102 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 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 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 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 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.

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.

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.

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.

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.

A non-transitory computer-readable recording medium storing a model generation program for causing a computer to perform processing including: changing first data and generating a plurality of pieces of data; calculating a plurality of values indicating a distance between the first data and each of the plurality of pieces of data; determining whether or not a value indicating uniformity of distribution of the distance between the first data and each of the plurality of pieces of data is equal to or greater than a threshold based on the plurality of values; and in a case where the value indicating the uniformity is determined to be equal to or greater than the threshold, generating a linear regression model using a result obtained by inputting the plurality of pieces of data into a machine learning model as an objective variable and using the plurality of pieces of data as explanatory variables.

The invention provides an intelligent SQL question and answer mode access method. The method comprises the steps that S1, a basic environment and configuration are initialized; s2, inputting an SQL question and answer pair and constructing an original semantic knowledge base; s3, semantic analysis and feature preprocessing; s4, constructing a class ER structure storage model; s5, multi-dimensional model training is carried out; and S6, performing intelligent access execution and closed-loop updating. According to the method, full automation from natural language to SQL access is realized through an integrated process of basic configuration construction, question and answer pair input, semantic preprocessing, structured storage, multi-dimensional training, intelligent access and closed-loop updating.

The invention provides a data processing method of an industrial database, and relates to the technical field of data processing. The data processing method of the industrial database comprises the following steps: S1, constructing a stream batch integrated processing engine, and carrying out joint calculation on real-time data stream and historical data; s2, dynamically allocating a storage structure according to an entity identifier by adopting an entity partition storage model; s3, executing real-time query based on an incremental graph traversal technology, and dynamically pruning in a traversal process; s4, entity behavior abnormity is detected in real time through the space-time fusion model; and S5, compressing the event stream data according to a causal rule. According to the technology, real-time and historical data are processed by utilizing a stream batch integrated engine, and full-dimension rapid decision analysis is realized. Incremental graph traversal, space-time fusion anomaly detection and the like respectively improve the query efficiency and accurately position faults. The expenditure is reduced through causal rule compression and the like, the end-to-end credibility is guaranteed through the quaternion metadata block and verification technology, the storage read-write efficiency is improved through thermal scheduling, and the problems of response, detection and storage are solved.

A system includes an interface, a processor, and a memory. The interface is configured to receive a version of a model from a model registry. The processor is configured to store the version of the model, start a process running the version of the model, and update a proxy with version information associated with the version of the model, wherein the updated proxy indicates to redirect an indication to invoke the version of the model to the process. The memory is coupled to the processor and configured to provide the processor with instructions.

The invention discloses a process-closed-loop-oriented three-dimensional model lightweight and real-time verification method, which belongs to the technical field of aircraft design, and comprises the following steps: step 1, receiving a lightweight model loading request initiated by an assembly outline identifier, step 2, judging whether a lightweight model associated with the assembly outline identifier exists or not, and step 3, if yes, carrying out process-closed-loop-oriented process-closed-loop-oriented process-closed-loop-oriented process-closed-loop-oriented three-dimensional model lightweight and real-time verification. And step 3, if yes, obtaining and loading the associated lightweight model for rendering display. By establishing accurate mapping of model nodes and process procedures, automatic highlighting and visual guidance of an assembly path are realized, the system supports local model uploading and real-time state feedback, the flexibility of collaborative design is enhanced, and by asynchronously extracting and versionally storing model attributes, automatic comparison of multiple versions of models is realized, so that the efficiency of collaborative design is improved. The error caused by manual checking is effectively avoided, a closed-loop data link from design to process is finally constructed, and a data breakpoint is broken through.

The embodiment of the invention discloses a process optimization method and device, electronic equipment and a storage medium, and relates to the technical field of business process management.The method comprises the steps that a business process is obtained, business logic and key nodes are obtained by analyzing the business process, the business process is divided into a plurality of milestone stages, and names are set; obtaining a plurality of milestone nodes; building a storage model, designing and binding a visual UI component, and recording the attribute of each milestone node by using a custom field; the milestone nodes and the service approval nodes are visually displayed according to the corresponding relation by designing lane type layout, and the rendering effect of a visual UI component is dynamically updated by using the Ajax technology; and integrating the process data of each milestone node by constructing a data ledger query window to obtain resource configuration data of process optimization, and carrying out business process optimization. The problems that in the prior art, the model stage is not transparent, the progress is difficult to track, and data statistics is low in efficiency are solved.

The present disclosure relates to a data collection method and device, electronic equipment and computer readable storage medium, and relates to the technical field of databases, and can be applied to a scenario of data collection operation through a configured data model. The method comprises: obtaining a data collection task; obtaining a pre-configured data collection model, the data collection model comprising any one or a combination of multiple of a domain name network address model, a network layer data model, a software model, a storage model and a rack device model; performing a data collection operation based on the data collection model and the data collection task to obtain target collection data, the target collection data being stored in a topological structure. The present disclosure uses a pre-configured data collection model to quickly and accurately perform a data collection operation, which can effectively improve data collection efficiency and ensure data quality.

This invention discloses an on-network fault recovery method for distributed model training. By introducing a fault-tolerant aggregation mechanism based on window constraints and integrity constraints into the parameter server, the model parameters for the current training round can still be updated when a training node fails. Simultaneously, the model recovery parameters are stored in the top-of-rack switch (ToR switch) and updated during data forwarding. This allows the ToR switch to maintain a copy of the model recovery parameters corresponding to the current training round. Thus, when a training node fails, the corresponding ToR switch directly sends the model recovery parameters to the failed node, thereby accelerating the on-network fault recovery process for the training node.

The invention discloses a data weight quantification method and system based on operator-level blood relationship analysis. The method comprises the following steps of: analyzing a to-be-analyzed SQL (Structured Query Language) statement corresponding to a target scene into an abstract syntax tree; traversing the abstract syntax tree, extracting a plurality of operators, and constructing a dependency relationship among the plurality of operators; constructing an operator directed acyclic graph based on a plurality of operators and dependency relationships; on the basis of an operator directed acyclic graph, an operator-level blood relationship storage model comprising operator nodes, table nodes and an association relationship among the nodes is constructed, attributes of the association relationship comprise edge weight values, and the edge weight values are obtained through weighted summation of a data traffic weight, an operator complexity weight, a dependency relationship weight and a path depth weight; and normalizing the edge weight value to obtain a normalized edge weight value so as to realize data weight quantization. According to the application, fine-grained and quantifiable blood relationship analysis support can be provided for data quality traceability in data management and key operator positioning in query optimization.

The application discloses a distributed database operation separation method and device, a database and electronic equipment, and relates to the technical field of data storage. When local input data to be stored is obtained, target local parameters of the distributed database are collected, wherein the target local parameters include current local parameters of the local database and current local parameters of the off-site database. Then, the local input data and the target local parameters are input into a predetermined storage model in the local database to output a storage mechanism corresponding to the local input data, wherein the storage mechanism is used for determining a storage position of the local input data in the local database and the off-site database. Finally, the local input data is stored in the local database and the off-site database according to the storage mechanism.

This invention discloses an artificial intelligence work platform for enterprise knowledge operation, belonging to the field of enterprise management technology. The platform includes: a data access module, a knowledge storage module, an intelligent interaction module, a knowledge structuring and accumulation module, a knowledge proactive empowerment module, a knowledge element permission control module, and an operational value optimization module. This knowledge structuring and accumulation mechanism, triggered by business events, and its knowledge empowerment architecture deeply integrated with enterprise business processes, differ from the traditional static storage model relying on manual uploads and the service logic that can only passively respond. It can automatically transform scattered business data in the daily workflow of an enterprise into standardized knowledge units, achieving precise knowledge delivery at key nodes of business execution, realizing real-time matching between knowledge supply and business needs, and achieving automated and systematic accumulation of enterprise knowledge assets, significantly improving the efficiency of knowledge reuse and the adaptability of business empowerment.

The digital material block conveying and storing system comprises a conveying line, a storing line and a master controller, the conveying line comprises a front conveying unit and a layered conveying unit, the storing line comprises at least two slidable lines, the front conveying unit is connected with the at least two slidable lines through the layered conveying unit, and the front conveying unit is connected with the at least two slidable lines through the layered conveying unit. And the main controller is connected with the layered conveying unit, so that the material blocks are conveyed to the slidable line through the front conveying unit and the layered conveying unit and slide on the slidable line. According to the invention, a plurality of material block types are arranged in a plurality of layers, the material blocks are digitally conveyed and stored, and the weight and position information of each material block is accurately recorded, so that the corresponding material block is conveniently selected for subsequent smelting operation. And a driving conveying line conveying and driven roller line or sliding groove storage mode is adopted, and the material blocks are conveyed to the target storage position based on the storage mapping relation of the conveying and storage model.