169 results about "Service process" patented technology

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 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 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 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 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.

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.

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.

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 application discloses a kind of collaborative robot system and service process for active power grid marketing, belong to artificial intelligence and electric power marketing service technical field.System includes: robot perception interaction end, cloud intelligent analysis hub and dynamic customer memory bank, robot perception interaction end deploys lightweight vision model, real-time identification customer emotion, body state and explicit feature, and initiatively trigger service;Cloud intelligent analysis hub uses power grid knowledge enhanced large language model, carries out depth demand analysis and individualized strategy generation to multi-modal data;Dynamic customer memory bank stores customer historical interaction record and portrait information in the form of knowledge graph, realizes the continuity and memory of service.The application is through edge small model+cloud big model collaborative architecture, general AI capability and power grid business knowledge are deeply fused, so that robot can actively identify customer demand, provide marketing service, improve customer experience and marketing conversion rate of electric power business hall.

The invention relates to the technical field of pension service, and discloses a smart nursing home optimization auxiliary service system, which comprises a high-dimensional situation sensing module, which is used for collecting multi-modal biological and environmental signals in real time through an integrated sensor array; the professional cognitive reconstruction module is used for deducing a cognitive path sequence and a decision intention; the service behavior generation module is used for reversely deriving a complete language expression sequence and a limb behavior sequence required by the old-age care service; the dynamic calibration module is used for comparing the actual situation change with the service system expectation in real time; the body memory module is used for constructing body memory experience for immersive backtracking of service personnel; and the cognitive sharing module is used for anonymously extracting the situational cognitive fingerprints. According to the invention, the service instruction is transmitted to the service personnel without perception through the cross-sensory customized instruction transmission unit, the standardization and accurate control of the service behavior are realized, dynamic adjustment is carried out according to the actual situation change, and the real-time adaptability of the service process is ensured.

The application provides a project knowledge base-based work order service tracking method, platform and medium, relates to the technical field of data processing, and the method comprises the following steps: constructing a project knowledge base in a project operation process; reading work order information, performing semantic analysis processing, and mapping the analysis result to the project knowledge base to generate a knowledge mapping result; extracting a historical processing path and a standard implementation path associated with the work order and meeting a preset condition, and constructing a service processing baseline track corresponding to the work order; in the work order execution process, real-time collection of work order processing behavior information is performed, continuous mapping to the project knowledge base is performed, and a real-time knowledge evolution track is generated; and work order service tracking analysis is performed, and a service tracking result is established. The application solves the technical problem that work order management in the prior art mostly depends on manual input and processing, lacks an automatic knowledge mining and application mechanism, and work order processing efficiency is low, error rate is high, and project progress and quality are affected.

The invention provides a distributed cluster timed task scheduling method and device and a program product, and relates to the technical field of computers. The method comprises the steps of traversing timed task scheduling configuration when a service process is started; for each timed task scheduling configuration, trying to obtain a distributed lock through a database shared by the current service cluster according to the timed task name; if the distributed lock is obtained, applying for a thread resource from a service process, registering a timed task by using a scheduler, and recording the registered timed task after registration succeeds; if the distributed lock is not obtained, the service process is reminded that the current timed task is registered by other processes. According to the embodiment of the invention, on the premise of not depending on an independent scheduling center, it can be ensured that the timed task is only executed by one node, repeated task scheduling is avoided, and meanwhile system complexity and resource overhead are reduced.

The invention discloses an information evaluation method, and relates to the technical field of intelligent information evaluation, and the method comprises the steps: deploying a protocol conversion rule of a field identifier; automatically matching and corresponding the extracted diagnosis data with the one-stop certificate handling system terminal for the disabled to form an automatically matched and corresponding information item; and performing secondary evaluation on the generated preliminary evaluation suggestions. According to the method, standardized access and field unification of multi-source heterogeneous medical data are realized, the compatibility of data interaction between systems and the accurate matching capability of diagnostic information are improved, and manual intervention and subjective judgment errors are reduced; the information processing efficiency of the one-stop certificate handling service of the disabled is remarkably improved, the automation level and the system cooperation capability are evaluated, the intelligence of the certificate handling process is realized, and the response speed and the service quality of the government affair service are improved.

The invention discloses a heat treatment equipment service reliability comprehensive evaluation method, which comprises the following steps of: firstly, determining a reliability evaluation index according to the working content of a heat treatment equipment service process; related data of the service process of the heat treatment equipment is analyzed, corresponding scoring standards are established for different reliability indexes, and evaluation values of different evaluation indexes are obtained according to the scoring standards; and finally, weighting the different evaluation indexes, and calculating to obtain a service reliability comprehensive evaluation result of the heat treatment equipment by combining the evaluation values and weights of the different evaluation indexes. Through comprehensive evaluation of the service reliability of the heat treatment equipment, the reliability weakness of the heat treatment equipment is accurately identified, and the method has great significance in improving the production efficiency, guaranteeing the product quality, enhancing the enterprise competitiveness and promoting the high-quality development of the industry.

The application relates to the technical field of computer networks and discloses a TCP service behavior portrait method and system based on network flow metadata, which comprises the following steps: decoding captured TCP network flow and extracting distributed correlation identifiers to form application layer interaction records; converting the interaction records into unified standardized operation logs; organizing the logs into ordered behavior chains based on time windows; further analyzing the logs and the behavior chains, quantifying atomic behaviors by aggregation, and discovering frequent service process modes by sequence pattern mining; attributing the analysis results to specific service entities to form entity portraits containing service function portraits and service process atlases; and finally constructing a global distributed service atlas representing the calling relationship between services by using the distributed correlation identifiers. The dependence strength between services is accurately quantified, and data decision-making basis is provided for system operation and maintenance and architecture optimization.

The embodiment provides a kind of intelligent recommendation method and device, equipment, medium, belong to artificial intelligence technical field.The method comprises: obtaining user voice;The speech recognition processing is carried out to user voice, to obtain target speech text;At least one intent set is obtained by the NLP intent recognition model to the intent recognition processing of target speech text;According to intent set, obtain user intent list, and user intent list is used to provide at least one user intent to user;Receive the intent information that user feedbacks according to user intent;According to intent information, determine target recommendation rhetoric from pre-set rhetoric recommendation library.The scheme is cleaned by pre-set NLP intent recognition model to speech text, extracts intent set, and then obtains user intent list.To clarify intent to customer according to user intent list, to determine target recommendation rhetoric to carry out service sales, improve the conversion rate in service process.

The invention discloses a loan service application method and device, electronic equipment and a storage medium, and relates to the technical field of internet finance, and the method comprises the steps: obtaining a unified social credit code and contact information of an enterprise, and transmitting a registration request to a server to complete account registration; logging in a server based on the registered account information; guiding the user to sequentially complete tax information authorization and electronic signing of a credit investigation authorization file; using the server to generate and return a loan amount to the client based on an authorization success result; guiding a user to input loan information by using a client, and completing electronic signing of a loan contract and a guarantee contract; and performing signing processing on the successfully signed loan contract and guarantee contract by using the server, and executing loan expenditure in response to an expenditure instruction of the user. According to the invention, through registration, quota calculation and signing of the loan application, the technical problems of flow splitting, tedious operation, low efficiency and incoherent experience of the existing online loan business can be effectively solved.

The invention provides an automatic testing method and device for cloud native services and a testing tool. The method comprises the steps that an identifier of a target application service and an ip address of a target service node where the target application service is located are obtained; generating a hanging script according to the identifier of the target service node and a pre-stored script template; according to the ip address of a target service node where the target application service is located, creating a copy of a hanging script on the target service node; controlling the target service node to execute the execution of hanging up the copy of the script so as to control the target service node to automatically obtain and hang up each target process associated with the target application service; and obtaining monitoring state data of each target process associated with the target application service so as to determine a test result of the cloud native service. According to the scheme, the target process of the target application service can be automatically suspended, the service process can be quickly suspended by one key, the test efficiency can be improved, and the time cost of suspending the service process when a tester performs a non-function test is greatly saved.

The application discloses a network false connection processing method and device, electronic equipment and program product, and relates to the field of financial technology, and comprises the following steps: periodically reading heartbeat parameters from a data set file through a data exchange function module of a host end; matching the heartbeat parameters with each keep-alive attribute and filling in the parameter values corresponding to each keep-alive attribute; successfully establishing a network connection between the host end and a gateway end, starting a sub-service process in which the data exchange function module is located; detecting whether there is data interaction between the host end and the host end by using a data interaction function module, obtaining a detection result; in the case that the detection result exceeds the parameter value corresponding to the keep-alive attribute and there is no data interaction, confirming that the connection path between the host end and the gateway end is abnormal, and closing the sub-service process in which the data exchange function module is located. The application solves the technical problem that the detection of the false live phenomenon in the related art relies on human intervention, the efficiency is low, and the flexibility and response speed of the system are reduced.

A system receives identification of a machine learning model, stored by a vehicle, that is ready for training. The system determines, based on a configuration file associated with the model, whether batch-based or live training is to be used to train the model and collects data for training, defined by a configuration file associated with the model. The system calls a learning as a service vehicle process to load a kernel and configures the kernel based on configuration data defined in the configuration file. The system receives notification from the learning as a service process that the training is complete. Additionally, the system validates a model, responsive to the notification including indication that training was successful, using a vehicle model validation process to test the model with live data before deployment by background execution of the model and saves a copy of the model for deployment responsive to successful validation.

The invention discloses a service full-link real-time perspective and driving method, device and equipment and a storage medium, and the method comprises the steps: building a hybrid data integration system, and integrating data dispersed in each heterogeneous service system to a lightweight order center; obtaining a data change event of the core business system through database log mining and issuing the data change event to a message queue; updating the lightweight order center based on the incremental data; calling a source system interface to acquire and process missing data in real time during query through an on-demand back-to-source mechanism; assembling the basic data and the supplementary data into a complete business object, and generating a full-link state view through state mapping; and when the key service state node is identified to be changed, broadcasting an event to a subscription system to trigger downstream service process automation. According to the invention, real-time integration and second-level response of service data are realized, data blind areas are eliminated while stability is guaranteed, full-link state perspective and service process automation are realized, and service operation efficiency and reliability are improved.

The invention discloses a sequence self-coding service full-chain quality tracking and anomaly detection method and system. The method comprises the following steps: acquiring a service process data set as an original data set; performing standardized preprocessing on the original data set to obtain a time sequence data set aggregated according to a process; constructing a training data set based on the generated time sequence data set aggregated according to the process; constructing and training a business process sequence anomaly detection model; and inputting the preprocessed to-be-detected sequence into the trained business process sequence anomaly detection model, and outputting a quality tracking report and an anomaly result. According to the sequence self-coding service full-chain quality tracking and anomaly detection method and system, the service process sequence is taken as a processing object, and uniform data preprocessing, effective length modeling and structured coding and decoding are carried out on a full link, so that accurate learning of a service flow normal behavior mode is realized, and the service flow quality tracking and anomaly detection efficiency is improved. And quality deviation and abnormal behaviors are tracked and discriminated in real time on the basis.

This invention relates to a service process running method, system, device, and readable storage medium. The method includes: upon receiving a service process startup request for the current microservice, starting the target application service process while simultaneously disabling the external traffic interface; disabling the service traffic thread during application startup; after the application service process has started, checking whether the application service process started normally; if normal, determining whether to manually or automatically call the service's internal traffic management interface to enable service traffic based on the service instance's status; and re-enabling the external traffic interface. This method uses the internally provided traffic management service interface for flexible deployment, avoiding errors in business code execution caused by incoming access traffic due to application startup or shutdown failures.

1. The name of the appearance design product: graphical user interface of customer service process template creation of electronic equipment. 2. The use of the appearance design product: for electronic equipment. 3. The design points of the appearance design product: the content of the graphical user interface. 4. The picture or photo that best indicates the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: for creating customer service process templates. 7. The human-computer interaction mode of the graphical user interface: select the process node card to be edited in the interface shown in design 1 front view, fill in the information in the function bar on the right side of the interface shown in design 1 front view, and click the "OK" operation option in the interface shown in design 1 front view after filling in the information. The human-computer interaction mode of design 2 is the same as that of design 1, the human-computer interaction mode of design 3 is the same as that of design 1, the human-computer interaction mode of design 4 is the same as that of design 1, the human-computer interaction mode of design 5 is the same as that of design 1, the human-computer interaction mode of design 6 is the same as that of design 1, and the human-computer interaction mode of design 7 is the same as that of design 1.

The invention discloses a method for evaluating the fatigue performance of a service period composite structure driven by mass FBG monitoring data, and relates to the technical field of structural health monitoring and fatigue life prediction. An evaluation framework of deep fusion of data and a physical model is constructed, and key time-varying parameters of a fatigue damage evolution model are updated in real time to enable the fatigue damage evolution model to continuously approach a real service state of a structure, so that accurate evaluation of fatigue damage accumulation and dynamic and reliable prediction of residual life are realized. According to the method for evaluating the fatigue performance of the service period composite structure driven by the mass FBG monitoring data, the monitoring data accumulated in the long-term service process is directly introduced into fatigue performance evaluation, so that the monitoring data is upgraded to fatigue performance evaluation and life prediction from traditional state recording and threshold value alarming; and real and online inversion of the fatigue performance of the service structure is realized.

The application discloses a kind of equipment performance lightweight reliable integration evaluation method based on space-time progression rectification, it is related to equipment performance evaluation technical field, the method includes: first, the original sensing data in the service process of equipment is acquired, the high-quality data after rectification is output by weak abnormal sensing feature rectification model based on space-time progression, avoid the evaluation deviation caused by low-quality data;High-quality data is then input into the integrated monitoring model based on multi-scale deep residual shrinkage network, and a decoupled state vector is simultaneously generated to complete performance state identification and performance quantification, while calculating the probability distribution entropy value of each category, and accordingly adjusting the confidence level during performance quantification prediction, to improve the evaluation reliability of equipment boundary transition state;Finally, the model is lightened through lightweight strategy, deployed in industrial equipment, to meet the limited computing resources and real-time requirements. Efficient and reliable evaluation of equipment performance can provide strong support for industrial equipment performance evaluation.