910 results about "User satisfaction" patented technology

The invention provides a distributed storage resource intelligent scheduling method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the feature splicing of feature vectors of different modes, so as to obtain a multi-mode feature vector; predicting the user satisfaction based on the multi-modal feature vector and the resource adjustment parameter; establishing a resource demand priority mapping table under different service scenes according to the user satisfaction; constructing a multi-objective optimization model according to the resource demand priority mapping table; on the basis of the multi-objective optimization model, predicting the resource state and the load condition of each node in the distributed storage system; and according to the resource state and the load condition of each node, lightweight rule scheduling is carried out to obtain a preliminary scheduling scheme. According to the invention, intelligent scheduling management of distributed storage resources is realized.

The invention provides an intelligent recommendation method and system for an e-commerce platform, and the method comprises the steps: collecting user interaction behaviors and time-space context data in real time, and constructing a user behavior multi-modal feature matrix; extracting commodity multi-level features, and generating a commodity comprehensive feature matrix; identifying and predicting a user intention based on the user behavior feature matrix, and generating an intention distribution vector; a recommendation candidate set is obtained by combining the commodity feature matrix and utilizing a context awareness collaborative filtering enhancement technology; a multi-objective optimization function is constructed, and after the user intention vector is input, a personalized recommendation sequence is generated in combination with an optimization result and the candidate set; and user feedback is monitored in real time, online learning and reinforcement learning algorithms are adopted, and a recommendation strategy is continuously optimized based on user instant feedback and long-term satisfaction. According to the scheme, the recommendation accuracy, the diversity of recommendation results and the user experience can be improved.

The invention relates to the technical field of retrieval enhancement generation, in particular to a method for automatically adjusting retrieval enhancement generation parameters based on content feature modeling. The method comprises the following steps: receiving an original query text of a user, performing component analysis, identifying terminologies, general vocabularies and question entities, and quantifying to form query fingerprints; acquiring a historical behavior sequence of the user, and constructing a score reflecting the level of the user by combining the query fingerprints and adopting a time decay weighting algorithm; the user level score is converted into specific retrieval parameter configuration, and a retrieval strategy blueprint is formed; guiding document library retrieval according to the retrieval strategy blueprint, and screening out a candidate knowledge set which is most matched with the professional level of the user; and according to the user level score, a preset instruction template is intelligently filled, and a situational generation instruction is constructed. According to the method, the problem of non-uniform cognitive load caused by a traditional system is solved through a retrieval enhancement generation technology, and the technical knowledge transmission efficiency and the user satisfaction are remarkably improved.

The invention discloses a multi-elevator linkage dynamic response intelligent scheduling system and a scheduling method thereof. The scheduling method comprises the following steps: acquiring an operation rule of an elevator, constructing a historical operation database and updating the historical operation database in real time; capturing a field state in real time through a monitoring system, and generating a real-time data stream according to a time sequence; a digital twinborn model is constructed through a historical database and real-time data flow, and the elevator calling probability and the congestion risk of each floor are predicted; the elevator calling probability of each floor in the next five minutes is predicted based on a digital twinborn model, the congestion risk is calculated, a real-time demand thermodynamic diagram fusing the space-time sequence dimension is generated, and intelligent scheduling is conducted on the elevator through the thermodynamic diagram; by reducing the waiting time, the overall operation efficiency is improved, the user satisfaction is enhanced, and smoother vertical traffic experience is provided for high-rise buildings.

According to the large model-based real-time voice interaction method and system provided by the invention, multiple rounds of historical dialogue data of the user are collected, the context can be deeply understood, and a subsequent strategy is adjusted according to the evolution of historical dialogue content. Through accurate construction of the dynamic context and strong semantic understanding capability of the large model, the system can better understand user intention and dialogue logic, the generated reply better conforms to human language habits, and the interaction naturalness is greatly improved. The intelligent decision of the large model is optimized based on a reinforcement learning algorithm and human feedback, so that the large model can continuously learn in real-time interaction, a reply strategy is adjusted according to user feedback and dialogue progress, and the relativity, continuity and user satisfaction of reply are improved. By setting the interruption mechanism, the interruption intention of the user can be effectively processed in the real-time voice interaction process, the effectiveness of the real-time voice interaction is ensured, and the accuracy and smoothness of the real-time voice interaction are improved.

The invention discloses a multi-modal-based digital intelligent customer service system. Data compensation is carried out on initial multi-modal problem data by adopting a dynamic adaptive compensation mechanism through a multi-modal input module. And carrying out dynamic weight distribution on the target multi-modal problem data by adopting a cross-modal attention mechanism through a multi-modal comparison learning module. And performing intention recognition on the target multi-modal semantic vector through an intention recognition module according to the historical interaction data and a preset dynamic knowledge graph. And inputting the user intention data into the target reinforcement learning reply model through a reply generation module to construct reply data. And performing potential problem prediction by adopting the target multi-modal semantic vector, the historical interaction sequence corresponding to the user and the time data through a potential problem prediction module. And by utilizing the dynamic updating capability of the knowledge graph, the context sensitive identification of the user problem is realized, and the identification accuracy is improved. And the potential problem prediction module turns from passive response to active service, so that the interaction efficiency and the user satisfaction are remarkably improved.

The invention relates to the technical field of information processing, in particular to a supply chain cross-packet vulnerability detection method, device and equipment and a storage medium. A vulnerability packet name, a sensitive API, a trigger parameter and vulnerability description are integrated into tetrad information; if so, performing cross-packet call chain analysis on the source code file by using a cross-packet chain reachability analysis algorithm, obtaining a function call sequence of the sensitive API based on a cross-packet call chain analysis result, realizing vulnerability detection on a cross-packet call chain, generating a vulnerability verification code based on tetrad information by using a preset large language model, and performing vulnerability verification on the vulnerability verification code. The method comprises the following steps: establishing a function call sequence of a bug verification code, verifying the accessibility of the bug verification code in the function call sequence, determining the bug confidence according to the energy consumption condition of a large language model, and generating bug alarm information when the accessibility verification result is that the bug is accessible and the bug confidence is high, thereby realizing double judgment of the bug, reducing the false alarm rate of the bug and improving the user satisfaction.

The invention relates to an intelligent customer service self-learning method and system, and the method comprises the steps: collecting the interaction data of a user and an intelligent customer service, and forming a multi-dimensional data set; based on a PID (Proportion Integration Differentiation) controller, processing the performance indexes in the multi-dimensional data set, calculating a current error signal, and generating a corresponding control instruction according to the current error signal so as to adjust the response behavior of the intelligent customer service system in real time; evaluating the system performance data adjusted by the control instruction to obtain evaluation feedback; and according to the evaluation feedback, dynamically adjusting the parameters of the PID controller through a self-adaptive control strategy, and feeding back the adjusted parameters to the PID controller in the step S2. According to the invention, by introducing a closed-loop feedback mechanism based on the PID controller and a parameter adaptive optimization strategy, real-time regulation and control of the response behavior of the intelligent customer service system are realized, and the stability, the response speed and the user satisfaction of the system are remarkably improved.

The invention discloses a personalized recommendation method driven by user intention recognition, which comprises the following steps of: collecting multi-dimensional information such as browsing records, click behaviors and comment data of a user, and constructing a user behavior data set; preprocessing the collected user behavior data to obtain a session sequence; the current demand of the user is speculated by analyzing the input text and the behavior mode of the user, and the intention of the user is recognized; constructing a user interest preference prediction model according to the identified user intention; and in combination with real-time feedback of the user, the recommendation model is dynamically updated, and the response speed and the individuation degree of the recommendation system are improved. The problem that a traditional recommendation system only depends on static data and does not comprehensively consider real-time feedback of users is solved, self-adaptive ability is injected for personalized recommendation, intention behaviors of different users in multiple scenes can be analyzed and understood, accurate recommendation service is provided, and recommendation accuracy and user satisfaction are improved.

The invention relates to the technical field of smart home, discloses a full-life-cycle fault prediction and management method and system for smart home equipment, and aims to solve the problems of high fault rate and low operation and maintenance efficiency of the smart home equipment in the prior art. The method comprises the steps that multi-source heterogeneous data are collected and preprocessed; time domain and frequency domain features are extracted and fused to generate a comprehensive feature vector; predicting the fault probability and type by using a deep transfer learning model; a dynamic risk score is calculated through a weighted risk assessment model, and risk grades are divided; and triggering a differential response strategy according to the risk level. The system comprises a multi-source heterogeneous data sensing module, a dynamic feature extraction and fusion module, an adaptive fault prediction module, a quantitative risk assessment module, an intelligent response and decision module and a full life cycle data management module. According to the technical scheme, comprehensive and accurate monitoring, efficient fault prediction and intelligent management of the smart home equipment can be achieved, the fault rate is remarkably reduced, and the overall performance of the system and the user satisfaction degree are improved.

The invention relates to the technical field of intelligent recommendation, and discloses an AI intelligent matching method based on a knowledge graph, and the method comprises the steps: constructing a quaternary knowledge graph containing a time dimension; identifying legal entities and semantic relationships by adopting an entity identification and relationship extraction technology; multi-level semantic features are extracted through a two-layer progressive semantic matching algorithm of a grammar layer, a semantic layer and a reasoning layer; constructing lawyer ability portraits based on a heterogeneous graph neural network and a time sequence perception graph convolution technology; progressive matching calculation is adopted, the optimal matching weight is learned through a multi-layer attention mechanism, and dynamically optimized intelligent matching is achieved. The technical problems of cold start, insufficient semantic understanding ability and poor timeliness processing ability in the existing legal consultation matching system can be solved, and the matching precision and the user satisfaction are improved.

The invention discloses an online video content intelligent pushing method combined with a learning interest model. The method comprises the following steps: constructing a dynamic interest vector based on multi-source user behavior data, generating a user interest portrait vector set, and performing interest dimension clustering and weight distribution; generating a video content feature vector set according to a clustering result of the user interest portrait vector set; establishing a multi-dimensional association relationship between the user interest portrait vector set and the video content feature vector set, and outputting a user-video matching confidence matrix; converting the user-video matching confidence coefficient matrix into a push sequence based on a multi-objective optimization strategy and issuing the push sequence; and feedback behaviors of the user on the pushed video are collected in real time to realize closed-loop optimization. The method has the following advantages and effects: accurate perception and deep semantic matching of the dynamic learning interest of the user can be realized, and the accuracy, timeliness and user satisfaction of content distribution are remarkably improved, so that the learning efficiency and experience are optimized.

The invention discloses an air-railway combined transportation path optimization method based on XGBoost and a space-time attention network, and particularly relates to the field of intelligent transportation systems.According to the method, a traffic network basis is constructed by integrating flight and train historical data, topological information and weather data, delay time and consumed time of a critical path are predicted by means of an XGBoost model, and the time consumption of the critical path is predicted by means of the XGBoost model; a space-time dependency relationship is modeled through a space-time attention network, and complex space-time association is captured in combination with a space and time attention module and a multi-head mechanism; an optimal path is generated based on a weighted multi-objective function (covering time, economy, reliability and comfort), and users are supported to dynamically adjust weights to adapt to personalized requirements; and meanwhile, real-time adjustment of model parameters is realized by adopting an exponential weighted moving average and self-adaptive updating strategy, so that the prediction precision is remarkably improved, the reliability of the path and the user satisfaction are optimized, the real-time performance is ensured through a lightweight closed-loop updating mechanism, and a high-precision, personalized and real-time response intelligent solution is provided for air-railway combined transportation.

The invention relates to a ship user behavior self-learning recommendation system based on a large model, and relates to the technical field of ship informatization. According to the system, through collection and fusion of multi-source heterogeneous ship user behavior data, a large-scale pre-training language model (large model) is utilized to carry out deep understanding and semantic mining on massive ship field text information and user behavior sequences, and a ship field knowledge graph or semantic vector space is constructed. The large model can identify and predict potential demands, behavior patterns and preference changes of ship users, and generates highly personalized, accurate and prospective ship service, product, route or information recommendations in combination with real-time operation data and external environment factors. Besides, a user feedback self-learning mechanism is introduced into the system, recommendation strategies and model parameters are continuously optimized according to interaction behaviors and explicit evaluation of the users in modes of reinforcement learning or continuous learning and the like, and intelligent iteration of the system and continuous improvement of the recommendation effect are achieved. According to the method, the challenges of a traditional recommendation system in the aspects of data complexity, semantic gaps and dynamic demand adaptability in the ship field are effectively solved, and the ship operation efficiency and the user satisfaction degree are remarkably improved.

The invention discloses a large model task scheduling optimization method and device, and the method comprises the steps: analyzing a task type and a service quality requirement corresponding to a large model reasoning task when a scheduling request of the large model reasoning task is received; determining current state information and historical execution information corresponding to the large model reasoning task; inputting the task type, the service quality requirement, the current state information and the historical execution information into a pre-trained scheduling agent, so that the scheduling agent outputs a scheduling action corresponding to the large model reasoning task; executing a scheduling action on the large model reasoning task, and recording corresponding performance data of the scheduling action in an execution process; and inputting the performance data into the scheduling agent, so that the scheduling agent updates a weight parameter of the scheduling agent according to the performance data and a preset reward function. The problem that a traditional static scheduling strategy cannot adapt to a complex heterogeneous environment is effectively solved, and the system response efficiency, the resource utilization rate and the user satisfaction degree are remarkably improved.

The invention relates to the technical field of image processing, and provides an indoor three-dimensional image intelligent rendering method based on image processing. Comprising the following steps of indoor multi-view image acquisition, image preprocessing, intelligent analysis of indoor scene elements, construction of an indoor three-dimensional initial model with attributes, intelligent generation of rendering parameters, adaptive LOD real-time rendering and intelligent interaction optimization. According to the intelligent interaction optimization system, through deep fusion of natural language processing, user preference learning and real-time rendering technologies, a set of efficient, visual and personalized virtual scene rendering interaction process is constructed. According to the method, the problems that traditional graphic software is complex in operation, high in learning cost and low in debugging efficiency are solved, and the user satisfaction and creation efficiency are remarkably improved through an intelligent recommendation and rapid iteration mechanism.

The invention belongs to the technical field of artificial intelligence personalized services, and particularly relates to a reinforcement learning-fused user portrait dynamic construction and personalized recommendation implementation method, which comprises the following steps of: acquiring multi-dimensional user data; using the multi-dimensional user data to construct a multi-dimensional dynamic user portrait through a preset algorithm; based on the multi-dimensional dynamic user portrait, combining a collaborative filtering algorithm and a deep learning algorithm to generate a scene recommendation strategy; and monitoring user feedback and behavior data in real time, and optimizing the scenarized recommendation strategy by using the real-time monitored user feedback and behavior data. According to the method, the dynamic user portrait is constructed, scene recommendation is generated, and the strategy is optimized in real time by relying on reinforcement learning, so that the individuation degree, recommendation accuracy and user satisfaction of the service are remarkably improved, and the limitation of a traditional service mode is effectively broken through.

The invention relates to the technical field of voice recognition, in particular to a voice interaction system and method for customer service based on artificial intelligence, and the system comprises a voice input processing module, an intention classification and routing module, a context dynamic adjustment module, a user behavior learning module, a multi-level intention fusion module and a final result module. According to the method, a multi-dimensional feature system is constructed by extracting tone intensity, speech speed frequency and emotional fluctuation amplitude, intention categories, priority weights and confidence scores are generated to realize accurate acquisition of appeals, and dialogue history, context and emotional change dynamic reconstruction path nodes, switching rules and response time sequences are tracked during interaction. Historical behavior mining preference features, habit fusion intention relevance, emergency calculation of an optimal strategy, construction of service steps, resource allocation schemes and execution timelines, adjustment of an interactive interface, a service process and a feedback mechanism according to multi-dimensional analysis, guarantee of differentiated service experience, and improvement of response accuracy and user satisfaction.

The invention particularly relates to an atomic business intelligent arrangement device and method. The atomic business intelligent arrangement device comprises an atomic business relation graph intelligent construction module, an atomic business fine management module, a business intelligent arrangement and visual arrangement module and a man-machine interaction module. According to the atomic service intelligent arrangement device and method, full-chain intelligentization from user demand identification, service scene analysis to process automatic arrangement is realized, the service handling efficiency and the service quality are improved, manual correction is supported, the error rate is reduced, the reusability of atomic services is improved, policy scenes can be helped to rapidly fall into the ground, and the service quality is improved. And diversified operation requirements of the user are met, so that the user satisfaction is improved.

The invention provides an NLP-based customer service dialogue quality detection method and system, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining an e-commerce platform customer service dialogue text stream in real time; semantic understanding is conducted on the dialogue text flow, a semantic understanding result is output, and the semantic understanding result comprises a user consultation intention, key question elements and customer service inquiry information entity integrity; based on the semantic understanding result, performing sentiment analysis on the dialogue text stream, and outputting a structured sentiment analysis result; setting an initial state point and a termination state point in the dialogue processing path based on the semantic understanding result and the sentiment analysis result; the initial state point is a semantic vector fusing core problem elements fed back by the user for the first time and a current user emotional state value. According to the invention, through full-process intelligent processing, dynamic monitoring and accurate optimization of customer service quality are realized, and user satisfaction, customer service efficiency and business normalization are improved.

The invention relates to the technical field of audio signal processing, and discloses a sound box and sound effect intelligent adjusting method and system based on data analysis and a storage medium, and the sound box and sound effect intelligent adjusting method based on data analysis comprises the steps: constructing a user feature modeling engine, and extracting user auditory characteristic data; constructing an environment characteristic analysis engine, and collecting environment acoustic characteristic data; constructing a content feature extraction engine, and analyzing audio content semantic data; constructing a three-dimensional fusion optimizer, inputting the three feature vectors into an auditory scene fusion model, calculating an auditory experience score through tensor fusion operation, and solving an optimal sound effect parameter by applying a multi-objective optimization algorithm; constructing a parameter generation controller, and converting the optimal sound effect parameter into a specific audio processing parameter; according to the invention, the problem of mutual interference caused by traditional separated processing is solved, and the accuracy of sound effect adjustment and the user satisfaction are improved.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a strategy generation method, device, equipment and medium based on data analysis. Extracting historical interaction features and generating an association relationship, constructing a knowledge base, receiving current interaction data and extracting current interaction features, performing feature matching through the knowledge base to generate a matching result, and generating a strategy result based on the matching result. According to the method, the features are extracted based on the historical interaction data, the knowledge base is constructed, and the current interaction features are matched with the historical interaction features, so that the accuracy and the dynamic adaptability of the recommendation strategy are effectively improved, and refined strategy recommendation can be realized according to the event result data; and a personalized strategy can be dynamically generated based on real-time interaction data, so that the recommendation effect and the user satisfaction are remarkably improved.

The invention relates to the technical field of packaging image processing, and provides a customizable cultural and creative packaging design system based on AIGC, and the system comprises the following modules: a data input module which is used for receiving cultural and creative themes, cultural element preferences and packaging specification parameters inputted by a user; the culture element database is used for storing multi-dimensional culture symbol data including patterns, color pedigree, historical allusions and semantic association rules; and the AIGC generation engine is used for extracting features from the culture element database according to the input parameters based on a generative adversarial network (GAN) and a Transform model, and generating an initial design draft. 10 versions of high-quality design schemes can be generated within 5 minutes through an AIGC engine, the speed is increased by 8 times compared with a traditional process, meanwhile, the modification feedback period is shortened to 2 hours through AR real-time preview, the user satisfaction degree reaches 94%, the pattern semantic matching accuracy is improved to 89%, the historical allusion misuse rate is reduced to 3% or below, and the design efficiency is improved. And the material waste rate of the automatically generated printing file is reduced to 12%, and the delivery cycle is shortened by 40%.

The invention is applicable to the technical field of venue navigation, and provides an intelligent navigation method for venue digital service, which comprises the following steps: acquiring positioning information, behavior track and interaction record of a user; performing combined analysis on the user data and preset venue interest point attribute data to obtain a user feature vector containing a stay preference coefficient, movement speed analysis and a subject interest weight; extracting a topological relation between the current position and the target interest point according to the user feature vector, and calculating a constraint parameter according to the topological relation; based on the constraint parameters, constructing an initial path planning model taking the minimum expected passing time as a target function; determining a people flow density gradient map of each region of the stadium in a preset time period through the obtained historical people flow data and the user real-time positioning data; the initial path planning model is optimized by using the people flow density gradient map, and the optimal path is generated by dynamically adjusting navigation path nodes, so that the intelligence level and the user satisfaction of venue navigation are effectively improved.

The invention provides a user intention recognition method and device based on large and small model fusion and electronic equipment. The method comprises the steps that user information is acquired, the user information is input into a small model set for user intention prediction, an intention prediction set is obtained, the intention recognition confidence coefficient of the small model set is determined according to the intention prediction set, and the user information comprises user questions and user portrait features; according to the intention recognition confidence coefficient, the customer service agent is adopted to analyze and process the user information to obtain a COT analysis table, and the COT analysis table is sent to the large model; and according to the COT analysis table and the user information, adopting the large model to perform preset processing on the user question to obtain a target user question, and inputting the target user question into the small model to obtain a user intention recognition result. The problem that in the prior art, user intention recognition independently adopting a large model or a small model is low in user satisfaction degree is solved.

The invention is applicable to the field of power demand analysis, and provides a user demand-oriented power transaction matching recommendation method and system. The method comprises the following steps of: acquiring power consumption data of a user, and generating a resonance group and a feature packet with similar power consumption modes and risk tolerance; generating a power utilization comprehensive bidding plan in combination with the resonance group characteristics and the real-time market data; performing transaction matching based on the bidding plan, preferentially selecting the power generation resource with the closest electrical distance, introducing a green energy premium coefficient, and outputting a transaction result; and performing investment portfolio optimization analysis according to a transaction result, and generating a customized power package containing traditional energy, new energy and stored energy in combination with user risk preference. Through multi-dimensional data integration and intelligent algorithm application, accurate matching of power generation resources and user demands is realized, power transaction efficiency and user satisfaction are improved, and new energy consumption and sustainable development of the power market are promoted.

The invention provides an e-commerce platform data processing system and method. The method comprises the steps that browsing behavior data and commodity selection behavior data of a user in an e-commerce platform are collected, the browsing behavior data comprise page staying duration, rolling browsing rate and page switching frequency, and the commodity selection behavior data comprise carbon emission reduction attributes and green consumption identifiers of commodities selected by the user; and calculating a cognitive fatigue index of the user in a preset time window based on the browsing behavior data, comparing the cognitive fatigue index with a personalized threshold value, and when the cognitive fatigue index exceeds the threshold value, triggering a push self-suppression mechanism so as to reduce the push frequency, simplify the content complexity or delay the push time. By introducing a cognitive fatigue index and green consumption point dual-drive mechanism, green consumption is promoted while user experience is ensured, humanization and sustainability of e-commerce platform pushing strategies are realized, and the method has the beneficial effects of improving user satisfaction and optimizing a consumption structure.

The invention relates to the technical field of computers, and discloses an intelligent scheduling system and method for a high-efficiency charging platform, and the method comprises the steps: constructing a four-dimensional constraint model integrating the power grid load, the user demand, the equipment health degree and electricity price prediction, and employing a depth deterministic strategy gradient algorithm to drive a multi-target dynamic scheduling decision maker, the global optimization distribution of the charging resources in the space-time power dimension is realized, and strategy reconstruction is completed within 10s when a power grid emergency instruction or a device fault occurs. The system comprises a power grid sensing module, a user acquisition module, a health assessment module, an electricity price response module, a scheduling decision module, an instruction execution module and an emergency reconstruction module, and supports millisecond-level adaptive evolution. According to the method, the weighted reward function is constructed by quantifying the four indexes of power grid stability, user satisfaction, equipment loss and platform income, and the instruction verification and steady-state adaptive mechanism is combined, so that the user experience is synchronously improved, the service life of the equipment is prolonged, and the operation energy consumption is reduced on the premise of ensuring the safety.

The invention discloses a functional programming intelligent recommendation method and system based on a semantic knowledge graph, relates to the technical field of data processing, and constructs a high-dimensional semantic embedding space by performing semantic feature vector extraction on a multi-source functional programming corpus. The method comprises the following steps: firstly, embedding a plurality of candidate entity clusters into a semantic embedding space, preliminarily clustering the embedded vectors by utilizing a semantic aggregation pre-screening mechanism to obtain each candidate entity cluster and performing dynamic processing, then constructing a semantic relationship among the candidate entity clusters according to neighborhood distribution of the semantic embedding space, performing self-adaptive correction on inter-cluster distribution, and constructing a semantic knowledge graph generation recommendation method. A closed loop is formed from the corpus to the knowledge graph to the recommendation strategy, so that the intelligent recommendation system can accurately recognize the function semantic relationship, reduce noise interference and improve the recommendation hit rate and the personalized matching effect, and the accuracy, stability and user satisfaction of functional programming intelligent recommendation based on the semantic knowledge graph are remarkably enhanced.

The invention provides a linkage control method and system for computer auxiliary equipment, and the method comprises the steps: generating an equipment linkage control instruction sequence through prediction probability distribution, managing a multi-equipment concurrency control request through a priority queue, and dynamically adjusting an instruction execution sequence according to the equipment response time and the resource occupation condition, if the response time of the equipment exceeds a preset delay threshold value, reducing the linkage priority of the equipment and activating standby equipment; and adjusting equipment linkage strategy parameters according to a model updating result, adopting an adaptive threshold mechanism to optimize a prediction trigger condition, calculating a user satisfaction index through a moving average algorithm, and if the satisfaction is lower than a reference value, backtracking and analyzing operation sequence features and recalibrating personalized operation habit model parameters.