219 results about "Microservices" patented technology

This application relates to a real-time power supply and demand forecasting method and system based on a cloud-native architecture. The method includes: deploying a data access service in a cloud-native cluster using containerized microservices to receive real-time supply and demand data streams and historical time-series data from a power trading system; writing the data streams to distributed storage and pushing them to the forecasting pipeline via a message queue; performing timestamp alignment, missing data handling, normalization, and smoothing / denoising on the supply and demand data by a preprocessing service to obtain a low-noise supply and demand sequence; updating model parameters in a rolling window by an ARIMA forecasting service and outputting linear forecast values ​​as the first forecast result; calculating the forecast residuals based on the first forecast result and the actual observations, constructing residual time-series samples, and outputting residual forecast values ​​by an LSTM forecasting service; and superimposing the first forecast result and the residual forecast values ​​by a fusion service to obtain the real-time supply and demand forecast result and publishing it to the real-time trading business interface.

The present disclosure relates to a method and a system for communication between a microservice and a policy execution engine (PEEGN) The present disclosure encompasses receiving a request message comprising at least an event type and a publisher type, wherein the publisher type is at least one of the PEEGN and the microservice; determining a target publisher, associated with the request message, based on the publisher type, wherein the publisher type is the PEEGN; transmitting to the target publisher, the request message; receiving from the target publisher, a response for the request message, the response comprising a subscriber type associated with the request message; identifying a subscriber, associated with the request message, based on the subscriber type; an transmitting to the subscriber, the response associated with the request message.

The application discloses an active defense method against adversarial denial of service attacks in a microservice scenario. First, multi-source index collection probes are deployed in the microservice system and combined with centralized aggregation processing. Then, the system attack and defense process is modeled as a zero-sum game problem of double reinforcement learning agents. The defense strategy is optimized in the alternating update using the adversarial training mechanism, allowing the defender to gradually converge to a robust optimal strategy. On this basis, a multi-dimensional discrete deep Q network is introduced. Through shared feature extraction and multi-head independent output, efficient generation of multi-dimensional expansion and contraction defense decisions is achieved. A loss balancing mechanism is used to accelerate convergence. Finally, a closed-loop optimization mechanism is combined to trigger lightweight incremental training when new attack patterns are detected or defense effectiveness decreases, dynamically updating part of the network weights to maintain the adaptability of the strategy. This scheme can achieve intelligent, automated and efficient defense of microservice systems in adversarial attack environments, significantly improving system security and service quality.

Approaches presented herein relate to the distribution and processing of stream data in a networked computing environment. A set of streaming data sources can be identified for a given cluster of nodes and the data from those data sources can be tracked, managed, and / or routed to a number of different microservices. Stream distribution and routing (SDR) agents may be used to manage a routing table that includes an identifier for each of the data sources. The individual agents may distribute information about the sensors to individual microservices, which may determine which data streams are relevant and then initiate individual processes and functions using the relevant data streams. Output results from the individual microservices may also be assigned an identifier, which may further be used by the individual agents to route outputs toward their particular microservices, which may then use the outputs from other microservices within their own workstreams.

The application discloses a Java-based distributed transaction management system and relates to the technical field of distributed systems. In view of the contradiction between transaction consistency and high concurrency and high availability in a distributed system, the scheme comprises the following: a transaction coordination module, which is used for constructing a cluster based on a Raft protocol to guarantee continuous availability of a service, realizing transaction full-life cycle management by relying on an extended state machine engine, and dynamically selecting an optimal communication path of a transaction according to scheduling decisions and a real-time network topology state; a local transaction management module, which is embedded in each microservice and is used for realizing local execution and state reporting of a transaction; a multi-level log module, which is used for realizing traceability of a transaction state and recovery of a fault through multi-level storage and event tracing; an intelligent scheduling module, which is used for realizing dynamic optimization of transaction processing based on an LSTM model and QoS indicators; and a monitoring and alarming module, which is used for collecting transaction full-link logs and performance indicators in real time and realizing full-link observability. The application is suitable for high-reliability and high-concurrency scenarios.

A system and method are disclosed for propagating changes to a schema model. Embodiments include detecting a change to a system represented by the schema model, generating a change event based on the change to the schema model, determining microservice actors that execute within a container-based computation service, where the microservice actors are configured to be triggered by the change event, dispatching the change event to the microservice actors, and updating, by the microservice actors, the schema model. The system further comprises setting up and controlling one or more virtual machines to execute the microservice actors. The behavior of each of the microservice actors is specified by a configuration file, which is associated with a particular microservice actor and specifies the name of a particular microservice actor, what objects a microservice actor may update, actions a microservice actor may take and change events that trigger a microservice actor.

This invention belongs to the field of business travel expense management technology, and particularly relates to an intelligent dynamic business travel expense management method and system based on a microservice architecture. This invention utilizes an information parsing microservice, employing an improved BERT model to perform semantic parsing and entity extraction on travel booking requests, outputting structured itinerary information and user job level; it uses an expense benchmark calculation microservice to lock in a matching pool of benchmark users, splits itinerary units, and generates an initial expense range through cluster analysis; it uses a credit rating microservice to calculate personalized credit coefficients for users; and it uses a dynamic expense calibration microservice, combining credit coefficients to calibrate expense standards, generating compliant expense ranges constrained by compliance red line thresholds. This invention achieves dynamic and refined management of business travel expense standards, solving the pain points of poor adaptability and low efficiency of traditional static management models, and improving the intelligent level of enterprise travel cost management.

The embodiment of the application provides a kind of based on containerization's power grid algorithm management method and related equipment, belong to electronic digital processing technical field.The method comprises: obtaining the power grid algorithm characteristics corresponding to power grid whole link algorithm;According to the power grid algorithm characteristics, the microservice of power grid whole link algorithm is split, obtain multiple microservice information, and each microservice information is customized corresponding containerization image and resource model, obtain multiple initial microservice modules;Through container orchestration strategy and according to each initial microservice module corresponding link priority and traffic proportion, each initial microservice module is carried out grey iteration, obtain multiple target microservice modules;The management monitoring information corresponding to each target microservice module is obtained, and according to the management monitoring information corresponding to multiple target microservice modules respectively whole link monitoring report is generated.The embodiment of the application can improve the reliability, flexibility and system observability of power grid algorithm iteration.

A method of analyzing a performance of a microservices-based application comprises generating a plurality of traces from a plurality of spans associated with the microservices-based application. The method also comprises generating a plurality of data sets each associated with a respective analysis mode of a plurality of analysis modes using the plurality of traces, wherein each analysis mode extracts a different level of detail for analyzing the performance of the services in the application from the plurality of spans. Further, the method comprises selecting, based on a first user query, a first analysis mode from the plurality of analysis modes for generating a response to the first user query. The method also comprises accessing a data set of the plurality of data sets that is associated with the first analysis mode and generating the response to the first user query using the data set associated with the first analysis mode.

This invention discloses a satellite communication terminal simulation task orchestration system and device based on intelligent scheduling. It adopts a microservice architecture, with five modules working collaboratively via RESTful APIs or gRPC. The system constructs a multi-dimensional global resource view, combining historical data to drive a regression prediction model to quantify resource requirements. The intelligent scheduling decision module models complex task allocation as a multi-dimensional optimization problem, using the PSO algorithm combined with a multi-objective fitness function to find the optimal solution, supplemented by a task preprocessing mechanism to improve efficiency and effectiveness. The system manages task dependencies using a DAG and deeply integrates with Kubernetes to achieve automated task orchestration, status monitoring, and fault recovery. This effectively solves the pain points of traditional scheduling, such as resource fragmentation and static configuration waste, achieving dual-dimensional global dynamic scheduling of business and resources. This significantly improves cluster resource utilization and simulation execution efficiency, adapting to the needs of large-scale, high-concurrency simulation scenarios.

In an example embodiment, an embedding machine learning model is used to effectively and efficiently split a monolithic application into microservice. The embedding machine learning model is used to group the domain managers into multiple group, making sure that the semantic coupling among domain managers in the same group is the most and the semantic coupling among domain managers in different groups is the least. Once these groups have been form, the entities of the persistence layer are then assigned to corresponding groups, making sure that the semantic coupling between the domain layer and the persistence layer in the same group is the largest.

A system can intercept, by a kernel-space application of a node of nodes, a call from a microservice that is directed to a remote endpoint that is external to a decentralized service mesh architecture, wherein the kernel-space application operates in a kernel space of the node, and wherein the microservice executes in a user space of the node. The system can perform, by the kernel-space application, an authorization check on the call based on the microservice and a virtual address of the remote endpoint identified in the call, to produce an authorization result determine, by the kernel-space application, connectivity information of the remote endpoint based on the virtual address of the remote endpoint identified in the call. The system can relay, by the kernel-space application, network traffic between the microservice and the remote endpoint, based on the authorization result indicating that the call is authorized and using the connectivity information.

Systems, methods, and techniques that facilitate application fingerprint generation are provided. One or more embodiments described herein can comprise a computer-implemented method comprising determining, by a device operatively coupled to a processor, fault proneness of one or more microservices regarding one or more fault categories, generating, by the device, an ordered seed set of the one or more microservices based on the determined fault proneness, generating, by the device, an augmented ordered seed set of the one or more microservices based on topological relationships of the one or more microservices and based on the ordered seed set, and building, by the device, a set of one or more patterns of resource constraint faults and a set of one or more patterns of entity experience faults based on the augmented ordered seed set of the one or more microservices.

A hosting service enables streamlined scaling of features that relate to data storage management. The scaling is powered by deployment of microservices, each one directed to a discrete feature of a data storage management system. In addition to scaling on demand, the disclosed hosting service provides additional value-added features, such as streamlining intra-system handling, auditing, and reporting of logs generated by microservices; standardizing exception handling; mediating port assignments for co-located microservices to avoid collisions; streamlining formats for inter-microservice communications; streamlining local database access; etc. Advantageously, the disclosed solution off-loads the storage manager, and additionally improves system performance by scaling various functions and by streamlining interoperability among components. Furthermore, the disclosed solution also enables the hosting service in general, and each discrete microservice in particular, to be enhanced, deployed, and scaled separately and asynchronously from each other and from the storage manager.

This invention proposes a multi-robot operation and maintenance system and method based on digital twins and cloud-edge-device fusion. It belongs to the interdisciplinary fields of intelligent manufacturing, industrial IoT, and human-machine collaborative operation and maintenance. The method includes: constructing a human-machine collaborative data acquisition network by integrating industrial-grade biometric sensors and distributed data acquisition units; deploying standardized interfaces based on a microservice architecture to perform layered preprocessing of multi-source heterogeneous data to generate a fused dataset; and achieving a breakthrough improvement in operation and maintenance efficiency and security through deep integration of biometric perception and digital twin technologies.

This application provides a database sharding and virtual table query method based on SQL interceptors, relating to the field of data sharding technology. This application requires only one lightweight SQL interceptor component to be introduced into the microservice, without modifying the data source configuration; sharding rules are centrally managed in the configuration center, and changes are pushed to each service interceptor in real time without restarting any microservices, achieving true dynamic configuration refresh and greatly improving system flexibility and maintainability; by introducing the "virtual table" abstraction, the natural inheritance relationship in the business model is mapped to the database query layer; business code does not need to care about which sub-table the data is actually stored in, and can perform global queries based on common fields, greatly simplifying complex query business code and reducing development complexity.

Presented herein are system and methods for detecting anomalies in microservices. A server may receive a first plurality of metrics from a microservice of a plurality of microservices. Each of the microservices may provide a respective function for an application independently from other microservices. The server may apply the first plurality of metrics to an ensemble of anomaly detection models to generate classifications. Each of the classifications may indicate the first plurality of metrics as one of anomalous or normal from a respective model of the ensemble. The ensemble may be trained using a second plurality of metrics from the microservice. The server may identify a majority of the plurality of classifications as indicating the first plurality of metrics as anomalous. The server may determine that at least one of the first plurality of metrics satisfies a threshold. The server may generate an alert to indicate an anomaly event.

This invention relates to the field of automotive embedded technology, and discloses a method, device, chip, and storage medium for virtualizing onboard computing power in engineering machinery. It is applied to an onboard main control chip, which includes a main computing power module and a computing power expansion module. The main computing power module includes a central processing unit and a core computing power unit, while the computing power expansion module includes multiple extended computing power units. The method includes: acquiring computing tasks from microservice software running within the central processing unit; allocating target computing power resources from the core computing power unit or multiple extended computing power units according to a preset computing power allocation queue; and calling the target computing power resources to execute the computing tasks. This invention virtualizes onboard computing power through the main computing power module and the computing power expansion module, enabling unified scheduling and management of computing power resources. It supports elastic expansion of computing power, flexibly meeting the computing power requirements of different levels of intelligent driving and operational scenarios, supporting service-oriented software development, and increasing the flexibility of software and algorithm development.

This invention proposes a method for optimizing mobile social security services based on a distributed and microservice architecture. Its technological breakthroughs are manifested in three core aspects: Elastic Service Cloud Network: Based on the self-developed PDFC framework, Spring Cloud microservice architecture, and Kubernetes containerization technology, microservices are deployed in a split manner, enabling dynamic scaling of microservice container nodes and second-level fault self-healing. Intelligent Data Hub: A cross-domain data channel is built based on Apache Kafka, collecting social security business data and health record data in real time with millisecond-level transmission efficiency. Intelligent Reporting Engine: An innovative three-dimensional intelligent reporting system is constructed. Pre-event reports are generated by parsing physical examination information; during-event reports are generated through dynamic monitoring of risk warnings; and post-event reports are generated based on actual business processing, achieving an upgrade and transformation of the governance model from "people seeking services" to "services benefiting people."

Systems, methods, and non-transitory, machine-readable media may facilitate adaptive resource capacity prediction and control using cloud infrastructures. Specifications of resource allocations for resources provided by a cloud infrastructure system may be collected. Execution of a series of sets of parallel microservices may be caused. Each set may be a function of a particular type of resource data and may facilitate obtaining resource metrics data corresponding to the particular type. The series of sets may facilitate obtaining resource metrics data mapped to the resources provided by the cloud infrastructure system. Prediction rules may be selected as a function of particular resource metrics. The selected prediction rules may be used to predict resource capacities for a subset of the resources as a function of the particular resource metrics and generate resource capacity predictions. Preemptive actions with respect to incidents identified based on the resource capacity predictions may be facilitated.

This disclosure provides one or more embodiments of a container deployment method, apparatus, electronic device, and storage medium for a power load control terminal. The method includes: acquiring multiple microservices corresponding to at least one power service of the power load control terminal; constructing a container image file corresponding to the microservice based on the service level and security level of the microservice; in response to receiving a deployment request for any of the microservices, determining whether the microservice is allowed to be deployed on the power load control terminal based on the requirements of the microservice and the resources of the power load control terminal; and in response to determining that the microservice is allowed to be deployed on the power load control terminal, deploying a container corresponding to the microservice on the power load control terminal based on the container image file.

The application discloses a kind of modularization on-orbit edge intelligent computing platform, belong to spacecraft electronic information technical field.The platform uses distributed architecture, including at least one network and management module NMM and at least one general edge AI module UAM, NMM as the data exchange and control center of platform, including system management unit SMU, redundant network exchange unit and controlled wireless communication unit;UAM as the core processing unit of platform, responsible for executing data and computationally intensive tasks, using the separation type design of core board plus bottom plate, each UAM is connected the redundant network exchange unit by independent Ethernet link.The platform is through the distributed architecture with network exchange as center, in combination with hierarchical module level fault self-healing mechanism, multi-level redundant communication architecture facing application optimization and the flexibility of elastic computing power scheduling mechanism based on microservice, while guaranteeing high performance and flexible scalability, greatly improve the on-orbit survivability and task execution efficiency of platform.

This specification discloses a method and apparatus for canary deployment. The canary deployment system acquires traffic segmentation rules corresponding to a target microservice among multiple microservices. Then, based on the traffic received by the new version of the target microservice in the current time period, it obtains the operational data of the new version of the target microservice in the current time period. Finally, based on the operational data of the new version of the target microservice in the current time period and the traffic segmentation rules corresponding to the target microservice, it performs traffic segmentation on the traffic received by the target microservice to determine the traffic received by the new version of the target microservice in the next time period, and further obtains the operational data of the new version of the target microservice in the next time period, and so on, until the new version of the target microservice receives all the traffic, thus completing the canary deployment. This automatically adjusts the traffic ratio between the old and new versions of the code, avoiding errors caused by human operation, and improving the efficiency of canary deployment.

The application discloses a kind of silicon steel sheet visual quality detection method and experimental system based on microservice, including the following steps;Step 1, build remote service management platform based on microservice, meet the user's custom storage needs through database interaction API, namely design corresponding data storage structure and convenient and fast data interaction API;Step 2, offline acquisition industrial area camera's checkerboard image and defect silicon steel sheet sample image, calculate the row and column scale factor between pixel and actual size;Step 3, offline training model and deployment model;Using the method of deep neural network learning, build PyTorch deep learning framework, use YOLOV7 model to carry out visual quality detection;Step 4, online silicon steel sheet visual quality detection experiment;Step 5, visual quality detection result display.The application carries out visual quality detection experiment by machine vision software, with high degree of automation, wide measurement range, fast, high precision, the characteristics of accurate detection.

The embodiment of the invention discloses a performance root cause positioning method and device for a micro-service system, a medium and a product, and relates to the technical field of micro-services. The method comprises the following steps: in response to an abnormal instruction of a service performance index of a front-end entry service, determining a candidate service set which is in the same service calling link as the front-end entry service, and obtaining a response time sequence of each candidate service in a preset time window; determining a baseline value, converting the original response time into a normalized relative change rate, and determining a service subset having fluctuation similarity with the front-end entry service; performing timing causal analysis on the service subset to determine a timing causal score; and determining a comprehensive root cause score based on the fluctuation similarity, the influence weight and the time sequence causal score, determining a root cause service with the highest confidence coefficient according to the comprehensive root cause score, and outputting a root cause report. According to the scheme of the invention, when the performance of the front-end entry service is abnormal, the root cause service is automatically identified, and the fault positioning efficiency is improved.

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.

This application provides a key generation method, apparatus, and electronic device to improve the security of microservices. In this embodiment, the electronic device determines second data on interactions between the microservice and other microservices within a preset time period prior to the current time, based on first data of interactions between the microservice and other microservices within a preset time period prior to the current time. Then, based on the value of each sub-data in the second data, a target key management strategy is determined. This method can accurately identify the call information of different microservices, enabling strategy customization and dynamic adaptation. Intelligent key management is achieved, thereby ensuring the security of microservices.

This invention discloses a method for constructing logical associations and generating privacy-compliant code for heterogeneous databases in intelligent programming assistance. It introduces a custom metadata description file independent of the database's physical schema, defining cross-domain logical association attributes and data security attributes, including the target database identifier. The description file is parsed, comparing the associated target with the database identifier of the current context, and automatically selecting either an intra-database join strategy or an application-layer aggregation strategy based on the comparison result. An enhanced context hint containing architectural constraints and security rules is constructed, and the target code is generated by inputting a large language model. De-identification processing logic is automatically embedded based on the data security attributes. This invention effectively solves the problems of AI programming context breakage, cross-database association code generation errors, and data privacy compliance risks caused by missing physical foreign keys in microservice architectures, achieving automatic code generation and source-level data privacy protection for complex cross-service business logic.