44 results about "Services computing" patented technology

The invention relates to the technical field of distributed computing, and discloses a heavy truck charging load scheduling-oriented distributed energy intelligent matching method, which comprises the following steps of: establishing a charging service computing power mapping table at a scheduling node; maintaining a shadow counter in a local memory, extracting a pre-estimated computing power consumption value according to an event type and accumulating the pre-estimated computing power consumption value to the shadow counter, and executing linear numerical deduction on the shadow counter according to a reference logic subtraction rate so as to simulate a scheduling data throughput evolution process; adjusting a linear deduction rate parameter according to the deviation between the state feedback data and the numerical value of the shadow counter; and distributing the charging matching task to a charging station edge computing node of which the shadow counter value does not exceed a preset logic saturation threshold value. According to the invention, through an open-loop estimation and closed-loop calibration mechanism of a local logic state, an instantaneous congestion risk caused by physical feedback lag is eliminated; and logic state consistency and self-adaptive distribution of the whole network computing power resources are realized.

The invention relates to the technical field of streaming computing, and provides a real-time service computing system based on a dynamic rule engine, which comprises a rule management module and a streaming computing engine, the rule management module is used for providing visual configuration and release functions of service rules and performing versioning storage on the released service rules; the streaming computation engine comprises a rule synchronization unit and a plurality of parallel processing units; the rule synchronization unit is used for acquiring service rules from the rule management module and distributing the acquired service rules to each parallel processing unit based on a broadcast mechanism; and each parallel processing unit is used for receiving the business event flow and performing real-time rule matching on the business event flow according to the received business rule so as to output a decision result. According to the system, complete decoupling of business rule configuration and execution is realized, and the problem that rule updating in a traditional business system needs shutdown deployment is effectively solved.

The invention relates to the technical field of enterprise management and information processing, in particular to an intelligent contract-driven enterprise tax risk management system, which adopts a rule management layer and contract execution layer separation architecture: the rule management layer is responsible for configuring and testing a tax rule and generating a rule version identifier; and the contract execution layer is composed of intelligent contracts deployed in the block chain, and comprises a rule state contract for anchoring a rule version and a business risk contract for executing business logic. And coupling the off-chain rule with the on-chain contract through the rule version identifier. And the business risk contract monitors an on-chain evidence storage event, obtains a current effective rule version, delegates under-chain service calculation, verifies a returned result by signature, version, time and the like, and executes corresponding treatment after the verification is passed. For high-risk disposal, the system provides an on-chain arbitration mechanism by governing a contract. According to the method, dynamic and safe updating of the tax risk management rule is realized on the premise of ensuring that the process is automatic, credible and non-tampering.

The invention relates to the technical field of content distribution network resource management, and provides a resource scheduling method, device and equipment, a storage medium and a computer product, a service request is received, a feature vector of the service request is determined, and the feature vector represents the service processing complexity, the service quality level and the service weight factor of the service request; obtaining one or more of load information, performance information and fault information of the node to determine a computing power score of the node; and determining a resource scheduling strategy of the service request based on the feature vector and the computing power score. The computing power demand of the service request is measured from multiple angles, the computing power demand evaluation accuracy is improved, the resource scheduling strategy is determined based on the computing power demand of the service request represented by the feature vector and the computing power score of the node, the service computing power demand can be dynamically matched, and the resource scheduling efficiency is improved. The computing power score of the node is determined based on one or more of load information, performance information and fault information, and burst traffic and node state dynamic changes can be handled.

Optimizing the presentation of data is described. A payment service computing platform may apply a trained artificial intelligence (AI) model to user data associated with users of a payment service, wherein the trained AI model associates a value with each of the users, and wherein the value represents a predicted engagement metric indicating a change in a level of engagement if an individual user of the users is offered an incentive having a particular attribute. The payment service computing platform may generate the incentive having the particular attribute, identify a subset of the users based at least in part on the value, and cause the incentive to be presented via a user interface of a payment application associated with the payment service and executing on a device of a user in the subset.

There is provided a computer implemented method of managing access for user account, comprising: accessing a graph mapping a plurality of nodes denoting real users to a plurality of user accounts and a plurality of resources hosted by a plurality of service computing environments external to a target computing environment accessed by the user accounts, and defining different permissions for user accounts for accessing the different resources, receiving a query for identifying user accounts for a target user, executing the query on the graph, and providing details regarding user accounts of the target users from the execution of the query on the graph, the details including at least one of: identifiers of the user accounts, resources accessible to the user accounts, access privileges for accessing the resources.

The invention discloses a space science data collaborative computing method and system based on a no-service computing architecture. According to the method, the technical problems that in traditional space science data processing, resource elasticity is insufficient, multidisciplinary cross-domain heterogeneous computing environment collaboration is difficult and data islands are serious are solved by constructing a whole-process and service-free computing framework of data access-function encapsulation-collaborative scheduling-result aggregation. Specifically, tasks such as preprocessing, analysis and modeling of space science data are packaged into a lightweight and non-service calculation function, data calculation collaborative calling in the interdisciplinary field is achieved in combination with a multi-calculation-node permission management mechanism, and calculation resources are distributed according to needs through a dynamic resource scheduling algorithm. The multi-disciplinary cross-domain TB / PB-level spatial data processing method has the advantages of being high in resource utilization rate, low in operation and maintenance cost and high in expansibility, and can be widely applied to space science research scenes such as sun-earth space physics, deep space exploration and high-energy celestial body outbreak.

The quality of service guaranteed function-free computing system comprises an HTTP gateway, a scheduler and a work pool; the HTTP gateway serializes the incoming DAG request through a serializer, and sequentially adds the serialized function-free function in the DAG request to a waiting queue; the scheduler retrieves the function-free function from the waiting queue and the chain running queue to the work pool for processing according to a scheduling strategy. The quality of service guaranteed function-free system and method of the application process the incoming request through the producer-consumer mode by reforming the scheduler in the platform. In addition, a strategy framework containing rich strategies is added. Different kinds of strategies are set for different kinds of application requests, so that the queuing time is reduced to a certain extent, and the running performance is improved.

A method, system and apparatus for configuring a computing resource of a service are provided, which relate to the field of resource configuration. The method includes that: an online learning algorithm that is used for recommending, to each service on a platform, a computing resource quota adapted to a service type of the service is preset; when a new service is launched on the platform, a target computing resource quota adapted to the service type of the new service is recommended according to the online learning algorithm; and initial configuration is performed for a computing resource of the new service according to the target computing resource quota.

This invention discloses a space-based computing power network system, belonging to the field of integrated space-ground information network technology. The system includes space-based nodes deploying edge computing power resource pools, ground stations, and a ground-based cloud computing center. The ground station is equipped with a space-based computing power orchestration and management platform, including a computing power orchestration module and a network control module. The platform collects real-time information on the network's computing power, network, storage, and algorithm resources, forming a unified resource view. When a user station initiates a service computing power request including computing power scale, algorithm type, and network QoS requirements, the platform generates a computing power allocation strategy through the computing power orchestration module and a space-based network path through the network control module, supporting step-by-step or joint optimization to achieve resource collaborative scheduling and optimal configuration. Finally, the strategy and path are distributed to each node, and the system responds to the user station. This invention supports real-time on-orbit processing of services, reduces space-to-ground transmission latency and bandwidth requirements, and enhances the intelligence and autonomous operation capabilities of the space-based network.

A computer system for analyzing and mitigating risks associated with an event is provided. The computer system is configured to: (i) receive at least one of a city risk profile and a building risk profile from a database; (ii) receive city systems data from the city services computer system; (iii) utilize a trained machine learning model to determine at least one potential risk associated with the event; (iv) generate an event risk profile that includes the at least one potential risk associated with the event; and / or (v) generate a risk mitigation output based upon at least one of the city risk profile and the at least one potential risk, wherein the risk mitigation output includes at least one of a risk alert, a risk mitigation recommendation, and risk mitigation instructions. Computer systems for analyzing and mitigation risks associated with a city, a building, and a user are also provided.

A service computing system receives an API call, where an authorization token is included in a header of the API call, the authorization token including an identifier in content. The identifier is also included as a parameter that is introduced with the API call. The service computing system parses the API call to obtain an authorization token, and an identifier included in the authorization token. It also obtains an identifier that is introduced as a parameter of the API call. The service computing system compares an identifier obtained from the authorization token to an identifier introduced as a parameter of the API call to determine if they match. If they do not match, the API call will be processed as an unauthorized API call. A security system in the service computing system authorizes the API call based on the comparison.

The application relates to the field of service computing and discloses an edge microservice automatic expansion and reduction method combining time sequence prediction and reinforcement learning, which comprises the following steps: step 1) first, modeling the problem scene of an edge computing platform, a microservice application, a deployment scheme and a deployment cost; step 2) based on TimeMixer improvement, a time sequence prediction model ATimeMixer is proposed to obtain the future work load of the microservice; and step 3) designing a DDPG intelligent agent to make an optimized decision in combination with real-time information and predicted information; the original average pooling downsampling and moving average kernel time sequence decomposition in the TimeMixer are replaced by convolution-based downsampling and time sequence decomposition, so that the ability of ATimeMixer to capture diverse and complex feature patterns in the work load time sequence is improved.

A computer implemented method of deployment and resource allocation of microservices of a distributed computing environment is disclosed. The distributed computing environment includes a microservice deployment scheduler and one or more computing nodes. The microservice deployment scheduler includes a reinforcement learning based dynamic workload orchestration module. The method includes receiving microservice constraints descriptive of a microservice computing task by the microservice deployment scheduler. The method further includes receiving node specific properties from the one or more computing nodes by the microservice deployment scheduler. The node specific properties are descriptive of a computing capacity and / or computing capabilities of the one or more computing nodes. The method further includes orchestrating operation of the one or more computing nodes by the microservice deployment scheduler by inputting the microservice constraints and the node specific properties into the reinforcement learning based dynamic workload orchestration module.

The invention relates to the technical field of service computing, and provides a large language model job scheduling method based on reply length prediction.The method comprises the steps that a user request text is received, and whether a current dialogue scene is a single-round dialogue scene or a multi-round dialogue scene is judged; if the current dialogue scene is a multi-round dialogue scene, constructing a time sequence feature sequence; if the current dialogue scene is a single-round dialogue scene, constructing an equivalent time sequence feature sequence; the method comprises the following steps: extracting dense semantic embedding from a user request text by adopting an Embedding model, inputting a time sequence feature sequence into a pre-trained long short-term memory (LSTM) network regression device, and predicting a large language model (LLM) reply lexical element length corresponding to a current request; and based on the predicted reply lexical element length, constructing a resource-aware intelligent scheduling algorithm, and by using the predicted reply lexical element length as a priority and simulating the operation process of a job to be scheduled, determining that the job does not cause preemption and is used for determining a request set participating in batch processing in each round of scheduling.

Disclosed in the present invention is a joint resource allocation method for an underwater cloud-edge collaborative network. The method comprises the following steps: according to geographic locations, dividing a water region into several rectangular grids having equal areas; obtaining a Hamiltonian loop according to an APPROX-TSP-TOUR algorithm; calculating a system delay and an energy loss, wherein the system delay comprises: a service transmission delay, a service calculation delay and a service movement delay, and the energy loss comprises an underwater moving-ahead energy loss, calculation loss and communication transmission loss of an underwater mobile submersible vehicle; and constructing a resource allocation model in which both the system delay and the energy loss are taken into consideration, and performing calculation by means of linear programming, so as to obtain an optimal resource allocation strategy. In the present invention, a regional collection strategy is specified for underwater sensor data, and a collection path is programmed, such that the efficiency is improved, the energy is saved, and the phenomenon of uneven resource allocation is avoided; and a resource allocation model in which both a system delay and an energy loss are taken into consideration is constructed, such that a relatively short delay is ensured while the energy loss is reduced.

A service computes a self-attention of a long sequence transformer. The computation is two-dimensional, with a first dimension being along a Q-dimension and a second dimension being along a KV-dimension. The service determines that the Q-dimension does not carry any data dependencies but that the KV-dimension does carry one or more data dependencies. The service splits the Q-dimension and distributes those splits to a processor grid. The service splits the one or more data dependencies along the KV-dimension and distributes those splits to the processor grid. The service performs a reduction operation to obtain a final result. The service distributes the final result among the processors.

Client support for applications to access services can be provided. In an example, a computing system can receive, from a client device, text input for an in-progress application to access a service. The client may be prevented from accessing the service prior to the in-progress application being approved. The computing system can detect an error associated with processing the in-progress application based on the text input and contextual information. The computing system can determine that the error is associated with the text input or with a technical issue associated with the in-progress application. The computing system can generate a recommendation associated with the error based on determining that the error is associated with the text input or the technical issue. The computing system may output the recommendation to the client device for use in resolving the error with processing the in-progress application.

In some examples, a service computing device receives, from a vehicle computing device on a vehicle, a communication including sensor data acquired from at least one sensor on the vehicle. Based on the communication, the service computing device determines a transmission delay corresponding to a difference between a time at which the vehicle sends the communication and a time at which the vehicle receives the communication. Based on the communication, the service computing device determines a trajectory of the vehicle on the travel route. The service computing device determines vehicle control information for the vehicle based on the sensor data, the transmission delay, the wind direction and volume, the surrounding objects and trajectories. The service computing device sends at least one instruction to the vehicle based at least on the vehicle control information, causing the vehicle to perform at least one control operation to traverse one or more locations associated with a trajectory of the vehicle.

A method and system for provisioning credentials is disclosed. The method includes receiving, by a server computer from an authorizing entity computer, a provisioning request message. The provisioning request message can include resource provider identifiers for a set of resource providers and a set of credentials to be associated with the set of resource providers. The method further includes transmitting, by the server computer, a provisioning instruction message to an intermediate computer. The provisioning instruction message can include the resource provider identifiers and credential reference identifiers associated with the set of credentials. The intermediate computer can transmit one or more token request messages to a token service computer, receive tokens corresponding to the credential reference identifiers, and provision the tokens to the resource providers associated with the resource provider identifiers.

Systems and methods are described herein for transmitting offers based on a geofence via a transfer service computing system. Such systems and methods may use a transfer service computing system to receive, an offer relating to a merchant and corresponding to a geographical area associated with the merchant, identify a geofence including the geographical area, receive a location of the user device and determine that the location of the user device is within the geofence. The transfer service computing system may transmit, to the user device, the offer based on the determination that the location of the user device is within the geofence. The transfer service computing system may transmit a transaction request to the provider computing system or to a third-party computing system based on receiving an acceptance of the offer from the user device.

A messaging system is provided to communicate messages between computing devices. The computing devices can include vehicles and service computers. The messaging system includes nodes and virtual entities that may forward messages between computing devices. The messaging system may be a publish-subscribe system. The messaging system may provide one-to-one communication.

A service computing system receives an API call, where an authorization token is included in the header of the API call, the contents of which contain an identifier. The identifier is also included as a parameter passed in with the API call. The service computing system parses the API call to obtain the authorization token, and the identifier included in the authorization token. It also obtains the identifier passed in as a parameter of the API call. The service computing system compares the identifier obtained from the authorization token with the identifier passed in as a parameter of the API call to determine if they match. If they do not match, the API call is processed as an unauthorized API call. A security system in the service computing system authorizes the API call based on the comparison.

A method, a system, and a computer program product for generating name recommendations in a core data services computing environment. A dataset for training a name data model is received. The name data model is configured for determination of a recommendation for one or more names in a plurality of names associated with one or more artifacts in a plurality of artifacts of a database management system. The name data model is trained using the received dataset and applied to generate one or more names associated with the one or more artifacts.

A messaging system is provided to communicate messages between computing devices. The computing devices can include vehicles and service computers. The messaging system includes nodes and virtual entities that may forward messages between computing devices. The messaging system may be a publish-subscribe system. The messaging system may provide one-to-many communication.

A resource provisioning service allows users to provision multiple, different network resources in an atomic manner and with a single call to a resource provisioning service. In some instances, the multiple, different network resources comprise individual types of resources that form a portion of one or more cloud-computing platforms. For instance, one or more entities may host and operate a cloud-computing platform that includes different types of network resources, such a storage service, a load balancing service, a compute service, a security service, or any other similar or different type of network-accessible service.

The application relates to the technical field of distributed computing, and discloses a distributed energy intelligent matching method for heavy truck charging load scheduling, which comprises the following steps: establishing a charging service computing power mapping table at a scheduling node; maintaining a shadow counter in a local memory, extracting an estimated computing power consumption value according to an event type and accumulating the estimated computing power consumption value to the shadow counter, and simultaneously performing linear numerical deduction on the shadow counter according to a reference logic deduction rate to simulate a scheduling data throughput evolution process; adjusting a linear deduction rate parameter according to the deviation of state feedback data and the value of the shadow counter; and distributing a charging matching task to a charging station edge computing node whose shadow counter value does not exceed a preset logical saturation threshold, wherein the application eliminates the instantaneous congestion risk caused by physical feedback lag through an open-loop estimation and closed-loop calibration mechanism of a local logical state, and realizes the logical state consistency and self-adaptive distribution of the computing power resources of the whole network.

This invention relates to the field of data service computing, and particularly to a method and system for batch calculation of asset depreciation based on distributed load balancing. Addressing the centralized performance bottlenecks, insufficient system elasticity, and rigid core computing logic faced by traditional systems when processing massive amounts of cards, this solution deeply decouples the underlying data from the computation. The method includes calling a distributed database to perform data sharding queries, cutting and encapsulating asset data into computation task messages; asynchronously distributing tasks to idle asset service instances through the load balancing mechanism of a message middleware; dynamically parsing text expressions in application-layer memory using a rule engine to rapidly execute financial depreciation calculations; and finally, asynchronously writing the results back through an anti-replay mechanism to ensure data consistency. This application, through a pipelined architecture of data sharding, message distribution, and in-memory computation, significantly improves concurrent processing efficiency and system disaster recovery stability, and achieves flexible configuration management.

A kind of attribute heterogeneous information network node importance evaluation method based on CMGI-GAT. It relates to the field of computer service technology, in view of the problem that in the existing attribute heterogeneous information network node importance evaluation process, it is difficult to effectively handle the noise and redundant information in multi-source, multi-modal attribute characteristics, it is difficult to fully mine the high-order semantic relationship of network, leading to insufficient evaluation accuracy and robustness, the following scheme is proposed: differentially encode the category attribute, numerical attribute and text attribute, and combine cross-modal attribute interaction, multi-objective loss optimization, type-aware feature fusion, edge weight update, structure optimization, attribute embedding learning, structure embedding learning and cross-feature multi-scale fusion, finally obtain the node importance value through multi-path hierarchical prediction. The present application is suitable for key node identification and importance evaluation in social network analysis, recommendation system, knowledge graph and other attribute heterogeneous information network scenarios.

A service computes a self-attention of a long sequence transformer. The computation is two-dimensional, with a first dimension being along a Q-dimension and a second dimension being along a KV-dimension. The service determines that the Q-dimension does not carry any data dependencies but that the KV-dimension does carry one or more data dependencies. The service splits the Q-dimension and distributes those splits to a processor grid. The service splits the one or more data dependencies along the KV-dimension and distributes those splits to the processor grid. The service performs a reduction operation to obtain a final result. The service distributes the final result among the processors.