1399 results about "Sentiment analysis" patented technology

The invention discloses a multi-modal knowledge graph rule reasoning method and device based on a large model, and the method comprises the steps: carrying out the feature extraction and cross-modal alignment of input text data and image data, and generating a multi-modal feature vector of a unified semantic space; performing knowledge graph storage on the emotion entities and the relationships by adopting an attribute graph model to complete construction of an emotion knowledge graph; generating an interpretable inference rule from the emotion knowledge graph by using a large language model, and eliminating a conflict rule in combination with logic verification; calculating the confidence coefficient of a reasoning path based on an attention mechanism, and carrying out quantitative evaluation on a rule reasoning result; the knowledge graph and the rule base are updated online according to user feedback, and the real-time performance and accuracy of the inference system are optimized through weight adjustment and a forgetting mechanism. According to the method, through innovative technologies such as multi-modal data integration, dynamic knowledge evolution and interpretability reasoning, the limitation of a traditional sentiment analysis method in the aspects of evidence dimension, adaptive capacity, interpretability and the like is broken through.

The invention relates to the technical field of AIGC content generation, discloses an AIGC content generation method and system based on multi-modal fusion, and aims to solve the problems of decentralization, low efficiency and insufficient originality of a traditional content generation tool. Multi-modal data such as texts, images, videos and audios are integrated, user intentions are analyzed in combination with intelligent retrieval and a domain knowledge base, automatic generation from multi-modal input to high-quality creative content is achieved, a cross-modal collaborative generation technology is adopted, semantic features are dynamically aligned, and logically coherent content is generated. The content emotional value is enhanced through an emotional analysis and dynamic optimization strategy, the homogenization bottleneck is broken through, meanwhile, an automatic quality evaluation and format adaptation mechanism is integrated, deep application of scenes such as text travel, advertisement, e-commerce and interactive network television service is supported, marketing copywriting, short videos and cross-platform distribution schemes can be efficiently generated, and the market competitiveness is improved. And the content production efficiency and the creativity transmission are obviously improved.

The invention discloses an intelligent psychological intervention system based on multi-modal fusion, which is characterized in that a three-dimensional evaluation system is constructed by integrating speech sentiment analysis, keyboard dynamics monitoring and physiological signal acquisition, and time sequence alignment and feature weighted fusion of multi-source data are realized by adopting a cross-modal Transform model. The core of the system comprises an adaptive intervention engine which defines a multi-dimensional state space based on a hierarchical reinforcement learning architecture, optimizes an intervention strategy through a PPO algorithm, and realizes dynamic emotion interaction in AR and VR scenes in combination with a digital twin training module; according to the clinical decision support system, physiological behavior characteristics and psychological assessment trends are integrated by using a multi-time scale risk prediction model, and a personalized early warning threshold system is constructed, so that the psychological state recognition accuracy is improved, the intervention intensity self-adaptive adjustment response time is shortened, and the high-risk signal early warning timeliness reaches the minute level; and the problems of evaluation hysteresis and strategy stiffness of traditional psychological intervention are obviously improved.

The invention discloses a large-model complaint intention recognition method based on sentiment analysis, and the method comprises the steps: obtaining call voice data of a customer, and converting the call voice data into text data; preprocessing the text data, and removing noise and marking components; performing emotion feature analysis on the text data, extracting an emotion feature value, and generating an emotion vector; the emotion vectors and the text data are input into a large model together, and the large model combines the emotion feature values and context semantics to generate intention feature vectors; constructing an emotion-intention state vector, inputting the emotion-intention state vector into an asynchronous dominant actor commentator algorithm model, and generating a corresponding complaint intention probability value; judging a complaint intention probability, and generating risk early warning; and preferentially distributing high-risk customers and responding to customer demands. Through combination of voice data preprocessing, text semantic feature extraction, emotion intensity quantitative analysis and a multi-modal fusion algorithm and efficient risk assessment based on an asynchronous dominant actor reviewer model, the early warning and response capabilities of customer complaint risks are significantly improved.

The invention discloses a scene-based emotional interactive accompanying doll system and method, and relates to the technical field of artificial intelligence, and the system comprises a data collection module, a feature analysis module, a portrait construction module, an interactive decision module, an execution control module and a database module. The data acquisition module is used for acquiring interaction data and scene data; the feature analysis module generates an emotion feature vector and a scene feature vector by using a multi-modal feature network, and determines a user emotion state and a scene type; the portrait construction module is used for constructing a user portrait; the interaction decision module comprises an emotion evolution unit, a scene interaction unit and a physiological regulation unit and is used for generating an execution instruction sequence; the execution control module is used for controlling the doll to complete emotion interaction behaviors. Through the scene perception and sentiment analysis technology, accurate recognition and personalized interaction response of the doll to the user sentiment state are achieved, and intelligent sentiment accompanying service is provided.

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The invention discloses an urban safety risk assessment method and system based on big data, and relates to the technical field of big data, and the method comprises the steps: constructing a multi-source data collection network, and obtaining data from a government department database, Internet of Things equipment, a social media platform and a traffic monitoring system in real time; preprocessing the collected multi-source data, wherein the preprocessing comprises data cleaning, format standardization, unstructured data semantic analysis and sentiment analysis; a cross-department data security sharing mechanism is established, and the traceability and security of data exchange are ensured through a block chain technology; constructing a dynamic risk assessment model, analyzing multi-source data relevance based on a deep learning algorithm, and dynamically adjusting the weight of each risk factor; and generating a visual risk assessment report, and pushing the visual risk assessment report to related departments in real time through an early warning system. According to the invention, through multi-technology fusion and a dynamic optimization mechanism, the accuracy, real-time performance and cooperation efficiency of urban safety risk assessment are significantly improved.

A computer-implemented method includes receiving patient responses over a network from a recorded screening interview, utilizing a custom Large Language Model (LLM) to generate transcriptions and insights from the audio, performing video sentiment analysis to assess emotional states, and based on these data, generating an AI model to predict risk levels for various mental health conditions. The method further comprises presenting personalized questions based on previous screening insights, creating longitudinal summaries of patient histories, continuously improving the AI models through reinforcement learning, and integrating a recommendation engine to suggest targeted interventions. Additionally, the method includes tracking outcomes over time and enabling causal inference across multiple screenings, thereby providing a comprehensive platform for mental health assessment, intervention, and outcomes tracking.

The invention provides a text sentiment analysis method based on dynamic semantic segmentation and feature perception, and belongs to the field of natural language processing. Inputting the semantic vector sequence into a knowledge retrieval and dynamic graph construction model for multi-path context enhancement through the knowledge retrieval and dynamic graph construction model to obtain semantic features, semantic knowledge and graph structure information; the semantic vector sequence is subjected to multi-path context enhancement, the complex relation between different parts in the text can be comprehensively considered, and more comprehensive and deep semantic features and knowledge can be mined. Unified representation of semantic features is combined with heterogeneous graph features, an antagonism training strategy is adopted to train a knowledge retrieval and dynamic graph construction model, an improved ATOSS + module is introduced to carry out hierarchical attention fusion, and multi-granularity semantic enhancement features are obtained; therefore, emotion clues and semantic association hidden in the text can be captured, and the accuracy and integrity of semantic understanding of the text are improved.

The invention discloses an emergency material intelligent scheduling system and method based on an ant colony algorithm. A demand prediction module of the emergency material intelligent scheduling system dynamically predicts material demands by using a time-space diagram neural network, and constructs a hierarchical network model integrating multi-modal transportation of unmanned aerial vehicles, ground vehicles and the like. And the optimization calculation module adopts an improved ant colony algorithm, introduces a demand urgency degree weight factor and a green weight factor to construct a multi-objective fitness function, and optimizes a transportation path in combination with a dynamic pheromone updating mechanism and a multi-ant colony collaborative strategy. The system is equipped with an edge computing driven dynamic adjustment module to realize 30-second fast path re-planning, a psychological assistance priority model is innovatively integrated, and a psychological crisis index optimization scheduling strategy is extracted through sentiment analysis. According to the method, the problems of response lag and insufficient multi-objective optimization of a traditional scheduling system are effectively solved, the transportation efficiency is remarkably improved by 25%-35%, carbon emission is reduced, and high efficiency, fairness and humanity care of emergency scheduling are guaranteed.

The invention, which relates to the technical field of customer relationship management, discloses a multi-channel interactive customer relationship management system comprising a dynamic routing decision module, a multi-modal data fusion module and an intelligent feedback optimization module. The dynamic routing decision module evaluates channel load through a deep neural network, dynamically allocates client requests to an optimal node by utilizing reinforcement learning, and realizes load balancing and service continuity; the multi-modal data fusion module integrates text, voice and image data, constructs a space-time correlation graph, identifies a cross-channel behavior mode, and ensures data consistency through multi-dimensional verification; the intelligent feedback optimization module combines customer satisfaction evaluation and multi-modal sentiment analysis, optimizes a service strategy by using a genetic algorithm, and synchronizes the service strategy to a cross-channel knowledge graph to realize adaptive iteration; according to the method, the problems of unreasonable multi-channel load distribution, insufficient data fusion and consistency verification and inaccurate service strategy optimization are effectively solved, and the customer service quality and experience are improved.

The invention provides a personalized English education system and method based on multi-modal sentiment analysis. The system comprises a multi-modal interaction module for receiving and processing multi-modal data of students, an emotion recognition module for performing emotion analysis on the input multi-modal data, and a personalized module for evaluating real-time learning states of the students based on historical learning data of the students, and the core brain module is used for dynamically adjusting interactive feedback according to the emotional state and the real-time learning state. The emotion recognition module comprises emotion information fusion, the emotion information fusion adopts a weighting strategy, and the final output emotion state is adjusted through emotion consistency constraint and a conflict correction mechanism. According to the invention, through an emotion consistency loss function, a conflict correction mechanism and a knowledge graph-based super-outline control mechanism, the emotion and cognitive states of the students are accurately identified.

The invention discloses a cross-cultural customer service dialogue quality automatic evaluation method in combination with sentiment analysis, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the alignment of voice and text based on a transmission matrix in real time, extracting a speech, a metaphor and polarity, and generating a speech tag; constructing an emotion channel and a polite channel, and fusing expression and shielding intensity through sharing attention; comparing and aligning with the same language prototype in a regional culture baseline library to obtain a calibration representation and updating a language offset record table; the potential upgrading probability is represented and recurred according to round aggregation calibration, and a risk vector and a high-risk position are formed; fusing risk and business indexes by a capacity integral kernel, outputting a comprehensive quality score, and giving factors and round attributions; sample recovery is triggered according to score and feedback difference, a micro-weight training data set is constructed, gradient increment training is carried out under low-rank adaptation, and cross-language consistency, early recognition of upgrading risks and interpretable evaluation are achieved through a closed loop.

The invention relates to the technical field of artificial intelligence, in particular to a real-time anti-fraud monitoring system and method based on behavior reasoning and sentiment analysis, and the system comprises a multi-modal data collection unit, an edge preprocessing unit, a feature fusion and behavior reasoning unit, a large language model context reasoning unit, a risk assessment and decision unit, and an intervention execution unit. A log recording and federal incremental learning unit; the method has the beneficial effects that the traditional isolated single-mode detection is evolved into an emotion and behavior dual-channel collaborative multi-mode recognition system through millisecond-level coaxial alignment of voice, video and user operation logs; the robustness of dialect, noise and expression shielding is greatly improved through the multi-modal fusion model, so that the cross-scene recognition accuracy is improved by nearly three percent compared with that of a traditional single-voice scheme, and high-sensitivity capture of hidden and emotion control type fraud is truly achieved.

The invention relates to the technical field of user interaction, in particular to a live broadcast bullet screen real-time feedback method and system based on interaction semantic matching, and the method comprises the steps: receiving and caching user bullet screens in real time, carrying out the density analysis and priority evaluation of bullet screen flows through the combination of filtering and resource optimization rules, dynamically adjusting the response frequency, and screening key bullet screens; a distillation processing mechanism intention unit is introduced, a user intention label in a key bullet screen is extracted, a vector is generated, historical behavior data of a sending user is obtained at the same time, the intention label, the vector and current real-time scene data of a live broadcast room are fused, and a bullet screen vector is constructed; performing similarity calculation in combination with the live broadcast content and user historical behaviors to obtain first feedback content; based on a reinforcement learning sentiment analysis strategy, combining user preference memory to generate second feedback content; and embedding the second feedback content into the current live broadcast interface through the client. According to the invention, real-time forward feedback and intelligent guidance of the live broadcast bullet screen are realized through interactive semantic matching.

The invention provides a dynamic self-adaptive multi-modal sentiment analysis fusion method and system, and relates to the technical field of multi-modal sentiment analysis. The method comprises the following steps: synchronously acquiring voice, text, facial expression and limb movement data of a target user to form a multi-modal data set; the method comprises the following steps: firstly, extracting emotional characteristics of each mode, and constructing a cross-mode correlation model to capture a collaborative and complementary relationship among different modes; and calculating a real-time confidence score and a complementarity index of each modal based on the weight matrix of the cross-modal correlation model. Then, according to the scores and the indexes, a weighted average or maximum entropy algorithm is dynamically selected to fuse multi-modal emotion features, and a comprehensive emotion feature vector is generated; and finally, inputting the vector into a pre-training deep learning model, and outputting an emotional state category of the user. According to the method, the user emotion can be accurately and comprehensively captured, efficient emotion recognition and classification are realized, and the robustness and flexibility of an emotion analysis system in a complex scene are improved.

The invention relates to a multi-modal sentiment analysis method based on deep decoupling and cross-modal semantic alignment. The method comprises the following steps: carrying out preprocessing and feature extraction on input video data; the three modes are decoupled through a feature decoupling mechanism based on the HSIC criterion; performing cross-modal alignment on the three modal semantics; hierarchically predicting emotion categories; and finally outputting a prediction result. According to the method, by decoupling features of three modes of text, audio and vision, unique attributes of each mode are separated from shared emotional semantics, and the problem of redundancy and conflict information between modes in an existing multi-mode emotional analysis method is solved. On the basis, a cross-modal alignment mechanism based on text guidance is provided, shared features are aligned through a cross-modal attention mechanism, and semantic consistency among different modals is enhanced. Meanwhile, the decoupled unique features and the aligned shared features are effectively fused through a hierarchical fusion strategy, and the accuracy of emotion prediction is improved.

The invention relates to the technical field of customer service quality inspection, in particular to an online customer service intelligent quality inspection system and method based on artificial intelligence. And automatically extracting a user demand keyword, an emotion expression keyword and a potential violation term keyword, and generating a structured keyword sequence. Performing emotion analysis on each dialogue round through a Transform model, and accurately outputting a customer emotion classification and an intensity score; and semantic correlation of the context is carried out through a neural network model. And automatically identifying the content of each round of dialogue and counting illegal verbal skills. Furthermore, the customer emotion value, the context coherence score and the occurrence frequency of violation verbal skills are input into a quality inspection scoring model, a comprehensive quality inspection score is automatically calculated, and whether the service is qualified or not is judged according to the comprehensive quality inspection score, so that the dynamic evaluation of the service quality is realized, the quality inspection efficiency is improved, and the customer experience is truly reflected.

The invention relates to the technical field of customer services, in particular to an intelligent outbound call skill generation method, system and device using LLM and a medium, and the method comprises the steps: collecting user voice data and text dialogue data in real time, and converting the user voice data and the text dialogue data into a time sequence aligned text sequence matrix based on the user voice data and the text dialogue data; inputting the time sequence aligned text sequence matrix into a preset multi-mode sentiment analysis model, and calculating a user sentiment tendency value; extracting an intention keyword based on the text sequence matrix with the aligned time sequence, inputting the intention keyword into a preset intention recognition model, and outputting a purchase intention probability of the user; and according to the user emotion tendency value and the user purchase intention probability, calling LLM to generate a personalized outbound call, and outputting the personalized outbound call to a dialogue system. The method has the advantages that high-quality and dynamic-adaptability customer service is achieved, and user experience is remarkably improved.

The invention discloses a sentiment analysis method based on prototype guide mode fusion and prompt enhancement, and constructs a multi-mode sentiment analysis network which comprises a multi-mode coding module, a prototype guide mode fusion module, a dynamic mode weight adjustment mechanism and a context prompt generation module. The method comprises the following steps: firstly, extracting semantic features of each mode by using a multi-mode encoder, and constructing a prototype feature library based on a labeled sample to describe typical representations of different modes under each category; and then, dynamically evaluating modal contribution through prototype similarity to realize modal adaptive fusion. Furthermore, a context prompt is generated according to a similarity retrieval result of the input sample and the prototype library, and the pre-training language model is guided to complete sentiment classification. According to the method, the problems of modal inconsistency, information redundancy, weak small sample generalization and the like can be effectively relieved, and the accuracy and robustness of sentiment analysis are improved.

The invention provides a text user sentiment analysis method and system based on multiple modes and AI, and relates to the technical field of text analysis, and the method comprises the steps: obtaining and preprocessing text, image and audio information of a user, and extracting semantic, visual and acoustic feature vectors; multi-modal features are fused through a cross-modal attention mechanism; utilizing a graph neural network to construct a user emotion social graph to calculate emotion propagation intensity; and finally, obtaining an analysis result containing emotion category and intensity through an emotion classifier. The emotion state of the user can be comprehensively captured, the emotion analysis accuracy is improved, and complex emotion expression is effectively recognized.

The invention discloses an aircraft cabin environment personalized adjustment method based on sentiment analysis, and the method comprises the steps: collecting the facial expression, voice waveform and environment parameters of a passenger in real time through a cabin multi-source sensor, and generating a standardized physiological signal matrix and anonymized voice features through noise reduction and feature extraction; inputting the physiological signal matrix and the voice features into a pre-trained deep learning model, outputting an emotion index and a classification label, and updating model parameters through a federal learning framework; dynamically generating temperature, humidity and oxygen concentration adjusting instructions and dynamic weights by adopting a fuzzy reasoning system in combination with the emotion indexes and passenger preset preferences; environment adjustment is executed through a closed-loop control system, and environment parameter errors are fed back; synchronously updating a fuzzy inference system rule base and deep learning model parameters by utilizing reinforcement learning in combination with environmental parameter errors and emotion index changes, and completing optimization of a closed loop; according to the invention, real-time dynamic regulation and control and continuous adaptation optimization of the personalized cabin environment can be realized.

The invention discloses a self-adaptive teaching strategy adjustment method based on sentiment analysis and a computer device. The method comprises the following steps: obtaining a target emotion feature data stream according to multi-modal student emotion data, and then extracting visual deep features, audio deep features and physiological deep features from the target emotion feature data stream; processing the visual attention weight, the audio attention weight, the physiological attention weight, the visual deep feature, the audio deep feature and the physiological deep feature according to a preset fusion strategy to obtain a multi-dimensional fusion feature, and analyzing the feature to obtain basic emotion recognition information of the individual student; analyzing the basic emotion recognition information through a built personalized emotion model of each student individual to obtain corresponding emotion state evaluation information; and on the basis of the constructed teaching knowledge graph and the emotional state evaluation information, generating a personalized teaching adjustment strategy so as to dynamically adjust an actual teaching scheme. According to the method, dynamic adjustment can be realized, and personalized teaching requirements are met.

The invention relates to the technical field of data retrieval, in particular to an information technology retrieval consultation system and method. The method comprises the following steps: acquiring user input data; extracting modal key information of the user input data to obtain multi-modal key semantic information data; performing data preprocessing on the multi-modal key semantic information data to generate standard user input data; performing historical retrieval information retrieval on the standard user input data, and when a historical retrieval record is retrieved, performing accurate portrait construction on the standard user input data to generate a user accurate retrieval portrait; and when no historical record is retrieved, performing retrieval reasoning on the standard user input data to generate a user fuzzy retrieval portrait. By combining multi-modal information extraction, accurate and fuzzy portrait construction, cross-context inheritance, dynamic knowledge fusion and sentiment analysis optimization, the problems of low retrieval precision, incomplete information coverage and poor user experience in the prior art are solved.

The invention discloses a text travel sentiment analysis response method and system based on multi-modal fusion and incremental learning, and the method comprises the steps: obtaining a text, an image, an audio or a video input by a user, carrying out the query in a text and multi-modal knowledge base through employing a multi-modal retrieval technology, and optimizing a retrieval result through combining sentiment analysis, thereby achieving the purpose of improving the user experience. And finally generating a personalized tourism information response. The system integrates a large language model, text and multi-modal knowledge base construction, and a data vectorization processing and sentiment analysis technology, supports multi-modal input and output, can dynamically adjust retrieval and answer contents, and realizes personalized recommendation according to user feedback. The system also has the capabilities of multi-modal data expansion, dynamic knowledge base updating and voice interaction, can significantly improve the response speed and accuracy of tourism information service, is widely applicable to intelligent and personalized tourism information service scenes, and has relatively high innovativeness and practical value.

The invention discloses a prompt-driven mode missing-oriented multi-mode sentiment analysis method and system, and relates to the technical field of sentiment analysis, and the method comprises the steps: inputting multi-mode data containing a missing mode into a multi-mode prompt learning model for analysis, and obtaining a sentiment prediction result; the multi-modal prompt learning model comprises a missing modal generator, a dynamic prompt weight module and a modal prior guide fusion module; the dynamic prompt weight module adaptively generates a prompt weight by extracting global context information from an available mode, dynamically adjusts prompt intensity and an action range, and realizes accurate local compensation of missing mode features; the modal prior guidance fusion module carries out explicit modeling on a modal missing type, actively fuses prior correlation knowledge between modals, accurately guides a fusion network to realize deep complementation and global optimization of cross-modal features, and significantly improves the robustness and generalization ability of the model in various modal missing scenes.

The invention discloses an oral diagnosis and treatment patient service platform based on reinforcement learning, and relates to the technical field of diagnosis and treatment services, and the platform comprises a health scoring module which integrates multi-source data, generates personalized oral health scores through a dynamic weight distribution model, and displays high-risk items in combination with 3D visualization; the patient management and marketing module tracks patient behaviors, clusters and classifies the patient behaviors, generates a dynamic follow-up visit path by using multi-target reinforcement learning, and matches a precise marketing verbal skill through a knowledge graph; the in-diagnosis auxiliary module synchronizes health data of the patient to the doctor terminal in real time, detects prescription conflicts and performs early warning, and generates personalized communication verbal skills in combination with historical preferences of the patient and real-time emotion analysis; through multi-dimensional data integration and intelligent processing, the whole oral diagnosis and treatment process is optimized, the diagnosis and treatment efficiency and the patient satisfaction degree are improved, the operation cost is reduced, and remarkable innovativeness and practicability are achieved.

The invention relates to the technical field of public transportation systems, and particularly discloses a bus departure scheduling method, which comprises the following steps of S1, integrating multi-dimensional data; s2, a dynamic prediction model containing machine learning parameters is adopted to calculate the passenger demand in the future period; s3, calculating the number of required vehicles according to the predicted demand, the vehicle capacity and the dynamic load coefficient; s4, constructing a multi-objective function including energy consumption optimization, and solving the optimal departure interval and route; and S5, according to the real-time data, correcting a scheduling scheme, collecting real-time feedback data through a passenger mobile application, analyzing the emotion and demand of the passenger by using a natural language processing technology, based on feedback intention recognition of an emotion analysis model, constructing a passenger demand knowledge base in combination with historical complaint data, and optimizing a dynamic prediction model and a scheduling strategy. Through technology integration and system innovation, the static and single bottleneck of traditional scheduling is broken through, and an intelligent solution considering efficiency, low carbon and user experience is provided for urban buses.

The invention relates to the technical field of artificial intelligence customer service, provides an intelligent customer life cycle management AiCRM method and system, and is used for solving the problems of low customer service accuracy and poor response timeliness in the prior art. The method comprises the steps that in the interaction process of a target client and an enterprise, client voice, expression and text data are acquired, multi-modal sentiment analysis is carried out, and a client sentiment feature set is generated; dynamically updating a customer emotion evolution graph based on the customer emotion feature set, and generating an accurate customer emotion score by adopting an emotion calculation model in combination with historical data and real-time interaction information; and when the score is lower than a preset baseline, the system automatically generates a personalized service strategy adjustment scheme matched with the current emotional state and the historical change trend of the customer. According to the application, the customer service accuracy and the response timeliness are improved.

The invention relates to the field of natural language processing technology, computer vision and multi-modal sentiment analysis, in particular to a multi-modal aspect-level sentiment analysis method based on multi-scale text visual feature enhancement, which comprises the following steps of: 1, acquiring a multi-modal aspect-level sentiment data set; 2, inputting a pre-training language model to obtain global context features; 3, combining target emotion semantic fusion to obtain aspect-related visual emotion features; 4, obtaining a dependency relationship among text information words, and obtaining syntactic enhanced text features; 5, fusing the global context feature, the syntactic enhanced text feature and the aspect-related visual emotion feature to obtain a multi-modal fusion feature; the method has the following beneficial effects that the feature information is expanded, so that effective interaction of text images is realized, noise generated by text and picture data is reduced, and the quality of multi-modal fusion features is ensured.