524 results about "Customer requirements" patented technology

The invention discloses a large-model complaint intention recognition method based on sentiment analysis, and the method comprises the steps: obtaining call voice data of a customer, and converting the call voice data into text data; preprocessing the text data, and removing noise and marking components; performing emotion feature analysis on the text data, extracting an emotion feature value, and generating an emotion vector; the emotion vectors and the text data are input into a large model together, and the large model combines the emotion feature values and context semantics to generate intention feature vectors; constructing an emotion-intention state vector, inputting the emotion-intention state vector into an asynchronous dominant actor commentator algorithm model, and generating a corresponding complaint intention probability value; judging a complaint intention probability, and generating risk early warning; and preferentially distributing high-risk customers and responding to customer demands. Through combination of voice data preprocessing, text semantic feature extraction, emotion intensity quantitative analysis and a multi-modal fusion algorithm and efficient risk assessment based on an asynchronous dominant actor reviewer model, the early warning and response capabilities of customer complaint risks are significantly improved.

The invention discloses a parcel logistics multimodal transport cooperative scheduling method based on big data, which relates to the technical field of big data, and comprises the following steps: when an information monitoring terminal monitors an abnormal condition, triggering dynamic correction of a transport efficiency coefficient, a cost fluctuation coefficient and a service risk coefficient; and when the absolute value of the difference value between the updated evaluation index and the original evaluation index exceeds a preset threshold value, the scheduling decision terminal re-executes the scheme generation process to generate an emergency scheme. Through a built-in evaluation model, according to scheduling modes such as cost priority, timeliness priority and safety priority selected by clients, weights are dynamically allocated, evaluation indexes are output, a transportation mode combination is generated in combination with a genetic algorithm, time connection verification and equipment matching verification are carried out, multi-target collaborative optimization is realized, different client requirements can be flexibly adapted, and the method is suitable for large-scale popularization and application. And the transportation efficiency, the cost and the safety are balanced, and the multimodal transport resource allocation efficiency is improved.

The invention discloses a model construction method and system based on indoor decoration interactive design, and relates to the technical field of intelligent design, and the method comprises the steps: generating a diversified design sketch through an AI generation tool, collecting customer feedback data, determining customer demands, and generating a basic design scheme; creating an interactive ecology home 3D model by using a parameterized design tool, and adjusting a basic design scheme according to customer requirements; based on the adjusted basic design scheme, optimizing visual requirements of the basic design scheme through a V-Ray rendering engine; based on visual requirements, acoustic simulation software and a sensor are utilized to analyze spatial acoustic performance, ecological integration is completed, and intelligent control and AR interaction of an ecological environment are performed in combination with IoT and a user interface. According to the method, the interactive ecology home 3D model is combined with the AI generation tool, the parametric design, the V-Ray rendering engine, the biological feedback technology, the acoustic simulation software and the IoT and AR application, so that an efficient, flexible and personalized design process is realized.

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

The invention relates to the technical field of business service dynamic optimization, in particular to a customer demand dynamic analysis and consultation service optimization platform based on deep learning, which comprises a user portrait module, an intention analysis module, a knowledge graph module, a learning engine and a recommendation module, the intention analysis module is connected with the knowledge graph module, the knowledge graph module is connected with the learning engine and the recommendation module, the learning engine is connected with the user portrait module and the recommendation module, and the recommendation module is connected with the user portrait module; according to the method, the accuracy of new user demand understanding is remarkably improved, the system can still provide accurate services in the absence of historical data through dynamic extension of the knowledge graph and an incremental learning mechanism, the problem of insufficient personalization caused by user portrait missing is thoroughly solved, and the user experience is improved. And meanwhile, the security and adaptability of data processing are ensured through multi-modal analysis and federal learning.

The invention relates to an auxiliary verbal skill self-adaptive generation method fusing a knowledge graph and reinforcement learning, and belongs to the technical field of artificial intelligence and service compliance management crossing. The method comprises the following steps: collecting service data, extracting entities, attributes and relationships among the entities through natural language processing, constructing a knowledge graph marked with compliance attributes, and establishing an updating mechanism; determining a verbal skill generation main body according to an actual service scene, and integrating a conversation context, a client real-time state and a business target construction environment state; the verbal skill generation subject retrieves associated business knowledge according to the current environment state, and after the seat selects adaptive verbal skill interaction, verbal skill generation strategy parameters are updated through deep reinforcement learning; and constructing a multi-dimensional effect evaluation method based on the business data, feeding back an evaluation result to an iteration link, and optimizing reward calculation and knowledge graph association logic. Dynamic self-adaption of verbal skills in multiple service scenes is realized, and customer demand solving efficiency and business conversion quality are improved.

The invention, which relates to the technical field of digital management, discloses an artificial intelligence-based enterprise digital operation management system comprising a data integration module, a behavior prediction module, a feature recommendation module, a clustering subdivision module and a decision support module. The data integration module collects and arranges original data and transmits the original data to the behavior prediction module and the feature recommendation module, the behavior prediction module constructs a prediction model according to an artificial intelligence algorithm, analyzes customer behaviors and purchase modes and predicts customer demands and commercial intentions, and the feature recommendation module performs feature recommendation according to a collaborative filtering algorithm. The service recommendation module is used for providing service recommendation according to customer characteristics, the clustering subdivision module is used for subdividing customer groups according to a clustering analysis algorithm, and the decision support module is used for grading service grades according to customer group types. Transfer development of enterprises in the B2B business field and the B2C business field is met, and the enterprises are prevented from falling into data islands and information isolation.

The invention provides a defect detection model parameter construction method, a controller, a medium and a product. The method comprises the following steps: acquiring a plurality of product images; inputting the plurality of product images into a deep learning model to obtain a reasoning result, the reasoning result comprising a defect category; setting an initial strict threshold value, an initial balance threshold value and an initial loose threshold value according to a reasoning result; and based on the defect category, correcting the detection parameters of the deep learning model according to the initial strict threshold value, the initial balance threshold value and the initial loose threshold value to obtain corrected defect detection model parameters. The corrected defect detection model parameters are obtained based on the model reasoning result and analysis of actual production requirements, so that the defect detection model has the flexible adjustment capability of defect detection in the actual production process, the detection threshold value can be adjusted in real time according to customer requirements, and it is ensured that the detection result meets the specific quality standard, and the detection efficiency is improved. And the dynamic requirements of different batches are met, and accurate and efficient detection is realized.

The invention relates to the technical field of business process management, in particular to a data processing method, system and device based on business process management and a medium, and the method comprises the steps: constructing a business process model driven by an abstraction layer, and configuring a multi-protocol standardized interface; according to the business process model driven by the abstraction layer and the multi-protocol standardized interface, a process instance matched with the business scene is generated, and a task is distributed to a target client according to a task demand; managing form state circulation and node jump logic according to a task control engine; an event monitor is deployed through a process key node to monitor the process, and when the process reaches a specified node, external service calling operation is triggered to form an end-to-end automatic closed loop. Therefore, the problems of code redundancy, high maintenance cost, difficulty in quickly adapting to diversified customer requirements and the like due to the fact that business process management generally needs to be subjected to customized development for different subsystems in related technologies are solved.

The invention belongs to the field of artificial intelligence, can be applied to the field of finance, and relates to a product recommendation method, which comprises the steps of classifying historical insurance policies according to a preset dimension, and generating a corresponding classification label for each classification category; matching the customer tags of the potential customers with the classification tags to obtain target classification tags; screening out a target customer group according to the target classification label; inputting the obtained target customer information of the target customer group and the target claim settlement case into a verbal skill recommendation model, outputting a target recommended verbal skill, pushing the target recommended verbal skill to potential customers, and receiving voice data fed back by the potential customers; recognizing the voice data to obtain recognized text data; and matching a target product based on customer demand information extracted from the identified text data, and recommending the target product to potential customers. The invention further provides a product recommendation device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the client tag can be stored in a block chain. According to the method, clients can be accurately positioned, and the product recommendation effect is improved.

The invention discloses an efficient business opportunity pushing method based on feature selection, and relates to the technical field of big data analysis, historical data of clients and data collected by business personnel in client contact are integrated, and the integrated data are preprocessed; extracting features related to insurance purchase willingness from the integrated customer data set, and encoding the extracted features to obtain a basic feature sequence; by collecting and analyzing the multi-dimensional data of the customers, constructing the accurate customer portraits and based on accurate matching of the customer portraits, the pushing accuracy is greatly improved, invalid pushing is reduced, the satisfaction degree of the customers to pushing is improved, and due to the fact that the pushed content is highly matched with the customer requirements, the customers are easier to generate purchase willingness, and the customer experience is improved. In addition, the investment of invalid customers can be reduced through accurate pushing, more resources are input to potential high-value customers, and optimal configuration of the resources is achieved.

The utility model relates to an intelligent lifting table with a screen structure, a screen assembly comprises a rectangular frame formed by four side columns, and decorative panels are formed on two surfaces of the rectangular frame, so that the rectangular frame becomes a rectangular body which has a certain thickness and is hollow inside; an embedding groove penetrating through the top and the bottom is formed in the decoration panel; the lifting assembly comprises at least two groups of lifting mechanisms arranged in the rectangular body; a lifting groove corresponding to the embedding groove is formed in the surface of the lifting mechanism; a supporting cantilever is connected in the lifting groove; the lifting mechanism is connected with a controller; the table top plate is fixedly connected to the supporting cantilever; the supporting legs are fixed to the bottoms of the screen assemblies. According to the utility model, the decorative panel of the screen is cut into a plurality of modules, and the embedded groove penetrating up and down is formed, so that the support arms for supporting the table plate are simpler and more convenient to mount; the decoration panels of the plurality of modules can be customized according to customer requirements, and are more flexible; and the controller and other circuits are integrated in the screen, so that the disorder of many wires is avoided.

The invention provides a database access method based on an end-side cloud system and related equipment. The method comprises the following steps: constructing the end-side cloud system of a terminal, an edge gateway and a cloud server; deploying an enterprise metadata knowledge base, an AI model and an enterprise database in the end-side cloud system based on customer requirements; receiving a demand prompt word input by a user through an intelligent agent in the terminal; a routing strategy is obtained through an intelligent agent based on deployment situations of an enterprise metadata knowledge base, an AI model and an enterprise database and by taking local gateway processing priority as a criterion; and based on the routing processing strategy, the edge gateway and the cloud server carry out programmed processing on the demand cue word. According to the database access method, the AI model is used for code generation, the development difficulty is reduced, the enterprise metadata knowledge base, the AI model and the enterprise database are flexibly deployed based on customer requirements, the terminal intelligent agent can flexibly route, and the database access method has better adaptability and can better meet the enterprise requirements.

The invention relates to the technical field of bank customer marketing scenes, in particular to an intelligent marketing decision-making system based on dynamic customer grouping. Comprising a data acquisition and preprocessing module, a dynamic customer grouping module, a multi-channel marketing collaboration module, a marketing effect evaluation and optimization module, a data security and privacy protection module and a customer feedback and complaint processing module. According to the intelligent marketing decision-making system based on dynamic customer grouping, the real-time data acquisition and preprocessing module is adopted, customer behavior data are captured in real time through a stream processing engine, real-time cleaning and standardization processing are carried out, meanwhile, the dynamic customer grouping module carries out dynamic grouping according to the customer real-time behavior data by using a deep clustering algorithm, and the dynamic customer grouping efficiency is improved. A traditional static grouping rule is replaced, the data timeliness is improved, marketing decisions can be based on latest customer behavior data, and market changes and customer demands can be captured in time.

The invention belongs to the technical field of customer demand prediction, and particularly relates to a customer demand prediction system and method based on machine learning. According to the method, the accuracy of demand prediction is effectively improved by introducing a multi-dimensional fusion prediction method of historical demands, real-time behavior characteristics and preference information, abnormal changes of customer behaviors can be quickly responded through deviation behavior recognition, customer demands in a dynamic environment are adapted, and the accuracy of demand prediction is improved by dividing the first time period and the second time period. According to the method, real-time dynamic changes of clients are captured, prediction of long-term demand preferences is considered, scientificity and comprehensiveness of results are improved, enterprises can provide more targeted and personalized services through accurate prediction of client demands, and client experience and satisfaction are improved.

The invention discloses a design and manufacturing method of a polyamideimide composite coating gear, which comprises the following steps: S1, collecting customer demands, sequencing the priorities of the customer demands, and establishing a quality house based on a quality function expansion method; s2, formulating a design project according to the priority of the customer demand; s3, preliminarily evaluating the feasibility of the design project through probability evaluation and risk analysis tools; s4, key quality characteristics are obtained, and a tolerance limit range is determined; s5, on the basis of a multi-objective optimization machining parameter reverse adjustment method, a parameter-driven input and output model is established; s6, inputting the key quality characteristics into the input and output model to obtain a design scheme; s7, inputting the design scheme into the multi-objective optimization model to obtain optimized key quality characteristics; s8, planning a manufacturing process based on customer requirements and optimized key quality characteristics; and S9, manufacturing a composite coating gear sample, testing the composite coating gear sample, and verifying the service performance of the composite coating gear sample.

The invention discloses an intelligent voice calling method and system, and the method comprises the steps: extracting client interaction data of a to-be-called client from a client database, the client interaction data comprising client basic data, historical transaction records, call records and feedback information; analyzing the client interaction data, identifying potential demands of clients, and generating client demand features; according to the customer demand characteristics, utilizing a pre-trained customer demand prediction model to predict the interest degree of the customer for a specific product or service, and outputting a customer demand probability; and based on the customer interaction data and the customer demand probability, setting a call strategy with a to-be-called customer, and performing call operation according to the call strategy, the call strategy including call time, call content and call style. By using the embodiment of the invention, accurate calling can be realized through an intelligent means, the customer service quality is improved, and a more scientific and efficient calling strategy is realized.

The invention relates to the technical field of information management, in particular to a software development service management platform and method. The method comprises the following steps: acquiring customer demand change information, analyzing the customer demand change information, and determining demand change key information; obtaining a project development flow chart, analyzing the demand change key information and the project development flow chart, and determining a business modification starting node; obtaining a project milestone node list, modifying the initial node according to the business, screening the project milestone node list, and determining a key milestone node; analyzing a service modification starting node and a key milestone node, and determining a resource overall planning influence factor set; and according to the resource overall planning influence factor set, determining and outputting a resource overall planning plan for reference of a project development team. According to the method and the device, software development project resources are quickly and effectively planned, so that the software development business process is prevented from being disrupted by temporary requirements of customers, and the customer satisfaction is improved while project risks are reduced.

The invention relates to the technical field of data analysis models, and particularly discloses an SCRM-based data analysis model, which is used for acquiring customer data from social media, a customer relationship management system and customer feedback; cleaning and formatting the collected customer data, extracting feature data related to after-sales service, and performing data fusion; based on an output result of the data fusion model, establishing an after-sales service demand prediction model, and predicting a future after-sales service demand change trend of the customer; the distribution of after-sales service resources is optimized according to the after-sales service demand calculated by the after-sales service demand prediction model; and a customer demand trend and a resource allocation scheme are displayed in a graphical mode. According to the method, the multi-source data is integrated, the data island is broken, the resource utilization rate is maximized and the customer waiting time is minimized by optimizing the objective function, and the user satisfaction is improved.

The invention relates to a knowledge question-answering system based on a middle table operation manual and an implementation and use method thereof, and belongs to the technical field of computer technology, data management and knowledge management. The system designed by the invention comprises five parts, namely data access and data analysis, entity definition, entity relationship definition and entity attribute definition, graph construction, question and answer system construction and user feedback. According to the method, an optimal and most efficient data processing mode is designed for current questions and customer requirements, operation manual data are integrated, and efficient operation manual content retrieval and intelligent question answering are realized for the user questions by combining natural language processing (NLP) and a knowledge graph technology, so that timely and accurate operation process guidance is provided for the user, and the user experience is improved. The device can be used for assisting a middle table operator to quickly master, and the training time and training cost are reduced.

The invention relates to a product customization and configuration method and system based on a product model and a knowledge base. The method comprises the following steps: (a) associating a product structure model and a product knowledge base of a target product series; (b) constructing a product configuration model integrating a product structure model, a product knowledge graph and a configuration rule; (c) customer demand parameters are received, and a customized configuration scheme is generated through the product configuration model; (d) automatically generating a design material list and a process material list based on the customized configuration scheme; and (e) executing cost accounting according to the generated design bill of material and the process bill of material and outputting quotation data. The method has the advantages that the requirement of a user for customization can be met, the requirement for configuration can also be met, data such as technologies and processes can be automatically configured for the user, the data can be synchronously updated to related software systems such as PLM and PDM, and the intelligent design and intelligent manufacturing capacity of an enterprise is greatly improved.

The invention discloses an AI-based customer behavior analysis and prediction system and method, and the system comprises a data collection module which is used for automatically collecting customer behavior data from a plurality of data sources; the data processing module is used for cleaning and standardizing the collected data so as to facilitate analysis; the AI model training module is used for training a machine learning model by utilizing the processed data; the prediction analysis module is used for predicting future behaviors of the customer by using the trained AI model; the user interface module is used for displaying a prediction result and allowing a user to adjust prediction parameters; the report generation module is used for automatically generating a report containing prediction information according to the selection of the user; the customer behavior data can be automatically collected from multiple data sources, the customer behavior mode can be learned and recognized through advanced data processing and the AI model training module, the prediction accuracy can be improved, the user experience can be improved, and enterprises can better adapt to market changes and customer requirements.

The invention relates to the technical field of intelligent window scheduling, in particular to a multi-objective optimization intelligent window dynamic scheduling method and system combined with business rules. The method comprises the following steps: acquiring reservation queuing data, customer demand characteristics and window state data associated with each service window in real time; based on real-time collected data, a multi-objective optimization function including global average waiting time minimization, overall service efficiency maximization of all windows and global resource utilization balance degree maximization is constructed; solving the multi-objective optimization function based on a sorting genetic optimization algorithm; selecting an optimal scheduling strategy through a multi-factor fuzzy decision maker; and dynamically allocating window service resources according to the optimal scheduling strategy, and adjusting a window service queue in real time. By constructing the multi-objective optimization function, the resource waste problems of partial window congestion and partial idle windows caused by traditional fixed rule scheduling are effectively avoided, and the overall rationality and efficiency of window scheduling are improved.

The invention discloses a PCB device layout method based on a Polar mode, and the method comprises the steps: determining layout parameters: determining the layout parameters of a device according to customer demands and product design requirements; the paired devices are used as a whole, and the layout distance between the paired devices is set according to the layout parameters; creating a Polar layout template: creating a Polar layout template on a PCB (Printed Circuit Board) based on the layout parameters in PCB design software; device layout: placing the processed paired devices on the Polar layout template, and adjusting the rotation angle of each pair of devices; and generating a layout file: after the layout is completed, generating a final layout file. According to the invention, the layout of the devices can be rapidly realized, and compared with a traditional mode of manually placing and adjusting the devices one by one, the layout time is greatly saved, and the working efficiency is improved.

The invention, which relates to the technical field of printing production management, discloses a production processing tracking system and tracking method for a printing industry, and the method specifically comprises the steps: creating a corresponding production order in a preset printing production management system based on customer demand information, generating a unique identification code for each production order according to a dynamic random number algorithm, and associating and binding the unique identification code with each production order; obtaining a process node of each production order associated with the unique identification code to construct a process dependency graph of each production order; calculating the remaining delivery time of each production order associated with the unique identification code, and sorting each production order in real time to construct a self-adaptively updated priority scheduling queue; obtaining a current process node of each production order, and obtaining a state attribute of each process node of each production order and a corresponding execution progress; and carrying out full-link visual display on the state attribute of each process node of each production order and the corresponding execution progress, and inputting a unique identification code through a client to carry out query.

The invention discloses an internet scene-oriented date selection method and device and electronic equipment, and relates to the technical field of distributed systems or other related fields, and the method comprises the following steps: receiving an operation request initiated by user side equipment to a calendar panel, the interaction mode for initiating the operation request comprises manual input, visual selection or voice interaction; a date selection process is generated according to the operation request and the interaction mode, and the process logic of the date selection process is determined by the interaction mode; the date selection process is executed, an execution result is obtained, and the execution result is used for recording a user intention date corresponding to the operation request; and returning the execution result to the calendar panel of the user side equipment. According to the invention, the technical problems that the operation mode in the internet date selection scene is single and the customer demand cannot be met in the related technology are solved.

The invention discloses a transportation route optimization method and system based on the Internet of Things, and relates to the technical field of transportation route optimization, by integrating multi-source environment information and combining dynamic traffic data and transportation vehicle position data, transportation route planning is more intelligent and flexible, and when traffic jam, sudden accidents or road closing occur, the transportation route can be optimized more intelligently and flexibly. The system can detect the traffic condition of a transportation path in real time and recalculate and optimize the path through cloud computing when needed so as to ensure that the transportation task is completed on time, and the method not only can automatically adjust the transportation path, but also can continuously monitor the transportation process according to the real-time feedback data so as to ensure that the distribution is smoothly carried out, and meanwhile, the transportation efficiency is improved. A self-adaptive compensation algorithm is adopted to correct path deviation, so that the transportation route can be quickly adjusted, the transportation efficiency and customer requirements are ensured to be met in time, the transportation flexibility, timeliness and safety are greatly improved, and the development of intelligent transportation and logistics management is promoted.

The invention relates to the technical field of overseas warehouse system loading and unloading operation, in particular to an efficient intelligent warehouse loading and unloading method which comprises a loading strategy and an unloading strategy, the loading strategy comprises reference historical records, system recommendation and customer requirements, and the unloading strategy comprises a path priority principle, a storage location priority principle and other strategies. The system solves the problems that the existing logistics industry depends on manual cargo information recording and management, the efficiency is low, data input errors, cargo misplacement and cargo disorder and dispersion easily occur, the planning and utilization of the storage space are lack of intelligent means, the space is idle or the cargo accumulation is unreasonable, and the working efficiency is high. And resource waste is caused.

The invention provides an engineering investigation report automatic generation system and method based on a BERT model. The system comprises a data preprocessing module, a BERT model optimization customization module, an information extraction and structuring module and a report generation engine. According to the method, the limitation that time and labor are consumed and errors are prone to occurring in the traditional report compiling process can be overcome, the engineering investigation data can be automatically analyzed and processed by integrating an advanced BERT model for optimization and customization, then a high-quality report meeting the industry standard and the customer requirement is generated, and intelligent processing of the engineering investigation data and automatic report compiling are achieved.

A packaging cover and a packaging container are provided, which relate to the technical field of supplies. The packaging container comprises a container main body which is provided with an opening and a packaging cover which comprises a cover ring having an opening and a cover body covering the opening; wherein the cover ring and the container main body are detachably connected, the opening of the cover ring is communicated with the opening of the container main body, and the cover body can open or close the opening of the cover ring and the opening of the container main body under operations of a user. The above technical solution has the following advantages: the packaging cover can be installed on the packaging container according to customer requirements, and it is not easy to lose the packaging cover after the user opens the packaging container, and when the packaging cover is closed, it can prevent dust from entering the packaging container.