406 results about "Customer delight" patented technology

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 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 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 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 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 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 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 intelligent call dynamic response method and system fusing ASR (automatic voice recognition) and emotion recognition, and is applied to an interaction scene of a voice robot and a call center. The robot response strategy is dynamically adjusted by synchronously analyzing the text content (ASR) and emotional characteristics (such as intonation, speech speed and energy) of the user voice in real time. When the negative emotion is detected, a manual seat is automatically triggered to switch over or switch the pacifying verbal skill; and for the positive emotion of the high-value customer, a precision marketing module is started. The method solves the problems that an existing call center is single in response mode and cannot sense the emotion of a user, and the customer satisfaction and the service conversion rate are remarkably improved.

The invention provides an intelligent operation and maintenance inspection system and method for intelligent hotel guest control equipment, and relates to the technical field of intelligent hotel guest control equipment operation and maintenance, and the system comprises a data collection module which is used for collecting guest control equipment operation data and user feedback data; the data preprocessing module is used for carrying out missing value processing, anomaly detection and standardization processing on the collected data; the model training module constructs and dynamically updates a fault detection and prediction model; the real-time detection and prediction module processes real-time data to perform fault prediction; according to the system, through cooperative work of all the modules, real-time monitoring, fault prediction and intelligent operation and maintenance of customer control equipment are achieved, potential problems of the equipment can be found in time, faults are reduced, the operation and maintenance cost is reduced, and the hotel service quality and the customer satisfaction degree are improved.

System and method to flatten a hierarchical organizational structure by applying Magic Grid, specifically by generating a trained machine learning model using artificial intelligence using training data comprising a history of an organization's deep value customer satisfaction ratings and innovation (CSI) and associated operating status data of said organization, outputting indications of whether an alarm should be triggered, wherein said training model weights one or more nodes of an artificial neural network; providing said model with current operating status data, outputting a value indicating whether an alarm should be triggered, triggering said alarm based upon said value, receiving user input via a software interface, and further training said model based upon said user input.

The invention discloses a low-carbon cold-chain logistics path optimization method based on an improved marine predator algorithm. The low-carbon cold-chain logistics path optimization method comprises the steps of establishing a multi-target distribution path optimization model according to cold-chain logistics distribution task requirements and urban traffic network conditions; minimization of total distribution cost and maximization of customer satisfaction are taken as a comprehensive optimization objective function, and constraint conditions are determined; applying a Tent chaotic mapping mode to randomly generate an initialized population, wherein the population represents a set composed of a plurality of path planning schemes; performing iterative optimization on the initial population, updating each path planning scheme in the population according to global search and local search mechanisms of a marine predator algorithm in each iteration, evaluating the fitness of each path planning scheme according to a comprehensive optimization objective function, and updating the population according to an evaluation result; and when a preset termination condition is reached, outputting the path planning scheme with the highest fitness in the population as an optimization result.

The invention relates to the crossing field of artificial intelligence and hotel informatization management, in particular to a hotel knowledge base self-learning correction method, system and device and a medium. The method comprises the following steps: acquiring a client call voice stream, and preprocessing to obtain voice data; inputting the voice data into a preset multi-modal analysis model and an incremental learning model to generate a knowledge correction decision; updating a knowledge base version and an AI response generator based on the knowledge correction decision, and obtaining customer satisfaction feedback; and adjusting the knowledge correction decision through the feedback. And a knowledge correction decision is automatically generated by using a multi-modal analysis model and an incremental learning model, and a knowledge base version is quickly updated based on the decision. By means of the process, automatic correction of knowledge is achieved, the knowledge updating period is greatly shortened, the knowledge base can reflect latest service information and customer requirements in time, and the timeliness and accuracy of the knowledge base are improved. By incorporating the implicit knowledge into the knowledge base, the knowledge base is richer and more comprehensive.

The invention relates to the technical field of data processing, and discloses a claim settlement task processing method and device based on edge calculation, and a storage medium, and the scheme collects task evaluation data of a target object according to a target task type, ensures that the collected data is closely related to a claim settlement task, and improves the effectiveness and timeliness of information. And performing data evaluation on the task evaluation data by using the claim settlement evaluation model deployed at the edge node equipment, and generating a task evaluation report, thereby realizing quick response and timely identifying abnormal data. The task evaluation reports uploaded by the edge nodes are subjected to priority calculation through the central processing equipment, the task processing priority is determined, the processing sequence of the claim settlement tasks is reasonably arranged, and the overall claim settlement efficiency is effectively improved. The task processing strategy is determined according to the task processing priority, the claim settlement resource distribution is optimized, the processing efficiency of the financial insurance and medical insurance claim settlement process is improved, and the customer satisfaction is enhanced.

The invention discloses a training method of a reply key point determination model, a verbal skill reply method and a related product, and the method comprises the steps: obtaining instruction information used for model training, and the instruction information is used for instructing a to-be-trained model to output historical reply key points corresponding to historical dialogue data based on the historical dialogue data, the historical reply key points are to-be-replied keyword information analyzed from historical dialogue data; constructing a training data set, wherein the training data set comprises historical dialogue data and historical reply key points; the to-be-trained model is trained based on the instruction information and the training data set, a reply key point determination model is obtained after training is finished, and the reply key point determination model is used for outputting reply key points corresponding to the to-be-processed dialogue data based on the to-be-processed dialogue data. Therefore, the customer service reply efficiency and response speed can be remarkably improved, and the customer satisfaction degree is further improved.

The invention relates to the technical field of defect detection, and discloses a carton surface defect detection method based on machine vision, which comprises the following steps: collecting and analyzing carton images, judging whether defects exist on the surface of a carton and whether transportation information is complete, judging the carton with complete information according to the defect category and taking corresponding remedial measures, according to the carton surface defect detection method based on machine vision, transportation information of a carton with incomplete information is matched and supplemented, carton defects and transportation information problems are rapidly recognized and processed, delay and errors in the logistics process are reduced, the carton defects possibly causing damage to articles are found and processed in time, and the logistics quality is improved. The damage risk of articles in the transportation process is reduced, manual operation errors are reduced, the information accuracy and processing efficiency are improved, unnecessary processing on defect-free cartons is avoided, resources are reasonably allocated, the overall operation efficiency is improved, the accuracy and integrity of transportation information are ensured, and the customer satisfaction and credibility are enhanced.

The present invention discloses a multi-modal learning automated complaint content analysis and classification method and system, which relates to the technical field of semantic analysis, and includes: collecting multi-modal complaint data and preprocessing the collected multi-modal complaint data; extracting the features of the preprocessed multi-modal complaint data and fusing the features of different modal data; using the fused features to construct a complaint content classification model, performing model training, evaluating and optimizing the trained model; and deploying the optimized model for real-time classification. The multi-modal learning automated complaint content analysis and classification method provided by the present invention classifies complaint content quickly and accurately, and proposes targeted processing strategies according to the main categories, emotional tendencies and specific topics of complaints. Automatically trigger an emergency response mechanism to ensure that problems are processed immediately and ensure the effective utilization of resources. Learn from historical data, continuously optimize decision rules, and improve the intelligence level of processing strategies and customer satisfaction.

The invention discloses a message grading and sorting method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to an insurance service customer service system. According to the method, a lightweight pre-training language model is combined with a rule engine, an intelligent grading and sorting method for communication software messages is constructed, and deep semantic features of client messages and dialogue contexts are extracted through the pre-training model; multi-source features such as message sending time and customer identity are fused, dynamic weighting is performed by using an attention mechanism, and the understanding ability of the model to multi-dimensional features is enhanced; and a rule engine is introduced to carry out priority adjustment, so that accurate response to service logic is realized. Through collaborative optimization of the model and the rule, the accurate priority ranking result is generated, it is ensured that the customer service agent can respond to the high-priority request in time, delay and complaints caused by human negligence are reduced, and the intelligent level of service and the customer satisfaction degree are comprehensively improved.

The invention discloses an intelligent customer service system and method based on multi-agent cooperation and electronic equipment, and belongs to the technical field of computers. The system comprises a supervisor Agent, a processing Agent and an answering Agent, the supervisor Agent is used for acquiring multi-modal information input by a user, after analyzing the multi-modal information, assigning a target task to different processing Agents, calling a corresponding answer Agent according to a target task processing result fed back by the processing Agents, receiving a processing result of the answer Agent, and generating a multi-modal reply; the processing Agent is used for processing the target task and feeding back a processing result of the target task to the supervisor Agent; and the answer Agent is used for calling the corresponding capability and tool according to the calling instruction of the main Agent to obtain a processing result, and feeding back the processing result to the main Agent. According to the system, the problems of ordering, using and complaining of multiple products can be efficiently solved, independent and cooperative work of the intelligent agents is achieved through the intelligent agent center control, and the customer satisfaction is improved.

The invention discloses a vehicle-unmanned aerial vehicle distribution path optimization method considering customer distribution urgent degree, and relates to the field of logistics distribution path optimization. The method comprises the following steps: firstly, establishing a customer distribution urgent coefficient by considering a sensitive degree parameter of a customer for receiving time; secondly, a distribution time efficiency coefficient is established based on the customer distribution urgency coefficient, and a vehicle-unmanned aerial vehicle cooperative distribution path optimization model is constructed by taking the minimum distribution time efficiency coefficient as an optimization target; then, aiming at a dynamic order (a customer cancels or adds an order), calculating a dynamic deviation coefficient, and re-planning a path by taking minimization of the coefficient as a target; and finally, solving the established multi-target path planning optimization model by adopting a mode of combining a kmeans clustering algorithm, a greedy algorithm and a genetic algorithm. The method can effectively plan the collaborative distribution route of the logistics vehicle and the unmanned aerial vehicle, adapts to dynamic order changes, improves the distribution efficiency and customer satisfaction, and remarkably improves the overall service level of urban terminal distribution.

The invention provides a financial service recommendation method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology. The method comprises the steps of obtaining a user portrait label, behavior preference data of a user and a product portrait label; processing the behavior preference data of the user to obtain keyword label data representing the type characteristics of the financial service and / or the financial product, scoring the keyword label data, determining label integral data, and determining a grade score of the keyword label data based on the label integral data; and performing matching on the basis of the user portrait label and the product portrait label every preset duration, determining a target financial service and / or a target financial product, performing evaluation on the target financial service and / or the target financial product on the basis of the grade score, and determining a to-be-recommended financial service and / or a to-be-recommended financial product. According to the method, the recommendation accuracy and efficiency can be improved, and the customer satisfaction can be improved.

The embodiment of the invention discloses a third-party transport capacity resource scheduling method and device, a storage medium and a server, and relates to the field of logistics management. The method comprises the steps of receiving order attribute data of a customer logistics order; automatically judging whether the order exceeds the service capability of the logistics enterprise according to the order attribute data; if so, screening a conforming carrier from a third-party supplier library according to the order attribute; calculating the transportation cost and the transportation duration of processing the order by each carrier; obtaining personalized transportation cost and transportation duration weight preference of the client according to the unique identity label of the client; calculating a comprehensive service score of each carrier based on the weight; and finally, assigning the order to the third-party carrier with the highest score. According to the method and the device, automatic scheduling of third-party transport capacity resources can be realized, the problem of uncontrollable timeliness and cost caused by low manual decision-making efficiency under the condition that the logistics service capability is limited is effectively solved, and the logistics distribution efficiency and customer satisfaction are remarkably improved.

The invention discloses a property service satisfaction improvement method based on big language model reasoning, and belongs to the field of artificial intelligence and property service management. In order to solve the problems that customer satisfaction improvement measures depend on experience, native comments are insufficient in utilization and the like, the customer satisfaction improvement method based on fusion of a large language model and multi-source data is adopted in the scheme. Multi-source data of enterprise WeChat, interviews, business systems and the like are integrated, a training analysis model is designed, and a semantic recognition system is constructed to realize theme cognition and appeal analysis. And establishing a passenger full knowledge base, determining a hot scene according to historical data, and generating an evaluation report through a large language model in combination with index analysis. And fusing the hotspot and the native content by using the recommendation model, and outputting a TOP5 lifting measure by using the large language model and pushing the TOP5 lifting measure to a tracking system. According to the method, the value of multi-source data is mined, customer concerns are accurately positioned through analysis of the large model, the service level and the satisfaction degree are improved, and the model can be autonomously optimized along with data accumulation to adapt to different project requirements.

The invention discloses a hotel customer demand intelligent analysis and response method, device and equipment and a medium, and relates to the technical field of hotel management intellectualization, and the method comprises the steps: mapping a customer behavior time sequence data set into a hypergraph structure, calculating an attention weight coefficient based on a multi-head attention mechanism, and generating a hypergraph feature vector; constructing a resource state graph based on the resource state time sequence data set, inputting the hypergraph feature vector and the resource state graph into a multi-objective optimization model based on the game theory, defining a customer satisfaction function, a hotel revenue function and a resource consumption function, and taking the functions as game participants; calculating a non-dominated solution set of a game participant in an equilibrium state through a Pareto frontier solver, and screening out an optimal response strategy; according to the method, the multi-modal data are dynamically fused through the hypergraph feature vectors and the multi-head attention weights, and the problem of multi-modal data relevance missing is solved.

The invention discloses a power failure risk marketing and distribution co-processing system based on RPA, and relates to the technical field of power system automation and intelligent operation and maintenance. The system comprises a data preprocessing module, a risk influence factor analysis module and an RPA co-processing flow construction module, original data can be cleaned and standardized, power failure risk factors can be identified and analyzed, an efficient co-processing flow is automatically constructed based on the RPA technology, real-time monitoring and updating of power failure risk equipment and a sensitive customer list are achieved, and the real-time monitoring and updating of the power failure risk equipment and the sensitive customer list are achieved. And marketing and distribution collaboration is enhanced through closed-loop management and control on double-active I-line and single-line. In practical application, risk identification efficiency and repair efficiency can be greatly improved, power failure time and complaint amount are reduced, an automatic and intelligent solution is provided for power failure risk management, and power supply reliability and customer satisfaction are effectively improved.