185 results about "Computational environment" patented technology

A computing platform performs automated due diligence by acquiring project scope information and documents, standardizing all data across all document types into a uniform format optimized for analysis and subjecting the data to repeated querying by large language model agents to triangulate it against a body of human content expertise coded into the platform. The platform compares variables with a project proforma, verifies permits by parsing permit data and cross checking a permitting authority portal, evaluates design completeness by detecting standards of readiness status, and measures the bankability of project agreements compared to industry standards. The platform computes environmental, social, and governance metrics, assesses counterparty risk and community sentiment using searches of public sources, and validates a reported capital stack by analyzing documents and applying industry standards thereto. The platform calculates risk metrics, applies weights to produce two scores, and generates a report that includes remediation steps.

Disclosed is a system (200) and a method (500) for managing hierarchical relationship among network entities in a computing environment. The system (200) detects removed (or status-changed) parent entity. Thereafter, the system (200) retrieves a policy configuration from a cache memory (215) and identifies child entities linked to the parent entity using the policy configuration. This enables faster and modifiable execution of operations without any overheads from the computing environment. The system (200), using the policy configuration, determines a stalling condition for the child entity and initiates stalling the child entity for a stalling period based on the stalling condition. The stalling of the child entity enables an option of reassociation with an active entity, based on a receipt of association request from the active entity, when attributes of the child entity match with a reassociation eligibility criteria defined by the policy governing the hierarchical relationship.

Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a decision tree to generate a recommended action in response to detecting a change in a data map representing a computing environment. The disclosed system determines relationships among data objects representing digital data, digital assets, and data processing activities and generates a data map according to the relationships. The disclosed system monitors the data map for changes and, responsive to determining a change in the data map, traverses a decision tree by executing one or more calls to one or more application programming interfaces according to the change in the data map and one or more data policies relevant to the changes. The disclosed systems utilize the decision tree and application programming interfaces to generate a recommended action for modifying digital assets, digital data, and / or data processing activities.

This invention relates to the fields of computer vision and intelligent robotics, specifically disclosing a facial expression capture method and robot that integrates an edge-locking mechanism and a lightweight SAM-Tiny model. The method includes: generating a facial mask sequence using an improved lightweight SAM-Tiny model and calculating an edge confidence index; triggering mechanical locking matching the inference latency or calculating multi-axis drive parameters for pose compensation based on the index; recognizing facial expression modalities based on the mask sequence to generate interactive commands; and the robot comprising an electric chassis, a multi-degree-of-freedom support, a vision terminal integrating the improved SAM-Tiny model, a mechanical gripper, and a controlled locking mechanism. This invention, through an algorithm-mechanical timing closed loop, deeply couples edge confidence with mechanical locking frequency, solving the problem of the trade-off between tracking stability and real-time performance in edge computing environments, and achieving non-contact semantic interaction, suitable for medical monitoring and intelligent interaction scenarios.

An aspect of the present disclosure facilitates routing diverse incoming requests to optimal computing options while satisfying requisite performance metrics. In one embodiment, in a computing environment having multiple compute types, a historical data containing characteristics in transport payloads of incoming requests, and characteristics in processing corresponding incoming requests by respective compute types is collected. A system trains, based on the historical data, a machine learning (ML) model to select compute types for incoming requests. Upon receiving a new incoming request sought to be processed, the system extracts from a transport payload of the new incoming request, the first set of characteristics to create a new request context. The system then applies the ML model to the new request context to identify a target compute type and forwards the new incoming request to the target compute type.

A method for optimizing workload placement in a cloud computing environment includes collecting a sequence of snapshots of states of a plurality of workloads running on a plurality of compute instances. Based on the collected snapshots, a plurality of workload placement algorithms are determined, each applying a first sorting criterion for workloads and a second sorting criterion for compute instances. Each workload placement algorithm is applied to the snapshots to determine performance metrics associated with workload distribution and compute resource utilization. A workload placement algorithm is selected based on the performance metrics and applied to the cloud environment, causing at least one workload to be migrated from a first compute instance to a second compute instance.

A system associated with an enterprise computing environment infrastructure may include a chaos testing tool data store that contains a plurality of electronic records, each record including a chaos engineering fault parameter. A computer processor of a chaos testing tool may receive, from a chaos testing user, injection information associated with an element of the enterprise computing environment infrastructure. The chaos testing tool may then access the chaos engineering fault parameter from the chaos testing tool data store and automatically arrange to inject a chaos engineering fault into operation of the element of the enterprise computing environment infrastructure in accordance with the injection information and the chaos fault parameter. A response of the element of the enterprise computing environment infrastructure may then be measured subsequent to said injection, and the system may output an indication of the measured response to the chaos testing user.

A method comprises receiving a request to predict a type and a quantity of respective ones of a plurality of resources for a computing environment. Using a multiple output classification and regression machine learning model, the type and the quantity of the respective ones of the plurality of resources are predicted in response to the request. The machine learning model is trained with a dataset comprising historical resource data corresponding to respective ones of a plurality of users.

This invention discloses a robot environment perception method and system, belonging to the field of intelligent robot technology. The method and system collect environmental perception data through multiple perception links; establish a dynamic confidence model for each perception link and calculate the dynamic confidence level in real time; construct an environmental complexity assessment module to calculate the environmental complexity index in real time; and dynamically adjust the perception mode and fusion strategy within an adaptive fusion framework based on the dynamic confidence level and environmental complexity index. The adaptive fusion framework includes a predictive mode switching layer, a confidence-weighted fusion layer, and an adaptive computational resource scheduling layer. A navigation module receives the fused data and performs path planning and motion control. This achieves predictive switching of perception modes and confidence-weighted fusion, enhances system robustness, optimizes computational resource utilization, and improves the robot's autonomous navigation capability in complex dynamic environments.

The application provides a path-aware fault processing method for a cloud computing environment, and relates to the technical fields of cloud computing, computer network security and system reliability. The method comprises the following steps: obtaining a real end-to-end network path of target service traffic in a network communication system for cloud computing, and representing the real end-to-end network path as an ordered path sequence; determining a fault type of a target fault to be injected and a target path node corresponding to the target fault in the ordered path sequence based on a preset fault model and the real end-to-end network path, generating a fault specification based on the fault type, the position of the target path node and the fault parameters of the target fault, and controlling the target path node to inject a fault injection primitive corresponding to the target fault; and in the case of injecting the fault injection primitive, performing correlation analysis on running data and the fault injection primitive to generate an influence analysis result of the target fault. The technical scheme of the application improves the accuracy of fault influence analysis.

A system is provided for artificial intelligence-based remediation of computing infrastructure issues using federated and reinforcement learning. In particular, the system may use a federated learning strategy to monitor technical issues within the computing environment and identify patterns associated with the issues. Based on the patterns, the system may generate a rule template for remediating the issue. The system may further use a reinforcement learning strategy to orchestrate and execute the rules for remediating the issue on the affected computing devices or resources. The system may also use a fallback mechanism to ensure data and service availability while issues are being remediated. Local AI models may be installed on endpoint devices to support self-remediation, which in turn increases the computational efficiency of the remediation processes. In this way, the system provides an intelligent, efficient, and secure way to remediate issues within the computing environment.

This application provides a communication information collaborative processing method and system applied to an edge computing environment, relating to the field of communication information technology. First, the original communication data packets generated by each edge computing node in the edge computing environment are acquired, and intent mapping processing is performed on them, with a collaborative intent tag field appended to generate intent-based data packets. The intent-aware forwarding node receives and parses the intent-based data packets, retrieving a collaborative operation instruction sequence based on the collaborative intent tag field. Collaborative operations such as combination or splitting are performed on the intent-based data packets according to the instruction sequence, and finally, the processed data packets are forwarded according to the destination node address field. This invention can accurately identify business intents, achieve differentiated collaborative processing, improve data processing efficiency and accuracy, and ensure the stable and efficient operation of the edge computing system.

A system and method for evicting a resource from a unified network topology (UNT) of a computing environment, is disclosed. The method includes: generating a representation of a UNT for a network topology of a computing environment, the representation based on a unified data schema, wherein the unified data schema is mapped to a plurality of data schemas, each corresponding to a unique data source; receiving an event record at a first time, the event record including a resource identifier; updating in the UNT a representation corresponding to the resource based on extracted data from the event record at the first time; determining that a time period has lapsed from the first time which is greater than a threshold; detecting that a number of event records which include the resource identifier is less than a threshold value; and removing the representation of the resource from the unified network topology.

An apparatus includes: a memory configured to store a graph data structure comprising a plurality of nodes representing computing resources and workloads within a computing environment; and a processor configured to: instantiate, in the graph data structure in response to a migration of a workload from a first computing resource to a second computing resource, a first node representing a state of the workload at the first computing resource before the migration; associate an edge connecting the first node to a second node representing a state of the workload at the second computing resource after the migration, wherein the edge represents a performance metric associated with the migration; and process the graph data structure comprising the first node and the edge using a Graph Neural Network (GNN) to determine an action associated with the migration of the workload.

A system and method for detecting a protection gap in a cybersecurity system is provided. The method includes scanning a computing environment to detect a cybersecurity monitoring system connected to the computing environment; scanning the computing environment to detect a plurality of resources deployed in the computing environment; detecting a resource of the plurality of resources which is of a first resource type; determining that the cybersecurity monitoring system is not configured to monitor the first resource type; and initiating a mitigation action in response to determining that the cybersecurity monitoring system is not configured to monitor the first resource type.

Embodiments include methods performed by a monitoring function for a computing environment configured to host a plurality of atomic software functions (AFs) usable by applications executing in the computing environment. Such methods include performing pattern analysis on entries of a database to identify one or more candidate recurrent patterns. Each database entry includes an AF identifier uniquely associated with one of the AFs and a time at which the identified AF was invoked in the computing environment. Each candidate recurrent pattern includes a plurality of instances of a same sequence of AF identifiers in a same chronological order. Such methods include validating a first one or more candidate recurrent patterns as applications and / or use cases and assigning respective use case tags to the validated first candidate recurrent patterns. Such methods include updating the database such that each assigned use case tag is stored in association with the database entries that are associated with the corresponding validated candidate recurrent pattern. Other embodiments include monitoring functions (e.g., apparatus) configured to perform such methods.

A system and method are disclosed for managing integration patterns among a plurality of nodes in a networked environment. Each node is assigned a unique identifier, and integration patterns defining relationships between two or more nodes are represented as vectors comprising node identifiers and additional attributes such as protocol, security context, or dependencies. The vectors are validated using predefined logic and are indexed and stored within a multi-dimensional matrix according to their defining attributes. The system maintains metadata for each vector, including integration dependencies, approval status, and versioning. A graphical user interface presents the matrix as an interactive grid, enabling users to filter, select, and analyze integration patterns based on node attributes or pattern characteristics. The techniques disclosed support dynamic updates, automated governance, and compliance workflows, thereby streamlining the design, validation, and management of integration scenarios across complex computing environments.

A container process mapping can be used for container layer management in a computing environment. For example, a system can receive a mapping file indicating a mapping of one or more processes to one or more container layers of a container image. Each process can be mapped to a container layer in the mapping file. The system can determine, for an execution of a container associated with the container image, a first resource usage of each process of the one or more processes. The system can determine, based on the mapping file and the first resource usage, a second resource usage of each container layer of the one or more container layers. The system can perform, based on the second resource usage, an action for a container file that is executable to generate the one or more container layers.

This invention discloses a forward-secure dynamic searchable encryption method and system based on an XOR encryption chain. The invention includes a data provider dynamically establishing a searchable secure index for a data file; a data user generating a query token based on a keyword 'w' and sending it to a cloud server; upon receiving the query token, the cloud server first decrypts the file identifier at the last index position based on the query token, then decrypts the file identifier at the next index position based on the XOR encryption chain, sequentially using the query token and information from the previous index position, and finally sends the file identifier to the data user for retrieving the data file. This invention can dynamically establish a secure searchable index for data files and achieve forward-secure, efficient encrypted retrieval with extremely low computational and communication overhead, while ensuring data confidentiality and forward security. It can be applied in cloud computing environments to ensure the confidentiality of outsourced data.

Examples of the present disclosure describe systems and methods for state-based entity behavior analysis. In an example, entities of a computing environment may be represented using a hierarchical entity web. In some examples, an entity may have a state associated with it, which may be modeled using a place / transition (PT) network. Events within the computing environment may be evaluated by transitions of a PT network to determine whether an entity should change state. If an entity transitions from one state to another, one or more actions may be performed, including, but not limited to, taking a remedial action, generating a recommendation, and updating the state of one or more associated entities. Thus, aspects disclosed herein may provide a high-level overview of the state of entities of a computing environment, but may also be used to view in-depth information of entities at lower levels of the hierarchical entity web.

Mechanisms are provided to perform intelligent compliance verification for monitored computing environments. The mechanisms train a first artificial intelligence (AI) computer model to generate rules from natural language content, to thereby generate a first trained AI computer model. The mechanisms train a second AI computer model to verify compliance of a monitored computing environment with rules generated by the first AI computer model, to thereby generate a second trained AI computer model. The mechanisms receive a regulation electronic document that comprises natural language content describing a regulation and determining compliance of a monitored computing environment based on a processing of the regulation electronic document by the first trained AI computer model to generate a rule corresponding to the regulation electronic document, and application of the generated rule by the second trained AI computer model to verify compliance of the monitored computing environment.

This invention provides a method, apparatus, device, and medium for operating a plug-in system. Existing plug-in systems only allow developers' plug-ins to run in a single target computing environment. The plug-in system of this invention obtains a plug-in to be deployed, which includes business logic code and descriptive information; receives a deployment instruction for the plug-in; determines the target computing environment based on the deployment instruction; performs adaptation processing on the business logic code based on the descriptive information and the target computing environment to generate an executable adapted to the target computing environment; runs the executable in the target computing environment, establishing a mapping relationship between the executable and a unique identifier; and manages the executables running in different target computing environments uniformly based on the mapping relationship, enabling efficient and secure operation in different target computing environments without modifying the plug-in code.

An illustrative method for querying multiple datasets may include accessing a plurality of datasets including data associated with monitoring one or more compute environments and ingested at varying time intervals, receiving a query request for information that depends on the data included in multiple datasets of the plurality of datasets, querying, based on the query request, the multiple datasets, prioritizing, based on the varying time intervals, information that depends on the data included in the multiple datasets and presenting, based on the prioritizing, a query result representative of the information that depends on the data included in the multiple datasets.

The application discloses an edge-cloud collaborative computing power scheduling architecture and a low-power consumption management method, and relates to the technical field of edge computing.The application collects multi-dimensional resource indexes of distributed edge computing nodes, calculates resource availability scores through standardized processing and weighted comprehensive score calculation; extracts static code features and dynamic running features of tasks, adopts a gradient boosting decision tree algorithm to predict execution time, CPU occupancy, memory requirement and network transmission data volume of the tasks, and performs node performance correction; adopts an improved non-dominated sorting genetic algorithm to solve a multi-objective optimization problem, and generates a task offloading decision; the application solves problems such as difficulty in resource quantification under an edge computing environment, lack of accurate prediction in task scheduling, poor single-objective optimization effect, and unintelligent power consumption control, realizes efficient task scheduling and low-power consumption operation, and has good scalability and adaptability.

This invention discloses a computer-based method for evaluating the complexity of the working environment of unmanned ground vehicles (UGVs). First, it determines environmental complexity evaluation indicators and establishes an evaluation indicator system. Then, it acquires and normalizes multi-source environmental data using onboard sensors and a deep learning model. Next, it determines the weights of static environmental indicators using the Analytic Hierarchy Process (AHP) and calculates the static environmental complexity index using a nonlinear mapping function. Simultaneously, it establishes a potential energy function incorporating distance, speed, and predicted collision time, calculating the total environmental potential energy to output the dynamic environmental complexity index. For the state evolution complexity evaluation indicator, it constructs a state evolution complexity quantification model based on state change entropy, outputting the state evolution complexity index. Finally, it outputs the working environment complexity index by establishing a multi-dimensional coupled complexity model. This invention achieves real-time quantification and evaluation of working environment complexity, providing reliable data for autonomous decision-making, path planning, and safety control of UGVs.

Methods and systems for managing an edge compute environment comprising endpoint devices and an orchestrator are disclosed. In particular, an internal peer-to-peer file sharing infrastructure exclusive to the edge compute environment is established using the endpoint devices and the orchestrator. The internal peer-to-peer file sharing infrastructure exclusive to the edge compute environment may then be used to retrieve and distribute data used for configuring one or more of the endpoint devices to allow the endpoint devices to provide one or more computer-implemented services to one or more users associated with the edge compute environment. For security of the endpoint devices, only the orchestrator is able to communicate with data processing systems external to the edge compute environment.

A system can receive data that includes a first subset and a second subset relating to a first application and a second application in a cloud computing environment. The system can determine differences between the first subset and the second subset by comparing the first subset and the second subset. The system can generate, by using the differences, quality metrics for a transformation from the first application to the second application in the cloud computing environment. The system can generate, by using historical data that includes historical first subsets and second subsets, a trend that represents a progression of the transformation. The system can determine, by using the differences, the quality metrics, and the trend, code in the second application that is likely to cause the differences. The system can generate a command that is executable in the cloud computing environment to automatically update the code in the second application.

The application discloses a data cache optimization method in an edge computing environment, and relates to the technical field of edge computing data cache optimization, which comprises collecting original access logs from a target edge node and performing structured processing. Semantic distillation, periodic pattern mining and event intensity analysis are respectively performed on data topic labels, access heat time series and update event time series to obtain data semantic fingerprints, access heat evolution trajectories and update event intensity spectra. These features are input into a feature coupling device for collaborative evolution to generate a comprehensive cache feature map. A cache value evaluation network is constructed based on the map, data value is dynamically calibrated, and a hierarchical value list is formed, and then an adaptive strategy containing a cache order and a space pre-allocation plan is derived, which is finally converted into a configuration instruction set and issued to the target node. The application realizes accurate matching of cache strategies and multi-dimensional dynamic features of data, and improves the overall efficiency of the cache system.

A cybersecurity service (“service”) obtains alerts indicating security policy violations for assets in a computing environment and the corresponding issue category(ies) associated with each violation. For each alert, the service determines which asset in the computing environment to target for remediation of the associated violation by searching a graph representation of the computing environment based on the affected asset's type and / or the issue category. The service identifies a target asset and other assets related to the affected asset and the target asset as a result of searching the graph and generates a remediation plan indicating actions to take on the target asset to remediate the security policy violation for the affected asset based on the target asset type and the corresponding issue category. The service indicates the remediation plan, the related assets identified due to the graph traversal, and their corresponding security policy violations within the same issue category.