616 results about "Customer delight" 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 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 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 multi-modal learning automatic complaint content analysis and classification method and system, and relates to the technical field of semantic analysis, and the method comprises the steps: collecting multi-modal complaint data, and carrying out the preprocessing of the collected multi-modal complaint data; extracting features of the preprocessed multi-modal complaint data, and fusing the features of different modal data; constructing a complaint content classification model by using the fused features, carrying out model training, and evaluating and optimizing the trained model; and deploying the optimized model for real-time classification. According to the multi-modal learning automatic complaint content analysis and classification method provided by the invention, the complaint content is quickly and accurately classified, and a targeted processing strategy is provided according to the main category, the emotional tendency and the specific theme of the complaint. And an emergency response mechanism is automatically triggered to ensure that problems are processed in real time and effective utilization of resources is ensured. Decision rules are continuously optimized from historical data learning, and the intelligent level of a processing strategy and the customer satisfaction degree 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 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 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 discloses a telecommunication system service authorization method, and relates to the technical field of optical communication, and the system comprises the following components: S100, a user behavior data acquisition and analysis module, S200, an AI learning engine, S300, a personalized authorization strategy generation module, S400, a dynamic adjustment and feedback mechanism, and S500, a safety guarantee mechanism. According to the method, the diversified behavior data of the user is collected and analyzed in real time, the accurate user behavior portrait is constructed, and the prediction capability of the AI learning engine is combined, so that the future service demand of the user can be intelligently predicted, and a personalized service authorization strategy is generated according to the future service demand; according to the method and the system, telecommunication services which better meet the actual requirements of users can be provided for the users, the user experience is improved, the accuracy of service authorization can be ensured, unnecessary resource waste is reduced, and the service efficiency of operators and the customer satisfaction are improved.

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 relates to the technical field of big data intelligent decision making, and provides a household appliance commodity dynamic pricing system and method based on multi-source data fusion, and the system comprises a data collection server, a feature extraction engine, a model training cluster, and a strategy execution terminal. The feature extraction engine comprises a time sequence feature constructor used for generating sliding window statistics; the semantic analysis unit is used for analyzing a commodity detail page text description to generate a brand premium coefficient; and the image recognition module is used for predicting the willingness to pay by the user through the commodity main image quality score. Through collaborative design of the feature extraction engine and the model training cluster, the dynamic response capability and the data application depth which are deficient in a traditional pricing mechanism are realized, traditional household appliance merchants are assisted to adapt to market changes, the customer satisfaction and the market share are improved, and the survival and development of the merchants are guaranteed.

The invention relates to the technical field of digital data processing, in particular to an electric power communication network field operation and maintenance work order scheduling method and device, and the method comprises the steps: receiving and summarizing electric power communication fault information from a plurality of regions; classifying the fault information by adopting a support vector machine and generating an operation and maintenance work order; matching maintenance personnel with different maintenance skill levels and in a maintenance state based on personnel constraint conditions, and sending a scheduling notification; matching a solution corresponding to the fault information by using a historical database, and pushing the solution to a dispatched maintainer; and evaluating the scheduling process based on the fault processing time and the fault processing completion degree of the maintenance personnel. Therefore, the response speed and the processing efficiency of power communication network maintenance can be improved, the scheduling logic can be continuously optimized through continuously accumulated empirical data, and the overall service quality and customer satisfaction are improved.

The invention discloses an intelligent order flexible processing production scheduling management method, and relates to the technical field of order management. The method comprises the steps of obtaining demand data from a baking order management system and performing priority classification on orders to ensure timely delivery of important orders and improve customer satisfaction. The future consumption of the baking raw materials is predicted through a time sequence prediction algorithm, the inventory cost is reduced, and the production continuity is guaranteed. And configuration of production equipment, workers and raw materials is optimized through mixed integer linear programming, reasonable utilization of resources is achieved, and the production efficiency is improved. The production progress and the variation of raw material consumption are controlled and monitored through the statistical process, abnormal conditions are recognized in time, and stable product quality is ensured. And flexible processing scheduling adjustment is carried out according to the production abnormity index, and the adaptability of the production system is enhanced. In conclusion, according to the method, the intelligent level of production management is effectively improved, the production process is optimized, and powerful support is provided for enterprises in the market with fierce competition.

The invention discloses an overseas warehouse operation intelligent wave order management method and system, and the method comprises the steps: constructing a commodity incidence matrix, mining a frequent item set, determining the commodity correlation degree, carrying out the centralized picking of high-correlation commodities, reducing the round-trip times, shortening the walking distance, improving the picking efficiency, planning a picking path in combination with the warehouse layout and inventory information, and achieving the intelligent wave order management of the overseas warehouse operation. The method comprises the following steps: preferentially selecting short-distance commodities in the same area, further reducing the picking time, allocating priorities according to order emergency degrees and customer levels, ensuring that important orders are processed in time, improving customer satisfaction, dynamically adjusting the sequence in the picking process, avoiding invalid walking caused by stockout, enhancing path flexibility and adaptability, and collecting warehouse-in and warehouse-out information in real time. Inventory is updated immediately, data are ensured to be accurate and timely, labor intensity and operation time of picking personnel are reduced, labor cost is reduced, and storage equipment loss and maintenance cost are reduced.

The invention relates to the technical field of information processing, in particular to a question processing method, device and equipment based on artificial intelligence and a storage medium. Question recognition is performed on text information, a business consultation intention is determined according to a recognition result, and a corresponding business query model is called according to the business consultation intention; and inputting the identification result into a business query model for business query to obtain a query result, which is suitable for business data consultation, and when a business consultation intention cannot be determined, calling a preset artificial intelligence large model, inputting the identification result into the artificial intelligence large model for question answering to obtain an answering result, so that the intelligent degree is high, and the efficiency is high. The method is suitable for solving the logistics problem, is wide in application range, generates feedback information according to the query result or the solution result, sends the feedback information to the client, timely processes the logistics problem of the customer, reduces the workload of customer service, improves the quality and efficiency of express service, improves the user experience of the customer, and improves the customer satisfaction.

The invention belongs to the technical field of intelligent control, and discloses an energy optimization management system and method for hotel guest rooms, and the system comprises a central data pool unit which constructs a time sequence database cluster, and carries out the uploading and interaction of data between all units through a data bus; the existence sensing unit is used for integrating multiple sensors by adopting a sensing layer architecture to form a sensing network, capturing existence sensing data and generating a room existence state map based on the existence sensing data; the existence sensing data comprises guest room reservation information data, guest room environment data and guest information data; the partition environment control unit is used for obtaining customer preference data by analyzing a room existence state map; based on the guest preference data and the guest room environment data, selectively activating environment control of different guest room areas by adopting an intelligent optimization algorithm; and the hotel energy utilization rate and customer satisfaction are improved.

The invention discloses a multi-key index fusion service resource scheduling method based on AI large model driving, and relates to the technical field of intelligent resource scheduling, when new service resources are generated, a scheduling process is automatically triggered, and service task information of the service resources is identified and collected; and collecting latest engineer related data, and integrating engineer data and task information to obtain a multi-dimensional task data set. According to the multi-key index fusion service resource scheduling method based on AI large model driving, through comprehensive consideration of factors such as working saturation and distance, overfatigue of engineers and unnecessary path time waste are avoided, resources can be quickly responded, and the average duration of resource processing is reduced, so that the overall service efficiency is improved, and the service quality is improved. And the resources are allocated according to the skills and historical good reputation rates of the engineers, so that the resources are ensured to be processed by the engineers with corresponding capabilities and high service quality, and the success rate of resource processing and the customer satisfaction are improved.

The invention relates to the technical field of e-commerce replenishment, in particular to a cross-border e-commerce replenishment management system based on big data, which comprises a data interface connection module, a sales data analysis and prediction module, an inventory monitoring and replenishment decision module and a sales prediction and adjustment module, the inventory monitoring and replenishment decision-making module judges the replenishment date of the commodity A by sensing the inventory number of the commodity A in the cross-border e-commerce system, predicts the change trend of the daily sales volume of the commodity A in the current time interval through the commodity sales volume prediction unit, and calculates the replenishment number of the commodity A. Accurate inventory management and replenishment decision-making are realized. According to real-time inventory and accurate sales volume prediction, replenishment can be performed at a proper time, and a reasonable replenishment quantity can be determined. The problems of stock-out in the peak season and inventory overstock in the slack season caused by inaccurate experience judgment in a traditional method are avoided, the inventory cost is reduced, and the operation efficiency and customer satisfaction of enterprises are improved.

The utility model relates to the technical field of intelligent logistics transfer robots, in particular to an intelligent logistics transfer robot based on AI vision, which comprises a walking type robot body and a controller, the walking type robot body is of a double-layer structure, and a detection conveying device is arranged at the top of the walking type robot body. A damaged goods conveying device is arranged below the detection conveying device; the structure is simple, the automation degree is high, conveying monitoring of different goods is met, the requirement for manual intervention is reduced, the overall logistics efficiency is improved, it is ensured that damaged goods are not mixed with intact goods through an automatic classification and guiding mechanism, the goods safety is improved, and the goods quality is improved by reducing circulation of the damaged goods. The service quality and customer satisfaction of enterprises can be improved, the insurance cost and compensation expenditure of the enterprises can be reduced for a long time, and the profitability of the enterprises can be improved.

The invention discloses an offline multi-channel customer demand processing system based on grid control. According to the system, a layered system and a multi-module cooperation mode are adopted, WeChat, phone and other multi-channel customer appeal data are collected through the data collection layer, and after cleaning and fusion are conducted through the data processing layer, the functions of work order control, service quality analysis and the like are achieved through the service logic layer. The system utilizes a plurality of advanced algorithms, such as natural language processing, voice recognition, intelligent work order distribution and other technologies, improves the appeal processing efficiency and quality, constructs a service quality evaluation model, and carries out risk prediction and intelligent decision making. In the aspect of performance, a cache and distributed architecture is adopted to guarantee the response speed and the concurrent processing capability, and good expansibility and compatibility are achieved. In addition, data security and privacy protection are ensured through an AES encryption algorithm and a block chain technology, and an algorithm model is optimized regularly. The system effectively solves the problem of a traditional service mode, and improves the service level and customer satisfaction of a power enterprise.

The invention relates to the technical field of artificial intelligence, and discloses an insurance resuming method and device based on artificial intelligence, equipment and a medium, and the method comprises the steps: collecting the portrait information of a target user to be resumed, and constructing and obtaining a high-dimensional feature vector based on the portrait information; analyzing the high-dimensional feature vector through a continuation prediction model to obtain a continuation probability result and priority information of the target user; analyzing the continuous receiving probability result by using a large model scheduling engine to obtain a grouping result of the target user, and determining a continuous receiving work queue of the target user based on the grouping result and the priority information; and analyzing the call content of the target user based on the resumption work queue to obtain an insurance resumption text, and performing insurance resumption on the target user based on the insurance resumption text. The method and the device can be applied to a financial science and technology business program system, and can improve insurance resumption efficiency so as to increase customer satisfaction.

The invention discloses a power customer service real-time assistance and work order linkage method and system, and the method comprises the steps: collecting the voice data of an incoming call in real time, translating the voice data in real time to generate a translated text, and synchronously extracting the voice emotion features; performing semantic recognition on the translated text to extract a business appeal, performing risk analysis through a multi-dimensional risk early warning algorithm in combination with the translated text and the voice emotion features, and outputting a risk level; automatically judging and marking customer types and risk labels based on the translated text and the risk level; and automatically generating and filling a work order according to the business appeal, the customer type and the risk tag, and associating the work order with the translated text and the voice data. Through deep fusion of voice real-time translation and risk early warning, the problems of high translation delay, incomplete risk early warning coverage and low system cooperation efficiency in the prior art are solved, full-process automatic closed-loop management is realized, and the service efficiency, the risk management and control capability and the customer satisfaction are remarkably improved.

The invention discloses an order logistics channel allocation quotation method, which can automatically select and match a quotation table according to orders and channels by setting four charging modes and formulas of full toll, semi-toll, timber volume and cube, efficiently and accurately calculate final logistics quotation, reduce manual errors and improve the efficiency. The quotation table covers different logistics channels, service grades, destinations and weight / volume intervals, accurately matches order demands, provides diversified choices such as air transportation, sea transportation and land transportation, meets individual demands, improves customer satisfaction, and reduces logistics cost and improves operation efficiency by comparing quotations and enabling customers to choose channels with highest cost benefits. A clear quotation table and formula enhance transparency, improve customer trust, set different service levels to encourage high-quality service, guarantee customer experience and enhance loyalty.

The invention relates to the technical field of digital management, in particular to a data analysis system based on digital enterprise management. The system comprises a data acquisition module, a model establishment module, an inventory adjustment module and a risk assessment module. According to the invention, competitor data, market promotion data and raw material supply chain data are collected through the data acquisition module, a neural network model for predicting enterprise sales is established by using the model establishment module, and the inventory adjustment module calculates the order quantity of the product by using an economic order quantity model according to the predicted enterprise sales. The maximum inventory level of an enterprise is determined through a reorder point algorithm and safe inventory setting, the risk assessment module comprehensively analyzes various indexes by using an analytic hierarchy process to obtain a comprehensive risk assessment result, an inventory optimization strategy is formulated according to the assessment result, and the risk of the enterprise is optimized. The inventory turnover rate is improved, the customer satisfaction is improved, and the market competitiveness is enhanced.

A method, system, and program product includes an integrated artificial intelligent system for generating a work order by including processes configured to execute demand forecasting, tailored pricing, service provider matching, and dynamic job order management. An example leverages data analytics to forecast demand, determine tailored prices, match users with nearby service professionals, teams, and offers via a marketplace network, and manage job orders using a dynamic work order management tool. The system provides service delivery and enhances customer satisfaction. Generating the work order may include performing a task selected from a list that includes at least one of: demand forecasting, tailored pricing, service provider matching, and dynamic job order management. Based on the performed task, the system may match a user with nearby service professionals via a marketplace network to generate a job order and manage the job order.

The invention discloses an unmanned distribution vehicle path optimization method based on an attention mechanism and a graph neural network. The method comprises the following steps: 1, constructing an unmanned distribution vehicle distribution task model; and 2, constructing an algorithm solving model based on an attention mechanism and a graph neural network, thereby obtaining an unmanned distribution vehicle path optimization model corresponding to the optimal parameters, and outputting the optimal distribution path of the unmanned distribution vehicle. According to the method, offline training is carried out on the model through deep reinforcement learning and a pseudo-label method, so that the method is applied to unmanned distribution vehicle path online optimization considering battery capacity, unmanned distribution vehicle load and customer satisfaction, the transportation efficiency is effectively improved, and the total transportation cost is reduced.