313 results about "Storage model" patented technology

The invention discloses a BIM-based building electromechanical full life cycle management platform and an optimization strategy thereof, relates to the technical field of building informatization management, and solves the defects of the existing BIM technology in the aspects of processing efficiency, model precision, data synchronization and change management. According to the invention, a data layer is used for data cleaning and standardization through a machine learning model, and distributed storage is realized through a cloud management system; the model layer constructs and updates a high-precision BIM model; the synchronization collaboration layer ensures the consistency and traceability of data by using a real-time synchronization mechanism and a block chain technology; the decision analysis layer performs data analysis and prediction through a deep learning algorithm; the full life cycle management layer integrates historical data and current data and automatically triggers a management process; according to the method, the data processing efficiency and the model precision are greatly improved, and the seamless sharing capability of information and the transparency of data synchronization and change management are remarkably improved, so that the overall collaborative efficiency and the management level of a project are enhanced.

A resource set which includes multiple servers with a respective plurality of training computing devices is identified for training a machine learning model. The resource set is subdivided into partition groups, such that each partition group can store a respective replica of state information of the model. The model is trained using the partition groups. The training comprises a multi-stage gathering of a portion of the state information at training computing devices of a particular partition group. Different types of communication channels between training computing devices are used in respective stages of the gathering, including inter-server communication channels in one stage and an intra-server communication channel during another stage. A trained version of the model is stored.

The invention relates to an AI computing subsystem, end-side equipment and a control method of the end-side equipment, and the AI computing subsystem comprises at least one FLASH storage chip which is used for pre-storing weight parameters of at least one AI network model; the at least one DRAM storage chip is used for storing weight parameters and activation functions of the AI model; and the logic chip is connected with the FLASH storage chip and the DRAM storage chip, responds to an external instruction, reads data of the FLASH storage chip so as to write the data into the DRAM storage chip, and / or reads weight parameters and activation functions stored in the DRAM storage chip and executes a calculation task. The SoC system only needs to send parameters or start commands of the AI network, and the whole network runs in the AI computing subsystem; and the most complex part of the AI network part can be deployed in the AI computing subsystem, and the SoC system only needs to compute the simple operator of the part, so that the SoC system has larger space to complete other responsible tasks.

The present invention discloses a method for constructing an intelligent computation engine of an artificial intelligence cross-platform model based on knowledge self-evolution. The method comprises: determining source and target moments; dividing a discrete manufacturing system data set; initializing a dynamic discrete manufacturing system model; preprocessing data and constructing a task pool; constructing a meta learning framework; migrating a trained neural network to new tasks; iterating until convergence and storing model parameters; and testing in a new environment. This invention shortens the convergence time of model parameters, significantly benefiting the training of dynamic discrete manufacturing models subject to temporal disturbances in actual production.

Methods and apparatus for estimating an energy storage performance property of an energy system are provided. The method includes providing, as input to an energy storage model and a machine learning model, sensor information associated with the energy system, wherein the energy storage model comprises an empirical model and / or a physics-based model, providing as input to the machine learning model, one or more values based on an output of the energy storage model, and determining based, at least in part, on an output of the machine learning model, an estimate of the energy storage performance property.

The invention discloses a distributed storage and retrieval method for black soil cultivated land quality space vector big data, and relates to the technical field of data storage and retrieval. The method comprises the steps that firstly, according to space information and attribute information of black soil cultivated land quality space vector data, partition coding design is conducted on row keys, column families and Region partitions of a bottom storage system of a distributed non-relational database HBase, and a storage model used for storing the space vector data is obtained; then obtaining a Geohash coding grid of the space vector data in the storage model through a self-adaptive hierarchical division coding method; performing dimension raising conversion on the Geohash coding grid into a Geohash space grid, and constructing a distributed hierarchical spatial index model according to the Geohash space grid and an index unit of the R * tree; and finally, executing the spatial query request in the distributed hierarchical spatial index model through a spatial parallel retrieval method to obtain a spatial retrieval result. According to the method, the mass space vector data of the large-scale black soil cultivated land can be efficiently stored and retrieved.

The embodiment of the invention discloses a real-time warehouse management method and equipment based on digital twinning, relates to the technical field of Internet of Things, and is used for solving the problems of low efficiency and high cost of existing warehouse management. The method comprises the following steps: constructing a digital twin storage model according to building data and Internet of Things data corresponding to a current storage scene; clustering and grouping the current goods according to the arrival scanning information of the current goods and the historical storage information to obtain key storage information of the current goods in each group; querying the digital twin storage model according to the key storage information to obtain storage area information corresponding to each group of current goods; according to the warehousing starting point position corresponding to each group of current goods, the position information of each warehousing area and the key storage information, a logistics warehousing path of each group of current goods is planned, an initial logistics warehousing path is obtained, scheduling optimization is performed on the initial logistics warehousing path based on a data twin warehousing model, and the logistics warehousing path of each group of current goods is obtained. And warehousing and storing the current goods of each group.

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 embodiment of the invention provides a code compiling method, electronic equipment and a storage medium. The code compiling method is used for compiling a code comprising at least one thread local storage variable, and comprises the following steps: in response to existence of a target thread local storage variable, allocating the at least one target thread local storage variable to a thread local memory based on an attribute of the at least one target thread local storage variable, obtaining a thread local memory address corresponding to each target thread local storage variable, wherein the target thread local storage variable is the thread local storage variable used by the first function; updating an intermediate representation corresponding to the code based on a thread local memory address corresponding to each target thread local storage variable; and generating a machine code corresponding to the code based on the updated intermediate representation. According to the code compiling method, thread local storage model support under a parallel computing architecture is provided, and the problem that traditional thread local storage implementation cannot be directly applied to a GPU architecture is solved.

Automated machine learning (Auto ML) for creating and optimizing ML models using a model store for storing: trained ML models and hardware models; test metrics data corresponding to the stored models; ML advised-models. Using a model meta-services (MMS) for: accessing the stored models and the test metrics data; creating the ML meta-models based on the runtime test metrics data; and answering MPC queries. Using a models producer and consumer (MPC) for: selecting a ML advised-model; testing the selected ML advised-model using selected ML test inputs and outputs to provide runtime test metrics data; optimizing the selected ML advised-model using the runtime test metrics data; sending the optimized ML advised-model to the model store unit for storing as one of the stored ML advised-models; and sending the runtime test metrics data to the model store unit for storing as part of the runtime test metrics data; and sending the MPC queries.

The invention discloses a coal chemical industry standard metadata storage method and a machine readable storage medium, and the method comprises the steps: collecting coal chemical industry standard metadata, and carrying out the preprocessing of the metadata; performing feature classification on the metadata in combination with semantic information to obtain classification data, and constructing a knowledge graph for the classification data according to the association degree to obtain knowledge graph data; performing relation correction on the knowledge graph data to obtain optimized graph data, and constructing a coal chemical industry standard metadata storage model according to the optimized graph data; and optimizing the coal chemical industry standard metadata storage model, inputting to-be-stored data into the optimized coal chemical industry standard metadata storage model, and outputting a storage result. The method not only can improve the precision of coal chemical industry standard metadata storage, but also has good interpretability, and can be directly applied to a metadata storage system.

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 relates to the technical field of communication, in particular to an information interaction method and device and a readable storage medium, and the method comprises the steps that an ADRF receives a model storage request sent by an NWDAF including an MTLF, the request is used for storing a model, and the request does not include an allowed NFc list corresponding to the model; the ADRF generates an allowed NFc list for the model, wherein the allowed NFc list comprises an NF instance identifier of a second network element; the method is convenient to use during subsequent NWDAF retrieval containing the AnLF; and the ADRF returns a model storage response. By adopting the method and the device, the storage scheme of the model can be perfected, and under the condition that the NWDAF containing the MTLF does not specify an allowed NFc list, the model acquisition permissions at the two ends of the NWDAF containing the MTLF and the ADRF containing the MTLF can be aligned (or synchronized).

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 discloses a multi-source heterogeneous big data aggregation analysis method based on master data modeling. The method comprises the following steps: modeling main data, defining a relation mode of a business entity and a data entity, and outputting a graph structure storage mode; classifying the service entities, and defining different data entity fusion algorithms for different data entity types by taking the main attributes of the service entities as the identification basis of the data entities; accessing data, and cleaning the accessed data; service entities and data entities are analyzed from the cleaned data based on a data entity fusion algorithm, and a relation theme library is constructed according to the incidence relation between the service entities and the data entities; storing the cleaned data and a relation theme library thereof; and carrying out modeling analysis on the cleaned data by combining the characteristics of the business entity and the relation theme library. According to the method, unified management of multi-source heterogeneous mass data is carried out from a global perspective, the blood relationship of the data is clear, and the authority is controllable.

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 intelligent patrol control method, and relates to the technical field of intelligent patrol, and the intelligent patrol control method mainly comprises the steps: obtaining a resource data storage model according to patrol resources; according to the resource data storage model, a quick response police force task is obtained based on shortest path calculation and a task matching method; a major alarm condition or a safety event is confirmed, and emergency response processing is performed according to the quick response police force task; and performing multi-device cooperative scheduling according to the scheduling task and the patrol resources. By implementing the intelligent patrol management and control method provided by the invention, the police scheduling efficiency and response capability can be improved, the accuracy and collaboration of police resource integration can be improved, the system construction and operation cost can be reduced, the safety and reliability of the system can be enhanced, and the handling capability of emergency events can be improved.

The invention discloses a special tariff business settlement method and device, equipment, a storage medium and a product, and relates to the technical field of information technology data support, and the special tariff business settlement method comprises the steps: when a settlement instruction is received, extracting corresponding special tariff information from a real-time data warehouse, the real-time data warehouse synchronizes the service data of the special settlement service database in real time, the service data is obtained by performing data extraction on the obtained ticket file, and the service data is fragmented and stored to the special settlement service database through a distributed storage model loading fragmentation rules from a data source; and settling the special tariff information to obtain a special tariff settlement report. According to the invention, the distributed storage model is adopted to store the service data in the special settlement service database in a fragmented manner, so that super-large-scale file data volume can be borne; the special tariff information is extracted from the real-time data warehouse, the special tariff settlement report is output through automatic settlement, and the settlement efficiency of the special tariff is improved.

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.

The invention belongs to the technical field of Modelica language grammar analysis, and aims to solve the problem that model transmission and model details are difficult to read and recognize when an existing Modelica language uses a text form storage model. The invention discloses a grammar analysis system and method for a Modelica language. The system comprises an ANTLR4 grammar analysis module, a Modelica text code generation module, a data sequence and a deserialization module. According to the method, different model elements in a Modelica object are converted into character strings in a text format, and the character strings in the text format are combined to generate a complete Modelica model file in the text format. According to the method, mutual conversion between the text format and the structured JSON format of the simulation model based on the Modelica language is achieved, on the premise that the text format necessary for simulation of the Modelica model is met, the Modelica model can be transmitted and stored in a structured form, and large-scale collaborative modeling and model version tracking management based on the Modelica language are effectively supported.