176 results about "Storage model" patented technology

An apparatus of a user equipment (UE), the apparatus comprising a processor, and a memory storing instructions that, when executed by the processor, configure the apparatus to receive, from a base station of an operator network, Protocol Data Units (PDUs) carrying Artificial Intelligence (AI) model data in either a control plane or a user plane, and decapsulate the PDUs to obtain and store the AI model data, wherein the AI model data is indicative of an AI model configured for inference in Access Network (AN) protocol layers at the UE.

The invention relates to the technical field of automatic warehouse material, and discloses an intelligent goods allocation distribution system for automatic warehouse logistics, and the system comprises a goods allocation data collection module; a dynamic state detector; a multi-source data fusion unit; a three-dimensional storage modeling module; intelligently distributing a strategy engine; and a PLC control interface. According to the intelligent goods allocation allocation and scheduling system for automatic warehouse logistics, a goods allocation allocation scheme is generated through a multi-target Pareto optimization algorithm, the balance between carrying distance minimization min sigma alpha * Di and space utilization rate maximization Ut is achieved, and compared with a proportional static allocation rule, the warehouse space utilization rate is increased to 82%; a dynamic grid map technology is integrated, and a track cross risk area (red early warning grid) of the stacker and the AGV is marked in real time, so that the equipment collision probability is reduced by 90%; and in combination with a goods relevance knowledge graph, special goods such as medicines are clustered and stored according to temperature zones, and the energy consumption of cross-temperature-zone operation of equipment is reduced by 35%.

The invention discloses a multi-dimensional data asynchronous calculation method and system based on a dynamic dependency graph, and relates to the field of data processing. The method comprises the following steps: constructing a metadata, logic and instance three-layer separation storage model; analyzing the reference relationship to construct a directed acyclic graph, and generating a calculation priority; monitoring data change, and executing asynchronous serialization calculation through a message queue based on priority; the associated document is automatically updated based on anchor mapping. According to the method, the calculation deadlock and the performance bottleneck of large-scale data in the Web environment are solved, logic decoupling and dynamic expansion are realized, the final consistency of the data is guaranteed, and the high-concurrency throughput and the stability are remarkably improved.

The invention discloses a supply chain logistics real-time scheduling system and method, and relates to the technical field of production scheduling, and the system specifically comprises a production plan dynamic adjustment module, a digital twin simulation module, a dynamic response and optimization module, a space-time coupling resource scheduling model, a cross-enterprise cooperation module, and a visualization and decision support module. PLTI instructions are dynamically generated through digital twinborn simulation and a dynamic response mechanism, in-plant and out-plant real-time collaboration and in-plant and out-plant combined modeling are achieved, the inventory turnover rate is increased by 25%, the inventory turnover rate is increased through a space-time coupling resource scheduling model, an in-plant storage model and an Amap API road network are constructed through a Unity 3D engine, instruction conflict probability is rehearsed, and the real-time performance of the system is improved. According to the method, logistics scheduling information is dynamically updated every 15 minutes based on a production plan, the logistics scheduling conflict probability is reduced by 83% through digital twin rehearsal, and real-time collaboration and intelligent scheduling of logistics inside and outside a factory are achieved through digital twin simulation, a dynamic response mechanism and a space-time coupling resource scheduling model.

The invention discloses an electroencephalogram voice annotation calibration and scheduling system and method for individual differences. The system comprises an electroencephalogram signal collecting and preprocessing module, a voice instruction receiving and recognizing module, a rapid individual calibration module, a dynamic task scheduling module, an electroencephalogram feature online decoding and annotation module and an annotation storage and feedback module. Through cooperative work of the two core modules, namely the rapid individual calibration module and the dynamic task scheduling module, the problems that decoding performance is reduced during cross-subject and cross-task switching caused by individual differences, and multi-task labeling efficiency is low due to task scheduling strategy stiffness are solved accurately. Data persistent storage, model online updating and user interaction feedback are completed through storage, optimization and feedback links, periodic retraining is carried out by utilizing daily successful labeling data through a model continuous learning link, and finally, a zero-threshold individual rapid adaptation and high-efficiency multi-task collaborative labeling target is achieved.

The invention provides an Internet of Vehicles data sharing and storage model based on a double-layer block chain, and aims to solve the problems of low data interaction efficiency, poor expansibility, insufficient security and the like in the traditional Internet of Vehicles. According to the model, road side units are grouped geographically, an optimized Raft protocol is adopted in each group to realize rapid consensus, and an improved PBFT protocol is adopted among the groups to ensure global consistency. Self-adaptive leader election, batch processing and assembly line mechanisms are introduced into the Raft protocol, so that the throughput and response speed of the system are improved; a reputation value-based VRF main node election mechanism and a BLS aggregation signature technology are introduced into the PBFT protocol, so that the communication complexity is reduced, and the system security and fault-tolerant capability are enhanced. According to the method, the fault-tolerant rate and the message complexity of the system are analyzed theoretically, the advantages of the system in the aspects of throughput, time delay and fault-tolerant performance are verified through simulation experiments, and the method is suitable for large-scale and high-concurrency car networking application scenes.

The invention discloses a reasoning acceleration method and device of a pre-training model and electronic equipment, and relates to the technical field of computer application, the method comprises the steps of preprocessing a behavior sequence, combining the preprocessed behavior sequence with a neighbor sequence, inputting the preprocessed behavior sequence into the pre-training model, effectively extracting a dependency relationship through the design of a self-attention layer and a graph convolution layer, and improving the reasoning speed of the pre-training model. Performing aggregation processing to realize accurate reasoning of the pre-training model; meanwhile, the intra-core register which is located in the calculation core and used for storing the model parameters is arranged in the programmable logic unit, and the model parameters which are frequently used in the pre-training model are stored in the intra-core register, so that the reasoning efficiency of the pre-training model is improved.

The invention provides a micro-grid black start recovery optimization method and device based on adaptive Benders decomposition, and the method comprises the steps: providing a Benders two-stage solving algorithm fused with an adaptive penalty factor for the hierarchy and constraint complexity of a micro-grid black start two-stage optimization model. In the first optimization stage, a micro-source recovery sequence and a recovery path are optimized based on a Benders decomposition method of an adaptive penalty factor, and in the second optimization stage, load recovery is optimized in combination with mixed integer programming. According to the method, the precision of the optical storage model is improved through dynamic characteristic correction, the recovery efficiency and stability are balanced through a two-stage framework, the solving performance is improved through an adaptive algorithm, and efficient and safe recovery of black start of the micro-grid is effectively achieved.

The invention belongs to the technical field of data processing, and particularly discloses a computer vision model service scenarized configuration generation method, which comprises the following steps: constructing a basic information database comprising a plurality of computer vision models, the database being used for storing model identification information and feature parameters; extracting structured features of the scene according to a scene analysis engine, wherein the structured features are used for describing operation conditions of the scene; calculating a model matching score according to the structured features and the feature parameters, and obtaining a recommendation model through a multi-condition weight matching function; executing a model selection strategy based on the matching score, and obtaining a model matched with the scene; and generating a configuration file conforming to a predetermined specification according to the recommendation model. The objective of the invention is to solve the problems of low efficiency and error proneness caused by the fact that a computer vision model in the prior art depends on a manual selection model and cannot quickly select and adapt to an optimal model in diversified scenes.

The invention discloses a multi-dimensional index calculation method based on a graph database. The method comprises the following steps: defining an index graph storage model based on the graph database; registering a parameterized calculation task corresponding to the index in a built-in calculation framework of a graph database; when a user query request arrives, submitting a query transaction through a long connection channel established by driving the graph database, and directly accessing local storage data by a calculation framework to complete real-time calculation and return a result; when the original data in the graph database is changed, a database trigger captures a change event and pulls up the calculation task in the same process; user query triggering and data updating triggering share the same set of calculation task and graph traversal logic, and storage and calculation integrated real-time multi-dimensional index calculation and updating are achieved. The multi-dimensional index calculation method based on the graph database provided by the invention only depends on the graph database and its own calculation framework, simplifies a data processing link, and improves the index output efficiency.

The invention discloses an efficient storage method and system for distributed molecular dynamics simulation calculation, and the method comprises the steps: constructing a distributed storage model based on data access domain division, and dividing the data stored in a node into private access data and shared access data; a storage structure with particle multi-dimensional information separation is adopted, particle data is divided into static data and dynamic data, and different storage structures are adopted for different types of data. A hierarchical data mapping and addressing method is adopted, storage and access of particle data are divided into a simulation space level, a calculation node level and a space grid level, each layer is organized through a specific numbering rule, and effective distribution and efficient access of the particle data are ensured. According to the efficient storage method and system for distributed molecular dynamics simulation calculation, the problems of uneven particle data distribution and low access efficiency in the molecular dynamics simulation process are solved.

The invention discloses a standard metadata storage method and a machine readable storage medium, and the method comprises the steps: collecting operation data and associated data of a preset coal chemical industry, and carrying out the preprocessing of the equipment data and the associated data; performing multi-modal semantic information extraction on the associated data to obtain information data, performing structured processing on the information data and the operation data to obtain structural data, and constructing a process mapping table according to business terms and the process parameters; the information data comprises incremental information and general information; constructing a coal chemical engineering topological structure knowledge graph according to the structure data and the process mapping table based on a chemical engineering association type, and performing time sequence coding on the coal chemical engineering topological structure knowledge graph according to a time window and a process unit to obtain a dynamic code; and constructing a coal chemical industry standard metadata storage model according to the dynamic codes, inputting to-be-stored data into the coal chemical industry standard metadata storage model, and outputting a storage result.

The invention provides a model compiling method, electronic equipment, a storage medium and a program product, relates to the field of artificial intelligence, and can solve the problem of low model calling efficiency in related technologies. The method comprises the steps of obtaining compiling information corresponding to a target model from a database according to structure information of the target model; the database is used for storing structure information of the model and corresponding compiling information; and compiling the target model based on the compiling information. The model calling efficiency can be improved.

Aspects of the disclosure provide a method performed by a central Network Data Analytics Function (NWDAF) in a communications network. The communications network comprises one or more local NWDAFs configured to develop a model using federated learning, in which each local NWDAF stores a copy of the model and trains the copy of the model by inputting training data into a machine-learning process. The method comprises receiving, from the one or more local NWDAFs, a respective local model update comprising an update to values of one or more parameters of the model generated by training a respective copy of the model using machine-learning. The method further comprises combining the local model updates received from the one or more local NWDAFs to obtain a combined model update.

The platforms, systems and methods provided herein may provide a data-driven workflow platform. The method comprises: mapping selected data objects to a data storage model of the data-driven workflow platform, where the selected data objects are stored in a data cloud configuration that is operatively coupled to the data-driven workflow platform; and displaying a graphical user interface (GUI) for building a workflow that utilizes or manages the selected data objects, the workflow comprises at least one automated action triggered by a triggering event of the selected data objects and wherein the triggering event is a detected change of the selected data objects based at least in part on an output of a machine learning algorithm trained model.

Methods, systems, and computer-readable storage media for receiving, from probes, probe data indicative of gas storage, the probe data being collected by probes included in operating wells and observation wells within a field, the probe data comprising surface data and subterranean data indicative of a health of a gas storage reservoir within the field. A gas storage status is determined by using a gas storage model, the gas storage model correlating the surface data and the subterranean data within the field. A gas storage assessment report including a pressure map reflecting the gas storage status within the field is provided.

The invention relates to the technical field of information retrieval and processing, and discloses a cross-format document information traceability positioning method and system, a terminal and a medium. According to the method, document contents in different formats are decomposed into standardized element blocks and element blocks with position information, and a unified storage model is constructed, so that a corresponding file pre-conversion result is generated; based on the file pre-conversion result, vectorizing a to-be-positioned information extraction result, executing semantic similarity retrieval in a vector database to generate a candidate element block set, and on this basis, executing refined matching of text similarity and entity similarity in combination with candidate element blocks and associated element blocks, so as to obtain a file pre-conversion result; determining the accurate position of the information extraction result in the original document; according to the method, unified position sensing modeling of the cross-format document can be effectively realized, and a positioning blind area is eliminated.

The invention discloses a dam multi-source data management method, system and device and a medium, and relates to the technical field of data processing, and the method comprises the steps: receiving monitoring data from a plurality of heterogeneous data sources, and carrying out the classification processing of the monitoring data; performing format conversion, identifier unification and data association processing on the received monitoring data; dividing the standardized data into a shared type resource set, and performing data interaction according to a preset exchange mode; dividing the data according to the type, the structural feature and the access requirement of the monitoring data, and storing different types of data in a database; and according to the data type, a differentiated backup strategy and a storage model are adopted for management. According to the method, the organization efficiency and the query performance of mass monitoring data are improved by constructing the basic database, the monitoring database, the spatial database, the multimedia database and other multi-layer structures and combining hierarchical storage, index optimization and structural standardization table design, and the multi-dimensional data service requirement is met.

A device for editing large language models and multimodal AI models. The device includes a memory medium that stores the data and / or dataset of the model, a control unit that is configured to process the data within modules and according to requests and pre-defined configuration parameters from a user and / or client which communicates with the device with at least one communication unit to provide at least one output based on an input data; and runs the instruction on the trained AI model. The device includes a compression module, a lobotomization module and an inception module and at least one of the modules is configured to identify layers of the trained model with the weight matrices; and decomposing the weight matrices of the trained model into a tensor network structure. The device also includes an integration module that provides connection and data transfer between external systems and platforms and the device.

The invention discloses an intelligent parameter recommendation system and method suitable for an oil injector automation line adjusting procedure, and relates to the field of industrial production intelligent control, the intelligent parameter recommendation system comprises a data acquisition module, a data processing module, a model training module, an intelligent Agent module and a parameter execution module, generating a sample pair and performing structured storage; the model training module selects an AI model, carries out incremental training based on samples, and learns a working condition and propulsion quantity mapping relation and parameter adjustment experience; the intelligent Agent module obtains full-condition data, calls an AI model to generate candidate parameter tables in batches, and screens an optimal table through full-process simulation evaluation; and the parameter execution module writes the data into a database, a control system is triggered to download and synchronize to the equipment, and the equipment autonomously calls according to real-time working conditions. According to the method, the parameter adjustment precision and the production line adaptation efficiency are greatly improved, the parameter adjustment workload of engineers is reduced, the operation stability and the product quality are enhanced, and the industrial production requirements are met.

A model generation apparatus according to one or more embodiments may include: a generating unit that generates data using a generation model; a transmitting unit that transmits the generated data to a plurality of trained identification models that each have acquired, by machine learning using local learning data, a capability of identifying whether given data is the local learning data, and causes the identification models to perform an identification on the data; a receiving unit that receives results of identification with respect to the transmitted data executed by the identification models; and a learning processing unit that trains the generation model to generate data that causes identification performance of at least one of the plurality of identification models to be degraded, by performing machine learning using the received results of identification.

The application discloses a kind of high-resolution remote sensing image distributed processing system, the system includes remote sensing image input file format extension module, remote sensing image metadata management and optimization module and the distributed computing module of remote sensing image.The beneficial effects of the present application are that the scalability of distributed storage model can be used to store remote sensing images in different nodes, and as the number of storage nodes increases, the storage capacity of the image also increases accordingly.Using the corresponding distributed computing framework, the "computing moves to data" is realized, and the remote sensing image data stored in different nodes is operated simultaneously, which greatly improves the processing efficiency of image data.

A model generation apparatus according to one or more embodiments executes operations, with respect to each of learning data sets. The operations includes training a second estimator so that an estimation result obtained from a second estimator conforms to second correct answer data; training a coder so that an estimation result obtained from the second estimator does not conform to the second correct answer data; and training the coder and the first estimator so that an estimation result obtained from a first estimator conforms to first correct answer data. The model generation apparatus executes operation of the training the second estimator and the training the coder alternately and repeatedly.

The application relates to a DAG-based hierarchical data storage and loading method, which comprises the following steps: step S1, constructing a DAG hierarchical data storage model and storing hierarchical data; step S2, traversing the data in the DAG hierarchical data storage model, loading the hierarchical data by using a depth-first recursive algorithm; and step S3, performing index extraction of information through a relationship link between hierarchical data. The application effectively solves the problem of hierarchical data storage and loading and has the advantages of high data storage efficiency and fast loading speed.

This invention relates to the field of information retrieval and processing technology, and discloses a method, system, terminal, and medium for tracing and locating cross-format document information. The method decomposes the content of documents in different formats into standardized feature blocks and element blocks with location information, and constructs a unified storage model to generate corresponding file pre-conversion results. Based on the file pre-conversion results, the information extraction results to be located are vectorized, and a set of candidate feature blocks is generated by performing semantic similarity retrieval in a vector database. Based on this, a refined matching of text similarity and entity similarity is performed by combining the candidate feature blocks and their associated element blocks to determine the precise location of the extracted information in the original document. This invention can effectively achieve unified location-aware modeling of cross-format documents and eliminate location blind spots.

Methods, systems, and computer-readable storage media for receiving, from probes, probe data indicative of gas storage, the probe data being collected by probes included in operating wells and observation wells within a field, the probe data comprising surface data and subterranean data indicative of a health of a gas storage reservoir within the field. A gas storage status is determined by using a gas storage model, the gas storage model correlating the surface data and the subterranean data within the field. A gas storage assessment report including a pressure map reflecting the gas storage status within the field is provided.

The invention discloses an equipment state monitoring system and monitoring method for a new-generation centralized control station system, and the system comprises a model configuration tool which is used for configuring and generating a model rule; the instance configuration tool is used for generating an instance rule according to the model rule; the rule base is used for storing model rules and instance rules and providing a rule basis for equipment state monitoring; the state monitoring module is used for sensing state changes of associated measuring points in real time through a message bus, obtaining real-time data and historical data of the measuring points through a real-time library interface and a historical library interface of a new-generation centralized control station system, analyzing and judging the state of the equipment according to judgment logic of the state of the equipment in a rule library, and sending the state of the equipment to a server. And alarm information is sent through the message bus. The method is efficient and flexible in rule configuration, and timely and accurate in equipment state monitoring.

The present disclosure provides platforms, systems, and methods that can provide a data-driven workflow platform. The methods of the present disclosure can include mapping a selected data object to a data storage model of the data-driven workflow platform, wherein the selected data object is stored in a data cloud configuration operatively coupled with the data-driven workflow platform; and displaying, on a graphical user interface (GUI), a flow for building a cloud application with or managing the selected data object. The interactive flow includes at least one graphical element corresponding to a rule for automating an action triggered by a trigger event of the selected data object.

This system and method provides users with the ability to create, customize, and manage applications for managing data flows and processes using distributed computing systems. [Solution] The method includes mapping selected data objects to a data storage model of a data-driven workflow platform, wherein the selected data objects are stored in a data cloud configuration operablely coupled to the data-driven workflow platform; and displaying a flow on a graphical user interface for building a cloud application that utilizes or manages the selected data objects. The interactive flow includes graphical elements corresponding to rules for automating actions triggered by trigger events of the selected data objects.

The invention provides a distributed processing method for a model weight, and belongs to the technical field of artificial intelligence, and the method comprises the steps: S1, carrying out the feature extraction of a model weight file, obtaining the feature information of a model weight tensor, and enabling the feature information to be the data information of a preset bit length of the model weight tensor; and S2, performing hash fragmentation processing on the feature information, determining a storage node corresponding to each fragment, and storing each fragment to the storage node corresponding to each fragment. The method has the beneficial effects that the model weights are fragmented and stored in a plurality of nodes, so that the capacity demand of single storage equipment is reduced, the hardware cost and the management difficulty are reduced, and the storage reliability and expandability are improved; the distributed storage model weight is adopted, weight data can be obtained from a plurality of storage nodes in parallel, the loading time is greatly shortened, the model loading efficiency is improved, and the method is suitable for application scenes with high real-time requirements.