27 results about "Multiple platform" patented technology

This application discloses a unified management method and system for heterogeneous signing platforms with multi-protocol adaptation. The method includes: receiving signing requests through a unified signing interface and determining the target signing platform type according to a routing strategy; calling the corresponding adapter from multiple platform adapters registered in a pluggable manner according to the target signing platform type; converting standardized request parameters into the calling format and authentication method of the target platform's native interface and initiating a signing task by calling its API; mapping standardized signing capabilities to the signing capabilities actually supported by the target platform; using a dual-channel mechanism combining active query and passive callback to synchronize the signing status of the target platform to a unified state machine in real time; and converting the signed document and evidence chain returned by the target platform into a unified format and sending it back to the core service layer for archiving. Implementing the embodiments of this application can achieve unified access, scheduling, and management of multiple heterogeneous signing platforms, reducing the technical complexity of multi-platform integration.

This invention discloses a method and system for data fusion of multi-platform commercial outlets based on multi-source heterogeneous data. It relates to the field of data processing technology. The method involves acquiring commercial outlet data from multiple platforms and performing validity verification to generate an initial data foundation for each platform; extracting outlet coordinate information and performing a unified format conversion to obtain a coordinate-standardized commercial outlet dataset; constructing a multi-industry knowledge graph and an industry-wide common affix library; using algorithms to remove common name affixes and retain core feature identifiers to generate a feature-identified dataset; constructing a similarity algorithm to extract multiple features of outlets, calculate their similarity separately, and fuse them to obtain an overall similarity, generating a similarity matching result set; and identifying and removing duplicate data from multiple platforms based on the matching results to generate a full dataset of multi-source fused commercial outlets. Compared to traditional fusion methods, this method achieves accurate feature identification and quantitative determination of duplicate data, improving the completeness and accuracy of the fused data and making more efficient use of computing power.

This invention relates to the field of inventory management technology and discloses a cross-border e-commerce supply chain multi-platform collaboration and dynamic inventory forecasting management system. The system includes: a data acquisition unit that processes field formats, time granularity, and unit systems based on the heterogeneous data protocols of various cross-border e-commerce platforms, and indexes the processed data using timestamp numbers and platform codes; a structure fusion unit that resamples the data from each cross-border e-commerce platform based on the timestamp numbers in the acquired dataset, and merges records of the same product across multiple platforms based on the product code; a sequence parsing unit that segments the cumulative sales field according to the window span based on the fused structure matrix, and constructs a sequence feature chain based on the feature vectors of each segment; and a trend inference unit that performs difference calculations on the current inventory quantity based on the predicted sales quantity, and determines the replenishment quantity based on the result of the difference calculation. This invention ensures multi-platform collaboration and inventory adaptability to the market.

The application relates to the technical field of e-commerce live broadcast, and discloses an e-commerce live broadcast content distribution monitoring system supporting multi-platform synchronization, which comprises the following modules: a live broadcast content collection module, a multi-platform adaptive distribution module, a real-time monitoring module, an intelligent early warning module, a blockchain storage module, a data summary analysis module, a closed-loop intervention module, a user interaction module and a storage module. The live broadcast content collection module is used for preprocessing and multi-protocol adaptation of the multi-platform adaptive distribution module, multi-thread parallel streaming, CDN acceleration and dual-machine hot backup, so that the live broadcast content can be synchronously distributed on multiple platforms, the distribution process is stable and reliable, and the problems of asynchronous distribution, complicated operation, easy freezing and interruption and high labor cost are solved.

This application belongs to the field of data processing technology, specifically involving an integrated traceability method for language learning and practice results across multiple platforms. The method includes: acquiring learning behavior data from multiple platforms; standardizing and converting the multi-platform learning behavior data and writing a unified event serial number to generate a unified learning record; writing user identifiers, timestamps, record summary values, and previous record summary values ​​into the unified learning record to generate a chained record sequence; storing the chained record sequence in a storage network, and generating a restricted access copy and an integrity verification copy when the sharing request is verified; performing behavior fragment coverage checks and supplementary collection based on the integrity verification copy to generate a complete set of learning behaviors; and integrating the complete set of learning behaviors across platforms to generate a learning trajectory archive. This application can balance record reliability, sharing controllability, and trajectory integrity.

Systems or methods disclosed herein may provide artificial intelligence-based aggregation of social media content across multiple platforms. In an example, a computing system may receive content queries and may retrieve relevant content from various social media platforms. The computing system may generate consolidated content feeds that include first-party content from platforms owned by a primary entity and third-party content from external messaging platforms. The consolidated feeds may be arranged into content slots with sponsored content intelligently placed between organic content slots based on user engagement metrics. The computing system may implement machine learning models to optimize content selection or arrangement based on user interaction patterns.

A method and system for managing and calling BIM design resources across multiple platforms, relating to the field of BIM technology, includes: configuring Revit platform plugin information in a document and converting the document into a first XML file; downloading resources based on user permissions; matching the downloaded resource information with the first XML file and retrieving the corresponding DLL file to load plugin functionality; saving the function selection information as an INI file, and loading plugin function buttons based on the INI file when Revit starts; configuring Bentley platform plugin information in a second XML file, and copying the target resources to the Bentley directory based on the second XML file; and loading plugin function buttons based on the Bentley directory after startup. This invention provides a method for software integration management and calling across multiple basic platforms under different operating environments, solving the problem of difficulty in switching and unifying resources during the design process.

This document provides a computing framework for a 6G system. This disclosure provides a computer-readable medium storing instructions that, when executed by processing circuitry, cause the processing circuitry to: construct a unified computing plane within a sixth-generation (6G) network to provide standardized access to artificial intelligence (AI) services; and configure the unified computing plane to act as an intermediary between multiple AI service consumers and multiple AI platforms. Other embodiments may be described and / or claimed.

The present application relates to the technical field of intelligent product selection, and particularly relates to an intelligent product selection and infringement early warning method based on cross-platform mining and a knowledge graph, comprising: obtaining product description texts on multiple platforms, user interaction behavior sequences and market dynamic indicators; using natural language processing to analyze the product texts and extract a technical feature vector, combining time series analysis to generate a product heat index, and calculating market saturation based on market dynamics to form a product market potential evaluation indicator; constructing a multi-dimensional product knowledge graph based on the technical feature vector, and selecting candidate products according to the potential indicator; mapping the candidate product technical vector to the knowledge graph to quantitatively generate a patent infringement risk evaluation value; when the risk exceeds a threshold value, generating a design avoidance scheme based on the technical substitution path and design variant scheme in the graph; and outputting intelligent product selection decision support information. The present application improves platform product selection efficiency and accuracy.

An innovative system for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. By leveraging advanced AI techniques, seamless data synchronization, intelligent task management, and predictive workflow optimization across multiple SaaS platforms are described. Key components include an AI-driven data synchronization system, an intelligent cross-platform task manager, and an adaptive user interface. The system also features a cross-departmental health monitoring and notification mechanism, alongside an intent-based interaction system. This solution enhances organizational efficiency, improves decision-making, and provides unprecedented levels of automation and insight in managing complex SaaS ecosystems.

The embodiment of the application belongs to the field of artificial intelligence, is applied to the field of cross-platform interaction, and relates to a cross-platform task execution method and device, computer equipment and a storage medium, which comprises the following steps: acquiring a system image of a first platform and caching the system image into a Docker engine; acquiring machine resources of a second platform, deploying the Docker engine, and mounting a preset NAS mounting volume into the deployed Docker engine; acquiring a pre-created AI processing task through the Docker engine, taking the system image as an execution program, taking the machine resources as a configuration program, and completing AI processing system construction; calling the constructed AI processing system to run the AI processing task, acquiring a task execution result, and storing the task execution result in the NAS mounting volume. The application realizes cross-platform task execution in a state where business logic and configuration resources are highly separated across platforms through a NAS mounting volume and a Docker engine, and avoids excessive coupling among multiple platforms.

The application discloses a kind of based on collaborative intelligence's offshore rocket self-lifting platform dynamic scheduling method, comprising the following steps: multiple platforms are constructed as intelligent unit that can be autonomously responded to task, and the adaptive matching between task and platform is realized by dynamic grouping through collaborative mechanism.In the method, multiple indicators are introduced as scheduling targets, and the weight parameters are dynamically adjusted according to real-time task, resource and environmental information to improve scheduling flexibility.Through learning optimization mechanism to generate scheduling strategy, control platform task allocation, path planning and execution timing.In the execution process, scheduling strategy and platform grouping structure can be updated in time according to environmental disturbance and task state change, to ensure smooth execution of task and stable cooperation of platform.The application is suitable for multi-task, high-frequency launch scene.

The application provides a kind of perception reliability test method, device, storage medium and electronic equipment, applied to automatic driving perception test technical field.The application can automatically select perception test cases covering multiple scenarios from data server in combination with development test and historical test requirements after the release of perception module, wherein the perception module includes mutually adapted perception model and perception software, then the perception test cases are input to the perception module running on different architecture platforms, the output results of each platform are obtained using automated backfill test, and are compared with real labeled data to form performance test results, then the performance test results of perception model and software are integrated to obtain perception reliability test conclusion, so that timely and comprehensive reliability verification can be carried out on multiple platforms after the release of perception module, which greatly improves the test efficiency and coverage of perception reliability test.

The application discloses a user social interaction data management system and method based on cloud computing and relates to the technical field of data processing.The user social interaction data management method based on cloud computing comprises the following steps: S1, collecting user social interaction data of multiple platforms, and storing and constructing an event timeline database after preprocessing; S2, performing homologous merging analysis through time sequence offset features, field matching features and link association feature data; S3, performing event chain integration mapping analysis by comprehensively merging result parameters and task queue operation data; and S4, performing time sequence stability analysis based on link segment integration results and task execution states, and performing timeline recovery, maintenance inhibition and instability alarm control operations according to the time sequence stability analysis results.The problems of homologous event misplacement projection, event chain semantic destruction and unstable downstream processing caused by multi-platform asynchronous writing, cache back flushing and breakpoint supplement transmission are solved.

This invention relates to the field of intelligent procurement technology, specifically an AI-based cross-platform intelligent procurement sourcing and price comparison system. It includes modules for cross-platform data acquisition, AI-powered intelligent sourcing, multi-dimensional price comparison analysis, intelligent approval, procurement execution, settlement and reconciliation, data review, and a central control module. Each module communicates bidirectionally with the central system via a distributed network. The central system includes a main control server, a distributed database, an API gateway, and a data encryption unit, ensuring unified data scheduling and security. The cross-platform data acquisition module standardizes data processing across multiple platforms; the AI-powered intelligent sourcing module accurately matches requirements; the multi-dimensional price comparison module comprehensively evaluates price, quality, and service; the intelligent approval module completes multi-level reviews according to rules; the procurement execution and settlement and reconciliation module achieves a closed-loop process; and the data review module provides decision support. This system boasts a high degree of intelligence, comprehensive process coverage, and can improve procurement efficiency and cost-effectiveness while ensuring compliance.

A multi-platform cooperative task allocation method based on multi-agent reinforcement learning. The method comprises: constructing a multi-platform cooperative task model, and determining a target function of multi-platform cooperative task allocation; based on the multi-platform cooperative task model, a distributed partially observable Markov decision process is established, wherein a reward function of the distributed partially observable Markov decision process is determined according to the target function of multi-platform cooperative task allocation; using a multi-agent reinforcement learning algorithm, an optimal decision of multi-platform cooperative task allocation is determined based on the reward function of the distributed partially observable Markov decision process; and a corresponding task is allocated to each platform according to the optimal decision of multi-platform cooperative task allocation.

The application provides an advertisement audience portrait intelligent construction and accurate pushing method and system, relates to the technical field of advertisement intelligent putting, analyzes the cross-domain behavior sequence of the collected user of multiple platforms, extracts high-dimensional semantic feature vectors such as visual style, application scene and emotional tone, realizes the fusion of multi-platform data and the understanding of the deep-level appeal of the user in aesthetic preference, use scene and emotional demand, and generates a dynamic evolution user interest graph to accurately capture the instantaneity behavior of the user and respond, thereby generating multi-version advertisement materials adapted to the user in multiple dimensions, improving the user experience and conversion potential of the advertisement, optimizing the allocation of pushing resources through the shunting pushing strategy of the community and the individual, improving the propagation range while also ensuring accurate pushing, calculating the surface effect index and the deep experience index after pushing, realizing self-optimization of the whole process, and continuously improving the pushing accuracy and user satisfaction.

Described herein are systems and methods for real-time integration of resources across disparate electronic platforms, including automated data mapping and message translation for improved resource platform access. The embodiments provide technical solutions and technical improvements that overcome technical problems, drawbacks and / or deficiencies in the technical fields involving compatibility with multiple third-party APIs, unified access to data from multiple platforms, and efficient synchronization with multiple platforms. Technical solutions and technical improvements herein include aspects of improved API call normalization for scalable and flexible API calls that integrate with various platforms, proactive and / or real-time caching of data associated with the various platforms, and efficient orchestration of client queries, platform API calls and cache querying to answer the client queries most efficiently and quickly with up-to-date, synchronized data.

This invention relates to the fields of e-commerce and artificial intelligence technology, and particularly to an intelligent mapping and publishing method and system for heterogeneous product information data across multiple platforms. Through the complete process of steps S1 to S4, it achieves end-to-end intelligent processing from source product data acquisition to automated multi-platform publishing. The method first parses the data model of the target platform, then establishes mapping relationships between heterogeneous data through an intelligent mapping engine, followed by data transformation and compliance preprocessing, and finally batch publishing through a one-click publishing engine. Compared with existing technologies, this method can improve the multi-platform publishing of product information from the "person-day / platform-SKU" level to the "minute-batch" level, reducing the time cost for operators in cross-platform product information processing by more than 90%, while fundamentally avoiding common errors in manual operations such as field mismatch and unit misuse.

This invention belongs to the field of video promotion and mainly relates to an AI-based cross-platform video promotion system. This system uses material version records and rule base version records as deterministic benchmarks to support versioned management and reproducible execution when the same promotional campaign is launched on multiple platforms in parallel. It performs unpacking and parsing of original materials, extracts text, and solidifies content fingerprints; it structurally merges the review constraints of each platform and generates a rule base version; it generates release versions for each platform according to the rule base version and performs multimodal compliance review; after approval, it submits to the platform and solidifies the release receipt to form a release ledger; it reads back the actual presented copy on the platform and verifies its consistency with the released version, supporting random checks, dispute evidence collection, and audit archiving; it monitors changes in laws and regulations and updates the system rule base in real time; and it achieves unified versioned management of multi-version materials across platforms, consistency verification of actual presentation after release, and traceable retention of evidence materials.

This invention discloses a cross-platform information visualization analysis method and system based on big data mining, comprising the following steps: collecting raw data from multiple platforms to generate a standardized data set; extracting correlations and supporting evidence to generate a data evidence relationship set; integrating the correlations to generate a cross-platform data evidence chain set; determining conflict and consistency relationships between evidence to generate a set of evidence conflict relationships; constructing game-theoretic constraints between evidence chains to generate a set of valid evidence chains; combining the cross-platform data evidence chain set and the set of valid evidence chains to generate a set of valid data structures; performing cross-platform information analysis and calculation, performing structure mapping, and generating cross-platform information visualization analysis results. This invention achieves consistent cross-platform data analysis through evidence chain constraints and structure reconstruction methods, possessing the advantages of high result stability and strong interpretability.

Methods, systems, or computing devices are disclosed for enabling shared social media sessions that may allow multiple users to synchronously experience personalized content across different devices. The system may leverage artificial intelligence to create dynamic, constraint-free content streams that incorporate preferences from multiple users while maintaining individual privacy controls. Users may initiate shared sessions through messaging interfaces or standalone applications, with content sourced from multiple platforms and tailored to the collective preferences of session participants.

This invention discloses a 3D printing platform leveling method and system based on time-series data. The method includes: after the 3D printing platform executes a leveling command, acquiring height parameters corresponding to multiple platform positions of the 3D printing platform; determining whether the 3D printing platform needs leveling based on the height parameters, and obtaining a determination result; if the determination result is yes, forming a parameter sequence by combining the historical height parameters of the 3D printing platform and the height parameters; and inputting the parameter sequence into a trained LSTM model to determine the leveling command corresponding to the 3D printing platform. Therefore, this invention can achieve precise platform leveling control based on height analysis and time-series prediction, improving the flatness and printing quality stability of the 3D printing platform, and reducing the risk of printing failure due to platform unevenness.

The application belongs to the technical field of underwater target search, and particularly relates to a multi-agent cooperative path planning method based on reinforcement learning. The application improves the structure on the basis of a deep Q network framework, designs a joint state space to enhance the information correlation degree between multiple platforms, uses a joint Q network to realize information sharing and collaborative updating, introduces a conflict detection mechanism to reduce path conflicts and resource waste, and constructs a multi-dimensional reward and punishment function to balance individual exploration and group cooperation, so that the deep Q network can be applied to multi-platform cooperative search while ensuring learning efficiency and completing dynamic cooperative optimization between multiple platforms.

A system is provided. The system includes a first platform including a first platform level agent configured to direct one or more actions of the first platform based on at least one of a selected target or a selected goal. The system also includes a computer system in communication with the first platform level agent. The computer system programmed to a) execute a supervisor level agent configured to select at least one of a target or a goal for one or more platforms including the first platform, b) receive targeting information including one or more targets, c) receive platform information for the one or more platforms, d) select, by the supervisor level agent, a target of the one or more targets based on the target information and the platform information, and e) transmit, to the first platform level agent, the selected target.

A one-stop solid-state drive (SSD) testing system and method are disclosed. The system includes: a test cluster comprising multiple platforms under test; a database server equipped with a database management system; a test terminal for acquiring device information and disk status of a specified platform under test, generating a platform ID for that platform, and organizing the platform ID, device information, and disk status into a record in the platform under test information table of the database management system; and a control terminal for receiving the platform ID and production information, and updating the production information in the platform under test information table based on the platform ID. The test terminal further includes: for platforms under test with a ready disk status, retrieving a compatible SSD mass production package based on the production information obtained from the platform under test information table, executing multiple test modules according to a predetermined program, and summarizing the test results in a test result table. This system has achieved one-stop SSD testing for different production batches of SSD products.