37 results about "Service composition" patented technology

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 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 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 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 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 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.

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.

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.

This invention discloses a method and system for atomic service decomposition and secure invocation in power edge-end collaboration. First, all services in the power edge-end collaboration scenario are decomposed into two types of atomic services. Then, the decomposed atomic services are freely combined and dynamically orchestrated for each combination to construct an atomic service orchestration database. Each dynamic orchestration scheme in this database corresponds to a specific business requirement. In practical applications, users only need to select the corresponding dynamic orchestration scheme from the atomic service orchestration database based on the current business requirement to execute the target service invocation task. This not only integrates multiple IoT devices on the edge and end sides into a single entity to provide services, offering users a more efficient and flexible collaborative effect than a single device, but also flexibly adapts to different business scenarios and quickly responds to different business needs.

The invention relates to the technical field of data analysis, in particular to a data analysis service recommendation method and system for constructing a data analysis process, and the method comprises the steps: receiving target and data features inputted by a user; constructing a service use relation graph with weights based on a historical process; extracting a context feature vector; based on the relation graph and the context, generating a plurality of candidate service combination sequences through an improved multi-objective optimization algorithm; scoring the candidate sequences by using a pre-training model; outputting a high-score recommendation sequence and supporting interactive adjustment; dynamically updating the relation graph and the model according to user feedback. The system correspondingly comprises seven functional modules. According to the method, process-level intelligent recommendation which learns from history, combines context sensing, balances multi-target performance and can be evolved continuously is realized, the construction difficulty of a data analysis process is effectively reduced, and the analysis efficiency and the result quality are improved.

The application discloses a kind of component orchestration optimization method and system, method includes: obtaining the service request of target object;Based on service request, establish service combination model;Wherein, the definition of service combination model includes task set, service component set, service component action set and service component attribute value set;Through service combination model, based on service component combination reliability optimization, obtain target service component combination;Wherein, service component attribute value in service component attribute value set is associated with service component combination reliability;Service component combination reliability includes service component discovery reliability, service component binding reliability and service component execution reliability.The present application takes enhancing service component combination reliability as the primary optimization goal, can effectively solve the problem of poor optimization ability in large-scale service combination scene, and then realize the reliable and flexible combination of industrial software components in cloud edge collaboration, efficiently and high quality meet user demand, can be widely applied in component orchestration technical field.

The invention discloses a service combination method, device and equipment and a computer readable storage medium, and is applied to the technical field of computers, and the method comprises the steps: obtaining a service volume pricing function corresponding to each service in a service dimension; the service in the service dimension is the service type provided for the user; the service volume pricing function is a function that determines a service price based on the volume of the vehicle and each service; determining a preset number of service combinations based on the decoupled services and user demands, determining a service volume pricing function corresponding to the service in each service combination, and obtaining a service volume pricing function combination corresponding to each service combination; and determining service combination parameters of each service combination based on the service volume pricing function combination corresponding to each service combination, and determining a target service combination based on the service combination parameters. According to the method, the service in the package is decoupled, so that the target service combination is determined based on the service combination parameters, and the accuracy of the service combination is improved.

The application relates to the technical field of cloud computing and cloud service management, in particular to a cloud service adaptive combination method based on a Decision Transformer and a decision support system; the method unifies heterogeneous service data into hierarchical service vectors through hierarchical tokenization and service representation construction; a multi-scale interaction feature extraction and semantic enhancement mechanism is constructed to capture the semantic and behavior dependence between cloud services at different granularities to generate enhanced service representation and compatibility matrix; the state, action and target are jointly embedded into a Decision Transformer model to generate a cloud service combination strategy sequence; through adaptive combination optimization and backtracking adjustment mechanism, the execution feedback and environmental changes are dynamically evaluated, and multi-instance knowledge migration and online strategy generalization are combined to realize cross-instance and cross-scene decision support. The application realizes continuous output of an optimal service combination scheme in a dynamic cloud environment.

This invention proposes a service composition optimization method for a collaborative service platform, comprising: matching a set of candidate services that meet functional requirements based on user needs; standardizing the service quality attributes within the candidate service set and constructing a service composition optimization model based on the standardized service quality attributes; calculating the optimal solution with the highest fitness value in the service composition optimization model based on an improved flower pollination algorithm; and obtaining the optimal service composition of the service composition optimization mathematical model based on the optimal solution. The service composition optimization method for a collaborative service platform proposed in this invention improves the execution efficiency of service composition optimization.

This invention discloses a novel method for adaptive robust service composition and optimal selection in cloud manufacturing, belonging to the field of cloud manufacturing technology. The method includes the following steps: 1) establishing an Adaptive Robust Service Composition and Optimal Selection (ARSCOS) model; 2) solving the model in step 1) using the Enhanced Multi-Objective Artificial Hummingbird Algorithm (EMOAHA). The ARSCOS model of this invention enhances the anti-interference capability of CMS and reduces the negative impact of uncertainty on the task execution process.

The application relates to a service combination method, device, computer equipment and storage medium. The method comprises the following steps: acquiring a reference service combination set; at least one reference service combination is included in the reference service combination set; the reference service combination is used for meeting a preset Web Service requirement; based on a genetic algorithm, the selection state of each reference service in the reference service combination is adjusted to update the reference service combination set; at least one reference service combination is selected according to the combination evaluation value of each reference service combination in the updated reference service combination set to construct a standard service combination set; the combination evaluation value of the reference service combination is determined based on the service quality index of each reference service in the reference service combination; the standard service combination set is updated according to a preset updating mode; and the standard service combination with the minimum combination evaluation value in the updated standard combination set is selected as a target service combination. The method can improve the accuracy of target service combination determination.

This invention provides an information processing device, information processing method, and program that can estimate user satisfaction with services provided by transportation operators without directly asking users about their travel purpose. [Solution] The system has a memory for storing a program and a processor for executing processing according to the program. The memory stores user information of a user who moves using a means of transport, movement history information of an information processing terminal, and a service list which is a list of multiple service sets that are combinations of ancillary services and the value provided. The processor estimates the purpose of the move and the desired value, which is the value sought by the user, based on the user information and movement history information. By comparing the desired value with the value provided, it selects a service set from the service list that includes ancillary services corresponding to the desired value, calculates the degree of match between the desired value and the value provided, and outputs the degree of match as the user's satisfaction with the ancillary services.

The embodiment of the application discloses a service combination visualization implementation method and device, wherein the method comprises the following steps: calling a service manager to acquire service lists of different sources and performing visual display; calling a service combination generator to create a visualization space for service combination, and inputting service visualization data of the service list into the visualization space through a drag-and-drop interactive operation mode to combine; calling a service combination controller to build a form of the service combination; and calling the service combination generator to save the visualization space and generate the service combination. The scheme of the application fuses different sources and different scenes of the combined services to perform visual abstraction, builds the form of the service combination through the interactive operation of the drag-and-drop, automatically generates the service combination and a corresponding service definition language source code, effectively improves the efficiency of the combined service construction, and reduces the difficulty of the combined service construction.

The invention discloses a multi-dimensional service process perception and dynamic optimization method based on an intelligent algorithm, particularly relates to the technical field of resource allocation and workflow optimization, and is used for solving the problem that an existing dynamic optimization method falls into local optimum due to the influence of historical decisions in an industrial micro-service system. The method comprises the following steps of: acquiring multi-dimensional perception data in a micro-service instance operation process, analyzing convergence of a historical decision sequence to identify a path dependence characteristic, and analyzing a calling relationship among micro-service instances when path dependence exists to identify a high-frequency collaborative service combination. The method comprises the steps of evaluating the decoupling property of a resource competition relationship in a service combination to identify a service interaction bottleneck, evaluating the structure reconstruction potential of a system based on the service interaction bottleneck, determining the exploration priority of an unexplored area in a solution space, and finally dynamically adjusting the resource allocation proportion and the workflow scheduling sequence of micro-service instances according to the exploration priority. And continuous optimization and performance improvement of the industrial micro-service system are realized.

The invention discloses a task time sequence tight coupling cloud manufacturing energy consumption and service quality collaborative scheduling optimization method, belongs to the field of cloud computing, intelligent manufacturing and resource scheduling optimization, and aims to solve the problems of preheating energy consumption waste, service occupation conflict, supply and demand benefit imbalance, insufficient optimization performance and the like in the prior art. Comprising the following steps: decomposing a customized task into independent sub-tasks and matching a candidate service set; constructing a bilevel planning model in which the preheating energy consumption of the upper layer focuses on the minimum supplier and the lower layer is based on the QoS weighting preference of the demand side; designing an energy perception scheduling generation scheme containing service occupation scheduling and connection degree scheduling; and solving an optimal Pareto solution through an adaptive NSGA-II algorithm integrated with MEOS, and outputting a service combination and a subtask execution plan. On the premise that QoS constraints are met, preheating energy consumption is averagely reduced by 2%-8%, service occupation conflicts are effectively avoided, benefits of a supply party and a demand party are balanced, the Pareto frontier is better, the method is suitable for cloud manufacturing scenes with preheating processes, and diversified requirements of personalized manufacturing tasks can be met.

The invention provides a ConfigMap-based method and a ConfigMap-based system for storing a large AI configuration file, which comprises the following steps of: dividing the content of the large AI configuration file into a plurality of small AI configuration files, respectively creating sub ConfigMap resources by using each small AI configuration file, setting names of the plurality of sub ConfigMap resources in a main ConfigMap resource configuration, and storing the sub ConfigMap resources in the main ConfigMap resource configuration. And combining the data key content of each sub ConfigMap resource into a configuration file by the node proxy service, and storing the configuration file in the workload mounting directory, thereby realizing the purpose of storing a very large AI configuration file by using the ConfigMap resource.

The embodiment of the invention provides a credit investigation service processing method and device, and the method comprises the steps: carrying out the recognition of a credit investigation intention according to a credit investigation behavior tag of a credit investigation user, a credit investigation service combination to be purchased by the credit investigation user, and a credit investigation service record, and obtaining the credit investigation intention of the credit investigation user, on one hand, credit investigation service matching of a credit investigation service combination is carried out based on a credit investigation intention to obtain a recommended credit investigation service, on the other hand, a resource mapping strategy of the credit investigation service combination is generated based on the credit investigation intention, and on the basis, the credit investigation service combination, the credit investigation intention, the resource mapping strategy and a credit investigation behavior label are input into a service mapping model; and carrying out resource mapping processing on the credit investigation service combination through the service mapping model to obtain service resource information, thereby carrying out service resource mapping on the credit investigation service combination to be purchased by the credit investigation user from a plurality of resource mapping dimensions.

The invention provides a home file-based maintenance service package generation method, apparatus and device, and a medium, and relates to the technical field of computer data processing and artificial intelligence. The method comprises the following steps: after a maintenance condition is triggered, matching home file data of a target user with a maintenance rule in a maintenance knowledge base, and generating an expired maintenance item list of the target user; wherein the home file data comprises basic attributes and use state information of home assets, and maintenance rules corresponding to various home assets are stored in the maintenance knowledge base; selecting a main service item and a value-added service item from the expired maintenance item list based on a service item screening rule; wherein the service item screening rule comprises a value priority rule and a security priority rule; and integrating the main service item and the value-added service item to obtain a maintenance service package of the target user. The method can actively and intelligently generate and push the personalized periodic maintenance service combination with high cost performance for the user.

The invention relates to the technical field of smart libraries and human-computer interaction, and discloses a library multi-terminal visual service recommendation method based on user behavior perception, which comprises the following steps: collecting multi-source heterogeneous behavior data of a user, and dynamically constructing a user service state vector representing the intention, situation, terminal and state of the user; when an intention initiation event is monitored, generating an ideal behavior sequence based on the vector and the library knowledge graph; acquiring an actual behavior sequence of the user in real time, and calculating a detuning score between the actual behavior sequence and the ideal behavior sequence by adopting a dynamic time warping algorithm; meanwhile, mistuning entropy is introduced to quantify the uncertainty of the current situation, and a self-adaptive mistuning threshold value is determined according to the mistuning entropy. According to the method, the accuracy of detecting the user service difficulty is remarkably improved by introducing an adaptive threshold mechanism, and the effectiveness of service intervention and the cross-platform user experience are optimized through the service combination package and adaptive presentation.

The application provides an artificial intelligence-based public service platform system, aiming to realize accurate service recommendation for user demand through multiple modules. The system includes a user interaction module for receiving user text, voice, image and other input request data, and combining time and location information processing. The data acquisition module is responsible for collecting multi-source heterogeneous city public service data. Based on the collected data, the knowledge graph construction module constructs a dynamic knowledge graph containing public service entities, user entities and their attributes. The intelligent analysis module uses the knowledge graph for time series analysis, spatial analysis and cross-domain correlation analysis, generating service demand prediction and spatial demand distribution results. The personalized recommendation module provides the user with the most suitable target service and associated service combination through demand matching, location optimization and service combination recommendation units. The system can improve the response efficiency of public services and provide personalized, multi-field service recommendations for users.

The invention relates to the technical field of artificial intelligence. The NLP-based sentiment analysis method, device and equipment and the medium are provided, and the method comprises the following steps: performing parameter analysis processing on configuration data input by a user to obtain a demand specification; performing service combination optimization processing on a preset micro-service capability knowledge graph based on the demand specification to generate an optimized service arrangement scheme; performing micro-service instantiation processing on the module description information stored in the micro-service registration center according to the optimized service arrangement scheme to generate a directional data flow pipeline; inputting the input text data into a directional data stream pipeline for stream processing, and generating an emotion polarity label; and performing monitoring processing on the directional data flow pipeline during operation to generate performance feedback data so as to achieve the technical effects of improving the stability of multi-dimensional feature expansion, reducing the obscure semantic misjudgment rate and balancing the resource consumption and the sentiment analysis precision.

This invention relates to the field of information technology and provides a resource virtualization and resource scheduling architecture for a general resource management platform. This application aims to solve the problems of difficult deployment and reuse of application services developed on a general resource management platform. The main solution includes a management agent, a device resource virtualization combination service, an application resource virtualization combination service, and a device / application resource scheduling combination service. The management agent is responsible for dynamically generating the above services and manipulating the resource tree to form a complete service resource virtualization framework process. During the initialization of the general resource management platform, it is responsible for loading the service virtualization configuration file and constructing the device resource tree and application resource tree on the platform according to the resource tree structure. Next, according to the service combination configuration, the source code and compilation scripts of each service module are recursively concatenated according to the service slot matching relationship to generate a single complete executable module, which is dynamically loaded into the general resource management platform for execution.

A service providing device according to the present disclosure includes: a request accepting unit that accepts request information at least including machine-specific information that uniquely identifies an industrial machine and requested service information that specifies a service desired to be used in the industrial machine; a determining unit that determines a recommended service recommended to be used in conjunction with the service desired to be used, on the basis of service combination information defining a combination of services that become suitable by being used in conjunction with each other among services that can be provided, service trial information related to a service provided by the industrial machine as trial, and the request information; and a service information providing unit that notifies information for providing trial of the recommended service determined by the determining unit under a predetermined trial condition.

The present disclosure is directed to the application of data governance within the domain of a data supply chain. Governance is specified and administered through a method and system for a Data Supply Chain Process Instance (DSCPI), which is a collection of elements representing a specific purpose / data object / data service combination. Further, the method and system specify and administer governance across a collection of DSCPIs within the jurisdiction of a Data Supply Chain Governance Domain (DSCGD), through which the health of the DSCPIs and DSCGD may be monitored and improved.