604 results about "Service process" patented technology

The invention discloses an intelligent management system for a service consultation platform, and the system comprises the steps: processing data through a data collection and processing module; using a data fusion unit to construct a feature matrix for the cleaned multi-source data; based on a demand prediction module, generating a prediction result of the user consultation demand; user input is recognized through a semantic analysis and question and answer module; the analyzed semantic information is transmitted to a knowledge management module, matched content is retrieved from a knowledge base based on a semantic similarity matching algorithm, and answers are generated after ranking according to priorities; information in the service process is collected through the service quality dynamic evaluation module for scoring, an optimization scheme is made through the service optimization strategy generation module, and the optimization scheme is fed back to the evaluation module to achieve closed-loop management. According to the method, the prediction capability and the service efficiency of the platform are remarkably improved, and more efficient and more personalized consultation experience is provided for the user.

Disclosed in the present invention is a fully automated log analysis and fault handling system and method based on an NLP large model. The method comprises: collecting a service state, an operating environment, and log files; extracting and generating log summaries from the log files, extracting key information from the log summaries to obtain state information of a service process; if an error is fed back, the NLP large model performing fault analysis on the error by using the service state and the operating environment as prior knowledge, and recording upstream and downstream key information involved in the error; on the basis of the upstream and downstream key information involved in the error, finding a fault cause and providing a fault handling action; and then executing a plan on the basis of the fault handling action. Log collection of the present invention is not limited to a prescribed format, and an NLP large model using a service state and an operating environment as prior knowledge is introduced for fault analysis.

The invention provides an operating system updating method based on cloud service, which comprises the following steps: acquiring operating system versions and patch installation conditions of different hosts by scanning system information of cloud hosts, and dividing the hosts into corresponding management groups according to operating system types and version numbers, formulating a unified patch strategy and an update plan for each group; a monitoring agent is deployed on a cloud host, operation indexes and log information of service processes are collected in real time, collected data are summarized to a centralized log analysis platform, logs are analyzed and mined through a batch computing framework, and a service operation behavior model is constructed; for data in a log analysis platform, a machine learning algorithm is used, an anomaly detection model is trained in a supervised learning mode, and the model can automatically discover various abnormal behaviors in the service operation process from massive log data and generate alarm information.

The invention relates to the field of data processing, and particularly provides a big data mining method and system based on digital enterprise management, and the method comprises the steps: obtaining multi-modal session data generated in the process of carrying out digital service interaction between a target enterprise and a user, extracting a user demand feature set from the multi-modal session data, and based on a preset multi-task learning framework, carrying out joint training on the user demand feature set, generating a demand analysis model, calling the demand analysis model to analyze the user demand feature set, generating an optimization strategy set associated with the target enterprise service process, and sending the optimization strategy set to the server. And updating the digital service execution logic of the target enterprise according to the optimization strategy set, and performing incremental training on the demand analysis model based on user feedback data. According to the invention, the response efficiency and the function adaptability of the digital service process can be improved.

The present invention discloses a method and apparatus for simulating thermal stress of a casting mold during a service process, and a storage medium. The method comprises: importing a pre-built three-dimensional geometric model of the casting mold, processing the geometric model, and then performing grid division to obtain a casting simulation finite element physical model; assigning material parameters, interface parameters, process parameters, and boundary conditions to the finite element physical model to obtain a casting simulation finite element calculation model; performing casting process simulation calculations to obtain casting simulation results such as mold temperature field and stress field, and exporting result data; interpolating a temperature-stress correction factor; multiplying all correction factor data under the same node numbers with mold stress data to obtain final thermal stress distribution data of the mold during the service process.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to an intelligent customer service method and system based on a large model, and the method comprises the steps: generating a unique session ID based on a user identity, and associating a historical context; multi-modal input of texts, voices, images and the like is carried out, and data standardization is carried out through a unified interface; realizing mixed intention recognition by combining a rule engine and a large model; constructing a dynamic customer portrait through multiple rounds of dialogue records, historical work orders and business data; according to the question emergency degree and customer value grading response, analyzing customer emotion in real time, adjusting a reply style, and outputting an answer; the manual operation process of the traditional customer service is converted into an AI automatic closed loop; the method has the beneficial effects that customer intention deep understanding, multi-mode interaction, emotion perception and service process automation are realized through a large language model (LLM) technology, and the service efficiency and the user satisfaction are remarkably improved.

The invention discloses a business process optimization method and system, relates to the technical field of computers, and provides a preventive business process optimization method which can identify and solve potential problems in advance so as to ensure stable operation of a preset business process. According to the main technical scheme, the method comprises the steps of obtaining a preset business process, wherein the preset business process comprises business nodes, a logic operation sequence between the business nodes and monitoring index information corresponding to the business nodes; executing a preset business process according to the logic operation sequence among the business nodes by utilizing a process execution engine, and outputting a monitoring index value corresponding to the monitoring index information in the execution process of the preset business process; judging whether each service node in the preset service process has a fault risk or not by using the monitoring index value; and if yes, adjusting each service node according to a preset adjustment method to generate an optimized service process. The method is used for optimizing the preset business process.

Disclosed are a method and apparatus for optimizing server system interrupts, a device and a medium, which relate to the field of servers. The method is applied to a server system, and comprises: when a peripheral driver of a kernel space monitors an interrupt request signal sent by a hardware peripheral, reading a hardware event queue in the hardware peripheral, so as to acquire a target hardware event in the hardware event queue; then, converting the target hardware event into a target software event by using the peripheral driver, and writing the target software event into a preset global event queue; reading the target software event in the global event queue by means of a user space, and forwarding the target software event to a corresponding target service process by means of the user space, such that the target service process processes the target. In this way, by using hardware interrupt coalescing and software event coalescing techniques, this solution may not only reduce the interrupt processing overhead, but also reduce the processor utilization.

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 a terminal capability management method and device under RAN servitization and a storage medium, and relates to the technical field of communication, and the terminal capability management method comprises the following steps: a terminal obtains terminal capability information, and sends the terminal capability information to a first network element; and the first network element receives the terminal capability information sent by the terminal and manages the terminal capability information. Wherein the first network element is an access network element under RAN servitization, or the first network element is a core network element under RAN servitization. According to the method and the device, each core network element can obtain the terminal capability information from the first network element, so that the corresponding service process is initiated according to the terminal capability information, and the normal operation of each service process under RAN servitization is ensured.

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, which relates to the technical field of the electric power supervision management and control system, discloses a supervision management and control system based on improvement of the electric power marketing service level, and the system integrates four modules of data acquisition, data analysis, supervision management and control and feedback improvement. Basic information, service requests and service process records of clients are collected through the data acquisition module; the data analysis module identifies problems and bottlenecks in the service process; the supervision and control module comprises a real-time monitoring and early warning system sub-module, a service quality evaluation model sub-module, a process monitoring and optimizing sub-module and the like and can supervise and control the service process in real time, and the feedback improvement module collects customer feedback and service evaluation and continuously improves and optimizes the service. The system also has a self-learning upgrading capability, continuously optimizes the service process through an internal feedback mechanism, improves the service quality, effectively solves the defects of an existing power marketing service system in the aspects of supervision and control, and provides a more efficient and convenient service solution for the power industry.

The invention provides an AI-based customer service intelligent interaction method and system, and a medium. The method comprises the following steps: collecting attribute information and behavior track information of a target customer, processing to obtain a customer information portrait, extracting personalized demand information of the target customer according to the customer information portrait, executing intelligent interaction service based on the personalized demand information, monitoring an interaction service process in real time, collecting service state parameters, and sending the service state parameters to the target customer. Whether manual customer service switching needs to be executed or not is judged according to the service state parameters, intelligent interaction service in a preset time period is monitored, service index data is extracted, quantitative evaluation processing is conducted according to the service index data, a service reliability coefficient is obtained, the reliability of the intelligent interaction service is judged, and corresponding optimization measures are taken. Therefore, the AI-based customer service intelligent interaction technology is realized.

The invention belongs to the technical field of data analysis and process mining, and particularly relates to a method for generating a parameterized interactive graphical process tree for business log data. The method comprises the following steps: firstly, collecting original business log data including database logs, application server logs, user behavior logs and the like, checking the integrity and timestamp accuracy of the original business log data, cleaning and standardizing the data, and extracting key process nodes; secondly, designing a tree data structure based on a DFG graph structure, defining attributes of nodes and connections, mapping data into the DFG structure to accurately represent the relationship between the nodes, and extracting key nodes; and then selecting a proper directed graph layout algorithm, optimizing node distribution, and distinguishing nodes by using visual elements. And finally, designing an interactive feedback system which comprises user interface design, specific service type screening and a real-time updating mechanism, so that the user can conveniently interact with the process tree, deeply analyze the service process and improve the understanding and optimization capability of the service process.

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 discloses an agent building workbench based on a multi-agent framework, and relates to the technical field of artificial intelligence. The system comprises an agent building module, a workflow configuration module, a release and integration module and an enterprise WeChat access module. The agent building module is used for quickly creating an agent according to business requirements and configuring parameters, a function module and a knowledge base of the agent at the same time; the workflow configuration module constructs a task allocation mechanism based on an open source multi-agent architecture, and the workflow configuration module is used for supporting configuration of a multi-agent workflow through a graphical interface and a programming interface, and embedding an open source tool and a self-research tool in the workflow to realize modular processing of tasks; by constructing the Roles intelligent agent construction workbench, the problem that an existing intelligent agent development tool is insufficient in flexibility and expansibility is solved, an efficient intelligent agent construction and management platform is provided for enterprise users, intelligence and automation of the service process are achieved, and the method has wide market application prospects.

The invention discloses an electric power marketing service behavior supervision and control method and system, and relates to the technical field of behavior supervision, and the method comprises the steps: collecting audio, video and position information in an electric power access service process, carrying out the multi-modal data fusion analysis of the collected information, and comprehensively judging whether the service behavior is compliant or not; the wearing condition of the customer manager is monitored in real time through the AI image recognition technology, and whether the behavior is correct or not is judged based on the fused data; performing encrypted storage and non-tampering recording on behavior data of a customer manager in each service link based on a block chain technology, and performing intelligent labeling and tracking on key operation steps in a service process; and verifying the audio and video data provided by the customer manager through a deep counterfeiting detection technology, and outputting a final verification result. According to the invention, by introducing a multi-modal data fusion technology, an intelligent behavior analysis technology and a block chain storage technology, brand new improvement of the power management and control system in customer manager service behavior supervision is realized.

The invention discloses a hotel guest room service task intelligent scheduling system based on a cloud platform, and relates to the technical field of hotel service management, the intelligent scheduling system comprises a mobile client, a cloud server and a database system; and the mobile client is used for configuring a special mobile application program running on mobile equipment such as a smart phone or a tablet personal computer for hotel guest room service personnel. According to the intelligent hotel guest room service task scheduling system based on the cloud platform, service personnel can quickly respond to service requirements through real-time task pushing and mobile operation, the task waiting time is shortened, the moving speed of the service personnel is increased through positioning and navigation functions, the service response time is shortened, and the service efficiency is improved. Through detailed recording and picture and video uploading in the service process, service quality tracing and supervision are facilitated, weak links of the service process can be found and improved through data analysis, in addition, special requirements of guests are met through personalized service recommendation, and the satisfaction degree of the guests is improved.

The embodiment of the invention discloses a method for analyzing a root cause of a user carrying network and electronic equipment, and the method comprises the steps: determining a first weight of each event in a personalized service process based on the personalized service process of a to-be-analyzed user and basic weights of a plurality of events, for example, the basic weight, and further, determining a second weight of each event in the personalized service process; according to the method, the basic weight of each event in the personalized service process is corrected, the corrected weight of the event reflects the influence degree of the event on the to-be-analyzed user carrying network, and further, the root cause (or root cause event) of the to-be-analyzed user carrying network is determined based on the corrected weight of each event in the personalized service process. According to the method, the root cause of the carrying network of the user can be analyzed differentially, and the accuracy of root cause identification is improved.

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 relates to the technical field of artificial intelligence management systems, in particular to an AI business management system based on enterprise large model data, which comprises an information acquisition module for receiving enterprise information and determining businesses in an enterprise, a classification module for carrying out category division processing on the businesses, and a processing module for processing the businesses, the detection module is used for recording time nodes when the terminals start to process the services and time nodes when the services are processed, and detecting loads and occupancy rates in the running process of the terminals; the analysis module is used for judging whether the processing of the terminal aiming at the service meets the standard or not, analyzing the reason for not meeting the standard and generating a scheduling instruction; and the scheduling module is used for performing scheduling processing on the service processed by the corresponding terminal based on the received scheduling instruction. According to the invention, the real-time monitoring and intelligent scheduling of the terminal for the business processing are effectively realized, and the management efficiency of the AI business management system for the enterprise large model data is effectively improved.

The invention discloses a physical examination quality control system. The quality control system is in interactive connection with a mobile terminal through the Internet; the quality control system comprises a quality control module and a data management and analysis module. The quality control module comprises a first-level quality control module, a second-level quality control module and a third-level quality control module. The primary quality control module comprises a problem unit, an analysis unit and a marking unit; the secondary quality control module comprises a quality control spot check unit, a quality control table unit and a rectification unit; the third-level quality control module comprises a quality control summary conference management unit, a conference content unit and a tracking evaluation unit; and the data management and analysis module provides a mobile terminal with query, statistics and analysis permissions to perform query, quantity statistics and query result analysis according to the screening conditions. According to the invention, comprehensive quality control of multiple inventions is realized, omissions and errors in the service process are reduced, and high quality and safety of medical services are ensured.

The invention relates to a customer insight platform in a financial deprotection scene, and belongs to the field of financial science and technology. The data processing and analyzing module is used for identifying customer behavior modes, preference changes and potential risk points; the customer insight module is used for updating customer labels and realizing personalized classification and prediction; and the application service module is used for designing a customized financial service scheme according to the insight result. The method has the advantages that the capacity of the financial institution in the aspect of customer right protection is greatly improved, consumption disputes are reduced through accurate insight of customer requirements, the service process is optimized, customer trust and satisfaction are enhanced, and meanwhile sustainable development of the financial institution is promoted.

The embodiment of the invention provides a flow management method, device and system and a storage medium. In the embodiment of the invention, the bandwidth flow control is firstly carried out on the service process based on the throughput threshold of the service process, so that the network flow sent by the service process does not exceed the throughput threshold of the service process, and the flow control is carried out at the home terminal, so that the flow sent by the home terminal does not occupy all resources of the home terminal; and thus, the traffic of the home terminal can be prevented from occupying all resources of the opposite terminal. Afterwards, for a plurality of second requests read in unit time, the plurality of second requests are sent to the destination end in batches according to the scheduling priorities and / or delay sensitivities of the plurality of second requests, and the requests with high scheduling priorities and / or delay sensitivities can be preferentially sent to the destination end, so that the requests with high scheduling priorities and / or delay sensitivities can be preferentially sent to the destination end. The queuing time of the requests with the high scheduling priority and / or the high delay sensitivity is shortened, the processing efficiency of the requests with the high scheduling priority and / or the high delay sensitivity is preferentially guaranteed, and the service quality can be improved.

The invention discloses an SOP flow rapid generation method based on activity features, and particularly relates to the technical field of computer application, and the method comprises the steps of S1, activity feature dynamic extraction, S2, knowledge graph enhanced semantic understanding, S3, reinforcement learning flow optimization, and S4, real-time feedback and dynamic adjustment. Through multi-aspect technical innovation, the method has remarkable effects on flow generation, execution and knowledge updating, in the flow generation stage, multi-source heterogeneous data collection is combined with an advanced algorithm, the generation time is greatly shortened, in the execution process, knowledge maps, reinforcement learning and a real-time feedback mechanism are coordinated, the error rate is reduced, the resource utilization rate and compliance are improved, and the method is suitable for large-scale popularization and application. Meanwhile, the knowledge graph can be continuously updated according to the optimization process, the adaptability of the SOP process to service changes is enhanced, the intelligence and automation level of the service process is comprehensively improved, and an efficient, high-quality and flexible process solution is brought.

The invention relates to the technical field of maintenance service management and intelligent scheduling, in particular to a one-stop maintenance service management platform based on a cloud platform, which comprises a user demand analysis module, a service resource scheduling module, a maintenance process collaboration module, a data tracing analysis module and a service report generation module. Wherein the user demand analysis module is used for analyzing and extracting equipment type parameters and generating maintenance aging parameters; the service resource scheduling module is used for generating a resource scheduling instruction containing a service provider ID and committed arrival time; the maintenance process collaboration module is used for generating an aging verification parameter containing an actual time difference of arrival; and the data tracing analysis module is used for generating fitting compliance parameters. According to the invention, through constructing the maintenance service management platform which integrates demand analysis, resource scheduling, process collaboration and result evaluation, structured processing and quality traceable supervision of the whole service process are realized.

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.