995 results about "Satisfaction level" patented technology

The invention discloses a material identification method based on a multi-modal large model, and particularly relates to the technical field of data processing. The method comprises the following steps: inputting image data and associated text data of advertisement materials into a multi-modal large model, and carrying out preliminary preprocessing and feature extraction; respectively extracting visual features and semantic features of the image and the text by using a convolutional neural network and a natural language processing technology, and optimizing a fusion effect of the image and text features through a multi-modal contrast learning strategy; the feature alignment condition is verified by comparing a loss function, and the recognition precision of the model is improved by combining a double-path fine adjustment mechanism and multi-label classification supervision; finally, an improved cue word mechanism is adopted to carry out accurate classification on the advertisement materials, a putting engine and a feedback mechanism are combined, the auditing effect of the advertisement materials is monitored and optimized in real time, the accuracy of advertisement compliance management is remarkably improved, auditing errors are reduced, and the customer satisfaction degree is improved.

The invention discloses a motor vehicle maintenance management service system, and relates to the technical field of motor vehicle maintenance management, and the system comprises a client information management module which builds data for storing client multi-information, supports multi-format import and export, intelligently recognizes and updates, and guarantees the information to be timely and accurate; the vehicle fault diagnosis module is provided with various interfaces and expert systems, analyzes and diagnoses faults based on big data, and gives reasons, suggestions and cost estimation; the maintenance work order management module supports work order full-process operation, automatically distributes personnel and updates the progress in real time for clients to inquire; the maintenance accessory management module is provided with an inventory database and can intelligently match accessories and provide position query navigation; and the maintenance personnel management module is used for storing personnel information and evaluating performance. According to the invention, management efficiency and accuracy can be improved, accessory management and cost are optimized, personnel quality and service quality are improved, customer experience and satisfaction are enhanced, powerful support can be provided for enterprise decision making, and intelligentization of the motor vehicle maintenance industry is promoted.

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 relates to a bank marketing model construction method and system based on machine learning, and the method comprises the following steps: constructing a customer multi-dimensional feature engineering system which is used for integrating customer multi-dimensional features, and extracting customer behavior period features through employing a time sequence feature coding technology; meanwhile, a graph neural network is adopted to construct customer social influence features; constructing a dynamic customer value analysis model integrated with XGBoost and establishing a dynamic attenuation function of customer life cycle value; establishing a marketing response prediction rate model of a hybrid model architecture combining LightGBM and Transform, and embedding an adversarial training sample generation mechanism; a marketing strategy generation model is constructed, the marketing strategy generation model is based on a multi-objective optimization function considering the marketing response rate, the marketing cost and the customer satisfaction, and a marketing strategy is generated through a marketing path planning algorithm of Monte Carlo tree search and the multi-objective optimization function.

The invention provides an AR intelligent shopping guide interaction method and system applied to store management, and the method comprises the steps: firstly obtaining an interaction data set of a user in a store, covering a user position movement track, a commodity attention action sequence and voice consultation content, carrying out the user intention analysis of the interaction data set, and carrying out the user intention analysis of the interaction data set; the method comprises the steps of obtaining commodity demand characteristics and interaction preference characteristics, generating AR shopping guide interaction content, including commodity three-dimensional display information and an interaction guide instruction, adaptive to a user based on the commodity demand characteristics and the interaction preference characteristics, aligning and fusing the AR shopping guide interaction content with a store physical scene space, generating an AR shopping guide interaction scene capable of being displayed in real time, and displaying the AR shopping guide interaction scene in real time. And finally, responding to user interaction operation in the AR shopping guide interaction scene, generating feedback information and updating the interaction data set to trigger interactive process iterative optimization, thereby providing personalized and immersive shopping guide experience, and improving store operation efficiency and customer satisfaction.

The invention discloses an intelligent customer service interaction method and system, and relates to the technical field of intelligent customer service interaction, and the method comprises the steps: obtaining a user request; calculating a comprehensive emotion score through multi-modal emotion analysis based on a user request; according to the comprehensive emotion score, classifying the user emotion state, according to a classification result, initializing a dialogue context, and constructing a dialogue context graph; dynamically adjusting the response mode of the intelligent customer service according to the emotional state and the user question in the dialogue context graph, and collecting the feedback information of the user; and dynamically adjusting the dialogue strategy based on the feedback information of the user and the dialogue context. According to the method, the emotion state of the user can be comprehensively captured from multiple aspects such as texts, voices and facial expressions through multi-modal emotion analysis, so that the accuracy and robustness of emotion recognition are improved, a reliable basis is provided for subsequent dialogue management and response modes, and the experience satisfaction degree and the service quality of the user are improved.

The invention discloses a digital intelligent service system for enterprise services, which belongs to the field of enterprise digital services and comprises a user demand analysis module, an intelligent data adaptation module, a service resource matching module, a service efficiency evaluation module and a user-friendly interface. The user demand analysis module is used for semantic analysis and classification decision making; the intelligent data adaptation module identifies and analyzes the protocol type, and establishes an intelligent data transmission channel; the service resource matching module carries out resource pre-screening and accurate matching; the service efficiency evaluation module tracks an identifier and captures a four-dimensional index system in real time, and the dynamic evaluation unit realizes service state grading early warning through a sliding window algorithm; according to the invention, accurate demand identification is realized, the response speed and the data transmission efficiency are improved, the user satisfaction is improved, the service matching accuracy is enhanced, the resource utilization efficiency is optimized, the service capability and the customer experience are improved, and maximization of economic benefits and social values is realized.

The invention discloses a credit marketing intelligent recommendation system. According to the system, for the existing credit marketing problem, a customer portrait is constructed through multi-channel data acquisition, cleaning and preprocessing, and personalized credit product recommendation is carried out based on content and collaborative filtering. And training a recommendation model by using a neural network model and a reinforcement learning algorithm, updating recommendation in real time or at regular intervals according to real-time conditions and feedback of customers, establishing a system evaluation effect including recommendation accuracy, customer satisfaction and business indexes, and optimizing model parameters, improving the algorithm and updating data according to evaluation. The method can improve the customer satisfaction and the business conversion rate, reduces the risk, adapts to the market change, and effectively solves the defects of the existing credit marketing channel and strategy.

The present invention relates to the field of data analysis technology, and specifically to a customer relationship management data analysis method and system based on artificial intelligence, comprising the following steps: step one, collecting customer data; step two, storing the data; step three, analyzing and mining the data; step four, building a customer portrait; step five, providing intelligent decision support for customer relationship management based on the customer portrait and data analysis results; the customer relationship management data analysis method and system based on artificial intelligence of the present invention can significantly improve the customer relationship management level of an enterprise. Through artificial intelligence deep learning, the system can automatically analyze a customer's massive conversation records, social media interactions, and transaction data, quickly identify customer needs and preferences, and then generate personalized customer portraits and behavior analysis reports, to help enterprises accurately locate potential customer groups, optimize service strategies and marketing plans, and thus improve customer satisfaction and loyalty.

The invention relates to the technical field of supply chain management, and discloses a commodity supply chain management method based on the Internet of Things. According to the commodity supply chain management method based on the Internet of Things, errors caused by manual data entry are avoided through time sequence analysis and data filtering processing of the cloud analysis management platform, enterprises can accurately predict future market demands, and the inventory turnover rate, the optimal inventory level and the transportation and storage cost are dynamically calculated, so that the inventory management is optimized, and the enterprise management efficiency is improved. In addition, according to the method, by comprehensively evaluating the delivery timeliness rate of suppliers and the quality defect rate of goods, it is ensured that enterprises can select reliable suppliers, the stability and efficiency of a supply chain are improved, the real-time logistics monitoring and dispatching capacity enables goods to be quickly and efficiently delivered to clients, and the efficiency is improved. The customer satisfaction is comprehensively improved, and the competitiveness and market adaptability of enterprises are promoted.

The invention relates to the technical field of logistics order delivery management, in particular to a cross-border e-commerce logistics order management system based on big data, which comprises a data acquisition processing unit, an order clustering delivery unit and a delivery execution feedback unit, the data acquisition and processing unit collects orders, geographic information and real-time traffic data from a cross-border e-commerce platform and performs cleaning preprocessing, repeated orders are removed by using a Hash algorithm, the order clustering and delivery unit clusters the orders by using a density peak clustering algorithm, and the clustering effect is optimized by combining the order emergency degree and the customer loyalty, so that the service quality of the cross-border e-commerce platform is improved. A genetic algorithm is adopted to optimize the delivery sequence, the crossover and mutation probability is dynamically adjusted, a fitness function is calculated by considering the delivery distance, time cost and regional limitation penalty factors, a delivery execution feedback unit sends an optimized route to a delivery vehicle, client feedback evaluation data is collected, a delivery strategy is dynamically adjusted by means of an upper confidence bound algorithm, and the delivery efficiency is improved. And the delivery efficiency and the customer satisfaction are improved.

The invention discloses a purchase management decision support system driven by an AI large model, and particularly relates to the technical field of intelligent purchase management. A data acquisition module obtains historical and current purchase, market price, inventory, sales and customer feedback data of commodities; the clustering analysis module divides purchasing groups based on inventory states and market prices; the self-portrait construction module generates a comprehensive self-portrait through client feedback data; the market analysis module analyzes sales data and predicts future demand changes; the optimization adjustment module optimizes the purchase quantity in combination with demand prediction, customer preferences and purchase group data, so that the accuracy and efficiency of purchase decision making are improved; through intelligent data processing and model prediction, the limitation of dependence on experience and static rules in traditional purchase management is avoided, fluctuation of market demands can be responded in real time, the purchase quantity is ensured to be matched with the market demands, inventory overstock and shortage phenomena are avoided, and customer satisfaction and market competitiveness are improved.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to an intelligent customer service method and system based on a large model, and the method comprises the steps: generating a unique session ID based on a user identity, and associating a historical context; multi-modal input of texts, voices, images and the like is carried out, and data standardization is carried out through a unified interface; realizing mixed intention recognition by combining a rule engine and a large model; constructing a dynamic customer portrait through multiple rounds of dialogue records, historical work orders and business data; according to the question emergency degree and customer value grading response, analyzing customer emotion in real time, adjusting a reply style, and outputting an answer; the manual operation process of the traditional customer service is converted into an AI automatic closed loop; the method has the beneficial effects that customer intention deep understanding, multi-mode interaction, emotion perception and service process automation are realized through a large language model (LLM) technology, and the service efficiency and the user satisfaction are remarkably 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 artificial intelligence, in particular to an AI digital human-based customer service method and system, and the method comprises the steps: receiving a voice stream, a visual stream and auxiliary sensor data inputted by a user, achieving the time-space alignment of multi-modal data through a cross-modal feature synchronization engine, and fusing the multi-modal data into a unified fusion data packet; based on the fused data packet, extracting an emotion intensity value and an emotion type code in real time, generating a dynamic emotion label, and extracting service demand characteristics to generate a demand vector; inputting the dynamic emotion label and the demand vector into a strategy decision tree, and generating a strategy identifier containing an emotion pacifying coefficient and a response emergency degree; the AI digital human is driven to generate the multi-modal response based on the strategy identifier, deep perception and flexible response to the user state can be achieved, the interaction naturalness and the user satisfaction of a digital human service system are effectively improved, and the method is suitable for multiple intelligent service scenes such as online customer service and intelligent terminal assistants.

The invention discloses a service scheduling system based on artificial intelligence, and belongs to the technical field of service scheduling. Comprising a data acquisition layer, an artificial intelligence engine layer, a scheduling execution layer, a monitoring management layer and a function module. The data acquisition layer acquires and preprocesses data related to insurance services; the artificial intelligence engine layer integrates natural language processing and machine learning technologies, and identifies and analyzes customer demands; the scheduling execution layer realizes access of customer service staff through a scheduling algorithm according to customer requirements and customer service staff information; and the monitoring management layer monitors and optimizes the service scheduling process. According to the invention, customer demands are intelligently analyzed, appropriate professional service personnel are accurately matched, and zero-delay docking between demands and services is realized; service personnel are intelligently dispatched, the waiting time of clients is greatly shortened, and the satisfaction degree is improved; tasks are deployed according to the insurance business rhythm, the potential of service personnel is mined, idleness is reduced, and the overall service capacity is improved.

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 an unmanned aerial vehicle take-off and landing site selection method for urban end logistics, and relates to the technical field of facility site selection, and the method comprises the following steps: S1, obtaining a building contour of a region to be subjected to site selection, and constructing a customer buffer region according to the building contour; s2, based on the customer buffer area, performing noise analysis and safety analysis on the unmanned aerial vehicle, and constructing an overall restricted area; and S3, based on the overall restricted area, constructing a target function taking the lowest cost as an optimization target, a target function taking the highest satisfaction as an optimization target and a plurality of constraint conditions, and obtaining a site selection layout result. According to the method, the applicability of the grid map method in the complex urban environment is verified, and a new thought is provided for the layout of the urban logistics unmanned aerial vehicle take-off and landing points.

The invention discloses a workshop task allocation method and system based on multiple dimensions, and relates to the technical field of production control management. Comprising the steps of obtaining order data and then performing preprocessing; based on the preprocessed order data, an advanced machine learning algorithm is adopted to construct a decision tree model, and the processing complexity of each order is identified; determining a task allocation scheme by using an optimization algorithm according to the identified order processing complexity and the current state of the production line; monitoring the actual operation condition of each production line in real time, starting a dynamic adjustment mechanism when the work load of a certain production line is unbalanced, and re-evaluating and adjusting task distribution; through intelligent task allocation and resource optimization, the production efficiency and customer satisfaction are improved, and meanwhile, dynamic adjustment and intelligent management of the production process are realized.

The invention discloses an intelligent customer service question and answer optimization method and system based on sentiment analysis, and relates to the technical field of language data processing, and the specific steps are as follows: reading voice data, and carrying out sentiment analysis and feature extraction on the voice data; judging the priority level of the voice data based on the extracted features; performing demand prediction on the voice data in sequence based on the priority level; and generating personalized reply content according to the extracted features, the prediction demand and the historical interaction record. By introducing emotion recognition, the emotion change of the user can be accurately captured, the reply tone and content are adjusted accordingly, more intimate service is provided, the negative emotion of the user can be relieved, and the trust feeling and satisfaction degree of the user to a power enterprise are increased.

The invention belongs to the technical field of intelligent manufacturing, and discloses a production order automatic scheduling correction method and system, and the method comprises the steps: obtaining a product material list; determining a product process route according to the product type and the process comparison result; marking resource configuration of a process route and a production scheduling state of the product; creating a production order for the products of which the production scheduling states are to be scheduled, and forming a production order level material list according to order information of each level and a product material list; according to the production requirements of the products, the production scheduling logic of the products is selected from the shortest construction period logic, the earliest delivery time logic and the emergency order insertion logic, and production scheduling of the production work orders is carried out; when the production work order is arranged, generating a processing equipment Gantt chart based on the processing information of the processing equipment and the production work order information; and correcting the production scheduling plan of the production order by using the automatic correction logic in the Gantt chart of the processing equipment. The automatic production scheduling efficiency of production is improved, and the customer satisfaction is reduced and improved.

The invention provides a call center dialogue sentiment analysis method and system fusing voice and text, and relates to the technical field of voice signal processing, and the method comprises the steps: obtaining a voice signal of a call center and a corresponding transliteration text, extracting intonation, speed, sound intensity and pause sentiment features from the voice signal, meanwhile, deep language analysis is carried out on the transliterated text, and semantic emotion features related to context are extracted; and performing cross-modal correlation analysis on the voice emotion features and the semantic emotion features, and generating a time sequence correction coefficient for feature alignment by constructing a corresponding relation analysis framework between feature sequences. According to the method, language emotion information in voice and text is integrated, more accurate and comprehensive recognition of conversation emotion of the call center is realized, customer satisfaction is improved, and reliable emotion analysis basis is provided for efficiently processing customer appeals.

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses a service strategy adjustment method based on emotion recognition, which comprises the following steps: acquiring voice data in a service session, performing segmentation processing, extracting feature data, inputting the feature data into an emotion classification model to recognize emotion tags, and generating an emotion tag sequence; and further generating an emotional fluctuation graph, analyzing a service anomaly probability, generating a risk early warning signal when the service anomaly probability reaches a preset threshold, and adjusting a dialogue strategy of the service session based on the risk early warning signal. The emotion label is identified through the emotion classification model, and the service abnormity probability is analyzed in combination with the emotion fluctuation graph, so that the risk early warning signal is generated when the service abnormity probability reaches the preset threshold value, the service strategy is adjusted in time, real-time monitoring and service dynamic adjustment of the customer emotion state are realized, the customer satisfaction can be effectively improved, and the customer experience is improved. And economic loss and reputation loss caused by complaints are reduced.

The invention relates to the technical field of customer relationship management, and discloses an intelligent customer life cycle management SCRM system, which comprises a multi-source data acquisition and fusion engine; a dynamic customer portrait modeling engine; a customer life cycle state evaluation engine; a predictive intervention engine; an intelligent strategy decision engine; a task distribution and execution engine; and a feedback closed loop and learning engine. According to the intelligent customer life cycle management SCRM system and method, by setting a multi-modal decision engine, taking a customer state as input, learning an optimal strategy of maximizing a long term value (LTV) through a DQN / PPO algorithm, when a customer is in a high loss risk state, triggering an exclusive retention scheme for the customer, and generating a strategy according to a comprehensive conversion rate, cost and satisfaction target, so that the customer life cycle management is realized. Moreover, the personalized content template can be automatically generated, the optimal information channel is accurately sent to different clients, personalized matching is carried out for the clients, and the loss rate of the clients is reduced.

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 relates to the field of data analysis, in particular to a customer service interaction system and method based on natural language processing. The system comprises a multi-channel access module used for supporting interaction of at least one channel and providing a uniform interface; the semantic understanding module is used for carrying out semantic analysis on the input problem through a natural language processing technology so as to determine a problem type and a corresponding intention; the knowledge base retrieval module is used for maintaining a knowledge base containing common questions and answers and quickly retrieving related answers through a semantic similarity matching algorithm; the dialogue management module is used for tracking the dialogue state, understanding the context and managing multiple rounds of dialogue processes; and the natural language generation module is used for converting the answers retrieved by the knowledge base retrieval module into natural languages and replying. And intelligent customer service interaction is realized by utilizing a natural language processing technology, so that efficient and accurate solutions are provided, and the customer service efficiency and satisfaction are improved.

The invention discloses a service processing method based on emotion recognition and related equipment thereof, belongs to the technical field of artificial intelligence, and is applied to processing of insurance elimination service. Firstly, unified format conversion and feature extraction are carried out on three data sources of voice, video and text; then, a multi-modal fusion algorithm is combined with an attention mechanism to carry out weighted splicing on the features, and a comprehensive emotion feature vector is constructed; and emotional expression deviation caused by cultural difference is effectively corrected by combining a regional cultural emotion mapping table and customer regional information. And when the corrected emotional feature vector triggers an emotional threshold, the specific emotional state of the customer is accurately judged by a classification algorithm. And finally, determining a corresponding service response strategy according to the emotional state of the customer. According to the invention, the accuracy and response speed of emotion recognition are improved, the dynamic optimization of the service process is realized, the customer experience and satisfaction are enhanced, and the insurance industry is assisted to realize intelligent and precise customer service management.

The invention discloses a customer service response evaluation method and system based on a large language model, and relates to the technical field of customer service responsiveness, and the method comprises the steps: obtaining customer service dialogue data, carrying out the emotion score assignment through an emotion dictionary and an emotion label library, and obtaining a customer service emotion text; obtaining a plurality of groups of different emotion threshold combinations, and layering the customer service emotion text to obtain the emotion level of the customer; acquiring emotion matching data and customer emotion change data; constructing an emotion threshold evaluation model based on machine learning; constructing an effect display model, carrying out data analysis on the effect display model to obtain an optimized emotion threshold combination, and applying the optimized emotion threshold combination to customer service response evaluation; according to the method and the device, the problem that the customer satisfaction and the service quality are finally influenced due to inaccurate analysis result and unstable service effect caused by experience-based setting of a sentiment analysis threshold value is solved.

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 field of customer relationship management (CRM), in particular to a business recommendation method and system based on a CRM customer portrait, and the method comprises the steps: inputting the key information and behavior data of a customer into a pre-established CRM customer portrait for matching when a server side receives an access request from a client, and obtaining the grouping information of the customer; and performing upper-layer application management on the customer grouping information, formulating a targeted strategy according to characteristics of different customer grouping information, pushing corresponding target customers to related sales personnel, determining demands and value degrees of the target customers, and formulating a personalized business recommendation scheme. Therefore, the problem that content cannot be screened and recommended according to user requirements in the prior art is solved, accurate grouping of users is achieved by deeply mining user portraits in a CRM system, deep association with the system is established on the basis, the response rate and satisfaction of clients are improved through an accurate pushing and follow-up mechanism of a foreground, and the user experience is improved. And the continuous growth of businesses is promoted.