267 results about "Customer delight" patented technology

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 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 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 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 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 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 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 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.

The invention discloses a sales demand analysis method and system based on AI and portraits. The method comprises the steps of collecting multi-source heterogeneous data such as basic information, transaction records and online behaviors of customers; according to the method, a self-developed large model of the Chinese human life research and development center is adopted, implicit requirements in a customer behavior sequence are deeply mined through an attention mechanism, a hierarchical clustering algorithm is used for generating a tree-shaped clustering graph based on a Ward connection method, and high-value features with variances larger than 0.8 and absolute values of correlation coefficients larger than 0.5 are screened out in combination with a rule engine; it is ensured that the extracted features have statistical significance and are directly associated with business targets, invalid feature redundancy is avoided, the efficiency and accuracy of subsequent portrait construction are improved, product recommendation priorities are determined through a decision tree algorithm, and a Q-learning framework is combined to optimize communication verbal skills and cross sales strategies, so that the sales strategies meet the current demands of customers, and the customer experience is improved. Potential requirements are guided, and the conversion rate and the customer satisfaction degree are both improved.

The invention discloses an insurance customer resource optimal configuration method and system, and mainly relates to the technical field of insurance service resource management. Comprising the following steps: acquiring customer label data of a plurality of customers and salesman label data of a plurality of salesmen; performing preprocessing and feature coding on the customer label data and the salesman label data, and converting non-numerical labels into numerical features; carrying out feature importance analysis on the basis of the preprocessed numerical features, and selecting key features; based on the key features, calculating an association degree matrix between the customer tag and the salesman tag; based on the correlation degree matrix, calculating the matching degree between the to-be-distributed customer and the plurality of salesmen; and selecting a target salesman from the plurality of salesmen according to the matching degree, and allocating the to-be-allocated customer to the target salesman. The method has the beneficial effects that accurate and automatic distribution of customers and salesmen based on portrait label matching is realized, and meanwhile, the service efficiency and customer satisfaction are improved.

The invention discloses a logistics network multi-objective optimization method based on an aging demand, and the method comprises the following steps: obtaining logistics order information, dividing the logistics order information into a high aging grade, a middle aging grade and a low aging grade according to the aging demand, and setting a differential punishment mechanism according to different aging grades, a multi-target collaborative optimization model of cost-carbon emission-customer satisfaction is constructed; limiting the transportation mode of each logistics order based on the aging grade; based on the high-timeliness order proportion, the transport capacity distribution proportion is adjusted in real time through a dynamic threshold value control formula; and solving the multi-target collaborative optimization model by adopting an improved genetic algorithm and combining a dynamic time window matching strategy to obtain an optimal logistics network optimization scheme. According to the method, multiple targets such as cost, carbon emission and customer satisfaction can be considered, time-sensitive customer requirements are met, an effective method is provided for optimizing resource allocation for logistics enterprises, and the operation efficiency and competitiveness are improved.

The invention relates to an intelligent work order recommendation processing method and device based on multi-algorithm fusion. The method comprises the steps of obtaining original work order data of a work order to be processed and capability indexes of multiple processing parties; extracting a comprehensive feature vector of the original work order data; screening out a plurality of candidate processing parties from all the processing parties through a double-tower model according to the comprehensive feature vector and the capability indexes of the plurality of processing parties; and determining a recommended processing party list meeting a preset condition from the plurality of candidate processing parties by adopting a Thompson sampling algorithm. According to the scheme, intelligent allocation of the work orders is realized by fusing multiple algorithms, the processing efficiency and accuracy are remarkably improved, the resource utilization is optimized, the self-learning ability of the system is enhanced, and a virtuous circle is formed, so that the customer satisfaction and the overall performance of the system are improved.

The invention relates to the technical field of electronic commerce, and discloses a multi-modal dialogue analysis system and method based on digital electronic commerce. The invention constructs a complete multi-mode intelligent dialogue analysis system oriented to a digital e-commerce scene. The system comprises a keyword library, a multi-mode fusion module, an association mapping module, a self-service reply module, a semantic optimization module and an auxiliary optimization module. Firstly, user multi-modal input is unified and standardized through a unified multi-modal message representation pipeline, a unified intention vector capable of accurately expressing the complex intention of a user is generated based on a multi-modal fusion strategy of an attention mechanism, and a keyword result with confidence is output according to a matching mechanism of vector semantic similarity. The reply feedback is executed accordingly; and if automatic feedback cannot be carried out, the manual customer service is called, new knowledge is automatically extracted after the service is finished to update the keyword library, different requirements of pursuing efficiency and autonomous exploration are met at the same time, and the customer satisfaction and the problem solving efficiency are improved.

The system (108) in the present disclosure for service barring workflow is designed to automate and streamline the process of managing service barring actions in a telecommunications network It provides centralized control, efficient processing. and granular control over service restrictions by implementing a method (400) involving various nodes. Tracking Area Code (TAC) and Network administrators. The system (108) offers advantages such as enhanced operational efficiency. accurate enforcement of service barring rules. real-time monitoring. and reporting capabilities. It integrates with network elements and ensures compliance with regulatory requirements. With its flexible and scalable architecture, the system (108) enables administrators and users to efficiently handle service barring requests while maintaining a positive customer experience. Overall, the system (108) for service barring workflow optimizes the service management process. enhances security, and contributes to effective network control and customer satisfaction.

The invention discloses an ERP intelligent management system and method based on big data. The method comprises the following steps: firstly, arranging historical sales volume data and historical price data of multiple days according to a time dimension, and then performing local time sequence feature analysis to obtain a sequence of historical sales volume local time sequence feature vectors and a sequence of historical price local time sequence feature vectors; performing fusion processing on each group of corresponding historical sales volume local time sequence feature vectors and historical price local time sequence feature vectors to obtain a sequence of historical sales volume-historical price local time sequence fusion feature vectors; and determining a short-time yield demand prediction value of the to-be-predicted commodity based on historical sales-historical price time sequence fusion features obtained by sequence cascade of the historical sales-historical price local time sequence fusion feature vectors, and judging whether loss will be caused or not. In this way, the situation of commodity inventory excess or stockout can be avoided, and the production efficiency and the customer satisfaction degree are improved.

Traditional methods often result in long response times to customer inquiries, leading to decreased customer satisfaction. Furthermore, it reduces the productivity of sales representatives and places a heavy load on product servers and systems. This system addresses these issues by providing a rapid and accurate customer response solution. [Solution] The system receives input from the user, tokenizes the user's input, passes the tokenized data to a generative AI model to generate a response, converts the generated response into JSON format, and sends the converted response back to the user.

The application discloses a kind of through deep learning automatically identifying design draft element generation activity method, it is related to artificial intelligence technical field, the method includes the following steps: based on the marketing activity design drawing and user information obtained in advance, and by customer group identification and marketing scene identification mode, obtain marketing activity scene;Utilize convolutional neural network model, identify marketing element in marketing activity design drawing, obtain activity component element feature, and according to the position information of activity component element feature extraction activity component, obtain descriptive data;Based on the component library obtained in advance, combine descriptive data and marketing activity scene to obtain activity component template, and according to the activity component template obtained to carry out page rendering, generate marketing activity page.The application generates marketing activity page with complete interaction automatically by convolutional neural network model, to greatly improve marketing activity page development efficiency and customer satisfaction.

The invention discloses a marketing customer label management system and management method based on the electric power field, and relates to the technical field of electric power marketing customer label management, and the system comprises a data collection module, a data storage module, a label generation module, a customer grouping module, a marketing strategy generation module and an interface module. According to the invention, through multi-dimensional data real-time acquisition, dynamic label management, demand elasticity modeling and physical layer accurate intervention, the problems of single data dimension, insufficient real-time performance, unquantified demand elasticity and the like of an existing static label system are solved, synchronous updating of a marketing strategy and user behaviors is realized, the customer satisfaction is improved, power grid resource allocation is optimized, and the user experience is improved. According to the method, the policy compliance is automatically checked, the management risk is reduced, data fusion without additional hardware is realized through power line carrier communication, and a full-closed-loop precision solution from data acquisition to policy intervention is provided for power marketing.

The invention provides a multi-factor pricing transaction method and device and a core system, and the method comprises the steps: receiving transaction information uploaded by a peripheral system, and extracting a basic price of a pricing object in the transaction information; the core system is retrieved according to the transaction information, whether a corrected pricing rule corresponding to the pricing object exists or not is determined, and the corrected pricing rule comprises an external pricing rule, a final pricing rule, a scene pricing rule and a client pricing rule; under the condition that the corrected pricing rule corresponding to the pricing object does not exist, transaction with a peripheral system is carried out according to basic pricing; and under the condition that the corrected pricing rule corresponding to the pricing object exists, calling a corresponding preset collaborative pricing strategy to correct the basic pricing according to the type of the corrected pricing rule, obtaining a transaction pricing, and carrying out transaction with a peripheral system according to the transaction pricing. The method solves the problems that in the prior art, a business pricing system depends on customer groups and money to carry out gradient pricing, scene channel pricing is single, and customer satisfaction is low.

The invention discloses a customer group classification method and device based on machine learning and electronic equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the dimension reduction processing of an original data set through employing a kernel principal component analysis algorithm, obtaining a data point set after the dimension reduction, calculating the outlier factor of each data point through employing a local density factor algorithm, and obtaining an outlier classification result; removing abnormal data values by using outlier factors, calculating connection strength between nodes according to a relationship between data points, establishing a linear equation set, solving the linear equation set, obtaining an initial clustering center point, performing clustering analysis based on the initial clustering center point, classifying each data point in a data point set after dimension reduction into a cluster to which the closest centroid belongs, and performing clustering analysis based on the clustering center point. And obtaining a financial management customer group classification result until a clustering convergence condition is met. The technical problems that the real-time updating demand of large-scale customer data cannot be adapted during financial management customer classification in the related technology, the recommended product does not meet the demand, and the customer satisfaction is reduced are solved.

The invention relates to the technical field of enterprise data intelligent management, in particular to a service optimization method and system based on multi-dimensional enterprise data fusion, and the method comprises the following steps: taking a unified social credit code as a main key, and combining an enterprise name and an operation address as auxiliary matching keys; industrial and commercial registration data, tax declaration data, energy consumption monitoring data, social insurance payment data and operation development data are fused; the method has the beneficial effects that a real-time calculation framework and an intelligent algorithm are adopted, the sudden risk of an enterprise, such as sudden energy consumption increase and tax decline, can be captured in time, and an early warning and service intervention mechanism is automatically triggered. Therefore, enterprises can quickly respond to market changes, the operation risk is reduced, and the competitiveness is improved. And by accurately sensing enterprise demands and service states, the system can realize optimal configuration and efficient utilization of the service resources. Therefore, the service cost can be reduced, the service quality can be improved, and the customer satisfaction and loyalty can be enhanced.

The invention provides a business travel product management and control method, system and device and a program product. The management and control method comprises the steps that management and control conditions are configured according to requirements of a client company to which a user belongs; responding to the condition that the business travel product reserved by the user meets the corresponding control condition, and taking a corresponding control measure according to the standard exceeding condition of the business travel product; wherein the management and control conditions comprise at least one of an amount management and control condition, an advance reservation management and control condition, a range management and control condition and a low-price management and control condition. Multi-dimensional management and control measures are provided for reservation and modification of business travel products, users can reasonably select the business travel products according to different situations, and the management and control measures can help optimize business travel expenditure management of customer enterprises, promote advanced planning, improve management efficiency, guarantee compliance and improve customer satisfaction; and the business travel company and the client company can be helped to collect reasons of modification and cancellation each time, so that potential problems in the process are identified, the business travel management process is continuously improved, and the overall efficiency is improved.

An AI-based hospitality management system and method for providing tools to manage, optimize, and streamline hospitality performance, resulting in improved revenue models, as well as increased customer satisfaction. The system and method offer a dual interface, catering to both management and guests. For management, it provides tangible tools to receive and analyze competing amenity provider rates and services, and to implement in-house amenity rate change records to update its own in-house service rates thereby improving revenue. The guest profiles are centralized, and communication streamlined through automated responses and analysis of guest reviews via machine learning algorithms, thereby providing personalized recommendations, assistance with inquiries, and facilitating communication with the front desk.

The invention relates to the technical field of household broadband installation and maintenance, in particular to a method and system for constructing a household broadband intelligent auxiliary installation agent based on a large language model, and the method comprises the following steps: a pre-construction stage: an intelligent auxiliary installation agent senses work order distribution, construction community characteristics and customer characteristics; task arrangement reminding, construction difficulty reminding and low-full customer reminding are provided, and networking scheme intelligent planning is carried out; wherein the task arrangement reminding comprises the total workload of the day and the optimal construction path suggestion; the method has the beneficial effects that the intelligent assistance of home-wide installation and maintenance is realized, the installation and maintenance efficiency and customer satisfaction are improved, the receipt duration is shortened, and the installation and maintenance process is optimized by carrying out task reminding and networking scheme planning before construction, providing scheme guidance in construction, completing completion acceptance after construction and the like in combination with a large language model and an intelligent algorithm.

To provide a customer service system, a customer service program and its providing method for improving the efficiency of the whole industry and customer satisfaction.SOLUTION: In the customer service system, the communication control server includes a communication protocol control unit that executes control corresponding to each communication protocol for transmitting and receiving voice information, character information, or data by a call, and the application server includes a communication history recording unit that records history data transmitted and received through the communication protocol control unit and information on the communication protocol when the history data is transmitted and received in association with each other as thread data, and a display control unit that displays a list of the thread data. The communication control server is also provided with a communication protocol switching part 110a for executing the transmission / reception of information in the communication protocol control part by the communication protocol corresponding to the selection of a manipulator or the communication protocol associated with the existing thread.SELECTED DRAWING: Figure 2

The invention provides an insurance industry meeting place seat number automatic distribution method. The method comprises the steps that client data are acquired through a system input interface and an API interface and preprocessed; calculating a customer value score based on a predefined mathematical model and generating a customer tag; configuring a seat distribution rule set, setting priorities, and establishing a mapping relation between rules and areas; executing automatic seat distribution by adopting a hierarchical-backtracking model, and optimizing a distribution result through a greedy algorithm and a backtracking mechanism; providing a visual interface to display a distribution result and supporting manual adjustment; and finally generating a complete seat arrangement report. According to the invention, the problems of low efficiency and poor accuracy of traditional manual distribution are solved, intelligent and precise management of seat distribution of the meeting place in the insurance industry is realized, and the distribution efficiency and customer satisfaction are remarkably improved.

By accurately grasping customer needs in real time and providing appropriate property information instantly, the system improves the work efficiency of sales staff, increases the accuracy of proposals, and increases customer satisfaction. [Solution] In an information processing system that matches real estate properties, the recommendation device comprises a memory unit that stores customer property needs information and property information, a transcription result acquisition unit that acquires real-time transcription results of conversational audio with the customer, a property needs information acquisition unit that acquires customer property needs information automatically extracted by AI analysis based on the transcribed conversation content, a property extraction unit that extracts properties that match the customer property needs information from the property information stored in the memory unit based on the customer's property needs, a reason acquisition unit that acquires recommendation reasons for the extracted properties generated by AI analysis based on the characteristics of the property and the customer's property needs information, and an output unit that outputs recommendation results including recommendation reasons to a user terminal.

Embodiments of the invention relate to an object recommendation method, a medium and a program product. The method comprises the steps of determining a target demand type of a target customer from multiple types corresponding to target attributes of a to-be-recommended object; obtaining a target recommendation strategy corresponding to the target demand type, and screening out a first recommendation object from a plurality of first candidate objects conforming to the target demand type based on the target recommendation strategy; determining a target recommendation object based on the first recommendation object; and providing an object recommendation service for the target customer based on the target recommendation object. According to the embodiment of the invention, the target recommendation object suitable for the target customer can be efficiently and reliably screened out, the accuracy of the recommendation result is ensured, and the customer satisfaction is improved.