2738 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 discloses an AI-driven abnormal transaction intelligent monitoring and analysis system and method, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining structured features and unstructured features, carrying out the feature fusion, and constructing an account feature vector; constructing a cross-domain heterogeneous graph, learning an embedded vector of an account node in the cross-domain heterogeneous graph based on a time sequence heterogeneous graph neural network, and generating an account portrait vector; training an abnormal transaction detection model, and predicting the abnormal probability of the current real-time transaction record of the account by taking the account portrait vector and the transaction record as input data; dividing all accounts into account groups based on the account portrait vectors, calculating a group abnormal transaction threshold value, and calculating an adaptive abnormal transaction threshold value based on the group abnormal transaction threshold value and the similarity between the account portrait vectors and the center vectors of the account groups to which the account portrait vectors belong; and if the abnormal probability is greater than a self-adaptive abnormal transaction threshold, marking the transaction of the account as an abnormal transaction, triggering risk control measures, and regularly updating the self-adaptive abnormal transaction threshold.

The invention provides a high-density RFID tag group scanning system based on a multi-antenna array and beam forming, and relates to the technical field of the Internet of Things. The system comprises a multi-antenna array, an RFID reader-writer, a beam forming unit, a signal processing unit, a feedback unit and a data management unit. Through a digital beam forming technology, the system dynamically adjusts the direction and width of a beam, and achieves the precise scanning of a target area. And in combination with real-time signal feedback, a density peak clustering algorithm and a gradient descent algorithm are adopted to optimize beam parameters, so that the recognition efficiency is improved. The system further utilizes an anti-collision algorithm to dynamically adjust frame parameters, and performs logic grouping based on spatial positions and type characteristics of tags, so that signal collision and missed reading rates are remarkably reduced. Meanwhile, the data management unit stores and outputs the label data in a classified mode, and intelligent management capacity is provided for multi-scene application.

A method and apparatus comprising computer code configured to cause a processor or processors to obtain, from a bitstream, a mesh representing an encoded volumetric data of at least one three-dimensional (3D) visual content; partition a plurality of vertices of the mesh into a plurality of groups; and decode the encoded volumetric data by predicting the vertices in each group of the plurality of groups based on a plurality of traversal orders depending on an adaptive reference vertex of the vertices, and the plurality of traversal orders includes a first order from the adaptive reference vertex to a first alternative reference vertex of the vertices, and wherein the plurality of traversal orders includes a second order from the adaptive reference vertices to a second alternative reference vertex of the vertices.

The invention discloses a method and system for searching a risk enterprise group based on a knowledge graph, and the method comprises the steps: obtaining the related information of an enterprise industrial chain graph, carrying out the layering of hierarchical labels, selecting the related elements of the labels of the enterprise industrial chain graph according to the basic information of the enterprise industrial chain, forming an enterprise industrial chain key element knowledge base, and carrying out the searching of the risk enterprise group. Performing label keyword extraction and matching, generating an enterprise portrait and enterprise data report information, constructing an enterprise industry chain knowledge graph, performing enterprise risk evaluation according to the enterprise industry chain knowledge graph, and then performing enterprise investment attraction management strategy processing; according to the method, the business feature tag of the enterprise can be formed, and after the business feature tag is accurately matched with the industrial chain graph, the enterprise industrial chain knowledge graph is constructed, and the risk of the enterprise ethnic group is evaluated and analyzed in multiple dimensions, so that the risk evaluation of the enterprise ethnic group is more accurate, efficient and comprehensive compared with a traditional method, and the risk evaluation efficiency of the enterprise ethnic group is improved. And an enterprise investment attraction management strategy is given.

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 discloses a purchase management decision support system driven by an AI large model, and particularly relates to the technical field of intelligent purchase management. A data acquisition module obtains historical and current purchase, market price, inventory, sales and customer feedback data of commodities; the clustering analysis module divides purchasing groups based on inventory states and market prices; the self-portrait construction module generates a comprehensive self-portrait through client feedback data; the market analysis module analyzes sales data and predicts future demand changes; the optimization adjustment module optimizes the purchase quantity in combination with demand prediction, customer preferences and purchase group data, so that the accuracy and efficiency of purchase decision making are improved; through intelligent data processing and model prediction, the limitation of dependence on experience and static rules in traditional purchase management is avoided, fluctuation of market demands can be responded in real time, the purchase quantity is ensured to be matched with the market demands, inventory overstock and shortage phenomena are avoided, and customer satisfaction and market competitiveness are improved.

The invention discloses a multi-terminal data transmission detection method and system based on artificial intelligence, relates to the technical field of network communication, and aims to solve the problems of data consistency, security and efficiency in a current heterogeneous network, and the adopted scheme comprises the steps that a cloud connection layer is constructed, a plurality of edge nodes are configured, and each edge node is connected with a specified terminal; performing logic grouping on the edge nodes corresponding to the plurality of terminals with interaction requirements; each edge node is responsible for monitoring the real-time operation state of the terminal connected with the edge node and collecting monitoring data; the cloud connection layer receives the monitoring data, evaluates the consistency among the monitoring data in the same group based on an artificial intelligence prediction model, and obtains a difference value; and the cloud connection layer generates and sends an adjustment signal according to the difference value, and the edge node adjusts the corresponding terminal according to the adjustment signal so as to ensure that the data interaction between the associated terminals in the same group is kept consistent. The method is suitable for the current complex heterogeneous internet environment.

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 provides an Internet of Vehicles trust management method and system based on a block chain, and the method comprises the steps: dividing intelligent networked vehicles running on a road into a plurality of vehicle groups according to a preset region coverage range, calculating a group trust value set of vehicles in each group in a node dimension and a trust value offset set and a vehicle message score set in a message dimension in a vehicle group form, and calculating a global trust value and a total trust value offset amount of each vehicle in different vehicle groups based on the group trust value set, the trust value offset set and the vehicle message score set; carrying out weighted summation to obtain a comprehensive trust value of each vehicle; comparing a preset warning trust threshold value with a malicious trust threshold value based on the comprehensive trust value, and endowing each vehicle with a vehicle label; and performing communication management on the corresponding vehicle based on the vehicle management rule matched with the vehicle label. According to the invention, malicious nodes in the network can be effectively identified and processed in real time, the trust value is prevented from being maliciously tampered, the communication delay is reduced, the network load is reduced, and the vehicle privacy is protected.

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 network configuration management method and device, a program product and a medium, and relates to the technical field of computers. The method is applied to a data center operation and maintenance management platform, and comprises the following steps: when it is monitored that network configuration needs to be performed on a data center in a current service scene, obtaining physical link interconnection information between network devices in the data center through a preset hardware management protocol; before the service is issued, grouping servers in the network equipment, and issuing configuration to a target port of a switch accessed into the network equipment based on the physical link interconnection information to obtain a configured port; and dividing the servers into different virtual local area networks according to the groups to which the servers belong and based on the configured ports so as to construct a logic network isolation domain, and performing corresponding service operation on each group based on the logic network isolation domain after the service is issued. Through the technical scheme of the invention, the network configuration change can be realized, and the impact on the network flow of the data center is avoided.

Various embodiments comprise a wireless communication network configured to anonymize network Key Performance Indicators (KPIs) to train a machine learning model. In some examples, the wireless communication network comprises a network analytics system and a KPI store. The network analytics system retrieves the network KPIs generated by network KPI sources. The analytics system filters the network KPIs based on an intended function of the machine learning model. The analytics system identifies correlated ones of the filtered KPIs and groups the correlated ones of the filtered KPIs into KPI groups based on a network condition. For each KPI group, the analytics system sorts the filtered KPIs into KPI ranges. For each KPI range, the analytics system tokenizes the KPI range by converting the filtered KPIs that compose the KPI range into strings. The KPI store stores the tokenized KPIs in a KPI database accessible by the machine learning model for training.

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 provides a wind power wind speed prediction and optimization arrangement method, system and device. The wind power wind speed prediction and optimization arrangement method comprises the steps of obtaining wind power wind speed basic data of a target area; constructing a neural network model based on physical information based on the wind power wind speed basic data; acquiring a wind speed at a specific position in a speed field from a neural network model based on physical information, and creating a training data set; training the neural network model based on the physical information based on the training data set; reconstructing wind speed distribution in a speed field through the trained neural network model based on physical information, and predicting to obtain a wind speed prediction value of the wind power plant in the next time period; and optimizing the arrangement of the wind turbine group according to the wind speed distribution in the reconstructed speed field. According to the method, the two-dimensional flow velocity field of the wind power plant can be reconstructed by training one PINN, and accurate wind speed ultra-short-term prediction is realized based on the trained model.

The invention provides a method and device for calculating the trafficability of a line under the operation of a heavy haul railway train group, and relates to the technical field of train group analysis, and the method comprises the steps: calculating an interval time parameter under the operation of the train group; drawing a train interval working diagram; constructing a single-line working diagram cycle model based on the train interval working diagram and the interval time parameters; constructing a train group tracking model of the double-track heavy haul railway under train group operation based on the interval time parameters; linearizing and then solving the single-line working diagram cycle model to obtain a minimum limit interval working diagram cycle, and calculating the trafficability of the single-line heavy haul railway line under the operation of the train group based on the minimum limit interval working diagram cycle; and according to the train group tracking model, calculating the trafficability of the double-track heavy haul railway at different stages under the train group operation. The method solves the problem that in an existing line trafficability calculation method, the particularity of a group operation organization method is not considered, and the method is not suitable for capacity calculation under group operation.

The invention relates to the field of data management, and provides a data management system and method based on an industrial Internet of Things data center, and the system comprises an industrial Internet of Things user platform, an industrial Internet of Things service platform, an industrial Internet of Things management platform, an industrial Internet of Things sensing network platform, and an industrial Internet of Things sensing control platform. The industrial Internet of Things management platform is configured to determine future acquisition parameters of the sensing network sub-platform based on the residual storage space, the first calling feature and the future calling feature; determining a pre-increased storage amount based on the future acquisition parameters; determining a to-be-deleted group based on the pre-increased storage amount, the residual storage space and the group calling features and the group data amount corresponding to the plurality of data groups; generating a parameter updating instruction based on the future acquisition parameters; and generating a data deletion instruction based on the to-be-deleted group. According to the invention, the normalization and flexibility of data management can be improved, and the data management efficiency is improved.

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.