2143 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 invention relates to the technical field of charging pile group load intelligent regulation and control, and particularly discloses a charging pile group load intelligent regulation and control method and system based on dynamic power equalization, and the method comprises the steps: collecting the oil temperature of a transformer in real time, calculating a total output power threshold value, and correcting the actual output power of a charging pile through combining the line loss and the equipment aging degree; a multi-dimensional load regulation and control decision matrix including user priority, equipment health degree and power regulation sensitivity is established, a dynamic power strategy is allocated through matrix weight, power allocation is inclined to high-priority users and health equipment, emergency power allocation and progressive readjustment during newly-added demands are supported, and the power regulation and control efficiency is improved. Impact of sudden power change on a power grid is avoided, system stability is ensured when newly added loads are accessed, refined intelligent regulation and control of loads of the charging pile group are finally realized, transformer safety, charging efficiency and user experience are improved, and power grid stability and resource optimization configuration are ensured.

The invention relates to the technical field of exhibit recommendation, and particularly discloses an exhibit first push item optimization method combining a user portrait and user positioning, which extracts user positioning data from an indoor positioning system, and determines a target exhibit group based on real-time position and orientation information of a user, and then introducing a deep learning technology to perform semantic analysis on the user portrait data to extract user portrait features, and performing semantic matching analysis on the user portrait features and feature tags of each exhibit in the target exhibit group to determine an exhibit first push item from the target exhibit group. Through the mode, the behavior preference of the user and the real-time physical environment information can be comprehensively considered, a more accurate and personalized exhibit recommendation service is provided for the user, then the visiting experience of visitors is improved, and the resource utilization efficiency of culture display places such as museums and exhibition halls is optimized.

The invention discloses a comprehensive online monitoring system for a high-voltage cable of an electrified railway, and particularly relates to the technical field of cable monitoring. The clustering analysis module divides similar groups based on factory and use environment data; the prediction mode selection module evaluates the stability and nonlinearity degree of the environmental data, and selects an optimal prediction method to predict the environmental data; the threshold value judgment module selects a fixed or dynamic threshold value to judge a fault through comparison with similar group data; the dynamic adjustment module optimizes a fault threshold based on historical data and improves the detection precision; the alarm module triggers early warning when the monitoring data exceed a threshold value; according to the method, the operation mode of the cable is accurately matched, the monitoring precision is improved, misjudgment caused by a single data source is avoided, it is ensured that the optimal prediction method is always used under different environmental conditions, the prediction precision of future environmental data is improved, and reliable support is provided for fault judgment.

The invention relates to the technical field of bidirectional PCS converters, and discloses a VSG control method and device for a DSP numerical control high-frequency bidirectional PCS converter, and the method comprises the steps: carrying out the multi-signal classification of an output voltage, an output current, a DC bus voltage and a PWM modulation waveform of the PCS converter through a DSP chip, and obtaining a frequency spectrum feature vector; calculating a VSG parameter group based on the spectrum feature vector, inputting the VSG parameter group into a depth deterministic strategy gradient network for parallel training, and generating a VSG real-time control strategy; applying phase-locked loop compensation to the VSG real-time control strategy to obtain a PWM modulation signal after synchronous compensation; and carrying out real-time fault early warning on the PWM modulation signal after synchronous compensation, and carrying out seamless switching on a grid-connected and off-grid mode by adopting voltage feed-forward decoupling control to obtain a smooth transition control sequence. According to the method, the control period and the calculation load of the PCS converter under the high-frequency switching condition are reduced, and the control precision under the complex working condition is ensured.

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 a product collaborative design management method and system based on data driving, and relates to the technical field of product design management, and the method comprises the steps: constructing a double-flow heterogeneous quantitative collection network; establishing an incidence matrix and calculating dynamic coupling strength among the parameters; designing a recursive deep belief network based on the incidence matrix and the dynamic coupling strength, encoding product structure parameters and design process data into a probability graph model, and constructing a design knowledge base; dynamically distributing the design rules in the design knowledge base by adopting a swarm intelligent optimization algorithm, generating a collaborative decision-making unit, and establishing a constraint propagation link; and generating a multi-target collaborative optimization scheme group, and screening an optimal scheme group based on Pareto frontier. According to the invention, omnibearing acquisition of product structure parameters and design flow data is realized through the double-flow heterogeneous quantitative acquisition network, and the data integrity is improved.

The invention relates to the technical field of lens parameter optimization, and discloses an out-of-focus lens structure parameter optimization method and system based on multiple optical parameters, and the method comprises the steps: determining a lens wearing user and an optimization target, carrying out the eyeball parameter collection of the lens wearing user, obtaining a user eyeball parameter group, determining an optimization structure type group based on the optimization target, constructing a prevention effect function, a visual effect function and a wearing comfort function, generating a fitness function, confirming a standard structure parameter group according to the optimized structure category group, initializing a particle swarm based on the standard structure parameter group, a user eyeball parameter group and the fitness function, obtaining an initial position group and an initial fitness group, and obtaining an initial position group and an initial fitness group; and iterating the particle swarm based on the initial position group and the initial fitness group to obtain an optimal structure parameter group. According to the invention, the comprehensive performance of the out-of-focus lens can be improved, and personalized lens design can be realized according to different requirements.

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 a human resource information management system and method based on big data, and relates to the technical field of human resources, and the system comprises a project demand analysis module which is used for extracting a skill list required by new project post personnel, determining task complexity, and generating skill parameter requirements; the employee screening module is used for obtaining the basic information of all the employees in service and dividing the employees in service into two types of crowds, one type is the crowd meeting the new project employment, and the other type is the crowd not meeting the new project employment, through big data analysis and multi-dimensional evaluation, accurate matching of the project demand and the employee ability is achieved, and the work efficiency is improved. Project execution quality and efficiency are improved, enterprise internal human resource potential is fully excavated, personnel can be reasonably allocated, and waste and idleness of human resources can be avoided.

The invention provides a fault diagnosis intelligent analysis method and system for industrial equipment, and a storage medium. The method comprises the following steps: segmenting monitoring time sequence data into at least one sub-sequence based on statistical characteristics and working conditions of the monitoring time sequence data; determining the length of an embedded window based on the local characteristics of the subsequences, constructing a trajectory matrix by using the length of the embedded window, and performing singular value decomposition on the trajectory matrix to obtain a primitive matrix and a corresponding singular value; forming candidate groups for the primitive matrixes of different preset combinations, calculating a separability metric value of each candidate group, and determining a component group based on the separability metric values and the singular values; performing diagonal averaging reconstruction on the selected component group to obtain signal components, and calculating preset characteristic parameters of each signal component; and according to the preset feature parameters, target signal components related to the preset fault mode are screened out, feature vectors representing the fault state are extracted from the target signal components, the feature vectors are input into a pre-configured fault diagnosis model, and fault diagnosis information is output.

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 belongs to the technical field of automatic control, and discloses an automatic equipment control method and system based on multi-source data. Comprising the steps of collecting multi-source data of an industrial equipment group in a mining area, integrating the multi-source data into multi-equipment operation data, and preprocessing the multi-equipment operation data to obtain basic equipment operation data; constructing a multi-modal hybrid model based on the basic equipment operation data, updating the multi-modal hybrid model, and performing equipment health state evaluation on each industrial equipment by using the updated multi-modal hybrid model to obtain comprehensive risk data; constructing a deep learning framework, generating an equipment control strategy priority based on the comprehensive risk data and the deep learning framework, and generating an instruction sequence based on the equipment control strategy priority; performing dynamic parameter adjustment on each industrial device in the industrial device group based on the instruction sequence to obtain an optimal operation parameter combination; sending the optimal operation parameter combination to a preset mining area equipment parameter database; and automatic control of each device is realized.

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.

Methods and apparatus are described for delivering streams of media content in ways that maintain compatibility among different streaming protocols for inserting secondary content into the streams of media content. This is accomplished by encoding media content the same for each streaming protocol but generating different output groups based on each streaming protocol.

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.

An edge device is for a distributed traffic engineering system with quality of service control of a plurality of flow groups routed over a set of overlay links. The edge device is configured to receive one or more service-level agreement requirements for the plurality of flow groups from a network controller, receive first control information from one or more other edge devices in the distributed traffic engineering system, obtain monitoring information for the plurality of flow groups and the set of overlay links, and adjust a plurality of queuing parameters for the plurality of flow groups based on at least one of the first control information, the monitoring information, or the one or more SLA requirements.

The invention relates to a remote fault diagnosis method and system for a sand absorbing, screening and separating all-in-one machine based on the Internet of Things, and the method comprises the following steps: obtaining the historical data of equipment, and deploying an Internet of Things sensor group based on the key part of the sand absorbing, screening and separating all-in-one machine, the method comprises the steps of collecting vibration data, temperature data, pressure data and displacement data of equipment in real time, preprocessing historical data of the equipment, performing normalization processing on the historical data of the equipment, extracting features from the historical data, fusing the historical data of the equipment, performing fault prediction on time series data through an LSTM network, and obtaining a fault prediction result. The equipment fault mode can be recognized in advance, early warning, edge computing and cloud computing are combined, delay is reduced, more accurate diagnosis can be provided, the deep reinforcement learning is introduced to optimize the equipment self-adaptive repair process, automatic adjustment and intelligent repair are achieved, and through the multi-task learning and big data analysis technology, the equipment fault diagnosis efficiency is improved. And the precision and reliability of fault diagnosis are improved.

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.