93 results about "Service composition" patented technology

A multi-channel ticket creation and service management system that includes: a graphical interface controller configured to provide a graphical user interface (GUI) accessible through a plurality of predefined Uniform Resource Locators (URLs), the GUI including interactive service request forms associated with a plurality of hierarchical service and subservice classifications; a form processing circuit operatively coupled to the graphical interface controller and configured to receive a selected service-subservice combination from a user and dynamically populate subsequent input fields based on a predefined service taxonomy, the input fields including contact information, email address, subject, location identifier, area of ​​responsibility field, and a description field; a rule execution logical processing unit coupled to the form processing circuitry, the rule execution logical processing unit configured to enable or disable one or more context-sensitive fields based on logical rules stored in a service-subservice dependency tree, the logical rules governing the enablement of date and time fields exclusively when the selected service is journalism and the selected subservice is newspaper reporting; an authentication mapping processor comprising a session parser and a user-to-department resolver, wherein the session parser is configured to extract user credentials and associated metadata from session cookies or authentication tokens, and the user-to-department resolver is configured to auto-populate an activity-driven scope field based on the extracted metadata and an organizational mapping scheme; and a validation and transmission control unit configured to check the completeness and data type conformity of all enabled fields, generate a structured ticket record, and transmit it to a central ticket management platform.

The invention belongs to the field of vehicle service, relates to a data analysis technology, is used for solving the problems that matched service recommendation cannot be provided for a user and service type optimization cannot be carried out according to the service condition of the user in the prior art, and particularly relates to a vehicle service management platform which comprises a server, the server is in communication connection with a correlation analysis module, a service management module, a store recommendation module, a store management module and a database; the association analysis module is used for performing association analysis on service types of the vehicle service management platform: generating an analysis period, obtaining all service data of the vehicle service management platform in the analysis period, and screening associated objects through the service data; according to the invention, association analysis can be carried out on the service type of the vehicle service management platform, the service data in the analysis period is analyzed to obtain the association data set of the vehicle interval, the corresponding service combination package can be generated for the vehicle interval according to the association data set, and the user experience is improved.

The invention discloses a service-based killer chain analysis construction method, which belongs to the technical field of cooperative combat, and comprises the following steps: constructing a service resource pool, and setting a service discovery mechanism; on the basis of the fusion situation set, a situation switching event is recognized so as to perform local updating, and a three-level situation view is constructed; constructing a directed hyperedge, generating a directed hypergraph in combination with function category constraints, generating a dimensionality reduction state space based on multi-dimensional constraints, and generating an initial combat candidate set; configuring a management node, sending task invitation information to the initial combat candidate set in combination with the subtasks, generating a response scheme, performing adaptive screening, generating an initial killing chain, and performing failure verification; constructing a servitization twinborn body, deducing the initial killing chain, and executing delay at a calculation end so as to perform elimination recombination or delay optimization; a service combination template is constructed, an efficiency feedback mechanism is established, the superposition influence of frequent situation switching and state space catastrophe is cooperatively solved, and the real-time performance of generation of the killing chain is remarkably improved.

The invention provides a public service platform system based on artificial intelligence, and aims to realize accurate service recommendation required by a user through a plurality of modules. The system comprises a user interaction module which is used for receiving input request data such as texts, voices and images of a user and processing the input request data in combination with time and position information. The data acquisition module is responsible for acquiring multi-source heterogeneous urban public service data. Based on the collected data, the knowledge graph construction module constructs a dynamic knowledge graph comprising public service entities, user entities and attributes thereof. And the intelligent analysis module performs time sequence analysis, spatial analysis and cross-domain association analysis by using the knowledge graph to generate service demand prediction and spatial demand distribution results. And the personalized recommendation module provides the combination of the target service and the associated service, which most accord with the request of the user, for the user through a demand matching unit, a position optimization unit and a service combination recommendation unit. According to the system, the response efficiency of public services can be improved, and personalized and multi-field service recommendation is provided for users.

The invention relates to the technical field of internet risk collaborative management, and discloses an internet data risk early warning and collaborative management system and method.The internet data risk early warning and collaborative management method comprises the steps that a service grid enhanced federal learning unified framework is constructed, and a tetrad structure is formed; a seamless integrated architecture of federal learning and a service grid is realized; developing a federal contribution evaluation mechanism based on a service level agreement; a self-adaptive service discovery and dynamic combination technology is realized, and an optimal service combination scheme is generated; constructing a federated aggregation algorithm of data quality perception, and introducing a quality weight factor optimization model for training; designing a multi-level cache strategy; according to the invention, through the fusion of federal learning and the micro-service architecture, the purpose of keeping efficient collaboration while protecting data privacy is realized, the original data of each participant is always kept locally, only necessary model parameters are shared, and the privacy leakage risk is reduced.

The invention discloses a service combination method for driving benefit balance of both parties by cooperation capability in a cloud manufacturing environment, which comprises the following steps of: loading a manufacturing task to be processed at the current moment, decomposing the manufacturing task into a plurality of sub-tasks, acquiring a plurality of state characteristics of each sub-task at the current moment, and splicing the state characteristics into a state vector representing the current state of the sub-task; and inputting the state vector representing the current state of the subtask into a reinforcement learning model of the cloud manufacturing platform, selecting a real-time optimal service with the maximum probability distribution from the candidate service set according to a pre-training parameter of the reinforcement learning model, and sequentially selecting a corresponding real-time optimal service for each subtask according to the execution sequence number, the real-time optimal services of all the sub-tasks are combined in sequence until the optimal service combination chain of the manufacturing task is constructed, the cooperative feasibility and operation stability between the services are considered, the service combination chain with the higher adaptive capacity is generated, and the service ecological sustainability and the development capacity of a platform are remarkably improved.

To solve the problem that arises when combining existing provision services to provide a new service.SOLUTION: A system for combining existing provision services to provide a new service, comprises: means for selecting relevant and necessary departments, constructing a project team, and creating a WBS (Work Breakdown Structure) while considering concerns; means for identifying applicable services and determining combinations of provision services; and means for designing the provision process of the new service and ensuring collaboration with relevant departments and teams.SELECTED DRAWING: Figure 1

The invention discloses a service adaptation method and device based on a skill dependence graph and QoS perception, and a robot method comprises the steps: responding to a task execution request sent by a client, and according to task demand information carried by the task execution request, presetting the skill dependence graph, and retrieving a plurality of available alternative atomic micro-services; the preset skill dependence graph is composed of a plurality of nodes and edges between the nodes; under the resource constraint of a preset multi-objective optimization model, determining an optimal service combination scheme from the plurality of alternative atomic micro-services; carrying out containerization deployment on each atomic micro-service in the optimal service combination scheme to obtain a deployed micro-service instance for task execution; in the execution process, when it is detected that service performance degradation or resource bottleneck exists according to the QoS index and resource information sensed in real time, the optimal service combination scheme is optimized, and the step of containerization deployment continues to be executed. Therefore, by adopting the embodiment of the invention, the execution efficiency of the task can be improved.

The invention relates to a multi-service combination recommendation method based on a large language model and a twin neural network, and the method comprises the steps: extracting entities and relationships from the text information of a service and a Mashup through an LLM, and constructing a service-entity graph or a Mashup-entity graph; thirdly, fusing and simplifying the service-entity graph and the Mashup-entity graph through a text vectorization means in combination with the calling relation and the vector similarity between entities; then, the vector representation of the relation is brought into an attention scoring function, a heterogeneous graph attention neural network is utilized to generate vector representations of services and Mashup requirements on a service-Mashup graph, and a group of services with the highest matching degree is returned; and finally, processing the service and a Mashup code through a twin neural network, calculating a demand feature which is not satisfied between the Mashup demand and the candidate service, and carrying out multi-service combination recommendation based on the feature. The result of comparison with the similar method shows that the recommendation accuracy of the multi-service combination recommendation task can be remarkably improved.

A method and system that allows on-the-fly synthesis and evaluation of services a scale has been provided. The method and system provide a mechanism where the service offering and their price points are accessible in a machine format and allow themselves to be lent to synthesize new service offerings with budgetary constraints, within the parameters of the predefined time windows. The system allows both a service provider and a service consumer to use a shared screen environment for service synthesis and valuation with the service provider playing the role of navigator. The system comprises of re-routing and navigating a plurality of nodes in the service composition graphs based on specified optimization parameters as chosen by the service consumer and tuned by the service provider. The method comprises of generation of the graph and graph traversal algorithms for along with service composition nodes and their specifications.

The invention discloses a service combination method based on a colored Petri net and a deep reinforcement learning agent. The method comprises the following steps: S1, constructing a colored Petri net model for manufacturing a service combination system; s2, constructing a deep reinforcement learning environment based on the model, and defining a state space, an action space and a reward function; s3, interacting the intelligent agent with the environment, dynamically generating an action mask by using a Petri net model to constrain an action space, and training the intelligent agent to converge by using a near-end strategy optimization algorithm; and S4, taking the strategy obtained by training as an optimal service combination strategy, and triggering transition in the Petri network to generate a final manufacturing service combination scheme. According to the method, the formalized modeling capability of the colored Petri network and the self-adaptive decision-making capability of deep reinforcement learning are deeply coupled, so that the problem that in a dynamic complex industrial interconnection environment, the formalized modeling capability of the colored Petri network and the self-adaptive decision-making capability of the deep reinforcement learning are influenced is solved; the problems of low decision-making efficiency, poor adaptive ability and opaque optimization process caused by huge decision-making space and complex logic constraints in a manufacturing service combination method are solved.

The invention discloses a graph analysis-based government and enterprise customer cross-marketing method and system, and belongs to the technical field of computers. The method comprises the following steps: acquiring basic data; constructing a knowledge graph according to the basic data; performing community division on the knowledge graph to obtain a plurality of communities; based on average or similarity calculation, screening high-quality communities from all communities; and recommending the high-quality community. Through cross marketing, an enterprise can provide more product and service combinations for government and enterprise customers, and the diversified requirements of the government and enterprise customers are met. For example, while an informatization system is provided for government customers, related data security services, cloud computing resources and the like can be provided, so that the customers are more dependent on an overall solution of an enterprise, and the stickiness and loyalty of the customers are improved.

The invention discloses an internet-based telecommunication service generation system, which relates to the field of machine learning and data mining and comprises an acquisition module for acquiring and preprocessing flow data, a verification module, a user connection telecommunication service generation platform, identity verification, user information acquisition after passing, a demand module, user interface entering and service type input by the user. The method comprises the following steps: analyzing information input by a user, extracting a service demand of the user, configuring a module, providing a service combination based on the service demand of the user, generating a service configuration file, detecting network traffic of a new service, establishing a traffic trend prediction model based on preprocessed traffic data, and obtaining a traffic trend prediction result; according to the method, the traffic trend in a period of time in the future is predicted by establishing the traffic trend prediction model, a scientific basis is provided for allocation of bandwidth resources, the bandwidth resources are reasonably allocated in different periods of time, network congestion is avoided, and the resource utilization rate and the service quality are improved.

The invention provides an Internet of Things service combination dependency real-time monitoring method and device and electronic equipment, and the method comprises the steps: calculating a specified dependency value between each service pair according to a specified parameter value of an obtained Internet of Things service during operation; determining dependency relevance among the service pairs by combining a standard deviation and entropy-based conjoint analysis weight; discrete signals of the three dependency values are fitted in a similar integral mode, continuous representation of dependency intensity is obtained, and the continuous representation is decomposed into two branches of positive accumulation and negative accumulation so as to achieve differentiation judgment; and generating a dynamic service topological structure on the basis of the dependency relevance between the service pairs, and judging whether the service combination of the Internet of Things meets the operation constraint or not under the given spatial-temporal logic specification in combination with an expression under the spatial combination signal sequential logic. According to the invention, real-time, accurate and explainable monitoring of the dependency relationship, the topological change and the constraint satisfaction degree can be realized in a complex, dynamic and heterogeneous edge environment.

The invention discloses an application program testing method and device, equipment, a storage medium and a program product. Any one of the to-be-tested services in the application program selected according to the first selection instruction serves as the first service. And then determining a first queue where the first service is located, determining a corresponding test strategy based on the first queue, and determining interface information of the first service based on the test strategy. And finally, sending the interface information to the test platform, and controlling the test platform to execute the test case of the first service. Thus, any one of the multiple to-be-tested services can be selected as the first service according to requirements, automatic testing is carried out based on the testing strategy of the first queue where the first service is located, and it is not needed to compile the testing strategy for each to-be-tested service or to-be-tested service combination of the application program in sequence; and the automatic testing efficiency of the application program is improved.

The application discloses a base station load balancing method and device, a storage medium and an electronic device, and relates to the technical field of communication. The method comprises the following steps: predicting the distribution characteristics of network service demand of a target scene in a target time period based on historical service data; adjusting the resource distribution of the base station according to the distribution characteristics; receiving a target service combination in the target time period, wherein the target service combination comprises M services and the priority of each service, and M is an integer greater than or equal to 1; based on the resource distribution of the base station, adjusting the target model according to the target service combination and a target algorithm, and generating a load balancing strategy for the target service combination based on the adjusted target model. The application solves the technical problem that the traditional load balancing algorithm is inefficient and cannot realize active load management when the base station load balancing scheme in the prior art faces rapid changes and burst conditions.

The invention discloses an industrial software service combination method based on an improved multi-objective grey wolf algorithm, and the method comprises the following steps: building an industrial software resource service combination optimization problem framework, and completing the evaluation of QoS indexes including service time, service cost, service reputation, service delivery quality and the like and availability indexes; an SCOS model with optimal service quality (QoS) and availability as targets is constructed; tent chaotic mapping and a Levy flight improved differential evolution strategy are fused into a multi-target grey wolf algorithm model, the improved multi-target grey wolf algorithm model is used for solving an SCOS model, and the effectiveness and feasibility of the method are verified through algorithm performance verification and example testing. According to the improved multi-target grey wolf algorithm model, the convergence speed can be effectively increased, manual combination is liberated, and the problem of local optimization of industrial software combination is effectively solved.

This invention provides a method and recommendation system for mining service patterns in exploratory service composition. The method includes: acquiring a dataset comprising multiple failed service composition processes, each of which is marked to distinguish between success points, debugging points, and failure points; based on the failed service composition processes and their markings in the dataset, focusing on mining service patterns related to debugging points and / or failure points to obtain failed service patterns. This invention utilizes the markings in the failed service composition processes to focus on mining the data related to failures and ignore the data unrelated to failures, which can significantly reduce the amount of data that needs to be processed and reduce the time required to mine failed service patterns.

The application discloses a kind of service-based kill chain analysis construction methods, belong to the technical field of cooperative combat, including: constructing service resource pool, and set service discovery mechanism;Based on fusion situation set, identify situation switching event, to carry out local update, and construct three-level situation view;Directed hyperedge is constructed, combined with function category constraint, generates directed hypergraph, generates dimensionality reduction state space based on multidimensional constraint, and generates initial combat candidate set;Configuration management node, combined with subtask to the initial combat candidate set sends task invitation information, generates response scheme and carries out adaptation screening, generates initial kill chain, and carries out failure check;Build service twin, deduce the initial kill chain, calculate end execution delay, to carry out elimination reorganization or delay optimization;Build service combination template, and establish efficiency feedback mechanism, cooperatively solve the superimposed influence of frequent situation switching and state space catastrophe, significantly improve kill chain generation real-time performance.

The deep learning model-as-a-service and composition method based on knowledge graphs and the Internet of Things includes: a service composition step and a recursive composition step. This method combines services by understanding the relationships between the inputs and outputs of the models corresponding to services in the knowledge graph. This allows multiple services to be combined into new services. Through continuous combination, all services in the model service knowledge graph can be arbitrarily combined to form many new services. Multiple services within the combined service can be provided by different users, enabling the same service to be provided collaboratively by different users. This achieves the function of multiple users providing services collaboratively, enriching the service offerings by combining different services.

A Web long-tail service recommendation method based on multi-level views and comparative learning comprises the steps that firstly, a global interaction structure view and a local interaction cooperation view are constructed, the global interaction structure view focuses on the comprehensive interaction relation among users, API services and Mashup, and the local interaction cooperation view focuses on the local interaction cooperation view; a user-API service graph in the local interaction structure view mainly focuses on fine-grained interaction preferences of users, and a Mashup-API service graph focuses on service combination association preferences among API services; furthermore, in order to enable the long-tail service and the long-tail user to have better embedding representation quality, performing data enhancement operation of adding nodes on two sub-graphs of the local interactive cooperation view in the graph convolutional neural network; and finally, performing comparative learning among the multi-level views to encourage similar representations of the same user and service under different views and punish the similar representations of different users and services so as to improve the overall efficiency of service recommendation.

A graph-based hierarchical hybrid enhanced service combination recommendation method comprises the following steps: performing MLP enhancement on original embedding based on an MLP residual error to generate MLP enhanced embedding; constructing a graph structure propagation model comprising a post-graph mixing enhancement module and a cross-view mixing enhancement module; respectively inputting the bipartite graph and original embedding and MLP enhanced embedding into a LightGCN network of an enhanced module, and generating graph enhanced original embedding and graph enhanced MLP embedding; performing interpolation on a Mixup unit of the mixed enhancement module after the graph enhancement original embedding and the graph enhancement MLP embedding are input into the graph, and generating interpolation embedding including final global user embedding and final local user embedding; and inputting the final global user embedding and the final local user embedding into a cross-view hybrid enhancement module for cross-view interpolation to obtain final user embedding. According to the method, by introducing the embedded enhancement module based on the MLP residual error, non-structural recessive features can be modeled, and preference information of users except for graph propagation on atomic services and combinations of the atomic services can be captured.

The invention provides an application system of equipment detection and inspection on a GIS mobile platform, and relates to the technical field of equipment detection, the application system comprises an architecture layer and a system layer, the architecture layer comprises a data source layer, a business service layer, a service container layer, a business system layer and a portal platform, the data source layer is used for integrating equipment detection data and associated business data, and the system layer is used for integrating equipment detection data and associated business data; constructing a unified data center; the business service layer provides data transparent access capability for a business system through a service interface; the service container layer comprises an enterprise service bus and a service combination module; according to the system, a layered architecture is adopted, functions of equipment detection, data acquisition, data analysis, early warning notification and the like are divided into independent units, system expansion and maintenance are facilitated, a cloud computing technology is utilized, a data center is built, centralized storage and processing of data are realized, a big data analysis technology is adopted, data are mined and analyzed, abnormal states are automatically identified, and the system is convenient to use. Early warning is carried out in advance, and overdue detection and missing detection of equipment are avoided.

A server system may receive two or more Quality of Service (QoS) dimensions for the multi-objective optimization model, wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension. The server system may maximize the multi-objective optimization model along the first QoS dimension, wherein the maximizing includes selecting one or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension, wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and whereby there is a tradeoff between the first QoS dimension and the second QoS dimension.

The application discloses a satellite service combination selection method and system based on reinforcement learning multi-agent cooperation, comprising the following steps: determining the residual resource values of each sample satellite corresponding to the sample satellites respectively, and the sample service item resource demand values corresponding to each sample service item in the sample service item set respectively; inputting the sample service item set, the sample satellite residual resource values and the sample service item resource demand values into a first multi-agent reinforcement learning network model to obtain first joint action values output by the first multi-agent reinforcement learning network model; calculating loss values of the first multi-agent reinforcement learning network model based on the sample service item resource demand values and the first joint action values; and adjusting first parameters of the first multi-agent reinforcement learning network model based on the loss values to obtain a target multi-agent reinforcement learning network model.

The invention relates to a production element collaborative awareness service combination optimization method under an industrial internet, and the method comprises the steps: obtaining a production process related to a user demand, and decomposing the production process into a plurality of subtasks; based on the historical cooperation relationship and the current cooperation relationship, calculating internal and external cooperation coefficients of the task and the current cooperation relationship, and obtaining time consumption and cost consumption of the sub-task; time consumption and cost consumption are aggregated, and an optimal service combination scheme is obtained through iterative analysis by means of a teaching optimization model. According to the method, the historical cooperation relationship and the current cooperation relationship are introduced, the internal cooperation coefficient and the external cooperation coefficient of the production element service are combined, and the cooperation effectiveness of the service is comprehensively evaluated. Meanwhile, the cooperation effectiveness index is combined with a multi-objective optimization model based on a teaching optimization algorithm (TLBO) so as to screen an optimal service combination.

The embodiment of the invention provides a service recommendation method and electronic equipment. The method is applied to terminal equipment. The method comprises the following steps: acquiring a first scene service combination comprising a first scene condition set and a first scene service set; service recommendation is carried out based on the first scene service combination; other scene conditions are collected, and / or a service starting condition is acquired; a second scene service combination is obtained, service recommendation is carried out based on the second scene service combination, and the second scene service combination comprises a second scene condition set and / or a second scene service set; the second scene condition set is a scene condition set generated according to the first scene condition set and the candidate scene conditions; the candidate scene conditions are scene conditions related to the first scene condition set and obtained according to other scene conditions; the second scene service set is a scene service set generated according to the first scene service set and the candidate service; the candidate service is a service related to the first scene condition set and obtained according to the service enabling condition. According to the method provided by the embodiment of the invention, the service matched with the user demand can be recommended to the user, and the user experience of service recommendation is improved.

The application relates to the technical field of internet risk cooperative management, and discloses an internet data risk early warning and cooperative management system and method, wherein the internet data risk early warning and cooperative management method comprises the following steps: a service grid enhanced federal learning unified framework is constructed to form a four-tuple structure; a seamless integration architecture of federal learning and a service grid is realized; a federal contribution evaluation mechanism based on a service level agreement is developed; adaptive service discovery and dynamic combination technology is realized to generate an optimal service combination scheme; a data quality-aware federal aggregation algorithm is constructed to introduce a quality weight factor optimization model for training; and a multistage cache strategy is designed; through the fusion of federal learning and a micro-service architecture, the application realizes the target of protecting data privacy while maintaining efficient cooperation, the original data of each participant is always kept locally, only necessary model parameters are shared, and the risk of privacy leakage is reduced.

A method may include presenting a user interface on a computing device, the user interface including: a service provider input element identifying a service provider; a service identifier input element identifying a service; and a graph presentation area; executing a knowledge graph database query using a combination of the service provider and the service as input to a knowledge graph database; receiving tuple results in response to the executing, the tuple results including an allocation value property of the service provider attributable to the service provider with respect to the service; and generating in the graph presentation area, an interactive graph based on the tuple results including: representations of entities including the service provider and the service in the tuple results as nodes in the interactive graph, wherein a representation of the service provider includes the allocation value; and links connecting the representations of entities.

Device for controlling the execution of a mashup WoT service, the device comprising: a Web of Things mashup service function entity, WoT mashup service function entity, (52) configured to execute a mashup WoT service adapted for a WoT service user (10); a WoT service execution function entity (54) configured to handle the execution of simple WoT services for the execution of the mashup WoT service of the WoT mashup service function entity; and a WoT service repository (56) configured to store WoT service execution descriptions (56a, 56b, 56n), each describing execution logic for each of the simple WoT services; wherein the WoT mashup service functionality entity comprises: a WoT mashup engine (52b) configured to execute the mashup WoT service in accordance with the service instruction read from the relevant WoT service execution description;wherein each of the WoT service execution descriptions includes information about the mashup WoT service, a service instruction designed to execute the mashup WoT service created by combining the two or more simple WoT services, and service profiles representing parameters designed to identify the mashup WoT service customized for the WoT service user.