spectrum; s5, performing clustering processing on the path load characteristic spectrum to form a path strategy optimization library; s6, determining a data transmission strategy and path combination by using a double-library dynamic mapping mechanism; and S7, monitoring the network state in real time, and dynamically updating the path strategy optimization library and the network state feature set. According to the invention, the maritime communication bandwidth utilization efficiency and the transmission stability are improved.

The invention provides an intelligent message pushing method and system, and the method comprises the steps: collecting the social behavior data and personal attribute data of a user in a social network; dividing the users into a plurality of groups by using the social behavior data and the personal attribute data based on an ant colony algorithm; analyzing a social relation and an interaction mode among users in the group; collecting message resources in the social network, and classifying and labeling messages; performing collaborative filtering processing on the to-be-pushed message, determining a target user group, and generating a message recommendation list; and pushing the recommended message to users in the target user group according to a preset pushing strategy. The user social behavior data and the personal attribute data are converted into the ant feature vectors based on the ant colony algorithm, similar feature vector ants are gathered through pheromone updating and path selection mechanisms, different user groups are formed, the user group division accuracy is improved, and user requirements are more accurately grasped.

The invention discloses a storage method and system for an intelligent storage cabinet, and the method comprises the steps: receiving authority parameters of an online examination and approval request and user biological characteristics, and generating an encrypted digital token; based on the encrypted digital token, performing identity authentication by adopting a multi-mode biological feature fusion algorithm, associating face features with voiceprint features through a space-time attention mechanism, and outputting a dynamic identity verification code; activating a target storage cabinet group according to the dynamic identity verification code, and generating an article unique identifier set; based on difference analysis of the unique article identifier set and a preset inventory list, outputting an optimal cabinet grid allocation scheme; and driving a modular cabinet group execution mechanism to adjust a physical structure according to the optimal cabinet grid distribution scheme, synchronously encrypting and writing article state data into a distributed account book, and triggering an edge computing node to update a global inventory topological graph. According to the embodiment of the invention, intelligent, self-adaptive and distributed collaborative management of the storage cabinet can be realized on the premise of ensuring the safety.

The invention relates to the field of data mining and machine learning, and relates to a bridge group deformation mode evaluation method and system based on PS-InSAR and complex network theories. The method comprises the following steps of: 1, performing refined preprocessing on high-dimensional time series data of engineering structure monitoring points obtained by multiple sources; 2, constructing a behavior pattern distribution vector of each monomer structure by adopting a UMAP-KDPI algorithm; and step 3, constructing a group association network by calculating behavior similarity between structures, and performing community discovery, key node identification and multi-level association characteristic deep analysis by using a network science theory to realize bridge group deformation mode evaluation. The problem that in an existing monitoring data analysis method for an engineering structure group, potential and common behavior patterns in complex high-dimensional time series data are difficult to effectively recognize, and an effective means for accurately quantifying and comparing behavior similarity among structure individuals is lacked is solved.

A method for providing advertisements in a linear media service includes (a) obtaining first list information about a first advertisement group to be provided during a commercial break; and (b) performing signal processing and providing at least a part of one advertisement of the first advertisement group based on the first list information during the commercial break. Step (b) includes receiving a first user input for skipping the first advertisement while providing, by signal processing, the first advertisement having a skippable attribute in the first advertisement group; in response to the first user input, stopping the first advertisement, and obtaining second list information about a second advertisement group to be provided in a first remaining time during the commercial break; and performing signal processing and providing at least a part of one advertisement of the second advertising group based on the second list information during the first remaining time.

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 relates to the technical field of network communication, in particular to a tourist group non-inductive cross-AP roaming method based on multi-stream graph neural network optimization hierarchical reinforcement learning. Comprising the steps of group identification and Bluetooth sideband control establishment, multi-stream scene graph modeling and foresight anchor point labeling, opportunity window inference and group-level constraint projection, HRL high-level sub-target planning, HRL low-level sideband collaborative execution, steady-state shaping, congestion suppression, rollback bundling and the like. Group movement and session states are sensed and represented in a unified manner at a network side, a cross-AP switching plan with a controllable time sequence is generated for groups rather than individuals, and resources and time slots of Bluetooth and Wi-Fi are coordinated, so that low-interruption, low-delay and consistent non-inductive roaming experience is realized in a process that the groups pass through boundaries of adjacent cells.

The invention discloses a multi-unmanned aerial vehicle cooperative task dynamic allocation method, which belongs to the technical field of unmanned aerial vehicles, and comprises the following steps: constructing an initial allocation model of a multi-unmanned aerial vehicle cooperative task according to static factors; performing optimal grouping on the task points by adopting an improved Kmeans clustering algorithm; determining an optimal sequence of the task points in each group based on an ant colony algorithm; based on the initial allocation model and the grouping, adopting an improved genetic algorithm to carry out initial allocation solution; after initial allocation is carried out according to an initial allocation solving result, a dynamic reallocation model of the multi-unmanned aerial vehicle cooperative task is constructed according to the dynamic factors; based on the dynamic redistribution model, carrying out dynamic redistribution solving by adopting an operation planning thought, and carrying out dynamic redistribution according to a dynamic redistribution solving result; according to the method, the genetic algorithm and the operation planning are combined, the advantages of the genetic algorithm and the operation planning are fused, more efficient multi-unmanned-aerial-vehicle cooperative task allocation is achieved, and the method is suitable for cooperative multi-task scenes such as routing inspection monitoring, disaster rescue, logistics distribution and fixed-point surveying and mapping.

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.

Artificial intelligence techniques for connections networking are described. In one embodiment, for example, a method comprises receiving a natural language query to group a set of entities by a first machine learning model trained on a public dataset, generating a set of entity groups by the first machine learning model based on the natural language query and the set of entities, generating a set of connections entity groups based on the set of entity groups by a second machine learning model trained on a private dataset, selecting a member identifier (ID) representing a member of a connections networking system associated with an entity from a connections entity group of the set of connections entity groups, and sending a recommendation for a networking service to an electronic device based on the member ID. Other embodiments are described and claimed.

The invention relates to the technical field of safety and stability control of a power distribution network, in particular to a regional power distribution network collection point voltage reactive coupling characteristic evaluation method. The method comprises the following steps: deploying a synchronous phasor measurement unit for a plurality of transformer area convergent points, collecting electrical parameters and generating original state data of the convergent points; performing quality verification, feature extraction and time sequence reconstruction on the original state data to obtain a convergence point key feature parameter set; based on the key characteristic parameter set, an inverter group initial model is constructed through inverter operation characteristic clustering and control link characteristic analysis; performing electromagnetic transient-steady state hybrid simulation on the inverter group based on the model to generate a transient-steady state hybrid simulation scheme; on the basis of the scheme, through multi-working-condition interactive simulation and interactive gain calculation, a key coupling path of a collection point is mined. According to the method, the dynamic interaction influence between the inverter group and the power grid can be accurately captured, and the resonance path and the reactive coupling path can be effectively identified.

The invention discloses a source load risk management and control method for cooperative operation of multiple types of adjusting power supplies and new energy, and the method comprises the steps: depicting a prediction error tail dependency relationship through employing an extreme value theory and a Copula function, constructing a distribution robust optimization model in combination with a Wasserstein distance, and obtaining the standby capacity sharing, triggering threshold and risk index of the multiple types of adjusting power supplies; performing conformal prediction based on the prediction residual error to generate a dynamic confidence interval, and mapping the dynamic confidence interval into a pipeline radius of model prediction control; executing rolling MPC containing information bottleneck regularization under the constraint of the reserve capacity and the pipeline, and generating a robust scheduling strategy; and constructing a polyhedral feasible region model of the flexible load group according to the scheduling strategy and the risk index, and performing scaling to obtain a group control action. And the new energy consumption level and the system operation safety are obviously improved.

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 provides a control data collection method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the grouping of to-be-collected data point locations based on a point address and an address region, obtaining a plurality of point location groups, and obtaining a plurality of data collection point locations according to the point address and the reading offset of each to-be-collected data point location. Sorting the to-be-acquired data point locations in the plurality of point location groups from small to large according to the reading offsets, sequentially calculating the first communication byte number and the second communication byte number of each to-be-acquired data point location in the point location groups from front to back according to the sorting and based on the reading offsets, and partitioning the to-be-acquired data point locations into blocks, according to the method, the point location blocks are combined based on the number of block communication bytes to obtain a plurality of point location batches, a data acquisition request is sent to the controller based on the point location batches to acquire control data, and the technical problems of low communication efficiency and high delay of data acquisition for the programmable logic controller are solved through the method.

The invention relates to the technical field of audio control, and discloses a distributed audio group mute and recovery method and system, a storage medium and a device, and the method comprises the steps: determining a master device in a distributed audio network, and enabling the master device to find and register a plurality of slave devices to establish an audio group; receiving a group mute or recovery instruction sent by a user through the master device, and performing validity verification on the instruction; the master device allocates a uniform timestamp to the verified instruction, and sends a synchronous control command to all slave devices in the group based on the timestamp; the slave equipment executes mute or recovery operation at the moment corresponding to the timestamp, synchronous response of the equipment in the group is realized, all the equipment is ensured to accurately and synchronously execute the mute / recovery operation through unified management of the master equipment and the adoption of global timestamp synchronization and adaptive delay prediction technologies, and an intelligent state monitoring and exception recovery mechanism is provided, so that the service life of the slave equipment is prolonged. Finally, efficient and reliable multi-room audio group control with excellent user experience is realized.

The invention discloses a method, a system and equipment for carrying out resource management on a database and a medium. The method comprises the following steps: creating at least one resource group in a database, and defining configuration information of each resource group; based on the binding relation table of each resource group, binding each resource group, and determining the binding type of each resource group; based on the binding type of each resource group, updating the configuration information of each resource group through a resource group operation interface to obtain target configuration information of each resource group; executing a corresponding query operation based on the target configuration information of each resource group and the state of each resource group; monitoring whether the state of each resource group is changed or not, triggering a corresponding resource adjustment strategy when the state of each resource group is changed, and recording change information; according to the method, resources of different tenants are isolated through a resource group mechanism, so that database-level resource isolation and fine management are realized, and the stability of a multi-tenant environment is improved.

The invention discloses a plain river gatekeeper group intelligent regulation and control method based on deep reinforcement learning. The method comprises the steps that a flow prediction module, a knowledge constraint reinforcement learning module combining domain knowledge and an experience library and a multi-agent reinforcement learning module are built; integrating the calibrated knowledge constraint reinforcement learning module with a flow prediction module and a multi-agent reinforcement learning module to form a complete plain river network intelligent regulation and control model; and on the basis of the calibrated plain river network intelligent regulation and control model, future meteorological and hydrological prediction data are accessed, and a reasonable gate group scheduling scheme is rapidly output. The method has the advantages that the scheduling scheme cluster is intelligently generated, the complex and dynamically changing plain river gatekeeper group scheduling problem is efficiently solved, the flexibility, timeliness and intelligent level of a gatekeeper group scheduling system are improved, the possible'non-realistic solution 'problem of a machine learning method in practical application is solved, the interpretability of a result and the practical operability of a scheme are enhanced, and the method is suitable for large-scale popularization and application. And a self-adaptive closed loop of model-environment-feedback-regulation and control correction is formed.

A method is disclosed for enhancing subband full duplex operation. The method may include receiving, by a user equipment (UE), a configuration for subband full duplex (SBFD) operation and Physical Random Access Channel (PRACH) repetition, and constructing, by the UE, a RACH Occasion (RO) group based on the configuration, wherein the RO group comprises one or more ROs from a set of SBFD symbols and a set of non-SBFD symbols.

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.

The invention relates to an automatic interface test management method and device, equipment and a medium, and the method comprises the steps: obtaining an interface document of a to-be-tested interface, carrying out the analysis of the interface document, generating interface case data containing the primary configuration content of an interface case, and transmitting the interface case data to a server; performing execution grouping on the interface cases configured with the input parameter value set, generating a case execution group based on interface grouping information and a preset test execution strategy, setting an execution sequence and an exception handling rule for the case execution group, and generating a corresponding execution link at the same time; calling an execution link based on the trigger request to sequentially use each group of request content execution cases in the input parameter value set to execute the interface cases in the group, and generating corresponding case execution results; high automation and intelligentization are achieved in the whole interface test process, manual intervention is remarkably reduced, the test coverage rate is increased, the problem response speed is increased, and therefore the working efficiency of testers is effectively improved, and the human error risk is reduced.

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.

This invention relates to the field of robot scheduling technology, and more particularly to a multi-robot cloud scheduling and collaborative method and system. The method includes the following steps: acquiring building elevator group data, and dividing the building elevator service groups based on the building elevator group data to obtain building elevator service group data; acquiring robot target path data; segmenting the robot target path data and building elevator service group data into path resource segments to obtain split path data; acquiring current transfer floor data, and estimating transfer waiting time based on the current transfer floor data and split path data to obtain transfer waiting time estimation data; constructing a transfer map based on the transfer waiting time estimation data to obtain transfer map data; obtaining path conflict data based on the transfer map data; and obtaining robot scheduling data based on the path conflict data to perform multi-robot collaborative scheduling operations. This invention can improve task completion efficiency and scheduling intelligence in multi-robot cross-floor collaborative scenarios.

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.