316 results about "Service process" patented technology

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 relates to the field of emerging software and information technology service, in particular to an artificial intelligence-based application system integrated management method, which comprises the following steps of: analyzing an API (Application Program Interface) log, structured data, an unstructured document, business work order data and business image data by pre-training a large model, and generating a unified semantic representation in combination with multi-modal contrast learning; a knowledge distillation technology is adopted to migrate the large model capability to a lightweight neural network, and a domain knowledge base is generated based on fine tuning of business work order data; a user multi-mode instruction is analyzed, a DAG service agent template library is matched, and an execution chain is dynamically combined based on a reinforcement learning strategy, so that text processing, multi-system collaborative reasoning and RPA service process automation are realized; and collecting an execution log, and updating lightweight network parameters and an intelligent agent strategy library through an online distillation algorithm to form a closed-loop optimization mechanism. According to the method, the service response efficiency can be improved, and the problems of enterprise multi-service system data splitting and process stiffness are solved.

The invention provides an SCRM customer management method and system, and relates to the technical field of customer management, and the method comprises the steps: collecting customer data; preprocessing the customer data, and obtaining integrated data based on a preset integration rule; performing multi-dimensional analysis on the integrated data, and mining rules and trends of the integrated data to obtain an integrated data analysis result; based on an integrated data analysis result, through an association rule mining technology and a clustering algorithm, constructing a customer portrait of the customer; according to the customer portrait, personalized marketing content is pushed to the customer through a preset marketing rule; and collecting a feedback problem of the customer, providing a solution of the feedback problem according to the customer portrait of the current customer, and updating the customer portrait according to customer data generated in the service process. According to the method, the client feedback is continuously collected and the client portrait is updated in real time, so that the latest behavior change of the client is reflected, and the accuracy of marketing decision making is further improved.

The invention discloses an AI infrastructure performance detection method and device and a storage medium, and belongs to the technical field of data processing. The method comprises the steps that according to chip information of container nodes, dynamic instrumentation is conducted on a kernel layer and a calculation layer by expanding a Burkley packet filter, first performance indexes of the kernel layer and the calculation layer are collected, a hardware management library function is called, second performance indexes of a chip hardware layer are collected, and on the basis of a control group mechanism, the kernel layer and the calculation layer are subjected to dynamic instrumentation; and obtaining meta-information of the business process group in the container node, constructing an association relationship between the first performance index and the business process group and between the second performance index and the business process group through association marking according to the meta-information, and performing association analysis on the first performance index and the second performance index based on the association relationship to obtain a performance detection result of the business process group. According to the method, the performance parameters are collected through a non-intrusive scheme, the performance indexes of different service processes are determined through a service association mode, and the accuracy of AI performance problem positioning is improved.

The invention provides a resource leakage point positioning method, electronic equipment and a storage medium, and relates to the technical field of terminals. In the method, in a service process, when a service thread calls a resource application interface to apply for resources, corresponding resource calling information is obtained, and the resource calling information comprises function stack memory information and register information corresponding to the resource application interface; the business process comprises one or more business threads; according to the technical scheme, the target thread is newly added in the business process, the stack return is carried out on the nat ive layer based on the function stack memory information and the register information, and the backtracking from the nat ive layer to the java layer is not needed during the stack return, so that the performance overhead and the resource consumption caused by the stack return are reduced, and the technical effect of improving the positioning efficiency of the resource leakage point is realized.

The invention provides an RPA operation report automatic generation method based on full-business process data fusion and a financial robot thereof, and the method comprises the steps: collecting task logs, business action events and context data generated in the execution process of the financial robot, and standardizing the task logs, the business action events and the context data into a unified event unit; constructing a flow path diagram based on the event time sequence; performing structure comparison on the trajectory diagrams of different service periods, identifying node addition and deletion or path rearrangement, and generating a structure attribution result; combining the attribution result and the event data to construct a structure perception type semantic index; and finally, automatically filling the indexes and attribution contents into the financial operation report template to generate a complete report. The method does not need to depend on a process definition file, can automatically restore a real execution path, achieves the interpretability analysis of process change and the full-automatic generation of a report, and remarkably improves the accuracy, integrity and decision support capability of financial RPA operation analysis.

The invention discloses a method for managing service nodes capable of being dynamically expanded based on a Camuda process engine, which belongs to the technical field of electric digital data processing and comprises the following steps of: uniformly configuring service process nodes into a pointing proxy delegation class; querying an external mapping table based on the execution context to obtain a business logic unit identifier; obtaining metadata from a logic registration center according to the identifier, and dynamically loading and instantiating a business logic implementation class through a sandbox class loader; driving service logic execution through a security context object of a white list only exposure method; and recording a full-link audit log. According to the method, structural decoupling of process definition and service logic is realized, so that the process definition does not need to be modified during service change, and dynamic replacement and gray release are supported; through sandbox isolation and security agent, isolated operation of dynamic codes and security and stability of an engine are ensured; and in combination with an audit tracking and fusing mechanism, the observability, the reliability and the operation and maintenance efficiency of the system are improved.

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 an insurance service full-process processing method and device based on multi-agent cooperation, and belongs to the technical field of artificial intelligence. A user request is received through the user interaction layer, a natural language processing technology is utilized to identify a user intention and extract key entity information, and structured user input data is generated; dynamically routing the request to a corresponding AI agent through a task distribution module according to the intention type, wherein the AI agent comprises a customer consultation agent, a risk assessment agent and a claim settlement processing agent; each agent cooperatively processes a business process based on shared session context information, exchanges structured data through a unified communication protocol and executes a cross-link task; and finally, integrating processing results of all agents, generating a service response conforming to insurance business logic, and feeding back the service response to a user through a user interaction layer. According to the invention, efficient automation and intelligent cooperation of the whole insurance service process are realized, and the accuracy and response speed of service processing are remarkably improved.

The invention provides an intelligent gas station optimization scheduling method and system, is applied to the technical field of vehicle scheduling, and aims to guide a vehicle to enter a proper initial parking position by obtaining the identity and service demand of the vehicle when the vehicle enters the station and generating a service voucher and combining real-time resource information in an energy station. And when the service resources in the station are available, selecting the most suitable target vehicle for resource allocation according to the type of the available service resources and the demand information recorded in the waiting vehicle service voucher. In the service process, if service demand update information exists, the service voucher of the vehicle is dynamically adjusted according to the update information, and according to the adjusted service voucher, the system can provide an accurate second guide signal for the vehicle completing the current service to guide the vehicle to go to the next stop, so that the service quality of the vehicle is improved. The whole process of the vehicle in the station is ensured to be smooth and efficient, and path conflict or congestion caused by demand change is avoided, so that optimal scheduling of the vehicle in the whole life cycle in the energy station is realized.

The invention relates to the technical field of data security, can be applied to business system platforms of medical health, financial science and technology and the like, and discloses an intelligent processing method for business process abnormity, which comprises the following steps: acquiring a business task, analyzing the business task, and generating a business component; sequentially executing the business components according to a preset execution sequence; capturing an abnormal execution state of the business task through an exception capturing mechanism, and classifying the abnormal execution state to obtain an exception type and a business identifier; selecting an exception handling mode according to the exception type and the service identifier; and processing the exception according to the exception processing mode and the service identifier. According to the method, the service assemblies are generated by obtaining and analyzing the service tasks, the assemblies are executed according to the preset sequence, and the execution state is monitored in real time. And if the exception occurs, the exception state is classified and processed, the exception type and the service identifier are determined, and then the exception processing mode is selected, so that the exception in the service process is effectively processed, and stable operation of the service is ensured.

The invention provides a government affair element universe digital person-based system for realizing intelligent guide of affair handling personnel, and belongs to the technical field of government affair service intellectualization. The system deeply integrates element universe, intelligent interaction, big data and artificial intelligence technologies, realizes natural interaction with the affair handling personnel by means of an intelligent virtual digital person, and realizes intelligent guide of the affair handling personnel. And the government affair service process can be smoothly completed. A local intelligent knowledge base system and a knowledge base exclusive to multiple government affair departments are built, and a DeepSeek large model is applied. Voice or text query of business knowledge, handling processes, policies and regulations is supported, and contents cover latest policies of tax payment services, meta universe, offline business handling places and modes and the like. The digital human has abundant functions, provides business consultation, hall navigation guidance and intelligent explanation, can also perform daily question and answer interaction, and creates a relaxed communication atmosphere. And after logging in the meta universe cloud hall, the accompanying real-time efficient interactive guide service can be obtained by clicking the guide digital person, so that the government affair service efficiency and the user satisfaction are remarkably improved.

The invention relates to the technical field of intelligent management, and discloses an after-sales service management method based on intelligent scheduling and automatic order dispatching, and the method comprises the steps: obtaining the maintenance and after-sales work order of a customer, and extracting the work order information; classifying the work order information according to a preset classification rule, generating different types of work orders, and distributing the work orders to a to-be-processed queue; matching a plurality of engineers for the work order to be processed by adopting an intelligent matching algorithm, and calculating an engineer score based on factors such as skill matching, load, response time efficiency and historical evaluation; assigning engineer processing work orders according to the scores, marking the work orders as to-be-processed work orders if the work orders cannot be matched, and transferring to manual auditing; if the distribution is successful, tracking the position of the engineer and the service progress in real time, and pushing information to the client; after the work order is completed, client evaluation is collected, and engineer scores are updated in real time. According to the method, the problem that a multi-dimensional scoring judgment, real-time position information tracking and service process visualization feedback mechanism is lacked can be solved.

The invention discloses an intelligent system for customer service quality inspection based on multi-source data, and particularly relates to the technical field of customer service quality inspection. The method comprises the following steps: acquiring session texts generated in a customer service process and associated product purchase and consumption data in a customer account in real time, and performing cleaning and standardization processing; a large-model automatic marking algorithm is constructed to carry out category marking on customer problems, and recognition of problem backgrounds and customer appeals is realized through context correlation analysis; performing double evaluation on customer attitude and customer service quality by using an emotion analysis model and a customer service verbal skill analysis model; and generating a comprehensive evaluation result of the overall quality of the customer service through fusion analysis, finally predicting a customer conversion rate and a contract extension rate based on the overall quality evaluation result, and generating a targeted customer service optimization strategy, thereby realizing accurate evaluation of the customer service quality and intelligent management of the customer relationship.

The invention discloses a cross-modal call receiving robot based on a large model and a full-scene intelligent control system and method, and the system comprises a cross-modal understanding engine based on a multi-modal large model, which obtains multi-modal data and carries out semantic understanding to generate unified semantic representation; the MCP manager is used for managing an MCP and realizing dynamic discovery, tool calling, multi-task arrangement and cross-modal interaction; the scene intelligent control center realizes docking of various customer service dialogue scene channels and manages a call receiving agent; the agent scheduler is used for dynamically creating agents according to user requirements, each agent is responsible for processing a specific task, identifying the user requirements through an intention decomposition algorithm in a service process, decomposing a complex scene into a plurality of atomic service units, and realizing load balancing by dynamically allocating tasks; and the adaptive interface is used for realizing compatible communication with various devices. According to the invention, the emergency response capability of the system is obviously improved.

The invention relates to the technical field of computers, particularly provides a service building method based on a low-code-free platform, and aims at solving the technical problems that in an existing low-code platform, the coupling degree of a service process engine, a form engine and a rule engine is high, troubleshooting is difficult and the like. The method comprises the following steps: generating a unified metadata model through visual interface configuration, and defining a business process, a form, a business rule instance and an association relationship thereof in a structured mode; analyzing the model and converting the model into deployment configuration information which can be loaded by each engine according to a grammar mapping rule; a coordination rule for coordinating a multi-engine execution sequence and a data interface is automatically generated based on the incidence relation between the instances, and the coordination rule is integrated with deployment configuration information; and driving the corresponding engine to execute the service. According to the method, business and engine decoupling is realized through the unified metadata model, and pluggable replacement of the engine is supported; a collaboration rule is automatically generated through visualization, and the development threshold is reduced; and through unified time axis debugging, the operation and maintenance efficiency is improved.

The invention provides a multi-service system integrated management method and system based on a unified data base, and the method comprises the steps: firstly obtaining a service demand description set of a multi-service system, mapping the service demand description set with data source capability information of the unified data base, and obtaining a service demand mapping result; constructing an integrated execution rule of the multi-service system based on the service demand mapping result, issuing the integrated execution rule to the service processing module of each service system, and triggering service processing to obtain a service collaborative execution result; and finally, generating an integrated management instruction according to a business collaborative execution result, and sending the integrated management instruction to a unified data base management module and business processing modules of the business systems to execute integrated optimization operation, so that integrated management of the multi-business systems in the ecological environment monitoring field can be efficiently realized.

The invention provides a document conversion service stability optimization method and system based on artificial intelligence, and the method comprises the steps: obtaining a real-time operation state data set containing service process operation data, resource occupation data and conversion task processing data, and obtaining a service operation state feature set through feature extraction; calling a pre-trained service exception tracing module to identify an exception mode, generating a result containing an exception type identifier and an exception association feature, calling a corresponding service adaptation model based on the result, adjusting parameters, generating a service stability regulation and control strategy, inputting the strategy into an execution control module for execution, and obtaining a service operation parameter adjustment result; finally, the training sample set of the service adaptation model is updated according to the adjustment result, and an optimized service adaptation model is generated through iterative optimization. Through multi-link cooperation, comprehensive optimization of the stability of the document conversion service is achieved, and the service performance and reliability are improved.

The invention provides a counter service monitoring system, method, device and equipment, a storage medium and a program product, and relates to the field of artificial intelligence. The system comprises a sensing module used for acquiring multi-modal data in a counter business handling process; the multi-modal data comprises a virtual identifier, visual information and core transaction data; the knowledge base module is used for storing business compliance rules, historical error cases and anti-fraud features; the processing module is used for identifying risk control information associated with counter business based on the storage content of the knowledge base module and the multi-modal data, and generating an execution instruction when an abnormal operation or a potential risk is detected based on the risk control information; and the execution module is used for responding to the execution instruction to execute at least one of the following operations: pushing operation correction guidance to the business personnel, and pushing business verification information or risk prompt to the customer. According to the system provided by the invention, the security of counter business handling is improved.

A process control system software security architecture, that is more effective at preventing zero-day or other types of malware attacks, implements the use of “least privileges” when executing the applications and services run within a computer device. The least privileges based architecture separates “service” processes from desktop applications that run on behalf of a logged-on user by partitioning the global namespace of the software system into service namespaces and logged-on user namespaces, and by strictly controlling communications between the applications and services in these different namespaces using interprocess communications. Moreover, the security architecture uses custom accounts to assure that each service process has the least set of privileges that are needed for implementing its function regardless of the privileges associated with the calling application or user.

The invention provides a smart campus task collaboration method and system oriented to multi-group service optimization, and relates to the technical field of campus service management. According to the method, historical service logs are called, and storage nodes are distributed according to a service process to generate data sets before and after collaboration; determining a group cooperation state based on the data set, and constructing an exclusive task measurement set and a group behavior feature set; quantifying a cross-group task cooperation priority through a double-evaluation mode mark; generating a group-task collaborative set in combination with iterative operation, and screening and optimizing by using a segmentation coefficient (cohesion + separation degree); the system correspondingly comprises a data acquisition and storage module, a task and feature construction module, a collaborative priority quantification module and a collaborative adaptation analysis and optimization module. According to the method, the problems of lack of precise adaptation, fuzzy priority quantization and the like in multi-group coordination in the prior art are solved, directional optimal configuration of service resources is realized, task coordination efficiency and group satisfaction are improved, and an efficient solution is provided for multi-group differentiated services of the smart campus.

The invention discloses a smart city power marketing service simulation evaluation method and system based on digital twinning. The method comprises the steps of obtaining power marketing service data, performing space-time semantic analysis, extracting attribute dimensions, establishing a cross-domain attribute association graph, establishing a cross-domain attribute association graph, and constructing a digital twinborn model; the target marketing service process is decomposed into service units, the service units are decomposed, a local simulation domain is constructed for parallel simulation, and a combined simulation result is obtained through an inter-domain coupling operator; and comparing a simulation result with real data, identifying virtual and real deviations, performing reverse traceability decomposition on virtual and real deviation distribution, mapping the virtual and real deviation distribution back to each local simulation domain, determining a local contribution magnitude of each local simulation domain, and constructing a hierarchical evaluation index to obtain a service quality evaluation result. According to the invention, accurate evaluation of the power marketing service is realized, and the service quality supervision efficiency is improved.

The invention relates to the technical field of artificial intelligence, discloses a human outsourcing intelligent customer service body based on a large model and reinforcement learning and a service method, and aims to solve the problems that an existing customer service system is rigid in service process, broken in customer experience and incapable of learning evolution from artificial expert experience. The intelligent customer service body comprises an intention analysis unit, a dynamic strategy generation unit, a closed-loop learning unit and the like. Wherein the dynamic strategy generation unit is used for generating and executing a service strategy by utilizing a large language model; and the closed-loop learning unit realizes continuous learning and capability self-optimization of the intelligent customer service body through explicit reasoning chain monitoring, correction instruction analysis, multi-dimensional reward calculation and double-track system model iteration. Through adoption of the technical scheme, the service flow flexibility, continuity and customer experience can be improved, and the technical problems of insufficient system rigidity and learning evolution are effectively solved.

The embodiment of the present specification provides a logistics service object scheduling method, which obtains service data of a target object; then performs data statistics on the service data through a service evaluation index to obtain a service evaluation parameter; and performs data statistics on the service data through a parameter influence index to obtain an influence factor; then adjusts the service evaluation parameter based on the influence factor to obtain a regional familiarity of the target object to a target region; and further schedules a service process corresponding to the target object according to the regional familiarity. Thus, the logistics service capacity resource dynamic scheduling process is realized. Since the multi-dimensional comprehensive service evaluation index is used to quantify the logistics service execution effect, and the multi-dimensional influence factor is used for quantitative correction, the evaluation accuracy of the service effect of the target object in a specific region is improved, and the service process is scheduled based on the obtained regional familiarity, so that the execution efficiency of the logistics service is improved.

An intent recognition method based on a large language model and a customer portrait classification model, the method comprising: obtaining a customer's current relevant order and background information according to customer ID information; a customer portrait classification model obtains a first score vector according to the relevant order and the background information; the first score vector is used to indicate a current customer portrait classification score; a fine-tuned large model performs semantic recognition on the context of an input customer conversation to obtain a second score vector; the second score vector is used to indicate a customer current emotional feedback score; the fine-tuned large model is obtained by fine-tuning a large language model; a comprehensive score vector is obtained by fusing the first score vector and the second score vector; the comprehensive score vector is used to indicate different tendency weights in different business scenarios; an inference large model performs inference according to the comprehensive score vector, and outputs a customer intent; a corresponding business logic is driven to be executed according to the customer intent, and the corresponding business logic comprises providing customized suggestions or triggering a specific service process.

The invention provides a business data feature sharing method and system based on machine learning, and the method comprises the steps: firstly obtaining a business link feature set of a talent service field, including a personnel core, post association and service process features, and a business demand description of a feature calling main body; then constructing a feature anchoring rule to perform association processing to obtain a feature anchoring result, inputting the feature anchoring result into a pre-training machine learning model to calculate a shared adaptation parameter, and constructing a shared execution link including feature extraction, processing and delivery nodes according to the shared adaptation parameter; and carrying out association mapping on the features output by the sharing execution link and a talent service business system of the feature calling main body, generating a feature sharing result containing a plurality of identifiers, and feeding back the feature sharing result to the business system to support business decision operation, thereby realizing efficient and accurate sharing of talent service business data features.

The application relates to an observable system based on service context enhancement, which is built based on an observable architecture and comprises a data acquisition layer, a data processing layer, a data storage layer and a data display layer. The data acquisition layer is used for acquiring metadata of service requirements and technical requirements, and generating technical monitoring data under a service context. The data processing layer is used for associating enhanced data including external service data and software life cycle data with the technical monitoring data under the service context to generate derivative data. The data processing layer is also used for performing semantic analysis on the derivative data to generate pragmatic data, wherein the pragmatic data includes service level operation steps in a natural language form and service optimization suggestions. The data storage layer is used for storing the derivative data and the pragmatic data. The data display layer is used for displaying the derivative data and the pragmatic data. Compared with the prior art, the application can realize deep integration and comprehensive observability of technical data and service processes, and reduce communication barriers between technical teams and service teams.

Embodiments of the present application provide a task execution method, an electronic device and a computer readable storage medium, and relate to the field of electronic devices. The task execution method comprises: creating a client task according to a task request sent by a client; allocating a service process to the client task, and controlling the service process to execute the client task; and when the time length of the service process executing the client task reaches a preset slice time length, releasing the service process and allocating the service process to another client task. Compared with the prior art, the task execution method, the electronic device and the computer readable storage medium provided by the embodiments of the present application have improved the use efficiency of system memory.

The application discloses a kind of based on intelligent service handling process management and control platform, specifically related to computer technology field, management and control platform is built-in dynamic process engine module, dynamic process engine module is used to parse third-party platform service process structure, support visual configuration process node and rule, realize the automatic execution and scheduling of process, and monitor feedback is carried out to process execution process.The application is adapted to the dynamic change of third-party platform by automatic analysis, strengthens learning dynamic scheduling resource, whole-link monitoring and intelligent diagnosis, systematically solves the problem of high adaptation cost, inefficient resource scheduling, insufficient monitoring capacity in traditional process control, and comprehensively improves the automation rate, execution efficiency and stability of service handling process.