69 results about "Platform model" patented technology

The invention discloses an intelligent data question answering and visualization system and method for leader decision making, and relates to the technical field of artificial intelligence, and the system comprises a knowledge base unit, an intelligent question answering module, a dynamic visualization unit, a quality control unit and a performance optimization unit. According to the method, the customized knowledge base is constructed through the knowledge base unit, cross-modal alignment and layered long context processing are weakly supervised during data fusion, the defects that cross-modal data labeling cost is high and long context processing is redundant can be overcome, and an interaction platform of the knowledge base and a data engine in the knowledge base unit is constructed through the intelligent question and answer module; the model mixing module mixes an NLP model, constructs a combined architecture of an LLM + field fine tuning model, ensures understanding of natural languages and intention analysis precision, introduces an online expert error correction mechanism, and reduces misjudgment during question answering, so that the processing capacity of long texts or complex logic can be ensured, missing of key information in question answering is avoided, and then the answering quality is ensured.

The invention belongs to the technical field of low-code development platforms, and particularly relates to a low-code platform model construction and dynamic execution method and system based on a hybrid DSL (Digital Subscriber Line), and the method comprises the steps: describing static model information in an application by using a structure DSL in a declarative grammar; defining a behavior DSL in a mounting mode under a corresponding element of the structure DSL; analyzing the structure DSL into a meta-model object tree in the platform, and converting the behavior DSL into an executable expression tree / intermediate representation; jointly driving the meta-model object tree and the expression tree / intermediate representation by utilizing a template engine, and automatically generating at least one application code in a front-end page, a back-end interface and a database script; a common structure DSL and a behavior DSL are combined and packaged into a scene template, and the scene template is stored in a template library so as to be reused by different service scenes, and modularization and templating of service modeling are realized. The complexity of the system is reduced, and the development efficiency and quality are improved.

The invention discloses an information operation dimension intelligent management and control platform system and method, electronic equipment and a storage medium, and belongs to the field of intelligent operation and maintenance of an information communication technology. The system comprises an anomaly detection module, a multi-modal feature fusion module and the like, multi-source data is processed through an unstructured data preprocessing unit and a time sequence data alignment unit, model parameters are dynamically adjusted by utilizing a self-adaptive online learning strategy, and dynamic resource scheduling is realized by combining a reinforcement learning algorithm. The method comprises the steps of data receiving preprocessing, feature fusion alignment, model training analysis and the like. The electronic equipment adopts a heterogeneous computing architecture, and the storage medium stores corresponding program codes. According to the method, the problems of weak generalization ability, insufficient unstructured data processing and the like of a traditional platform model are solved, the anomaly detection precision and the response speed are improved, a closed-loop operation and maintenance system is formed, the operation and maintenance decision intelligent level is improved, and the generalization recognition ability and the environmental adaptability of the model to an unknown abnormal mode are remarkably enhanced.

The invention provides a method for joint answering of a user knowledge base and a service system under an AI platform, and belongs to the technical field of AI platform models.According to the method, hierarchical semantic matching is achieved by constructing a three-level knowledge vector cache and a three-level question intention vector cache and adopting vector representation of different dimensions; after user query is received, a semantic similarity algorithm is used for performing parallel retrieval to generate an initial matching degree, multi-dimensional similarity information is fused through a joint matching degree calculation function, and the precision is improved by using an intention recognition optimization model; calculating an optimal sequence length through an adaptive parameter adjustment function according to the long and short memory matching length to obtain real-time data of the service system, fusing the static answer of the knowledge base and the real-time data of the service system to generate a final answer, and updating a vector cache and optimizing algorithm parameters according to user feedback. And high-precision joint answering of the knowledge base and the business system is completed.

The invention discloses a software radio unified modeling method and system, and belongs to the field of software radio, and the method comprises the steps: determining modeling objects as platform modeling and waveform modeling; wherein during platform modeling, abstract modeling is performed on each calculation unit, and during waveform modeling, waveform component modeling and waveform application modeling are included; in the waveform component modeling, at least one modeled waveform component runs on a calculation unit, and a plurality of waveform components can be deployed on the calculation unit at the same time; the waveform application modeling comprises the following steps: establishing a connection relation between waveform components, and assembling into waveform application; then executing a modeling process of creating a platform model, creating a waveform model and establishing a platform and waveform connection relationship, and performing modeling detection; establishing a basic model set after modeling detection; the basic model set comprises a platform model subset and a waveform model subset. The modeling efficiency and the standardization degree are improved.

The application discloses a kind of floating platform for offshore wind power generation and its design method, comprising: 1) theoretical numerical simulation analysis, establish floating platform model;2) manufacture platform structure and supporting assembly by three-dimensional printing technology;3) build test device in experimental pool;4) after each component is built, carry out equipment safety inspection;5) set initial environmental parameter, determine sampling frequency and sampling time;6) start experiment, adjust control system to obtain set sea state;7) carry out platform performance measurement, adjust parameter to obtain optimal state;8) collect the response data of platform under different environments;9) using orthogonal experimental design method, carry out multiple experiments to optimize platform design;10) collect and process all data.The application starts from platform structure design, and the optimization step is simple and easy to operate, suitable for floating platform design and optimization under different sea conditions, and provides reliable verification for offshore wind power generation.

The invention provides a low-code platform model generation method and device, equipment, a medium and a product, and relates to the technical field of artificial intelligence and low-code platform development. The method comprises the following steps: taking an interface type keyword; obtaining an association matching result of the first demand description and the keyword, and obtaining a second demand description according to the matching result; key information is taken from the second demand description, and a third demand description is obtained from the information; describing a first query language according to the third demand; splitting the first query language to obtain each minimum grammar unit, and screening to obtain each effective grammar unit; establishing a syntax tree, and when the syntax tree conforms to a pre-stored syntax rule, performing basic semantic check on the syntax tree to obtain a semantic check result; obtaining a second query language according to the check result; verifying the second query language rule according to a preset rule constraint condition to obtain a third query language; and obtaining a visual data model through the third query language and the low-code platform interface. The seamless connection from the natural language requirement to the visual application is realized, and the technical threshold is reduced.

The invention discloses a cross-platform model deployment framework, electronic equipment and a storage medium, and the framework is applied to the electronic equipment and specifically comprises a framework interface abstraction layer and a plurality of runtime libraries connected with the framework interface abstraction layer. The framework interface abstraction layer is used for being connected with an application framework layer and providing a uniform abstraction interface for the application framework layer; the runtime library is used for providing running support for the framework interface abstraction layer. Compared with a traditional model deployment scheme, the scheme provided by the invention can accelerate the deployment speed of the model, ensures the uniformity of evaluation standards of the model on different platforms, and improves the reasoning performance. If the running environment is inferred to be updated, deployment updating of the model on a new platform can be rapidly completed according to the framework, and manpower and material resources needed by deployment can be greatly saved.

The invention relates to the technical field of network security identity verification and game experience optimization, and discloses a self-adaptive user identity verification and game experience linkage optimization system based on federated learning. Comprising a federated learning cooperative training module, a user behavior feature baseline construction module, an adaptive identity verification module, a game experience dynamic adjustment module, a multi-dimensional data acquisition module, a security privacy protection module, a cross-platform model migration module and a system self-learning iteration module. And each module realizes data closed loop and function linkage through a distributed architecture. According to the method, a transverse federated learning architecture and an improved FedAvg + algorithm are adopted, cross-platform local node cooperative training is combined, only model parameter gradients instead of original data are uploaded, and a differential privacy disturbance mechanism is combined, so that the problem of data islands is solved, user privacy security is guaranteed, the privacy leakage risk of centralized training is avoided, and the user experience is improved. And the safety and generalization ability of model training are remarkably improved.

This invention provides a method, apparatus, and electronic device for modeling maritime rescue processes. The method includes: constructing a hierarchical model architecture driven by discrete event system specifications for the maritime rescue process, dividing the maritime rescue system model into a discrete event system layer and an object model layer; constructing a generalized rescue platform model and modular sub-models; the modular sub-models include a controller model, a maneuvering model, and a sensor model; constructing a complete rescue system simulation architecture based on the generalized rescue platform model; and performing repeated simulations and performance analysis on the complete rescue system simulation architecture to obtain a performance analysis report. This invention achieves accurate representation of the rescue system and solves the problems of poor accuracy and completeness in existing related technologies for rescue modeling.

The invention discloses a self-description southbound acquisition method based on a global Internet of Things platform, and the method comprises the steps: obtaining original physical feature parameters based on an interface protocol of an edge layer sensor, and constructing a feature vector of the sensor; converting the feature vector into initialization metadata which can be identified by a gateway terminal through a physical feature extraction module; the gateway terminal obtains an object model of the global Internet of Things platform, performs multi-modal binding on the feature vector and the object model, and updates the initialized metadata to obtain standardized metadata; a self-description coding and decoding rule is constructed, and the gateway terminal performs coding and decoding on the original data collected by the sensor based on the coding and decoding rule in combination with the standardized metadata; decoded original data is subjected to structured storage and protocol packaging, and is uploaded to a global Internet of Things platform through a gateway terminal, physical characteristic parameters are converted into metadata which is convenient to identify, and then dynamic adaptation from southbound original data to a global Internet of Things platform model is realized based on a constructed self-description coding and decoding rule.

The application relates to a power grid blockchain platform configuration method and device, computer equipment and a readable storage medium. The method is applied to a blockchain platform. In terms of hardware, node equipment is redundantly designed and cascaded. In terms of software, a plurality of different local aggregation coefficients are configured for the blockchain platform. According to the set local aggregation coefficients, corresponding blockchain nodes are deployed to obtain different blockchain platform models. Key data of a power grid is acquired, and the key data includes power grid equipment information, operation data and user data. In the case that communication between the blockchain nodes is normal, the performance of the blockchain platform models corresponding to the different local aggregation coefficients is evaluated and tested based on the key data of the power grid, and a normalized weighted score result is obtained. According to the normalized weighted score result, the blockchain platform model corresponding to the local aggregation coefficient with the highest score is selected as a target blockchain platform specially used for power grid book management.

The invention provides an intelligent inspection method and system for a booster station, and relates to the data processing technology, and the method comprises the steps: building a limited model of a limited area in the booster station based on the point cloud data collected by an unmanned aerial vehicle for the limited area, and the limited model comprises a collection platform model; according to a preset hovering height from the unmanned aerial vehicle to the acquisition platform model, constructing a swing space for suspending the acquisition device; and on the basis of comparison of the swing space and the model space of the limited model, a plurality of lowering stages and control parameters of the lowering stages are determined, and automatic inspection can be performed in combination with the actual condition of the limited area, so that the inspection data in the narrow space can be acquired on the premise of improving the operation efficiency.

The invention discloses a block chain-based federated learning cross-border trade service platform model verification method, and belongs to the technical field of block chain federated learning of cross-border trade, and the method comprises the steps: in the federated learning of the current round, each client node carries out the local learning of a global model through employing a first data set, and obtains a local model, submitting the data to all verifier nodes; each verifier node trains the global model by using the second data set to obtain a verification model, verifies the quality of each target local model based on the verification model to obtain each verification result, and submits each verification result and the corresponding target local model to an aggregation node; and the aggregation node aggregates each target local model based on all verification results to obtain a new global model, and adds the new global model to the block chain network. According to the method, the quality of each local model can be accurately verified, so that the quality of the aggregated global model is guaranteed.

This invention belongs to the field of electromagnetic field numerical calculation technology, specifically proposing a modular analysis method for the interfaced response of a platform antenna. By constructing a unified interface mode space, the platform's external domain response and antenna radiation behavior are mapped to finite-dimensional mode coefficients, respectively. The platform response operator can be formed with only one full-wave solution. The antenna is connected in the form of a surrogate operator, and the interaction relationship between the two is established through an interface coupling operator. This method can quickly predict the comprehensive performance after different antenna installations without repeatedly solving the platform model, and is suitable for the engineering needs of antenna loading analysis in complex equipment.

The application relates to the field of automobile testing technology and discloses a vehicle controller model-in-loop test system and method, which comprises a framework module, a calibration module, a script module and an interface module; wherein the framework module is used for building a physical model required by a model-in-loop test environment, the physical model is built according to a preset platform model architecture of vehicle controller software testing in response to preset test requirements; the calibration module is used for providing a vehicle controller software model to be tested after data calibration; the script module comprises a script program connected with the physical model and the vehicle controller software model to be tested, and generates a corresponding interface model in the interface module by running the script program, the interface model is used for communication between the framework module and the calibration module. The application can provide a comprehensive, effective and automatic model-in-loop test system of the vehicle controller suitable for different projects and products, improve the model-in-loop test efficiency of the vehicle controller software model and guarantee the safe operation of the vehicle.

This invention discloses a method, system, device, medium, and program product for optimizing tile data caching. The method includes: retrieving cache records of the model to be loaded from a browser database based on the unique identifier of the model; generating an octree-structured tile node queue based on the cache records and a preset maximum cache level, where each level of the tile node queue corresponds to the tile data at each level of the model to be loaded; and finally, asynchronously requesting the tile data corresponding to each node from the backend level by level based on the tile node queue. This invention achieves efficient caching and loading of digital twin platform models, significantly improving the user's browsing experience.

The invention discloses a converged media program list arrangement system and a generation method, and belongs to the technical field of media content management and intelligent scheduling. The system comprises a content acquisition module, a feature extraction module, a user portrait and platform model module, a program optimization and arrangement module, an intelligent conflict detection module, a self-adaptive generation module and a feedback optimization module. Unified management of program resources is realized through multi-source content collection and semantic tagging analysis; a multi-objective optimization model is constructed based on the user portrait and the platform strategy, and program lists adapted to different terminals are automatically generated; and dynamically adjusting a programming result by utilizing a conflict detection and self-learning feedback mechanism. According to the method, the intelligent level of convergence media program management can be remarkably improved, the cross-platform content distribution efficiency is optimized, and automatic generation and accurate pushing of the program list are realized.

This invention discloses an application deployment method, apparatus, device, and medium based on a cloud control infrastructure platform. The method includes: decomposing the various levels of the original cloud control infrastructure platform into multiple independent functional units according to business requirements; wherein each functional unit includes components, modules, sub-modules, and functional points in descending order of functional level; combining the functional units of each cloud platform in descending order of functional level to form a cloud control infrastructure platform model; creating an integration base; loading front-end application resources based on the front-end service base; and deploying back-end applications to the various levels of the cloud control infrastructure platform model according to the functional level of their corresponding functional units based on the back-end service base. By adopting the above technical solution, the different functional units of the cloud control infrastructure platform are decoupled in terms of business operations, the process of front-end application integration is simplified, and the flexibility of the cloud control infrastructure platform is improved.

The application belongs to the technical field of power systems, and specifically discloses a wind farm controller hardware-in-the-loop simulation method for a wind turbine test platform, which comprises the following steps: establishing a platform simulation model comprising a wind turbine transmission chain test platform model and a wind farm simulation model and setting a control strategy; configuring parameters of the platform simulation model; building a controller hardware-in-the-loop simulation system; loading the platform simulation model, the control strategy and model parameters into the system to complete data interface matching; carrying out simulation testing, obtaining and analyzing the operating state of the wind turbine; if the control effect does not meet the preset requirements, adjusting the control strategy or the model parameters and repeating the testing until the requirements are met. The method can realize dynamic coupling simulation between the wind turbine transmission chain, the wind farm operation and the power grid working condition, support control strategy verification and parameter optimization under multiple working conditions, and solve the problems of limited test range of the traditional test platform, insufficient strategy verification and high risk of real machine testing.

This invention discloses a cross-platform model transfer method combining CATIA and MIDAS. The method generates a skeleton model containing node, element, cross-section, and material information based on an engineering design scheme in CATIA. It then uses an EKL script to automatically extract model data and store it in a standardized skeleton information table (node ​​information includes coordinates and spacing; element / cross-section / material information is converted to MIDAS-compatible MCT format strings). A Python script then reads the table data to generate an MCT file, which is finally imported into MIDAS to complete model construction and structural analysis. For scenarios involving multiple scheme comparisons, unique labels are added to the skeleton models of different design schemes. After unifying boundary conditions and load cases, the optimal scheme is quickly selected based on the calculation and analysis results.

The invention provides a cross-platform acquisition method, device and system for model information. The cross-platform acquisition method for the model information comprises the steps that the model information of a design model is acquired from at least one computer aided design platform, and the model information comprises one or more of geometric parameters, assembly parameters, material parameters and annotation parameters; forming a data packet based on a preset data structure and the model information; performing verification processing on the data packet, wherein the verification processing comprises structure verification, integrity verification and consistency verification; an information view of the design model is constructed based on the verified data packet, the data packet is uploaded to a product data management system, and the information view comprises a model structure, a model tree structure and model attribute information. The method has the technical effect of realizing computer aided design cross-platform data processing.

The invention discloses an intelligent modeling analysis platform model data set downloading method comprising the following steps: sending a data set downloading request to obtain a data set; processing the data set to generate a unique fingerprint; comparing the new fingerprint with the database CRC fingerprint, and checking whether the request is repeated or not; if yes, storing or updating the downloaded data set, and checking the stored or updated downloaded data set; if the test is passed, a new data set object is assembled, the uniqueness of a new data set test point code is tested, classification processing is carried out on the new data set, batch operation is carried out, and data is stored in a corresponding database; if batch operation succeeds, related controller IDs are collected, deduplication operation is carried out, a downstream data set is notified of a downloading state, and downloading information is recorded to a database; and synchronizing the data set downloading instruction to the controller to complete data set downloading. According to the method, invalid data processing is reduced, and data processing efficiency and management convenience are improved by means of multiple data sources and code uniqueness verification.

The application discloses an industrial robot arm tool changing operation monitoring system based on digital twinning, and relates to the technical field of digital twinning. The system integrates a floating platform model, a robot arm model and a tool changing device model to create a complete digital twinning model, and sets up a perception sensor group to collect real physical world data of the platform, the robot arm and the tool changing device in real time. These data are transmitted to the digital twinning model in real time for processing and simulation, so that the virtual and physical world states can be bidirectionally synchronized. This highly integrated monitoring method can timely identify the resonance risk between the platform and the robot arm and the change in the tool locking trend. Through effective risk warning and adjustment mechanisms, the system can ensure the accuracy and safety of the tool changing operation, avoid tool changing failure or equipment damage caused by resonance or locking abnormalities, thereby improving production efficiency and reducing downtime.

1. Name of the product in this design: Graphical User Interface for Evaluation of Bit-Large Model Platform for Electronic Devices. 2. Purpose of this design: For displaying programs and interactions. 3. The key design features of this product are: the graphical user interface content that displays information. 4. The image or photo that best illustrates the design points: Interface change state diagram 2. 5. Since the design points have been clearly expressed, the rear view, left view, right view, top view, and bottom view are omitted. 6. Purpose of the graphical user interface: This graphical user interface is used for model evaluation on the bit-large model platform. The main view is the login interface of the BitBigModel platform. In the main view, after the user enters their account and password, they click the "Login" button to enter the interface change state diagram 1, which displays the BitBig Model Platform model management interface. In Interface Change State Diagram 1, when the user clicks the "Model Evaluation" tab on the left, they enter Interface Change State Diagram 2, which displays the model evaluation interface. In Interface Change State Diagram 2, when the user clicks the "Create Assessment Task" button, they enter Interface Change State Diagram 3, which displays the Create Assessment Task interface. In the interface change state diagram 3, the user enters the evaluation task information and clicks the "OK" button to enter the interface change state diagram 4, which displays the evaluation task creation completion interface. In the interface change state diagram 4, the user clicks the "View Evaluation Report" button in the operation bar to enter the interface change state diagram 5, which displays the model evaluation report interface.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: outputting, by a machine-learning model, a probability that a user will re-order two or more items at a present time; determining the two or more items to recommend to the user based on the probability exceeding a predetermined threshold that the user will re-order the two or more items at the present time; sending instructions to display the two or more items to the user, wherein the user interface comprises a single-click option to add to an electronic cart the two or more items; and after receiving the single-click option from the user interface, adding the two or more items to the electronic cart. Other embodiments are disclosed.

The invention discloses an application change automatic adjustment method and device, electronic equipment and a medium. Relates to the technical field of data processing. The method comprises the following steps: periodically obtaining a to-be-adjusted application change task and analyzing the to-be-adjusted application change task to obtain a current application change task analysis result; according to the current application level, matching with an application level execution condition table, and determining a current application level execution condition; and if the current application level is a first-level application level or a second-level application level, and a received current automatic change starting execution instruction determines that a current application level execution condition is satisfied, inputting current application change description information into an application change analysis and automatic adjustment platform model for automatic change adjustment, and obtaining a current application change automatic adjustment result. The problems of high time cost and low accuracy of application change caused by respective change of each department are solved, and the application change accuracy and efficiency are improved.

The invention discloses a multi-model service switching and effect perception mechanism in an AI application management platform, and aims to solve the problems that the existing platform model switching is not intelligent and the effect perception is lagged. The mechanism comprises a multi-model service switching module, an effect perception module and a central control module, wherein the switching module monitors model performance and a service scene in real time through a trigger unit, a decision unit screens an optimal target model, and an execution unit realizes smooth flow migration; the sensing module collects model performance and business data, compares and analyzes effects before and after switching and feeds back the results; and the central control module coordinates the interaction of the modules and optimizes a switching strategy according to feedback. According to the mechanism, automatic triggering and smooth execution of model switching are achieved, the switching effect is accurately sensed in real time, a'switching-sensing-optimization 'closed loop is formed, the AI application service stability and the service effect are improved, and the mechanism is suitable for AI application management in the fields of finance, e-commerce and the like.

The invention belongs to the technical field of industrial digital twinning, and discloses an industrial digital twinning platform model construction protocol and an implementation method. The implementation method comprises the following specific use steps of S1, equipment physical information collection; S2, software and control configuration collection; S3, physical attribute field definition; S4, software and control configuration field planning; by means of modularization design and a convenient configuration file generation function, an enterprise can quickly convert actual equipment into a digital model, construct a rich digital asset library, obtain physical model data from SolidWorks and input software control information from a data asset management page, so that the complexity of dispersive data acquisition is avoided, and by taking an automobile manufacturing enterprise as an example, the method has the advantages of being high in practicability and easy to popularize. And digital assets of all equipment of the production line can be constructed within one week.

The application provides a cross-platform model migration method and device, and belongs to the field of artificial intelligence. The method provided by the application comprises: managing chip information configuration in a chip configuration subpage; adjusting a target model migration task in a model migration management subpage, determining a target tab based on a model corresponding to the target model migration task, executing the target model migration task and displaying the corresponding task progress in the target tab; determining pre-migration chip information corresponding to a pre-migration model completing the model migration task; generating an evaluation task of the pre-migration model based on the pre-migration chip information, and modifying the number of model migration task display bars in the tab corresponding to the pre-migration model in the model migration management subpage based on the result of the evaluation task. The cross-platform model migration method and device provided by the application can realize efficient, reliable and visual cross-platform migration of artificial intelligence models from Nvidia chips to domestic chips.