2040 results about "Computational environment" patented technology

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

The invention provides a multi-domain computing resource aggregation method and system based on a virtualized user network, and the method comprises the steps: firstly collecting a real-time resource state data set of a computing resource node in a multi-domain environment, covering a computing power load rate and the like, and then calling a virtualized resource mapping model to carry out cross-domain resource feature extraction, the method comprises the following steps: generating a multi-dimensional resource feature vector containing dynamic load fluctuation and other features, performing resource association analysis on the feature vector based on an intelligent aggregation strategy network, determining a collaborative matching weight, aggregating dispersed nodes into a virtualized resource pool, and finally performing dynamic adaptation on aggregation resources in the virtualized resource pool. And generating a resource scheduling topological structure matched with the target business demand, and deploying the resource scheduling topological structure to a multi-domain computing environment to trigger a resource collaborative service, so that multi-domain computing resources can be effectively aggregated, and the resource utilization efficiency and the service matching degree are improved.

The present disclosure generally relates to a computer device, method and system utilizing machine learning for capturing and analyzing profile data communicated across a computing environment including but not limited to: each user's profile, online behaviors and career progression path and provides dynamic recommendations of online actions to be performed to reach a desired target state.

The invention discloses a cloud computing parallel task optimization scheduling method based on a priority dependency graph, which mainly solves the problem of parallel task scheduling in a cloud computing environment, and constructs the priority dependency graph (PDG) of cloud computing tasks by analyzing the dependency relationship and priority among the cloud computing tasks, so as to optimize the scheduling of the parallel tasks in the cloud computing environment. Nodes in the PDG represent cloud computing tasks, edges in the PDG represent dependency relationships among the tasks, the load condition of each computing node is continuously monitored on the basis of the PDG, the tasks are distributed to the most suitable nodes in combination with task priorities and the dependency relationships in the PDG, and if the load of a certain node is too high or the task running time exceeds the expectation, the tasks are distributed to the most suitable nodes. And automatically adjusting the resource allocation of the task. According to the method, the dependency and the priority among the tasks can be more accurately captured, so that the scheduling strategy is dynamically adjusted, the optimal arrangement of task execution is realized, the execution efficiency of the tasks is improved, and the overall resource utilization rate is maximized.

The invention relates to the technical field of data processing, and discloses a data processing method and system for cloud computing and a storage medium. The method comprises the following steps: acquiring preprocessed data through a distributed acquisition node to obtain a structured data packet; performing classification analysis through mixed feature extraction to obtain a safety disaster recovery data set; analyzing the incidence relation through a heterogeneous fusion network to generate a knowledge base; detecting abnormity through a self-adaptive threshold algorithm to form a risk library; determining a recovery scheme through a multi-objective decision to generate an execution log; and a protection strategy is formed through multi-angle comparison and optimization of parameters. According to the method, the data association relationship is mined and analyzed by constructing the multi-level heterogeneous fusion network, the optimal disaster recovery scheme is automatically determined through the multi-target decision process, and meanwhile, a continuous learning optimization mechanism is introduced; the intelligent level of data processing in the cloud computing environment, the timeliness of disaster recovery response and the stability of system operation are remarkably improved.

An illustrative data platform is disclosed that may receive, from an agent deployed in a cloud compute environment monitored by the data platform, data the agent has collected within the cloud compute environment in accordance with an agent operations ruleset. The data platform may determine, based on the data the agent has collected, an operational adjustment to the agent operations ruleset. Accordingly, the data platform may then direct the agent to implement the operational adjustment to the agent operations ruleset for use as the agent continues collecting data within the cloud compute environment in accordance with the agent operations ruleset. Corresponding methods, systems, and products are also disclosed.

Activity monitoring of a cloud compute environment based on container orchestration data may be performed by a data platform. For example, the data platform may obtain audit log data generated by a container orchestrator within a cloud compute environment, generate a data model based on the audit log data, and use the data model to monitor the activity for a security issue. The data model may be indicative of activity occurring with respect to one or more containerized applications executing within the cloud compute environment and the monitored activity may be activity with respect to the one or more containerized applications. Corresponding methods, systems, and products are also disclosed.

An illustrative method includes scanning a compute environment associated with an entity and identifying one or more attack paths from a network to one or more datasets associated with the entity. The one or more attack paths each include a series of risk artifacts within the compute environment that can be exploited by an attacker to access the one or more datasets. The method further includes generating one or more attack path risk scores associated with the one or more attack paths and indicative of one or more levels of risk that the one or more attack paths could be exploited to access the one or more datasets. A risk mitigation operation associated with the one or more attack paths is performed based on the one or more attack path risk scores.

The invention discloses an electric power data anomaly detection method and system combined with edge computing, and particularly relates to the field of electric power data anomaly detection.The electric power data anomaly detection method comprises the steps that a periodic abnormal behavior chain structure is established by extracting a continuous judgment result, actual physical feedback and a historical standard response track of edge nodes to electric power data; and a weighted residual trend path is generated, so that identification of a node judgment offset state and quantification of an abnormal trend are realized. By constructing a power data abnormal behavior chain and a multi-cycle residual error trend graph, an edge node misjudgment offset state is identified, and path suppression and channel structure correction are executed, so that the node safety judgment accuracy and the abnormal response stability in an edge computing environment are improved.

Data platforms described herein are configured to monitor a compute environment and to create and present composite events indicative of multifaceted security threats within the compute environment. Such a data platform may detect both a first event that occurs within the compute environment and is associated with a first alert score and a second event that occurs within the compute environment and is associated with a second alert score. The data platform may then identify an affiliation between the first event and the second event based on a predefined criteria of a multifaceted security threat. Based on the identifying of the affiliation, the data platform may present the first and second events as a composite event indicative of the multifaceted security threat and associated with a third alert score different from the first and second alert scores. Corresponding methods, systems, and products are also disclosed.

The invention provides a data all-flash storage optimization method and system based on cloud computing, and the method comprises the steps: receiving a storage operation request stream of a user terminal through collecting a multi-dimensional performance data set of each node of an all-flash storage cluster in real time, wherein the multi-dimensional performance data set comprises a storage space fragmentation rate, an input / output request queue depth and a network link bandwidth utilization rate; analyzing a to-be-stored data block identifier set and an operation mode label, traversing a historical access record library based on to-be-stored data block identifiers, extracting historical access time sequence characteristics and physical storage position proximity of associated data blocks, and combining the multi-dimensional performance data and the historical access time sequence characteristics to obtain a data block identifier set; according to the method, a dynamic access popularity prediction model in a preset time period is constructed, and finally, a cross-node fragmentation storage topological graph and a cache preloading strategy matrix are generated according to the proximity of the dynamic access popularity prediction model and a physical storage position, so that the optimization of all-flash storage is realized, and the data storage performance in a cloud computing environment is improved.

An AI orchestration system dynamically manages multiple artificial intelligence (AI) agents within a cloud computing environment to efficiently process user requests. A model orchestration subsystem determines whether a request is handled locally using a domain-specific database or by invoking one or more AI agents. The system maintains AI agents in active and inactive states, provisioning computing resources for inactive agents as needed. Real-time model metrics guide the selection of target AI agents, and if a degrading performance trend is detected, the system preemptively spins up additional AI instances. The system provisions processor cycles, memory, and network bandwidth through a cloud-based resource manager, instantiates containerized execution environments or virtual machines, and performs automated load balancing among AI instances.

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

The invention discloses a heterogeneous computing node task scheduling method and system based on an intelligent algorithm, the core idea is that task load prediction, node state monitoring and an intelligent scheduling algorithm are combined to realize efficient matching and load balancing of tasks and computing nodes, and the method mainly comprises task load prediction, node resource evaluation and scheduling strategy optimization. The method aims at achieving efficient matching and load balancing of tasks and computing nodes through an intelligent algorithm (reinforcement learning and a genetic algorithm are combined), and particularly optimizing the utilization rate of computing resources and the task execution efficiency under the condition that a dynamic computing environment and resources are limited.

The invention discloses a real-time computing system resource coordination and decision engine system based on a large language model. According to the system, a large language model is innovatively used as a central strategic decision engine, and a'decision-coordination-execution 'three-layer architecture is constructed. According to the system, macroscopic strategy generation and microscopic real-time control are decoupled by introducing a hierarchical decision-making mechanism (a strategic layer, a tactical layer and an execution layer), so that the core contradiction between LLM high reasoning delay and the microsecond / millisecond-level real-time requirement of the system is effectively solved, and the method is suitable for local computing equipment and a cloud data center. The system comprises a predictive strategy preloading system, and transient response can be achieved. Meanwhile, the system adopts an asynchronous event-driven decision-making mechanism for continuous intelligent optimization. According to the method, the top-down, semantic understanding-based and global collaborative intelligent management of the computing resources is realized, and the resource utilization efficiency, the system automation degree and the overall energy efficiency in a complex and dynamic computing environment are remarkably improved.

Cloud compliance monitoring for a cloud compute environment managed by a container orchestrator may be performed by a data platform. For example, the data platform may obtain cluster configuration data from a control plane of a cluster that is managed by a container orchestrator within a cloud compute environment, obtain node configuration data from one or more compute nodes associated with a data plane of the cluster that is managed by the container orchestrator, and generate a compliance statement based on the cluster configuration data and node configuration data. The compliance statement may indicate a configuration posture of the cloud compute environment according to a compliance benchmark. Corresponding methods, systems, and products are also disclosed.

A system and method for neural network weight compression with intrusion detection capabilities that optimizes model storage and transmission while providing security. The system analyzes weight characteristics to identify statistical properties within different neural network layers, generates optimized encoding schemes based on the analysis, and creates reference distributions for security verification. The compression process employs a multi-resolution approach that produces a progressive representation with base and enhancement layers, enabling flexible deployment across diverse computing environments. Security markers and statistical fingerprints can be embedded throughout the encoded representation, allowing for detection of unauthorized modifications during transmission or deployment. The system monitors encoded weight streams, measures distribution divergence against reference baselines, and generates alerts when statistical anomalies indicate potential tampering. This approach achieves superior compression ratios while maintaining model performance and providing robust protection against increasingly sophisticated attacks targeting neural network weights.

A framework provides an approach for utilizing contextualized content from a user's designed set of documents and private data sets to generate customized, contextualized, private, and domain-specific outputs of agents within an artificial intelligence computing environment and a supporting architecture. The agents and artificial intelligence computing environment include augmenting a language model with the contextualized content, and prompting the language model to generate defined, domain-specific outputs. Such agents enable computing systems to execute specific actions identified by a user that are external to the supporting architecture from the defined, domain-specific outputs.

An illustrative method includes identifying, based on a scan of a compute environment associated with an entity, a plurality of attack paths from one or more networks to one or more datasets associated with the entity and determining a set of one or more attack paths included in the plurality of attack paths that include a particular risk artifact. Based on one or more characteristics of the set of one or more attack paths, a risk score specific to the particular risk artifact may be determined and a risk mitigation operation associated with the particular risk artifact may be performed.

The invention discloses a three-dimensional monitoring data time sequence difference compensation and dynamic threshold adaptive adjustment method. The method comprises the following steps: collecting three-dimensional point cloud data and environment sensor data; environment feature extraction is carried out by adopting regional adaptive partition PCA, and the problem of spatial heterogeneity is solved; by utilizing a physical information enhanced environment field reconstruction technology, the environment field distribution of the whole monitoring area is accurately reconstructed from limited environment sensor data; constructing a cognitive physical-data dual-drive compensation model, and embedding a physical model into a data drive architecture; calculating expected displacement caused by the environment based on the environment-structure response model and deducting the expected displacement from the actually measured displacement; and dynamically adjusting the change detection threshold. According to the method, the problem of'false change 'caused by environmental factors is solved, and the accuracy and reliability of three-dimensional monitoring data are improved.

The invention discloses an extension equipment function configuration method and device, computer equipment and a storage medium, and relates to the technical field of data processing. According to the method, a hardware and firmware collaborative dynamic configuration framework is constructed, and a receiving queue is adopted at an extension equipment end to trigger a central processing unit firmware processing mechanism; in combination with virtualization design of a shadow configuration space and a shadow register, dynamic reconstruction of configuration parameters and an address mapping strategy during operation is realized; a host end supports configuration space access, base address register remapping and memory data transmission interaction through a transaction generation and routing mechanism driven by an event type. Hardware solidification limitation of traditional extension equipment is broken through through software and hardware decoupling design, the problems that function extension capacity is insufficient, resource dynamic allocation is difficult and protocol updating and virtualization scenes cannot be adapted are solved, and the technical effects of improving configuration reconstruction efficiency and address space utilization rate and supporting virtual function creation are achieved. And the adaptability of the equipment in cloud computing and edge computing environments is enhanced.

A system and method for dynamically visualize and modify user authority and security resource edge based on access behaviors in a cloud computing environment. The method provides a new access control perspective to ensure the credibility of database object usage patterns, build clearer and explicit security data consumption mode for data usage requests, and guide tenants to have a more comprehensive understanding and concise use. Processing components and method are configured to gather data and analytics including collecting data resource and database object usage tagging both resources and users with security level by machine learning and building an access model for each role. The method uses graph modeling with created, trained access patterns for users protected resources and produces in-memory implicit sub-groups for users with similar access requirements and limits their resource accesses according to their history behaviors. Further provided are outlier activities prediction and in-real time access authorization modification.

The invention provides an intelligent computing power scheduling method in a distributed computing environment, and relates to the technical field of distributed computing, and the intelligent scheduling method specifically comprises the steps of collecting resource information data, constructing a resource and task model, evaluating a computing power demand, formulating an adjustment strategy, distributing and scheduling the computing power, monitoring and adjusting in real time, and feeding back and optimizing. Through comprehensive modeling and dynamic computing power evaluation of computing nodes and tasks and in combination with multiple intelligent scheduling strategies, accurate allocation of computing power resources can be realized, the problems of resource waste and node load imbalance are effectively avoided, the overall utilization rate of resources in a distributed computing environment is remarkably improved, and the computing power of the distributed computing environment is improved according to the characteristics and requirements of the tasks. The execution nodes are reasonably selected, the resource allocation is optimized, the task correlation is considered, the data transmission overhead is reduced, the task execution speed can be increased, the task completion time can be shortened, and the processing capacity and response speed of the system are improved.

The invention provides a progressive privacy calculation method and system for a virtual-real fusion scene of an intelligent sweeper, and relates to the technical field of sweepers, and the method comprises the steps: collecting indoor space data through a multi-modal sensor, and constructing a scene knowledge graph through an entangled state registration network and a graph neural network; then, based on deep reinforcement learning and zero-knowledge proof, hierarchical encryption is carried out on the atlas, and scene data versions with different privacy protection intensities are generated through federated learning; and finally, performing path planning in a secure multi-party computing environment, and optimizing a cleaning strategy by using neural symbol reasoning and a graph attention network. According to the invention, scene understanding and path planning of the intelligent sweeper can be realized on the premise of guaranteeing the privacy of the user, the sweeping efficiency is improved, and the user experience is improved.

The present invention relates to adaptive multi-state encoding and processing in virtualized computing environments. The system includes a virtualized state selection module that dynamically transitions between binary, ternary, quaternary, and higher-order encoding states based on workload, bandwidth, security posture, and compliance requirements. A virtual encoding engine utilizes hardware-accelerated components, such as vFPGAs, vGPUs, and cTPUs, to enhance encoding throughput. A compliance-driven feedback controller continuously monitors encoding efficiency, threat levels, and adherence to mandates such as GDPR, HIPAA, and FIPS 140-3. Additional features include AI-based anomaly detection, federated model refinement, distributed ledger-backed audit trails, and quantum-resistant encoding techniques. By integrating intelligent encoding decisions with scalable compliance enforcement, the system enables high-performance, secure, and regulation-ready data processing across distributed cloud and edge environments, delivering measurable improvements in system responsiveness, data integrity, and operational trust.

An illustrative method includes receiving, by a data platform configured to monitor the compute environment, runtime workload data collected by an agent deployed to the compute environment, wherein the runtime workload data comprises user space data collected from a user space of the compute environment and kernel space data collected from a kernel space of the compute environment. The method further includes performing, by the data platform, a monitoring operation based on the user space data and the kernel space data of the runtime workload data.

A data collection system is disclosed to be communicatively coupled to a data platform configured to perform security monitoring of a compute environment. A sub-kernel component of the data collection system may collect sub-kernel data accessible from a sub-kernel space below an operating system of a compute resource in the compute environment. The data collection system may manipulate the collected sub-kernel data to prepare the sub-kernel data to be used by the data platform in performing the security monitoring of the compute environment. The data collection system may then communicate the manipulated sub-kernel data to the data platform. Corresponding methods, systems, and products for compute environment security monitoring using data collected from a sub-kernel space are also disclosed.

The invention relates to a dynamic CPU scheduling method, system and device based on a CPU busy state and a medium. The method comprises the steps that busy degree indexes of all CPU cores are dynamically generated through monitoring; service logic to be executed is analyzed into a task set, and task attributes which cannot be changed are distributed to tasks; determining respective target CPU cores based on busy degree indexes of all current tasks, generating binding parameters, and writing the binding parameters into task attributes of the corresponding tasks; executing the allocation logic according to the task attribute of each task, and submitting the task to a running queue of the target CPU core to wait for execution; and when the single execution duration of the task in the target CPU core reaches a preset threshold value or is terminated by external interruption, resetting the corresponding task into the operation queue. According to the invention, breakthrough improvement is realized in three dimensions of hardware resource utilization rate, system real-time performance and scheduling security, and a solution with response speed and stability is provided for task scheduling in a computing environment.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

Systems and methods for decentralized orchestration of event records within decentralized computing environments. A processing system including decentralized execution nodes dynamically allocates microservice instances based upon real-time node performance metrics. Transaction requests including transaction data digitally associated with decentralized identifiers (DIDs) are processed by allocated microservice instances. The processing includes verifying transactions, generating cryptographically-linked immutable memorialization records associated with respective DIDs, and distributing these records to a tamper-evident distributed ledger configured to enforce immutability through consensus nodes. The processing system dynamically reallocates microservice instances among decentralized execution nodes based upon monitored utilization metrics and predefined scaling thresholds. Each memorialization record is cryptographically secured and linked permanently to the DID for data immutability, enhanced security, and dynamic scalability for event data processing in distributed infrastructures.