4462 results about "Time information" patented technology

The invention discloses a supply chain intelligent management system based on dynamic collaborative optimization, and relates to the technical field of hotel supply chain management, and the system comprises a supply chain intelligent management platform which is in communication connection with the following modules: a multi-modal data sensing module, a multi-modal data processing module and a multi-modal data processing module. The multi-modal data acquisition module is used for acquiring multi-modal data in a hotel through a LoRaWAN + BLE hybrid sensor network, accessing an external data source and acquiring real-time information through an API (Application Program Interface); and the dynamic demand prediction module captures a time sequence trend through bidirectional LSTM based on an ASTGNN model. According to the method, external dynamic data such as social media public opinions and weather are fused, the multi-modal data analysis technology is combined, the accuracy of demand prediction is remarkably improved, the ASTGNN model is utilized, the time sequence trend and the demand association between the branches are analyzed in combination with bidirectional LSTM and GCN, and the feature weight is dynamically adjusted, so that the demand prediction error rate is greatly reduced, and the demand prediction efficiency is improved. A more reliable demand prediction basis is provided for enterprises, and optimization of inventory management and production plans is facilitated.

The invention relates to a man-machine hybrid autonomous navigation system in an unknown dynamic environment, which comprises an environment sensing module for acquiring environment information by using sensors such as a laser radar, a camera and an inertial measurement unit and performing data preprocessing; the local target point selection module selects a local target point set meeting requirements in the environment according to the real-time information provided by the environment sensing module; the decision planning module is used for generating an optimal path and a navigation strategy based on mixed training of a deep reinforcement learning algorithm and artificial experience; and the learning optimization module improves the generalization ability of the algorithm through online learning and transfer learning technologies, and realizes effective introduction and strategy adjustment of human experience in combination with the man-machine interaction module. The man-machine interaction module can work in cooperation with the local target point selection module so as to dynamically adjust target point selection according to external input and optimize path planning. According to the method, high decision stability can be kept in an unseen environment, the training cost is reduced, and the method is more suitable for autonomous navigation application in the real world.

The invention relates to the technical field of satellite communication, in particular to a high-orbit and low-orbit satellite communication resource scheduling method and system, which collects real-time state information of high-orbit and low-orbit satellites, performs digital simulation of a satellite network based on the real-time information, constructs a satellite network management model and predicts satellite trajectory change. The method comprises the following steps: performing multi-dimensional evaluation on a satellite network management model by using a pre-constructed network evaluation framework, generating cross-orbit link evaluation information, analyzing parameters such as link quality and bandwidth, obtaining current service demand information, analyzing a communication resource allocation strategy in combination with a cross-orbit link evaluation result, and formulating a resource scheduling strategy according to strategy analysis. And a high-orbit and low-orbit satellite communication network is constructed through execution of a hierarchical network management mechanism and a cross-orbit data transmission protocol.

The invention discloses a time sequence knowledge graph reasoning method based on a large language model, which comprises the following steps of: performing vectorization expression on query entities and relationships by utilizing an embedded model, and generating vectors containing structural features and semantic information in combination with adjacent entities, relationships and time information; a candidate entity set most relevant to query is screened out from the large-scale entity set by calculating the conditional probability; constructing an event evolution tree, and performing multi-hop sampling on adjacent nodes of a query entity and a candidate entity in a time window to generate a tree structure capable of completely reflecting a historical event evolution path; the method comprises the following steps: designing a structured prompt template, and performing instruction fine tuning on a pre-trained large language model by adopting an LoRA (Low Rank fine tuning technology), so that the model better follows a task instruction to generate a reasoning result. And efficient and accurate time sequence knowledge graph reasoning is realized.

Systems and methods for dynamic knowledge integration in LLM systems including receiving multimodal input data comprising text, image, audio, video, and / or code data, extracting information by processing the multimodal input data through a document processor, storing the extracted information in a dynamic knowledge base, receiving a user query at a query processor, identifying knowledge domains related to the user query using domain-specific agents, retrieving real-time information from the dynamic knowledge base responsive to the identified knowledge domains, integrating the real-time information into the processing of an LLM by a dynamic knowledge integrator, and generating a response using the LLM with the real-time information.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an agent-based webpage information processing method, device and equipment and a medium. And distributing the data to a capturing agent to obtain webpage original data. According to the method, model context information is generated by extracting webpage source information and release time information, page structure representation is constructed, key information nodes are positioned, structured data are generated, then multi-scene decision data are generated in combination with the structured data and the model context information in the analysis process, and a decision result is output through a decision reasoning module. By constructing a uniform data processing flow, webpage capture, context generation, structured analysis, analysis processing and decision reasoning are organically linked, efficient acquisition and intelligent processing of webpage information are realized, and timeliness and accuracy of decision are improved.

Techniques for processing with respect to a user input as contextual information is available are described. A system generates a first task prediction using first context data that is available when a user input is received. The system generates a second task prediction (e.g., updated first task prediction) when second context data is received, and then further generates a third task prediction when third context data is received. Example first context data may include device type information, time information, location, etc. Example second context data may include automatic speech recognition (ASR) data. Example third context data may include natural language understanding (NLU) data. Using the third task prediction, the system generates an output responsive to the user input.

The invention relates to the technical field of data linkage calculation, and provides a multistage document dependent freight trial calculation data linkage calculation method and system, and the method comprises the steps: expanding the attribute of a document node from a single business field to a composite vector containing a spatial-temporal feature through time information and geographic identification spatial-temporal dimension modeling; the dependency graph can capture the fine-grained dependency relationship of the freight service, and the space-time accuracy of data linkage is improved; when the basic information or the document information changes, the priority and the calculation path of the task queue are updated in real time through deep reinforcement learning, and calculation errors caused by change lag are avoided; through tensor synthesis operation, parallel subtask division, and a dual verification mechanism of a domain knowledge graph and a forced dependency edge, it is ensured that the whole process from dependency graph construction to calculation result output conforms to industry specifications, and the compliance and risk control ability of freight services are significantly improved.

The invention relates to the technical field of edge computing, in particular to a safety production monitoring method based on edge collaboration, which comprises the following steps: collecting multi-node clock pulses, calculating adjacent time differences to form a pulse difference parameter set, carrying out statistical distribution, setting a dynamic tolerance boundary, and constructing an effective deviation parameter set; the method comprises the following steps of: generating a weighted smooth deviation result by combining clock stability and link delay weighted normalization, sorting monitoring data according to a timestamp to identify duplication abrupt change exception, constructing a cross-node exception time sequence by associating nodes and time information, and judging a safety state transition result of equipment. A time difference parameter is constructed through multi-node reference pulse arrival time, a dynamic deviation constraint is formed by combining distribution characteristics, clock stability and link measurement weighted smoothing are introduced, cross-node time reference consistency is kept, operation data unified time sequence rearrangement is carried out, a sudden change event reliably reappears in a time window, and an abnormal identifier is associated with a node identity. And forming a cross-node event evolution link.

The invention relates to the technical field of resource scheduling, and particularly discloses a government affair collaborative emergency resource dynamic scheduling method and system.The method comprises the steps that event spatio-temporal information is obtained through a government affair data sharing platform, multi-source data are fused to construct a three-dimensional situation map, and a dynamic demand list is generated; positioning distributed resource nodes and establishing supply capability portraits; constructing an optimization model with response time efficiency, resource utilization efficiency and demand satisfaction balance degree as targets, and generating a multi-target scheduling scheme set by adopting a particle swarm algorithm; generating a visual decision billboard based on the scheme set, determining a final scheme through multi-party collaborative consultation, and analyzing the final scheme into an operable instruction; monitoring an instruction execution state and a resource track in real time, and automatically triggering scheme re-optimization when deviation or situation change occurs to form a closed-loop scheduling system; according to the invention, the method achieves the precise, intelligent and adaptive dynamic adjustment of resource scheduling, and improves the efficiency and robustness of emergency response.

The invention relates to the technical field of water conservancy management, and discloses a water conservancy dynamic decision-making method and system based on digital twinning and multi-source heterogeneous data, and the method comprises the steps: carrying out the real-time information capturing of a target water conservancy system, and generating a system operation information scheduling matrix based on a grid geographic model and a time dislocation mark; performing hierarchical slicing on the system operation information scheduling matrix according to a multi-scale time window to form a multi-channel flow framework of regional hydrological dynamic behaviors; analyzing the evolution trends of the water level, the flow and the water quality by using a multi-channel flowing framework and a dynamic fusion analysis operator; dividing a river reach, a reservoir and a regulation and control facility corresponding to the potential risk event as candidate scheduling areas; and based on the local risk coupling network, dynamically optimizing a scheduling strategy, a pump gate operation sequence and a data acquisition frequency, and generating a real-time scheduling and emergency response scheme of the candidate region. The method has the advantage of improving flood early warning accuracy and water supply scheduling efficiency.

The invention relates to a cross-attention mechanism optimization method and a cross-attention mechanism optimization system for multi-modal feature fine-grained alignment. The method comprises the following steps: constructing a space-time topological graph of a dynamic scene, and representing a space-time relationship among intelligent agents through nodes and various connecting edges; coding the heterogeneous spatio-temporal information by using a plurality of groups of parallel recurrent neural networks, and converting the heterogeneous spatio-temporal information into uniform dimension feature representation; establishing a multi-modal cross attention alignment component, and quantifying feature quality from multiple dimensions through a multi-criterion evaluation unit; an adaptive weight fusion system is adopted to dynamically integrate evaluation results, and a unified quality score is generated; constructing a progressive optimization architecture based on the quality score, and performing multi-task cooperative training on a double-layer attention mechanism by combining real-time sampling and a directional optimization strategy; and finally generating a movement decision. According to the method, fine-grained alignment and optimization of multi-modal features are realized, and the accuracy and adaptability of intelligent agent navigation and trajectory prediction in a complex dynamic environment are remarkably improved.

The invention relates to a priority scheduling generation method and system for Beidou satellite short message communication, and the method comprises the steps: collecting multi-dimensional features, such as message service types, user identities, spatio-temporal information, channel states and the like, dynamically calculating the priority score of each message through a configurable weighting model, and then, carrying out the dynamic calculation of the priority score of each message; the method comprises the following steps of: abstracting a message into a conflict graph vertex, establishing an edge according to a receiving conflict relationship, distributing candidate message sets which are not conflicted with each other for each scheduling time slot by adopting an independent set selection algorithm, and performing joint optimization on a message sending sequence and power distribution by using a genetic algorithm in each time slot by taking a maximized comprehensive utility value as a target, and generating a final scheduling instruction. Through dynamic evaluation, conflict avoidance and resource collaboration, intelligence and self-adaption of a scheduling strategy are realized, and the guarantee capability of a system for key messages and the overall resource utilization efficiency are remarkably improved.

The invention provides an intelligent data alignment method and system based on time sequence dynamic multi-source embedding mapping. The method belongs to the technical field of multi-modal data fusion and spatio-temporal information processing. The method comprises the following steps: performing time sequence dynamic feature extraction on a multi-source heterogeneous data source to generate a heterogeneous data sequence containing a time dependency relationship; and constructing a time sequence dynamic multi-source embedded manifold space based on a manifold learning theory, mapping a heterogeneous data sequence to a unified evolution geometric structure representation space, and generating embedded manifold data. Through the method, heterogeneous data from various different data sources can be effectively processed, unified mapping is carried out through time sequence dynamic feature extraction and a manifold learning technology, cross-source alignment of the data is achieved, and the method is particularly suitable for a data scene needing to consider a time dependency relationship.

The invention relates to the technical field of digital twinning equipment management, in particular to a digital twinning scientific research equipment intelligent management system integrating the Internet of Things and artificial intelligence. The system comprises a multi-source heterogeneous data acquisition unit which is used for performing real-time information acquisition on the operation environment and the equipment state of scientific research equipment; the digital twin mapping unit constructs a temperature and humidity coupling dynamic model fusing a temperature dynamic model and a humidity dynamic model by introducing a temperature and humidity coupling coefficient, and maps data collected from multi-source equipment and a laboratory environment to the temperature and humidity coupling dynamic model; the AI multi-dimensional prediction unit predicts the future running state of the equipment and the environmental risk; and the risk early warning unit analyzes the operation state of the air conditioning equipment and the environment coupling risk thereof in real time based on the prediction result of the AI multi-dimensional prediction unit, and sends out an early warning signal before potential abnormity occurs. The method is used for predicting the equipment fault risk in advance, thereby achieving the purposes of advanced prediction and active intervention.

The invention relates to the technical field of bearing fault diagnosis, in particular to a multi-bearing fault positioning and diagnosing method, which comprises the following steps of: acquiring bearing vibration signals of a plurality of sensing points, extracting peak time information to complete synchronous calibration, extracting high-frequency band energy and constructing a feature vector, screening deviation features and clustering and grouping, and identifying an energy abnormal channel, positioning a bearing position, extracting time domain and frequency domain features, comparing fault modes, and outputting a fault type diagnosis result. According to the method, the consistency of multi-channel data synchronization is realized by extracting the peak point of the vibration signal and calibrating the time offset, the high-frequency energy change rate is utilized to match the weight coefficient, the detection sensitivity of tiny fault features is enhanced, and the separability of a composite fault state is improved by combining feature relative deviation rate screening and density trend clustering. A frequency section energy proportion change recognition channel is adopted, accurate positioning of a fault bearing is achieved, and accurate judgment of a fault type is achieved in combination with a multi-feature comparison mode.

The embodiment of the invention discloses a resource scheduling method and device, equipment and a medium, and relates to the technical field of resource scheduling. The method comprises the following steps: acquiring a service quality constraint index of a data processing task, and resource states of a cloud center and an edge node; constructing a three-dimensional dynamic resource feature space according to the resource state fluctuation information of the edge node, the spatio-temporal information of the cloud computing and the edge node and the historical occurrence probability of the service quality constraint index; and performing particle swarm optimization based on the three-dimensional dynamic resource feature space to obtain an initial strategy set, scheduling computing resources in the cloud center and the edge node based on the initial strategy set, and processing the data processing task based on the scheduled computing resources. According to the technical scheme, scheduling is flexibly carried out according to the condition of the data processing task and the resource condition of the cloud center and the edge node, and the actual requirement of the data processing task is accurately met.

The invention relates to a self-adaptive interaction method based on multi-modal emotion calculation and a robot. The system realizes dynamic management of emotional values of different interaction objects by constructing a time sequence and a real time axis, and improves the memory weight in combination with key events. The system comprises a random event generation unit which simulates an environment event and gives an emotional influence; the user emotion judging unit is used for calculating emotion energy based on real-time input and historical interaction; the user impression dynamic adjusting unit is used for realizing impression weight attenuation and updating; the system emotional energy calculation unit fuses the virtual environment and the real time information to generate a system emotional state; the emotion response strategy unit is used for dynamically generating personalized emotion output according to the system emotion, the user emotion and the user impression; the aggressiveness detection unit prevents aggressiveness content from being output, and interaction safety is guaranteed. The system improves personalization, dynamic adaptation and safety of emotional response, and is suitable for scenes of intelligent accompanying, emotional counseling, customer service and the like.

The invention discloses a water conservancy knowledge graph intelligent question-answering system and method based on a large language model, and relates to the technical field of water conservancy information, and the method comprises the following steps: carrying out the fusion processing of multi-source water conservancy data in advance, constructing a triple knowledge graph containing a water conservancy field entity type and a relation system, and dynamic updating of the knowledge graph is realized through an incremental learning algorithm. According to the method, the defects of a traditional method in semantic understanding are effectively overcome, deep semantic association of professional query can be accurately captured, and answer deviation caused by keyword matching limitation is avoided. Meanwhile, a dynamic updating mechanism of the knowledge graph can integrate new knowledge such as new projects and industry standard updating in real time through an incremental learning algorithm and a time decay function, obsolete out-of-time information synchronously, ensure that a knowledge system of the system is synchronous with development of the water conservancy industry, and solve the problems that a traditional system is high in updating cost and long in period.

The invention discloses an intelligent cockpit driver intention prediction method and system fusing scene semantics, and the method comprises the steps: collecting cockpit interaction data and driving dynamic data, extracting a data sequence of a specified time length through a sliding window method, and constructing a driver interaction intention data set; constructing an instruction fine tuning data set of scene semantic understanding based on logic rules and manual inspection, and converting the driving dynamic data into a scene semantic text through a fine tuning large language model; training a deep learning model by using the constructed driver intention data set, and outputting the most probable driver interaction intention; according to the method, the limitation of independent modeling of a driver action sequence and spatio-temporal information in a traditional method is effectively solved, collaborative modeling of driver interaction actions and scene spatio-temporal characteristics is formed by fusing context information of a scene spatio-temporal causal relationship, and accurate prediction of interaction intentions of various services such as air conditioners, music and seats in an intelligent cabin is achieved.

According to some embodiments, a method by a computing system includes accessing a plurality of images captured by an imaging device. The imaging device and the computing system are coupled to a vehicle that moves within an intermodal container yard. The method further includes determining, by analyzing the plurality of images using a machine-learning module, that a shipping container is depicted within at least one of the plurality of images. The method further includes determining, using a map of the intermodal container yard, a parking location of the shipping container. The method further includes electronically communicating, in response to determining that the shipping container is depicted within at least one of the plurality of images, a message comprising data about the shipping container. The data includes the determined parking location of the shipping container and one or more identification markings of the shipping container.

The invention relates to the field of communication service support systems, and particularly provides a service provisioning process oriented anomaly identification and automatic compensation method and device, service provisioning process modeling is an ordered process chain composed of a plurality of processing nodes, and each node corresponds to a specific service system operation; a unique flow identifier Flow ID is generated for each service instance and used for whole flow state tracking, execution results, state codes and response time information of all flow nodes are recorded in a flow tracking database in real time, and after execution of each node is completed, events are published for other modules to subscribe to, so that an asynchronous observable link is formed. Compared with the prior art, the method can achieve the closed-loop processing of the abnormal process, remarkably reduces the manual intervention rate, improves the business activation success rate and customer service experience, and improves the stability and reliability of a service opening link.

A system and method of real-time optimization of drilling performance metrics during a well drilling operation, for oil and gas as well as geothermal wells, or wells drilled for any other purpose. In a preferred form, the system receives information about allowable drilling metrics and real-time information of performance indicators. The drilling performance metrics and performance indicators are used to build a model to predict drilling parameters likely to optimize one or more drilling performance metrics.

The invention provides a cognitive ability decline detection method and system based on physiological indexes of wearable equipment, and relates to the technical field of feature selection and machine learning. Comprising the following steps of multi-dimensional physiological data acquisition, data preprocessing and time alignment, cognitive ability state label definition, feature engineering and data balance, cognitive ability decline detection model training and optimization, and output of cognitive ability state prediction. Multi-dimensional physiological indexes and time information of a user are collected in real time through a wearable device, and the physiological indexes comprise heart rate fluctuation features, heart rate statistical features, body temperature features, blood oxygen saturation features, motion data, electroencephalogram state features, skin electrical features, near infrared spectrum features and the like. The wearable device is used for integrating multiple types of physiological signal sensors, and continuous and non-inductive collection of multi-dimensional physiological data such as heart rate variability, electrodermal response and oxyhemoglobin saturation is achieved.

The invention provides a data physical node isolation scheduling method and system, and belongs to the technical field of distributed storage and cloud computing. The method comprises the steps of collecting attribute information of tenants and establishing a tenant demand model; monitoring in real time and predicting the resource load of the physical node by using machine learning, and generating a resource evaluation report; formulating differentiated isolation strategies according to the data security levels of the tenants, adopting physical isolation for the tenants with high security levels, and distributing independent physical nodes for the tenants; the scheduling priority is allocated based on the business emergency degree and the resource demand of the tenant, the node load condition is combined, the high-priority tenant is preferentially scheduled to the node with the load lower than the preset threshold value, and meanwhile, the strategy is dynamically adjusted according to real-time information. The corresponding system comprises a tenant management module, a resource monitoring module, an isolation control module and a scheduling decision module. Unification of data security isolation and efficient and dynamic resource scheduling in a multi-tenant environment is achieved, data security and service quality are effectively guaranteed, and the resource utilization rate is increased.

The invention provides an electronic acupuncture closed-loop control method and system based on multi-modal information feedback. The method comprises the following steps: acquiring long-term, short-term and real-time representation information of a testee under an initial stimulation scheme; the long-term and short-term characterization information is input into a collaborative generative network model for learning, a target stimulation scheme is output, the collaborative generative network model comprises traditional Chinese and western medicine agents, the traditional Chinese and western medicine agents learn the long-term and short-term characterization information, and a corresponding traditional Chinese medicine treatment scheme and an electrical stimulation scheme are output. The collaborative generation network model carries out collaborative training on traditional Chinese and western medicine agents; calculating an index deviation between the real-time characterization information and the standard characterization information to generate a real-time information deviation matrix; inputting the target electrical stimulation scheme and the real-time information deviation matrix into a parameter deviation prediction agent for learning and prediction; and performing parameter adjustment on the target stimulation scheme based on the predicted stimulation parameter deviation matrix to realize long / short time and real-time double-layer closed-loop control of the electronic acupuncture.

The invention discloses a visibility regression prediction method based on multi-modal transfer learning and time coding, and relates to the technical field of artificial intelligence. The method comprises the following steps: S1, dividing a data set in different periods according to illumination characteristics, and splitting each period into a training set, a verification set and a test set; s2, preprocessing the data set; s3, constructing an initial model containing a pre-training deep learning network, a time coding module and a multi-layer perceptron regression head; s4, extracting image visual features and time feature vectors; s5, fusing the features and inputting the features into a regression head for prediction; s6, carrying out scheduling training by using layered parameter freezing, an AdamW optimizer and a dual learning rate, and combining with a mixed early stop strategy until convergence; and S7, evaluating the test set to determine a final model. According to the method, complementarity of image and time information is mined, high-precision prediction is realized, generalization is good under different illumination conditions, a layering strategy and an optimization mechanism guarantee stable and efficient training, a multilayer perceptron combination technology enhances expression, and overfitting is effectively prevented.

The invention discloses a multi-agent cooperation performance evaluation method and a medium. The method comprises the following steps: determining a task structure index according to task splitting information and task scheduling execution information; performing time sequence analysis and behavior pattern recognition according to the communication information among the intelligent agents to determine a cooperative communication index; determining a task consistency index according to the sub-task execution information and the main task execution information of each agent; determining a time resource efficiency index according to the task execution time information of each agent and the execution capability information of each agent; and determining an evaluation result of the multi-agent cooperation performance according to the task structure index, the cooperative communication index, the task consistency index and the time resource efficiency index. According to the invention, evaluation is carried out from multiple dimensions according to related information of multi-agent cooperation, the comprehensiveness of the evaluation result is improved, and the accuracy of the evaluation result is further improved.

The invention provides a heterogeneous resource dynamic sensing and self-adaptive scheduling method, which comprises the following steps of: dynamically acquiring basic attributes such as resource types, core configuration, memory capacity and bandwidth and real-time information such as load state and temperature through a heterogeneous resource feature extraction module by utilizing a hardware interface and software probe technology, and storing data in a feature table after processing, accurate scheduling decision basis is ensured; a self-adaptive scheduling algorithm is designed, matching with resource characteristics is carried out based on task calculation, memory and parallelism requirements, proper resources are preferentially allocated, competition is avoided by checking a load state, and the algorithm supports dynamic adjustment so as to optimize allocation efficiency; and finally, through a real-time monitoring and dynamic adjustment mechanism, regularly collecting resource and task state information, detecting a competition or low-efficiency condition, executing operations such as task migration, priority adjustment or resource reservation and the like, and ensuring efficient operation of the system. The heterogeneous resource scheduling efficiency and stability are improved, and the method is suitable for a modern computing environment.

The invention relates to the technical field of computational fluid mechanics data post-processing, and particularly discloses an OpenFOAM unsteady state simulation result-oriented boundary surface physical quantity calculation method, which comprises the following steps of: setting a working root directory, reading a grid file and establishing an example grid; accessing a time control function, and reading simulation time information of each time folder; establishing a global blank file to store boundary surface physical quantity results, first line writing time and physical quantity variable names at all moments; sequentially reading field quantity data required by calculation in the time folder; appointing and searching the name of a concerned boundary surface, creating a scalar field magSf of the boundary surface, and calculating and storing the area of a grid surface element; defining and initializing physical quantities and intermediate quantities of boundary surfaces, calculating physical quantities and intermediate variables of surface elements of each grid, and traversing the grids; and writing a result from the second line of the global file according to a time sequence, and traversing all times. By the adoption of the method, key data at all moments can be accurately extracted, errors and complexity caused by manual processing are avoided, and the calculation efficiency is greatly improved.