467 results about "Satisfaction level" patented technology

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

The invention relates to an intelligent customer service self-learning method and system, and the method comprises the steps: collecting the interaction data of a user and an intelligent customer service, and forming a multi-dimensional data set; based on a PID (Proportion Integration Differentiation) controller, processing the performance indexes in the multi-dimensional data set, calculating a current error signal, and generating a corresponding control instruction according to the current error signal so as to adjust the response behavior of the intelligent customer service system in real time; evaluating the system performance data adjusted by the control instruction to obtain evaluation feedback; and according to the evaluation feedback, dynamically adjusting the parameters of the PID controller through a self-adaptive control strategy, and feeding back the adjusted parameters to the PID controller in the step S2. According to the invention, by introducing a closed-loop feedback mechanism based on the PID controller and a parameter adaptive optimization strategy, real-time regulation and control of the response behavior of the intelligent customer service system are realized, and the stability, the response speed and the user satisfaction of the system are remarkably improved.

The invention 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 voice recognition, in particular to a voice interaction system and method for customer service based on artificial intelligence, and the system comprises a voice input processing module, an intention classification and routing module, a context dynamic adjustment module, a user behavior learning module, a multi-level intention fusion module and a final result module. According to the method, a multi-dimensional feature system is constructed by extracting tone intensity, speech speed frequency and emotional fluctuation amplitude, intention categories, priority weights and confidence scores are generated to realize accurate acquisition of appeals, and dialogue history, context and emotional change dynamic reconstruction path nodes, switching rules and response time sequences are tracked during interaction. Historical behavior mining preference features, habit fusion intention relevance, emergency calculation of an optimal strategy, construction of service steps, resource allocation schemes and execution timelines, adjustment of an interactive interface, a service process and a feedback mechanism according to multi-dimensional analysis, guarantee of differentiated service experience, and improvement of response accuracy and user satisfaction.

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

The invention provides an NLP-based customer service dialogue quality detection method and system, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining an e-commerce platform customer service dialogue text stream in real time; semantic understanding is conducted on the dialogue text flow, a semantic understanding result is output, and the semantic understanding result comprises a user consultation intention, key question elements and customer service inquiry information entity integrity; based on the semantic understanding result, performing sentiment analysis on the dialogue text stream, and outputting a structured sentiment analysis result; setting an initial state point and a termination state point in the dialogue processing path based on the semantic understanding result and the sentiment analysis result; the initial state point is a semantic vector fusing core problem elements fed back by the user for the first time and a current user emotional state value. According to the invention, through full-process intelligent processing, dynamic monitoring and accurate optimization of customer service quality are realized, and user satisfaction, customer service efficiency and business normalization are improved.

The invention discloses an AI customer service system dialogue strategy dynamic adjustment method and system, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining user portrait data of a user; describing a user portrait based on the user portrait data; matching and configuring initial customer service personnel from a preset customer service personnel library according to the user portrait; in the multi-round dialogue process of the user and the AI customer service, the emotion value of the user is analyzed in real time, and the emotion value comprises a positive emotion value and a negative emotion value; when the emotion value is a forward emotion value, the dialogue style of the large language model is enhanced in real time through the prompt word engineering module, so that the reply satisfaction degree of the user is improved; when the emotion value is a negative emotion value, adjusting prompt words of the initial customer service personnel and the prompt word engineering module, and continuously monitoring the emotion value in the dialogue; and when the emotion value is converted from the negative direction to the positive direction, strengthening the current adjusted customer service personnel setting and prompt words. According to the method, the user experience can be effectively improved in the interaction process of the user and the AI customer service system.

The invention provides a user intention recognition method and device based on large and small model fusion and electronic equipment. The method comprises the steps that user information is acquired, the user information is input into a small model set for user intention prediction, an intention prediction set is obtained, the intention recognition confidence coefficient of the small model set is determined according to the intention prediction set, and the user information comprises user questions and user portrait features; according to the intention recognition confidence coefficient, the customer service agent is adopted to analyze and process the user information to obtain a COT analysis table, and the COT analysis table is sent to the large model; and according to the COT analysis table and the user information, adopting the large model to perform preset processing on the user question to obtain a target user question, and inputting the target user question into the small model to obtain a user intention recognition result. The problem that in the prior art, user intention recognition independently adopting a large model or a small model is low in user satisfaction degree is solved.

The invention discloses an e-commerce intelligent customer service AI dynamic duty and automatic recovery system, and relates to the technical field of e-commerce service optimization, and the system comprises the steps: calculating a problem emergency degree value based on a multi-dimensional evaluation index through a problem emergency degree judgment module, and achieving the dynamic evaluation of the priority of a problem; through a manual customer service switching module, a dynamic weight distribution mechanism is adopted to distribute problems to manual service seats corresponding to professional skills; analyzing the dialogue content of the customer service and the user in real time through a dialogue context analysis module, and generating a dialogue satisfaction curve; through an AI customer service switching module, when the satisfaction curve tends to be stable, the dialogue is handed over to the AI customer service for continuous service; and service strategy optimization is carried out based on the dialogue historical record and the knowledge base through an optimization module. According to the invention, an efficient and intelligent service mechanism is constructed from the aspects of emergency degree evaluation, manual and AI customer service collaborative service, dynamic optimization and the like, and the response efficiency and user experience of a customer service system are improved.

The invention provides an enterprise employee demand recommendation method and device based on multi-dimensional data driving, and the method comprises the steps: collecting the associated data of enterprise employees, and generating a standardized data set; constructing a dynamic preference model based on the standardized data set; the method specifically comprises the following steps: mining strong association rules of employees-commodity categories by adopting an association rule mining algorithm to form a preference rule base; dynamically adjusting the weight of each rule in the preference rule base on the basis of a time attenuation coefficient; using the employee satisfaction score as a supervision signal, and iteratively adjusting the confidence threshold of each rule in the preference rule base to complete the iterative training of the model; and receiving welfare conditions input by an enterprise, screening commodities conforming to the preference rule base from the commodity pool based on the dynamic preference model, and generating multiple groups of personalized gift bag recommendation information containing the commodities. According to the invention, the defect that the diversified and personalized demands of enterprise customers on welfare demand schemes are difficult to meet at present is overcome.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence-based verbal skill partner training method, device and equipment and a storage medium, and the method comprises the steps: obtaining a dialogue state between an AI partner training person based on a DNQ model and a user in a service dialogue scene; obtaining an award generated by a service verbal skill adopted by the user in the conversation state and a new conversation state; obtaining dialogue experience between the AI training partner and the user according to the dialogue state, the service verbal skill, the reward and the new dialogue state; performing reinforcement learning training on the DNQ model according to dialogue experience; and performing parameter updating on the DNQ model to guide the user to perform service verbal skill adjustment. The method can be applied to a verbal skill partner training scene of financial insurance service or a verbal skill partner training scene of medical service, can provide highly personalized, real-time feedback and continuously optimized verbal skill partner training for the user, effectively improves the service quality, customer satisfaction and training efficiency of the user, and saves the training cost at the same time.

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 a multi-mode intelligent customer service adaptive dialogue interaction system based on generative AI, and relates to the technical field of intelligent customer service. The working process of the system comprises the following steps: collecting multi-modal data, carrying out sentiment analysis, OCR (Optical Character Recognition) and semantic analysis, extracting potential problem targets, and associating historical dialogue features; constructing a user problem risk field model, quantifying a current theme and related theme risks, dynamically adjusting parameters through historical data, and generating a risk perception index; fitting a user satisfaction function based on nonlinear regression, and setting a four-level response threshold value; historical dialogue resource consumption and progress data are extracted, the trend model is fitted in a segmented mode, and the current dialogue resource surplus and the solving progress are predicted; and monitoring resource abnormality and progress abnormality in real time, and triggering knowledge expansion, AI takeover or manual intervention. According to the system, the service efficiency and the user experience in a complex scene are improved through multi-modal perception, risk-driven decision and an active intervention mechanism.

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 discloses an e-commerce platform product selection and pricing method based on multi-agent reinforcement learning, aiming at the game decision problem between an e-commerce platform and a supplier, a platform agent and a supplier agent are constructed, the platform agent uses a deep Q network DQN to learn a product selection strategy, and the supplier agent uses an Actor-Critic algorithm to learn a pricing strategy. Through Stackelberg master-slave game modeling, a platform is used as a leader to make a decision for selection, and a supplier is used as a follower to make a decision for pricing after observation. A state space (including commodity sales volume, inventory, user score, supplier fulfillment rate and the like), an action space (discrete commodity selection and continuous pricing) and a multi-target reward function (balance profit, user satisfaction, inventory turnover and the like) suitable for an e-commerce scene are designed. The strategies of the two parties are converged to game equilibrium through alternate training, and online continuous learning is supported to adapt to a dynamic market environment. Experiments show that compared with an existing method, the method has the advantages that the overall income can be improved by 18-30%, the win-win situation of the platform and the suppliers is achieved, and the blank of multi-agent reinforcement learning in e-commerce product selection and pricing scenes is filled.

The invention discloses a smart retail scene consumer behavior analysis method and system based on big data, and the method comprises the steps: collecting consumer behavior data from a retail platform, and carrying out the preprocessing; and carrying out behavior feature extraction and modeling according to the preprocessed data. And constructing an optimized causal reasoning driven behavior attribution model, and providing personalized behavior prediction and recommendation according to a model prediction result. According to the method, multi-dimensional analysis and modeling of consumer behaviors are realized, and deep connection among the consumer behaviors is revealed through a causal reasoning technology. The innovation step can break through the limitation of the prior art and provide more accurate and personalized behavior prediction and recommendation services. Specifically, the system can accurately identify and predict future demands of consumers according to behavior data collected in real time, thereby helping retailers to formulate more targeted marketing strategies, and improving consumer satisfaction and enterprise income.

The invention provides a two-way emotion recognition and control customer service voice auxiliary method and system, and the method comprises the following steps: carrying out the word segmentation of obtained user voice data, and obtaining word vector data; obtaining an emotion score according to the word vector data; performing text conversion on the user voice data to obtain text information; different response modes and corresponding response schemes are generated according to the emotion scores, if the response modes are pacifying strategies, the response schemes are generated, and voice is synthesized and output to the user side after the response schemes are confirmed by customer service staff; if the response mode is manual switching, optimizing the text information, synthesizing the text information into voice and outputting the voice to the customer service side; performing word segmentation processing on the obtained customer service response data to obtain word vector data; emotion features are recognized according to the word vector data, and customer service emotion correction requirements are generated; optimizing the customer service response data according to the customer service emotion correction demand to obtain optimized voice information; and outputting the generated optimized voice information to a user side. According to the invention, the satisfaction degree and the acceptability of the customer service work of the user are improved, so that the quality of the customer service work is improved.

The invention discloses a product recommendation method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to a recommendation scene of financial products. According to the method, end-to-end data standardization, semantic analysis and privacy protection are carried out; performing disturbance and noise injection based on differential privacy; the method comprises the following steps: accurately capturing short-term fluctuation and long-term trend of customer demands by virtue of a multi-level time sequence algorithm, and extracting a typical demand change mode through segmented clustering and modeling; when significant demand fluctuation is detected, the online learning and deep reinforcement learning model can update a recommendation strategy in real time; the interpretability analysis module transparently presents a recommendation decision basis; user feedback is continuously absorbed through incremental learning, model parameters are continuously optimized, and finally it is guaranteed that an accurate personalized recommendation result is output under millisecond-level response. According to the method, the recommendation accuracy, the user satisfaction and the service conversion rate are remarkably improved, and the compliance and the expansibility are considered at the same time.

The invention provides a multi-dimensional data intelligent analysis and evaluation system for a building, and belongs to the technical field of data analysis, and the system comprises an information extraction module which is used for obtaining the living demand of a target customer, and carrying out the word segmentation processing and word segmentation mapping of the living demand, and obtaining a plurality of pieces of demand information of the target customer; the building determination module is used for determining a target residence of the target customer according to the plurality of pieces of demand information, and determining a plurality of target buildings based on the target residence; the demand analysis module is used for analyzing each piece of demand information based on a pre-trained demand analysis model and determining multi-dimensional demand data; and the building recommendation module is used for obtaining the building information of each target building, calculating the demand score of each target building according to the multi-dimensional demand data corresponding to each piece of demand information, and carrying out building recommendation for the target client. Intelligent and accurate matching of the demand and the building is realized, and the house selection efficiency and satisfaction degree are improved.

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 provides an information technology consultation process management system based on the Internet, and relates to the technical field of information technology management. A client interaction port of the system collects consultation texts and operation events in real time to generate a behavior track data set; the semantic path tracking module analyzes the data set to construct a demand evolution diagram; the path response arrangement module generates a service sequence according to the demand evolution diagram and binds consultant resources; the service recording module monitors data such as satisfaction and time delay to trigger path optimization, analyzes the weight of an efficiency attenuation point reconstruction demand evolution diagram and inputs the weight into the information pushing module during path optimization; and the information pushing module actively pushes a solution entry according to the information. The problems that dynamic changes of client intentions are difficult to accurately capture and the service process cannot be optimized in real time are solved, the intelligent level and the execution efficiency of the consultation process are improved, the experience feeling and the satisfaction degree of clients are improved, and meanwhile the utilization efficiency of consultation resources is improved.

The invention discloses a hotel online reservation service system based on Internet data intelligent service and an intelligent terminal, and belongs to the field of hotel reservation service. The hotel online reservation service system based on the Internet data intelligent service comprises a reservation management module, a guest room management module and a customer information management module. The method solves a problem that the customer distribution efficiency is affected because reasonable distribution cannot be carried out according to demands of different customers in the prior art, can automatically distribute hotel guest rooms according to customer preference factors, reduces manual communication errors, ensures that the check-in experience accords with expectations, improves the service response speed, improves the utilization rate and the room distribution efficiency, and improves the user experience. By evaluating the running state of the equipment of the hotel guest room, potential faults can be found in advance, the maintenance process can be automatically triggered, the situation that guests encounter equipment faults when checking in is avoided, the satisfaction degree is improved, the situation that the hotel guest room is temporarily non-sold due to sudden faults is avoided by eliminating equipment hidden dangers in advance, and the room resource utilization rate is improved.

The invention discloses a cross-platform member unified management and service optimization system. A data fusion processing module breaks a data island, fuses multi-platform member data, and constructs a complete member portrait; the member value evaluation module comprehensively and accurately evaluates the member value by using a member value evaluation formula based on the comprehensive data, insights the contribution and potential of the member value, and provides a basis for a marketing strategy; the member layering module divides levels according to values and dynamic thresholds, enterprises can formulate personalized management strategies accordingly, resources are reasonably distributed, and the management efficiency and the member satisfaction degree are improved; the service recommendation module calculates and recommends personalized services according to a matching degree formula in combination with the hierarchy and historical behavior data; all the modules are tightly matched, a complete closed loop is formed from data integration, value evaluation, hierarchical division and service recommendation, the member experience is effectively improved, and the recognition sense and stickiness of members to enterprises are enhanced.

The invention provides a canteen satisfaction quantitative evaluation method, electronic equipment and a medium. The method comprises the following steps: acquiring Internet of Things sensing data and sales volume of each dish; obtaining dining scene data based on the monitoring video stream of the canteen; obtaining an active feedback text of the diner; feedback information is extracted from the active feedback text; according to the data acquisition time, the dish name and the window information, data fusion is carried out on the Internet of Things sensing data, the sales volume, the dining scene data and the feedback information to obtain a multi-source data association model, and the multi-source data association model comprises multiple groups of association data; determining a dish taste index of each dish, a service efficiency index of each window and an environment comfort level of a canteen based on the multiple groups of associated data; and calculating the weighted sum of the dish taste index, the service efficiency index and the environmental comfort to obtain comprehensive satisfaction. According to the scheme, the satisfaction degree of the canteen can be quantified more accurately.

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 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.