2219 results about "Cross-platform" patented technology

The invention discloses a hydraulic engineering equipment data intelligent management system based on digital twinning, and belongs to the technical field of hydraulic engineering. Comprising an intelligent perception and data fusion module for realizing real-time acquisition and standardized processing of cross-modal data; the knowledge graph construction and causal reasoning module is used for constructing an intelligent knowledge system capable of autonomously learning and semantic reasoning; the digital twin modeling and simulation module is used for realizing dynamic simulation and scene deduction of a full life cycle and providing limit working condition simulation and risk assessment support; the intelligent prediction and health management module is responsible for performing real-time monitoring, fault early warning and residual service life prediction on the equipment state, and generating personalized intelligent maintenance strategies for different working conditions; the visualization and decision support module is used for visually presenting the equipment operation data and the analysis result and providing intelligent decision recommendation; and the cloud edge collaboration and system integration module realizes cross-platform interoperation and continuous integration through distributed computing and micro-service architecture.

The invention provides a cross-platform dynamic security baseline and vulnerability closed-loop repair method and system based on federated learning, and the method comprises the steps: constructing a heterogeneous policy mapping rule base of a Windows registry, Linux sysctl and iOS plist by configuring a semantic analysis algorithm, and forming a multi-operating system policy consistency guarantee mechanism by combining a configuration conflict detection model of formalized verification; a dynamic reinforcement decision engine is designed based on a vulnerability influence surface analysis model driven by a knowledge graph and a double-circulation reinforcement learning architecture (outer-layer strategy exploration and inner-layer parameter optimization); a special evaluation system is constructed for key fields such as electric power and finance, a domestic cryptographic algorithm is deeply fused, and an electric power monitoring system PCSR / IIR / SCAR multi-dimensional index and SM2 / SM4 / SM9 full-stack security scheme is developed. According to the intelligent security baseline reinforcement method, the problems of wide cross-platform strategy gap, extensive vulnerability repair decision and insufficient industry adaptability of a traditional scheme are solved, and the intelligent security baseline reinforcement method supporting dynamic confrontation, accurate adaptation and service fusion is provided.

The invention discloses an AI model automatic deployment platform based on a containerization technology, which relates to the technical field of automatic deployment of an artificial intelligence model, and comprises a container construction and intelligent configuration module, a transmission and cache management module, a security and multi-version warehouse module, a resource scheduling and optimization module and a deployment and interface management module, the container construction and intelligent configuration module adopts a three-layer mirror image construction strategy of a base layer, a framework layer and a model layer. According to the invention, all the modules cooperate to form a closed loop, after the container construction module generates an incremental packet and the incremental packet is subjected to security signature verification, the transmission module distributes the incremental packet according to network quality, the resource scheduling module dynamically adjusts bandwidth and quota, and the deployment module starts the container and monitors the container in real time. The problems of efficiency, safety and resource optimization of model deployment in an edge environment are solved, and cross-platform compatibility and service continuity are improved.

The invention relates to the technical field of data processing, in particular to an intelligent document updating processing method and system.The intelligent document updating processing method comprises the steps that part parameters and coding rules are extracted from engineering design file metadata, a part list and a PDM and PLM system, the semantic association relation between parts is analyzed through a knowledge graph, and the mapping relation between logic identifiers and physical files is constructed; generating an initial version file library; and monitoring file names and version changes in real time based on a micro-service architecture, calling a simulation service in combination with a knowledge graph to verify parameter compatibility, screening an optimal version, updating the optimal version to a main version library, optimizing historical conflict data in a block chain evidence storage index by using a genetic algorithm to generate a standardized code, and outputting a cross-platform parameter mapping table. According to the method, the problems of file name change, non-standard coding conflict, version control missing and cross-platform adaptation incoherence are solved, and the data tracing efficiency, the version management reliability and the system compatibility are improved.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

The invention relates to the technical field of data privacy protection, and discloses a cross-platform social privacy collaborative protection system based on federated learning and a block chain. The system comprises a federal learning initialization module, a private data encryption module, a cross-platform data synchronization module, a block chain consensus verification module and an intelligent contract execution module. Global model initialization parameters are generated through a multi-party security aggregation algorithm, user data privacy is protected through hierarchical encryption, data are synchronized through a Hash time lock protocol and an intelligent contract, model updating is verified through an improved Byzantine fault-tolerant algorithm, and a privacy protection rule is triggered based on a differential privacy noise injection algorithm. The system effectively solves the problem of cross-platform social privacy protection, guarantees data security and privacy, improves federal learning reliability, optimizes data sharing and utilization, and is suitable for various cross-platform social scenes.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a generative interface panel having multiple automated assistant services. Each assistant service may access a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The invention provides a smart campus-oriented multi-hyper fusion platform collaborative scheduling system and method, and is applied to the technical field of data processing. Resource sensing and dynamic modeling processing is performed on multi-hyper fusion platform resource pool data to generate target resource model data, and the target resource model data is composed of a resource real-time monitoring index, load prediction model output, a resource isomerism adaptation result and a cross-platform protocol conversion adaptation parameter; the target resource model data is processed, platform collaborative scheduling strategy parameters are generated based on reinforcement learning, and a campus business scene reward and punishment mechanism is introduced in the reinforcement learning process; processing the platform collaborative scheduling strategy parameters to generate a dynamic resource allocation scheme; processing the dynamic resource allocation scheme and the campus service demand data, and generating a service and resource matching agent model based on an intelligent optimization algorithm; and processing the target campus information based on the service and resource matching agent model to generate campus resource scheduling information.

The invention provides a multi-modal document content cross-platform analysis system, and relates to the technical field of data processing, and the system comprises a document preprocessing module which is used for receiving multi-source heterogeneous document input data, and generating a preprocessed document through format conversion, page segmentation, noise reduction and optical character recognition; the feature extraction module is used for extracting three types of features, including spatial layout features, semantic features and logic structure features, based on the preprocessed document; according to the method, the multi-source heterogeneous document is cooperatively processed, accurate extraction, calibration and association of features in cross-platform analysis are realized, finally structured data are generated, and the accuracy, consistency and cross-platform applicability of multi-modal document analysis are improved.

The invention discloses a cross-platform power transaction data interaction optimization method, and particularly relates to the technical field of power transaction data digital twinning. Simulation transaction data of market participants is generated based on an antagonistic neural network model; identifying a causal relationship among market variables from the simulation transaction data of the digital twin through a causal discovery algorithm, and constructing a causal graph; modeling spatial dependence and causal conduction paths among market participants by using a graph neural network model, and performing anti-factual reasoning in a single external impact scene in a digital twinborn body by intervening key variables in a causal graph; simulating dynamic game behaviors of market participants in an extreme weather parameter distribution scene; the power market failure assessment is carried out by combining the causal conduction path to obtain the power market failure assessment index, and early warning is carried out based on the power market failure assessment index, so that the adaptability and stability of the power market in extreme weather scenes are improved.

The present disclosure describes systems and methods for detecting and preventing data misconfigurations within a security-focused data fabric platform. The system integrates an advanced script migration engine designed to streamline the translation of security rules and scripts across different scripting languages while ensuring alignment with the fabric's unified schema. The method involves receiving inputs from data sources, mapping these inputs to entities of a target schema, monitoring real-time data changes, and simulating impacts on operational dependencies to detect misconfigurations proactively. Leveraging AI-driven mechanisms, including Large Language Models (LLMs), the system dynamically identifies breaking changes in third-party data streams, issues alerts, and provides suggested fixes. The script migration engine further enhances the platform's functionality by automating cross-platform script translations and enabling faster onboarding of security tools. Together, these innovations ensure scalable, accurate, and resilient integration and management of security data across heterogeneous sources, strengthening operational integrity and minimizing security risks.

The invention relates to a photovoltaic power station real-time monitoring management system and method based on a cloud platform, and belongs to the field of photovoltaic power station intelligent monitoring. The system adopts a three-layer architecture of an application layer, a platform layer and an edge layer deployed at a cloud end, the edge layer is composed of distributed edge nodes, data are acquired through a data acquisition interface, and local preprocessing is executed; the platform layer configures a data storage and machine learning model, and performs generating capacity prediction and equipment health degree evaluation; and the application layer provides a visual monitoring interface and an operation and maintenance management terminal, and supports multi-dimensional data display and alarm information pushing. And the edge layer and the platform layer adopt a calculation task dynamic allocation mechanism, so that the cloud load is reduced while the real-time performance is ensured. The full-life-cycle monitoring of the photovoltaic power station is realized through a pure software architecture, the system has the advantages of elastic expansion, cross-platform compatibility and remote maintenance, and the deployment efficiency and the operation and maintenance response speed of the monitoring system are remarkably improved.

The invention discloses a new media-oriented interactive marketing method, which comprises the following steps of: 1, multi-source data acquisition: acquiring behavior data of a user in social media, a short video platform and an e-commerce scene in real time through an API (Application Program Interface), a burying point technology and cross-platform data crawling, comprising click tracks, staying duration, comment content, sharing behaviors and shopping cart operation; through the technical innovation of a dynamic content engine, a real-time feedback closed loop, edge calculation, reinforcement learning, a multi-modal material library, a GAN automatic synthesis technology, federated learning, a block chain and the like, the core benefits of improving the interactive conversion efficiency, data-driven real-time decision, reducing the content generation cost, balancing the safety and the effect, performing scene-based accurate touch and the like are realized; the user conversion rate, the click rate, the marketing response speed, the negative comment processing efficiency, the portrait accuracy and the target user coverage rate are remarkably improved.

The invention provides an AI-based multi-user data storage docking and secure transmission method, which comprises the following steps of: acquiring a propagation path of a hot topic according to a multi-modal content association graph, performing fragmentation storage on the propagation path by adopting a clustering algorithm, and generating a fragmentation storage index table; when the relevance of different modal contents is higher than a preset threshold value, determining that the sensitivity grading result is consistent with the multi-modal relevance, performing dynamic rule adjustment by adopting a deep learning model, generating a dynamic auditing rule set, and obtaining boundary judgment parameters of the rule set; and distributing the dynamic auditing rule set and the decision transparency index to each service line by adopting a cross-platform data synchronization protocol according to the auditing decision log, generating a cross-platform consistency auditing standard, and obtaining a synchronization log of standard execution.

A platform for dynamically generating application experiences. The platform comprises a design management system, an agent orchestration system, an analytics system, a model management system, a user management system, and databases for storing design elements and templates. The design management system provides a portal for application owners / designers to create UX / UI designs, allowing them to select design elements from a set of categories or templates. The platform gathers existing websites / applications to identify common design patterns, stored in a design catalogue database, and suggests historical interfaces for design exploration. It enables the generation of templated applications that integrate with legacy systems. The agent orchestration system parses user specifications, selects generative AI systems, and generates UX / UI content based on the specifications. The analytics system collects and analyzes data to provide insights for improving UX / UI design and optimizing website performance. The model management system trains and maintains generative AI models used for content generation.

The invention provides a multi-agent-based low-code component development method and system and electronic equipment, and the method comprises the steps: obtaining a user demand, and converting the user demand into an initial business model through a first agent; obtaining a prefabricated component library of the low-code platform, and automatically generating a demand component by using a second agent according to the initial business model and the prefabricated component library; automatically adapting and integrating a cross-platform data source by using a preset cross-domain data integration mechanism, and performing intelligent scheduling on the automatically generated demand component through a third agent; and establishing an automatic service arrangement mechanism based on the micro-service architecture, automatically identifying each service dependency relationship, and dynamically adjusting the execution sequence and resource allocation of each demand component according to the service dependency relationship. Through modular component design, the low-code platform has high flexibility and expandability, efficient collaboration of the components in the complex industry is guaranteed, and resource allocation and use efficiency is optimized.

The invention relates to the related technical field of distributed computing power resources, and discloses a distributed computing power resource dynamic fusion and cooperative computing system which comprises a resource sensing module, a task analysis module, a dynamic scheduling module, a virtualization adaptation layer, a cooperative computing engine, a global controller and a hierarchical communication bus. The resource sensing module collects and quantifies the hardware computing power of the heterogeneous computing nodes in real time. According to the invention, a complete closed-loop control system is formed through a five-layer dynamic fusion architecture (a sensing layer, an analysis layer, a scheduling layer, a virtualization layer and a collaboration layer), so that a distributed computing system can adapt to a scene that edge nodes are dynamically added and pushed out; meanwhile, through the purpose of developing virtualization middleware supporting cross-platform instruction real-time conversion and based on a multi-target dynamic weight adjustment mechanism of online learning, the heterogeneous task scheduling success rate can be greatly improved through the overall structure of the system, and the fault recovery time is shortened.

The invention discloses an electric power communication network visual operation and maintenance method based on digital twinning, and the method comprises the steps: carrying out the coupling mapping of a global data mirror image and multiple sources, collecting data through a reconfigurable sensing array, and outputting a three-domain digital mirror image through time alignment; semantic topology reconstruction is carried out, and a dynamically growing semantic topology is generated by using a graph semantic engine; based on explainable causal chain fault tracing, an event-causal-countermeasure knowledge network is constructed; performing multi-target balancing operation and maintenance decision making, and screening an optimal strategy by using a three-dimensional Pareto model; immersive visualization and interactive intervention are performed, and a cross-platform engine is injected to realize multi-dimensional display. According to the method, through multi-source data accurate mapping, topology dynamic adaptation, causal interpretable traceability and scientific decision making, the problems of data islands, topology lagging and low decision making efficiency in existing operation and maintenance are solved, and the operation and maintenance accuracy and efficiency of the electric power communication network are improved.

The invention discloses a multi-platform Internet of Things equipment unified access management method, equipment and a storage medium, and relates to the technical field of Internet of Things integration. The method comprises the step of solving the fragmentation problem of cross-platform equipment access through dynamic configuration adaptation. The method comprises the following steps: starting from receiving a front-end equipment operation request, indexing a pre-configured mapping relation database through an equipment unique identifier, and obtaining a target platform type, an API address and an encryption certificate; dynamically calling a protocol adapter based on a platform type, and injecting an account certificate into a request header or a message body according to a platform specification; converting the original parameters into a format supported by a target platform through a protocol conversion engine, and combining the original parameters with the authentication information to generate a complete request; and realizing high-reliability communication by using an asynchronous message queue, and finally returning a platform response to a front end. According to the invention, through mapping management of the configuration driver and dynamic adapter scheduling, equipment access unification across the Internet of Things platform is realized.

The invention provides a cross-platform education data privacy protection analysis system, and relates to the technical field of data privacy protection. The cross-platform education data privacy protection analysis system comprises a multi-modal data acquisition unit, a dynamic privacy analysis engine, a federal learning processing core module, a block chain enhanced access control system, a privacy watermark traceability module and a compliance autonomy unit. Dynamic coupling of scene sensitivity and data protection intensity is realized through a dynamic privacy risk scoring equation and an adaptive differential privacy noise equation; the problems of privacy disclosure, compliance verification and traceability and responsibility investigation in cross-platform education data sharing are solved by combining federal learning, block chain evidence storage and privacy watermarking technologies. According to the method, the privacy risk assessment precision is remarkably improved, the model precision loss is reduced, full-life-cycle data security management and control are supported, and the method is suitable for multiple scenes such as K12 education and college educational administration.

The invention discloses a cross-platform authority unified management method and system based on a multi-WEB system, and the key point of the technical scheme is that the method comprises the steps: obtaining organization structure data of an enterprise system, analyzing the organization structure data, constructing a multi-level tree topology network, forming organization entity topology parameters, analyzing the organization entity topology parameters, and storing the analyzed organization entity topology parameters into a database; according to the scheme, post authority baseline parameters are generated, dynamic optimization of channel selection is achieved by constructing a dual-channel transmission decision maker and combining real-time network state data and an inter-system interaction characteristic score table, and compared with a traditional static configuration mode, the mechanism can achieve dynamic optimization of channel selection according to parameters such as department communication strength, system transmission performance and safety scores. According to the method, the optimal transmission path is automatically selected, network congestion or high-delay nodes are avoided, cross-system adaptation rules are generated through mapping calculation of an authority interface strategy and post authority baseline parameters, the problem of authority granularity difference between different systems is solved, and the accuracy and consistency of authority distribution are ensured.

The invention provides a cross-platform user behavior data intelligent aggregation and analysis processing method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-platform user historical behavior data, carrying out the time sequence sorting, carrying out the session segmentation based on the time sequence data, obtaining a behavior session sequence, and constructing a behavior migration probability graph; an intention recognition agent and a behavior prediction agent are deployed through a decentralized federal reinforcement learning framework, cross-platform cooperative training is realized by using a differential privacy mechanism, and anti-factual reasoning is performed based on a multi-layer causal relationship graph to generate a user intention portrait, so that the cross-platform data analysis accuracy is improved, and the privacy protection capability is enhanced.

An exemplary machine-learning-based method for generating a response to a natural-language query from an entity associated with an organization comprises: obtaining a sub-query from the natural-language query; determining if a cache associated with the organization stores a matching historical sub-query: if the cache stores the matching historical sub-query, retrieving a historical sub-response associated with the historical sub-query from the cache as a sub-query response; if the cache does not store any matching historical sub-query: inputting the sub-query into a cache-based enhancement machine-learning model to enhance the sub-query, wherein the cache-based enhancement machine-learning model is trained based on historical query data stored in the cache associated with the organization; and inputting the enhanced sub-query into a language model to generate the sub-query response; and generating the response to the natural-language query based on the sub-query response.

The invention belongs to the technical field of computers, and provides a video display and storage method and system based on a wayland protocol and a storage medium in order to solve the problems that an existing wayland video system is high in rendering delay, high in CPU load, low in storage efficiency and poor in cross-platform compatibility. Video frames are directly written into an Overlay layer through DRM-KMS and mixed by bypassing a synthesizer, meanwhile, CPU memory amplitude overhead is eliminated through zero copy transmission, an ISP-GPU-VPU direct transmission assembly line is constructed, full-link DMA-BUF direct transmission is achieved, and delay is greatly reduced; iSP preprocessing, GPU shader scaling and VPU coding are all executed by special hardware, so that the load is reduced, and the multi-path processing capability is improved; vPU dynamic code rate compression is utilized, segmented storage is carried out according to events / time, metadata is synchronized to a database, redundant storage is avoided, and space is saved.

The invention discloses a conversion method and system based on SysML and Modelica model semantic mapping. The method comprises the following steps that S1, a Modelica standard model library is imported into a SysML modeling tool; s2, completing the definition of behavior elements, and forming an SysML model instance; s3, exporting a Modelica model which can be executed by the Modelica simulator; s4, establishing a mapping relation table between SysML model elements and Modelica model elements; s5, when a change event occurs in the SysML model, updating the content of the corresponding Modelica model; s6, the Modelica model is imported into the SysML model, and a corresponding SysML model instance is generated; s7, simulation result mapping and behavior modeling generation are executed, a state transition diagram structure is formed, and structure updating of internal block diagrams in the system model is completed. According to the method, bidirectional conversion and joint simulation optimization between models are realized, and the method is suitable for cross-platform modeling, collaborative design and functional verification scenes of a complex system.

The invention discloses an intelligent personalized learning path recommendation system, and relates to the technical field of learning systems, and the system comprises a multi-source data collection module which is used for synchronizing behavior data of students in a cross-platform learning scene in real time; the cognitive feature analysis engine comprises a style recognition sub-engine and a demand prediction sub-engine to predict the potential learning demand intensity of students for unmastered knowledge points and generate a priority list comprising knowledge gaps; the personalized path generator generates a three-dimensional path plan; the proportion of guided questioning, example demonstration and autonomous exploration is automatically configured according to knowledge difficulty; the dynamic self-adaptive adjustment unit is used for designing a reward function including short-term progress speed and long-term ability growth potential by taking real-time performance of students as a state space and taking path adjustment action as a decision space based on a reinforcement learning framework; and a double-loop feedback mechanism is realized. According to the method, the dimension limitation of traditional learning analysis is broken through, the difficulty of stiffness of a static course template is broken through, and meanwhile, the semantic gap of subject cognition is broken through.

The invention discloses a BIM-based electromechanical modeling method and system, and the method comprises the steps: outputting an electromechanical parameterized model comprising a pipeline path, equipment layout and a connection relation according to a building structure model and an electromechanical design constraint rule library; generating a cross-platform compatible electromechanical data stream based on the electromechanical parameterized model; outputting optimized electromechanical model incremental data according to the user operation behavior data and the electromechanical data flow; based on the incremental data of the electromechanical model, generating a collaborative electromechanical model with consistent versions; and according to the collaborative electromechanical model, performing block rendering and parallel energy consumption simulation on the collaborative electromechanical model by adopting a distributed heterogeneous computing framework, and outputting an optimized electromechanical model and a performance analysis report. By utilizing the embodiment of the invention, automatic modeling, optimization design and dynamic coordination of the electromechanical system can be realized through an intelligent algorithm, so that the design efficiency, the operation intelligence level and the energy utilization efficiency of the building electromechanical system are improved.

The invention relates to the technical field of resource matching, and discloses a cross-platform e-commerce resource dynamic matching search method, which comprises the following steps: carrying out real-time analysis on commodity description information from different e-commerce platforms, extracting a core attribute of a commodity and generating a unified semantic representation; when cross-platform data conflicts occur, a conflict resolution strategy is dynamically generated, and the conflict resolution strategy is executed by a local preprocessing module; acquiring data content after conflict resolution, selecting a target edge node based on the data content in combination with user geographical location information, and dynamically configuring a cache data set of the target edge node; and generating corresponding multi-dimensional matching features based on user intention information in a natural query language in combination with the commodity data in the configured cache, determining a candidate commodity set and a corresponding search result based on the multi-dimensional matching features, obtaining feedback information of the user on the search result, and updating the intention analysis model and the matching strategy based on the feedback information. According to the invention, efficient cross-platform resource matching can be realized.

The invention discloses a non-perpetual culture immersive interactive experience device and system based on a virtual digital human, and relates to the technical field of intangible cultural heritage intelligent interaction, and the device comprises a multi-mode sensing module which collects data such as user actions and voices through a depth camera and the like; the non-perpetual knowledge base module stores a non-perpetual knowledge graph and a case library; the digital human modeling engine fuses the inheritor characteristics and the user data to generate a virtual digital human; the immersion interaction engine constructs a cross-platform rendering environment to realize five-sense fusion experience; and the adaptive learning module analyzes the interaction sequence to optimize the digital human behavior, and all the modules cooperate to realize the intelligent interaction experience of non-genetic culture. Through the multi-modal perception module, the digital human modeling module and the like, the immersive interactive experience of the non-perpetual culture is realized, the user behavior can be accurately captured, the characteristic virtual digital human is generated, the five-sense fusion experience is provided, the content can be optimized based on the user interaction, the inheritance and propagation of the non-perpetual culture are promoted, and the participation degree and the sense of identity of the user are improved.