257 results about "Interactive feedback" patented technology

The invention relates to a multi-modal intelligent robot system and an interaction method, and belongs to the technical field of robot digital data processing, and the method comprises the following steps: S1, multi-modal input collection; s2, carrying out multi-modal input preprocessing; s3, performing multi-modal information fusion, inputting the information into a pre-trained multi-modal feature fusion model, and performing feature fusion on each modal feature through a quantitative feature data fusion mechanism to obtain a user interaction intention vector and a user emotional state vector; s4, interaction response generation: matching a preset interaction response strategy library based on the user interaction intention vector to obtain basic response content, and performing emotion adaptation adjustment on the basic response content in combination with the user emotion state vector to generate multi-modal response content including text information, voice information and action information; s5, interactive feedback is executed; the method has the beneficial effects that the robot is fused with multi-modal information through the basic response content and the user emotional state vector, and interaction is dynamically optimized.

The invention discloses an intelligent programming assistance method and system combining a large model and a knowledge graph. The method comprises the steps that the system is initialized, and a programming domain knowledge graph body and an education programming large language model are loaded; constructing a learner initial portrait and associating the learner initial portrait with a knowledge graph; personalized programming learning experience is provided, including real-time programming tutoring, and thinking chain-based heuristic guidance is generated through combination of a large language model and knowledge graph information; and interactive feedback and system iteration are carried out, and a reinforcement learning mechanism based on human feedback is utilized to continuously optimize the large language model. Through deep fusion of structured knowledge of a knowledge graph and understanding generation ability of a large language model, the method aims at solving the problems that an existing programming auxiliary tool is insufficient in individuation and heuristic, heterogeneous knowledge is difficult to fuse, teaching resource modes are single, and an effective evolution mechanism is lacked. More accurate, personalized, heuristic and sustainable-evolution intelligent programming assistance can be provided, and the programming thinking and autonomous learning ability of learners can be effectively improved.

The invention relates to the technical field of artificial intelligence and language learning, in particular to a multi-modal language learning auxiliary system and method based on artificial intelligence, and the system comprises a multi-modal input module, a cross-modal feature fusion module, a dynamic adaptive learning module and an interactive feedback generation module. Wherein the multi-mode input module is used for receiving original text data, original image / video data and original voice data; the cross-modal feature fusion module is used for extracting a visual feature vector and a semantic coding vector and generating a cross-modal joint feature vector; the dynamic adaptive learning module is used for generating dynamic scene parameters; and the interactive feedback generation module is used for outputting a multi-mode feedback data packet through a prompt learning engine. According to the method, by fusing multi-modal feature alignment and dynamic context parameter modeling, linkage generation of grammar, culture and pronunciation feedback is achieved, and the context understanding ability and interaction feedback precision in the language learning process are improved.

The invention provides an immersive concert experience system based on virtual reality, and belongs to the technical field of virtual reality, and the system comprises a dynamic adjustment module which is used for predicting the preference of a user for stage arrangement, light effects and music types through a machine learning model according to the historical preference data of the user, and outputting the preference to the stage arrangement, light effects and music types; dynamically adjusting visual elements and sound field parameters of the virtual scene and a refresh rate and a field angle of the virtual scene in combination with the user physiological indexes and the user head motion trail which are acquired in real time; the action capture and feedback module is used for capturing an interaction instruction sent to the dynamically adjusted virtual scene by the limb action of the user in real time; and the interaction feedback module is used for enabling the virtual performer to respond to the interaction instruction of the user in real time based on the expression recognition and voice synthesis technology, and generating personalized interaction feedback content. A user constructs an immersive music interaction field domain, and experience logic of a virtual music meeting is completely reconstructed.

The invention discloses a multi-modal interactive fusion virtual reality emotion computing system, which comprises a multi-modal interactive fusion virtual reality emotion computing device, a multi-modal interactive fusion virtual reality emotion computing device and a multi-modal interactive fusion virtual reality emotion computing device, the virtual reality emotion computing device based on multi-modal interaction fusion further comprises a distributed multi-modal sensing device, a user adaptation interaction module, an interaction feedback module, an emotion extraction module, a multi-modal fusion module and a self-adaptation emotion computing model. The multi-modal fusion module is used for carrying out full-dimensional monitoring and intelligent decision making on a complex scene, the user adaptive interaction module can realize accurate identification and response to user requirements, and the multi-modal fusion module is used for making up for information limitation of a single modal, so that effective interaction and accurate identification can be realized on the whole.

The invention provides a VR scene adaptive rendering method based on AI and multi-modal perceptual interaction, and the method comprises the steps: firstly obtaining a perceptual signal set of a current interaction stage of a user through a multi-modal interaction interface, including user interaction actions, visual attention and scene physical feedback signals, and then analyzing the set to obtain a sensing signal set of the current interaction stage of the user; the method comprises the following steps: generating user concerned area features and scene dynamic sensitive area features, then determining a rendering area priority sequence and a detail retention reference through a rendering resource allocation model based on the user concerned area features and the scene dynamic sensitive area features, and adjusting a resource scheduling rule and a quality control rule of a VR rendering system according to the result. And executing the adjusted rule to generate the current frame of rendering picture, and updating the decision logic of the rendering resource allocation model according to the change trend of the interactive feedback of the user to the current frame of picture and the physical feedback of the scene, thereby realizing the dynamic self-adaption of VR scene rendering, improving the rendering efficiency and quality, and enhancing the user immersion.

The invention discloses a large language model knowledge base self-adaptive updating method combining documents and interactive feedback, and relates to the technical field of knowledge base updating, and the method comprises the steps that a real-time change detection module monitors streaming documents and interactive feedback, and captures and marks a single fact change event; the decision module calculates a shunting decision coefficient according to the heat density index and the linkage sweep index, and the event is shunted to a Micro-Edit or Incremental-Tune path; if shunting to the Micro-Edit path, the positioning module executes attention backtracking and gradient sensitivity analysis, and determines a to-be-updated weight address; the rank-one editing module implements rank-one updating and records an audit log; the verification module generates an online token or a rollback instruction through consistency comparison; the version management module manages a version, activates a new version route and cleans out expired branches. Through event granularity identification, intelligent shunting, local updating and multi-version management, the timeliness of knowledge base updating is remarkably improved, and resource consumption and maintenance cost are reduced.

The invention discloses a government affair service digital human intelligent interaction method and system based on deep learning, and the method comprises the steps: firstly obtaining government affair service specification policy data and interaction behavior data, including voice, text, facial expression, limb movement and the like, of a user; performing spatio-temporal feature extraction and fusion on the original interaction data to generate a user behavior semantic vector, performing feature extraction on the policy data to generate a standard vector, analyzing the user behavior semantic vector based on a government affair policy cognition map to obtain a government affair handling intention vector, and optimizing an intention generation service execution vector in combination with the standard vector; and multi-modal behavior decoding and cross-modal alignment loss function optimization are carried out to obtain an interaction behavior sequence conforming to the specification, and finally, the digital human is controlled to carry out interaction feedback with the user according to the sequence. According to the invention, pertinence and normalization of interactive feedback of the government affair service digital human and the integrating degree with the actual demand of the user can be improved.

The invention relates to the technical field of intelligent questioning and answering, in particular to an intelligent questioning and answering system implementation method and system.The intelligent questioning and answering system implementation method comprises the following steps of document collection and preprocessing, document dicing, document layout analysis and table layout analysis; the method has the beneficial effects that a semantic association network and a multi-modal index system are formed through offline document collection, preprocessing, slicing, layout analysis, data extraction and knowledge graph construction; in the online part, the capabilities of Embedding vectorization, multi-index joint retrieval, tensor reordering, AI database integration and large language model generation are combined, accurate semantic understanding and rapid knowledge matching of user questions are realized, high-quality answers are generated, and interactive feedback optimization is supported.

The invention relates to the technical field of interface interaction design, in particular to a GUI page element layout and typesetting optimization method and system based on artificial intelligence. The technical problems that a traditional GUI design mode is insufficient in GUI design flexibility, high in dynamic layout technical threshold, high in manual typesetting cost, limited in experience and unreasonable in resource allocation are solved. According to the technical scheme, the GUI page element layout and typesetting optimization system based on artificial intelligence comprises a natural language processing module, a context sensing module, an intelligent agent module, an interactive feedback optimization module and a cross-device adaptability module. Through the natural language processing technology, the user can describe the demand through the natural language, the system automatically identifies the natural language instruction, the layout and typesetting of the page elements are automatically completed by using the large language model and the intelligent agent, the GUI design efficiency is improved, the personalized display of the user interface is realized, and the user experience is improved.

The invention provides a robot interaction processing method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: in response to a character configuration operation for a robot, configuring the character of the robot as a target character; in response to interaction operation for the robot, the robot is controlled to interact in a multi-modal mode matched with the target character, and the multi-modal mode comprises at least two of voice, expression and action; in the interaction process of the robot, the target character is updated based on interaction feedback data, and the robot is controlled to interact in a multi-mode mode matched with the updated target character. By means of the robot interaction method and device, the naturalness and vividness of the robot interaction process can be improved.

The invention relates to the technical field of information flow intelligent recommendation, and discloses a real-time interactive feedback information flow content adaptive optimization method and system.The real-time interactive feedback information flow content adaptive optimization method comprises the steps that user interaction data are collected and subjected to robust preprocessing to obtain standardized interaction feature vectors; constructing a user personalized interaction baseline model to obtain a multi-level behavior reference basis, identifying a real-time interest signal of a user and updating an interest state, dynamically adjusting a recall strategy and executing multi-path recall to obtain a candidate content set, adaptively adjusting a sorting feature weight and executing sorting, and dynamically controlling diversity to execute rearrangement to obtain a final recommendation list, and meanwhile, obtaining an interest prediction score in a sorting stage by adopting a hierarchical reasoning architecture and a model distillation technology. The interest change of the user can be captured in real time, the matching degree of the recommended content and the instant interest of the user is improved, and the user experience and the recommendation effect are improved.

The invention relates to the technical field of electronic shopping, in particular to a garment matching customization management system and method.The method comprises the steps that firstly, the body size, personal characteristics, social attributes, historical behaviors and target dressing occasion data of a user are collected, and real-time interaction feedback is fused to generate a dynamic multi-dimensional user portrait vector; inputting the design parameters into a pre-trained personalized matching generation model to generate a personalized matching scheme containing the design parameters; a virtual fitting effect picture is rendered through three-dimensional modeling, and a user is supported to adjust in real time and confirm a final scheme; and finally, analyzing the scheme to generate a customization demand list, distinguishing finished product items from customization items, intelligently matching a target supplier based on a supplier capability matrix, and issuing a purchase order or a customization production task list. And the system realizes full-process automation. According to the invention, matching precision and user experience are improved, supply and demand cooperation efficiency is optimized, personalized and standardized demands are adapted, and accurate, personalized and comfortable experience is provided for online clothes purchasing of the user.

The invention discloses a graph calculation-driven complex publication derivative resource content layout planning system, which belongs to the technical field of digital publication and intelligent media, and comprises a multi-modal data analysis module for analyzing heterogeneous data such as texts, images, tables and the like in publications into structured node features; a feature fusion module; a content relationship modeling module; a graph calculation driving layout optimization module; performing layout constraint modeling: converting a layout problem into a graph constraint optimization problem; a modeling optimization module; a multi-terminal adaptive rendering module; the layout generation engine is used for converting a graph calculation result into a visual layout scheme; and the interactive feedback module is used for feeding back user operation to the graph in real time to trigger layout reconstruction. According to the method, advanced technologies such as graph calculation, multi-modal fusion and constraint solution are deeply fused, the limitation that a traditional typesetting tool depends on a fixed template is broken through, and a content-driven one-stop solution is provided for scenes such as academic periodicals, electronic textbooks and digital reports.

The invention belongs to the field of brain-computer interfaces, and particularly relates to a decision-making brain-computer method and device based on virtual reality induction, which combines two psychological decision-making tasks of auditory stimulation and visual stimulation and utilizes virtual reality equipment to create a decision-making brain-computer interface normal form of panoramic interaction, so that a subject can fit a scene facing a decision in reality to the greatest extent, and the accuracy of decision making is improved. The sensory motor cortex is effectively activated, and cooperative activation of the cognitive-motor neural network is induced; a decision interaction feedback link is added to enhance a decision stimulation effect and a cranial nerve feedback mechanism by analyzing related characteristics P300, power spectral density and brain network function connection quantity of the acquired electroencephalogram signals during normal form execution decision reaction. Compared with the prior art, the method has the advantages that immersive audio-visual stimulation and task interaction feedback are brought into a decision-making brain-computer interface for the first time, and a new scheme with neural rehabilitation and human-computer interaction functions is provided for neural feedback training of cognitive functions.

The invention discloses a multi-modal stimulation regulation and control system combining interactive feedback and AI self-adaption. The system comprises an AI main control and synchronization module which generates a unified rhythm time sequence; the acoustic stimulation output module outputs a rhythm acoustic signal; the visual LED stimulation module outputs rhythm visual signals; the game / animation interaction module provides an interaction task synchronized with the acousto-optic rhythm; the sensing acquisition and feedback module is used for acquiring multi-modal physiological and behavior data in real time; the amplifier output module drives the execution module or equipment; the power management module realizes voltage stabilization and low power consumption switching; the behavior habit forming mechanism module binds a stimulation signal and an interaction task; and the human-assisted management interface module supports remote parameter setting and real-time monitoring. The interactive feedback and AI self-adaptive multi-mode stimulation regulation and control system is combined, microsecond-level synchronization and multi-source closed-loop self-adaption of sound and light and animation can be achieved, a guidance scheme for personalized customization of treatment and behavioral habit cultivation can be achieved, and the efficiency, accuracy and effectiveness of system regulation and control are remarkably improved.

The invention relates to the technical field of adaptive testing, and particularly provides a WebUI adaptive testing system and method based on multi-agent collaboration and multi-modal perception, and the system comprises a multi-modal perception subsystem, an agent collaboration testing subsystem and an assertion and verification subsystem. The method comprises the following steps: adjusting an element positioning strategy for a multi-dimensional interface feature set through a continuous interaction feedback mechanism, and outputting an element descriptor set; deploying a multi-agent collaborative decision-making unit comprising an interface understanding agent, a test logic agent and a data agent; outputting to form a semantic test script through a collaborative fusion mechanism; comprising the following steps: carrying out pixel-level difference calculation, and identifying a visual inconsistent region through multi-scale image comparison; and performing service-level logic difference, analyzing the consistency of the interface element state and the service rule, and outputting a hierarchical verification report. According to the invention, high coverage rate, low maintenance cost and reliable defect positioning capability of the test can be maintained in an environment with frequent evolution of pages.

The invention discloses a DIKWP-driven individualized brain-map interactive feedback system, which is used for neural rehabilitation and brain-computer interface training. The system obtains brain activity data of a patient through electroencephalogram acquisition equipment, constructs a personal brain-semantic map in combination with cognitive evaluation, and establishes a mapping relation between semantic units and brain region responses. In the training process, the DIKWP semantic analysis module performs multi-layer analysis on indexes such as reaction time, accuracy and intention achievement, generates multi-mode instant feedback such as visual sense, auditory sense or tactile sense, and performs directional reinforcement on a weak semantic domain. The system has a dynamic target optimization capability, and the difficulty can be automatically adjusted according to training performance; when attention distraction, emotion abnormity or semantic deviation is detected, a safety intervention mechanism is automatically triggered, and training effectiveness and safety are guaranteed. According to the method, closed-loop individualized rehabilitation interaction is realized, the adaptation degree and efficiency of brain-computer interface training are remarkably improved, and the method is suitable for various rehabilitation scenes such as languages, movement and cognition and has a good industrial application prospect.

The invention relates to the technical field of intelligent travel, and particularly discloses an intelligent travel itinerary automatic recommendation system and method. Comprising the steps that a multi-source data fusion module aggregates user behaviors, POI attributes and environment dynamic data in real time; the dynamic user portrait module fuses dynamic and static data through a time sequence neural network and a graph neural network to generate a space-time preference portrait; the travel decision engine generates an initial scheme based on the portrait; the real-time optimization execution module dynamically adjusts node parameters in combination with positioning and event data; and the interactive feedback module realizes closed-loop optimization. The innovation lies in that the dynamic portrait improves the recommendation matching degree by 50% +; 2.1-minute-level re-planning is achieved through an elastic buffering mechanism (+ / -20% staying floating); the space-time network model supports cross-city transfer node insertion, and user fatigue is reduced by 35%; federal learning and differential privacy (epsilon is less than or equal to 0.3) guarantee data security. And the problems of high interruption rate and response delay are solved.

The invention belongs to the technical field of intelligent interaction, particularly relates to an intelligent interaction method, system and device based on AI communication and a storage medium, and aims to solve the problems that in the prior art, AI scene coverage is narrow and is separated from a business process; the system comprises four modules: a context sensing and modeling module which collects multi-source context information in real time, and generates a three-dimensional context tensor through structured alignment and time sequence normalization; the intention analysis and reasoning module performs intention level decomposition by using a deep semantic model based on the tensor, and outputs an intention recognition result and probability distribution; the response generation and adaptation module is used for generating multi-mode responses such as texts, voices, images or equipment instructions in combination with intention and context tensors, and outputting the multi-mode responses after consistency verification; and the interactive feedback and optimization module collects user explicit and implicit feedback and is used for updating model parameters and strategy weights online to realize continuous optimization of the system.

The invention relates to a digital human live broadcast method and system. The method comprises the following steps: acquiring a live broadcast room visual effect picture and a digital human live broadcast audio; generating digital human live broadcast content under the driving of the digital human live broadcast audio by taking the visual effect picture of the live broadcast room as a visual manuscript through an I2V model; in the process of playing the digital human live broadcast content, if the current interaction behavior of the user in the live broadcast room is monitored, generating digital human interaction content responding to the current interaction behavior; selecting an insertion frame from the key frames of the digital human live broadcast content so as to insert the digital human interaction content at the position of the insertion frame; the key frame is a video frame corresponding to a statement demarcation point in the digital human live broadcast content. According to the scheme provided by the invention, the contradiction between long content production period, high implementation cost and slow real-time interaction feedback in digital human live broadcast in related technologies can be solved.

The invention provides a weak password detection method based on multi-modal feature fusion and dynamic behavior analysis. According to the method, multi-dimensional feature extraction is innovatively introduced, including password entropy, semantic relevance, user historical behaviors, system login frequency and the like, so that user feature adaptation is improved, and dependency on a static dictionary is reduced; in addition, through real-time interactive feedback, instant pushing of password strength evaluation and safety suggestions can be realized. Therefore, by means of the method, the problems that in an existing weak password detection technology, the static dictionary dependency is high, the user feature adaptation is insufficient, and real-time feedback is lacked can be solved, the weak password detection precision and defense efficiency of colleges and universities are remarkably improved, and an efficient and easy-to-deploy password security solution is provided for the education industry.

The invention is suitable for the field of artificial intelligence and psychological health services, and provides a multi-mode psychological counseling system based on artificial intelligence. According to the system, face image data, voice data and text data of a user are collected through a multi-modal data collection module; the psychological counseling strategy generation module determines a corresponding psychological counseling strategy according to the comprehensive emotional state of the user and personal information of the user, and the psychological counseling strategy generation module generates a psychological counseling strategy according to the psychological counseling strategy according to the psychological counseling strategy and the personal information of the user. And finally, the interaction feedback module communicates with the user according to the policy, and optimizes the policy according to feedback information in the communication. The emotional state of the user can be judged according to various information sources such as facial expressions, voice emotions and voice texts of the user. Therefore, the accuracy and comprehensiveness of emotion recognition are improved.

The invention relates to the field of education, and discloses an interactive feedback system for modern education, which comprises a state monitoring module, a feedback optimization module, a learning recommendation module, a teaching analysis module and a long-term tracking module, the state monitoring module is used for collecting expression, voice and eye movement data of the student in real time to judge the emotional state and understanding condition of the student; the feedback optimization module dynamically adjusts a feedback strategy according to the learning state and presents the feedback strategy to the teacher; the learning recommendation module generates a learning resource list according to individual demands of students and pushes the learning resource list to the student terminals; the teaching analysis module analyzes the data of the student group and provides teaching strategy optimization suggestions for teachers; and the long-term tracking module records learning data of the students and performs time sequence analysis to form a long-term feedback report. According to the invention, real-time feedback, personalized learning support and data-based teaching strategy optimization for students can be realized, and the teaching efficiency and the learning effect of the students are improved.

The invention discloses an electric vehicle driving data management system, which comprises a multi-source data acquisition module, a processing module, a grading judgment module, a dynamic fusion module, an improvement module and an interaction feedback module, the multi-source data acquisition module is used for acquiring various driving data of the electric vehicle in real time; the processing module is used for receiving and processing various mass driving data; and the grading judgment module is used for dynamically judging the data grade and executing a grading storage strategy. The invention belongs to the technical field of electric vehicle driving data management, and aims at solving the problems that in the prior art, a data storage mode is single, distinguishing is not carried out according to the importance of driving data, and meanwhile a large deviation exists between a prediction result and the actual endurance mileage. The method has the technical effects that the data can be conveniently divided into the high importance level and the low importance level, the differentiated storage strategy is executed, and meanwhile, the actual energy consumption condition of the vehicle can be reflected more accurately.

The invention relates to the technical field of medical student clinical operation intelligent evaluation, and discloses a clinical basic skill operation identification and evaluation method and system based on artificial intelligence, and the system comprises a data collection device, an edge calculation platform, a cloud analysis engine and an interaction feedback terminal. The data acquisition device is used for acquiring original data in a user operation process in real time; the original data comprises action posture, time sequence video and time sequence audio data; the edge computing platform is deployed at a local terminal and is used for preprocessing original data; the cloud analysis engine is used for receiving the preprocessed data and executing operation identification and normativity evaluation; and the interactive feedback terminal is used for outputting a scoring report, an error correction prompt and a personalized training suggestion in real time. According to the method, a'perception-analysis-feedback 'closed loop can be formed, real-time and accurate operation identification and scoring are realized, the scoring result is objective and consistent, and the reference value is relatively high.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent matching system and method for entrepreneurship resources based on artificial intelligence. Comprising a multi-source heterogeneous resource acquisition module, a data processing module, a dynamic demand perception and analysis module, a multi-dimensional fusion matching engine, an interactive feedback optimization module, a personalized result presentation module and a data storage and management module. The invention aims to solve the problem that the efficiency, effect and user experience of startup resource matching are restricted in the prior art, and provides an intelligent solution which integrates an advanced artificial intelligence technology and has deep understanding, dynamic matching, continuous optimization and interpretable capabilities.

The invention discloses a community comment management method and system, and relates to the technical field of data processing, the method can capture user interest changes in real time based on a dynamic interest map generated by registration information and short-term interaction behaviors, quickly screen high-correlation comments through association matching of interest levels, and improve the user experience. It is ensured that the recommended content is deeply matched with user preferences, and meanwhile the redundant data processing burden is greatly reduced. And through comprehensive analysis of a multi-modal quality evaluation system fused text, image and user behavior, and dynamic adjustment of community type differentiation weight distribution and real-time interactive feedback, false, contradictory or low-quality content is effectively identified, and compliance review and high-quality content exposure are enhanced. According to the embodiment, high-quality comments are accurately recommended through the dynamic interest map and multi-modal evaluation, the content ecology is optimized in real time, the timeliness is guaranteed, and the system stability is enhanced.

The invention discloses an AR visual information real-time superposition system and method, and particularly relates to the technical field of augmented reality, and the system comprises a visual collection and preprocessing module, a multi-modal space positioning module, a multi-source information fusion module, a dynamic superposition rendering module and an interactive feedback optimization module. According to the AR visual information real-time superposition system and method, the interaction feedback optimization module locally recognizes a gesture or a touch instruction through a lightweight CNN model, feedback logic is executed locally in combination with edge calculation, and cloud delay is avoided; meanwhile, feedback delay time is monitored, when delay is larger than 15 ms, rendering precision of non-core information is automatically reduced, it is ensured that core operation feedback delay is smaller than or equal to 15 ms, an operation-feedback-information updating closed loop is achieved, and smooth interaction experience of a user in scenes such as real-time operation guidance is guaranteed.

The invention belongs to the technical field of computers, particularly relates to an enterprise consultation service system based on big data, and aims to solve the problems of data islands, decision lagging and insufficient predictive ability in traditional enterprise consultation service. The system comprises a global data access module, a knowledge graph construction module, a dynamic simulation engine, an intelligent consultation generation module and an interactive feedback optimization module, a time sequence evolution knowledge network is constructed by fusing internal and external multi-source heterogeneous data, multi-scene parallel simulation and intelligent suggestion generation are realized, and closed-loop feedback learning is supported. According to the scheme, the accuracy, foresight and response efficiency of enterprise strategic decision can be improved, and large-scale concurrent service and real-time dynamic optimization are supported.