5391 results about "Cloud computing" patented technology

The invention discloses a cloud monitoring service operation and maintenance dynamic optimization system and method based on an AI intelligent agent, and relates to the technical field of cloud computing intelligent operation and maintenance. The method is used for solving the problems of hybrid cloud cross-layer data splitting, hidden fault association failure and operation and maintenance action conflict. Heterogeneous data of a physical layer, a virtual layer and an application layer are collected through a containerized probe, and a standardized cross-layer index is constructed through layered tagging and dynamic time sequence alignment. And constructing a fault propagation map through incremental correlation analysis and a dynamic time window, and identifying space-time coupling nodes. And based on the directional disturbance verification causal relationship, combining with the resource scheduling and fault chain incidence matrix fitting coupling degree index, and generating a root cause positioning instruction. And optimizing an action priority based on a propagation cost gradient and an asymmetric game strategy, and updating the model and the rule through a feedback closed loop. According to the invention, cross-layer data fusion and accurate fault positioning are realized, and the cloud service stability and the resource utilization rate are improved.

The invention relates to the technical field of network security, in particular to a network security big data state evaluation method based on pattern recognition, which comprises the following steps of: extracting multi-modal features from a network flow log, a system event log, a host behavior log and threat intelligence data, generating a feature matrix, performing feature dimensionality reduction by adopting an auto-encoding network, and obtaining a network security big data state evaluation result; carrying out attack behavior classification and abnormal mode identification in combination with unsupervised clustering and a graph neural network; constructing an attack transition probability matrix based on a Markov model; forming a time sequence attack chain; predicting an attack development trend; and a dynamic protection instruction is issued to the safety equipment. According to the method, the unknown attack detection capability can be improved, the time sequence attack traceability is enhanced, the security situation assessment is optimized, and the method is suitable for security situation awareness in cloud computing, industrial internet and large-scale network environments.

The invention relates to the technical field of cloud computing, and discloses a cluster load balancing processing method based on cloud computing, which comprises the following steps of: S1, initializing a cluster state: determining computing power, power consumption parameters and a task allocation model of each node in a cloud computing cluster; s2, acquiring a node operation state: providing dynamic input data for construction of a load distribution model; s3, constructing a load distribution model: defining a global optimization target; s4, generating a preliminary task allocation scheme: satisfying an optimization target; s5, aiming at the dynamic change of the task arrival rate and the node state; s6, task migration is executed, and the state is updated; and task migration between the nodes is implemented. Through a closed-loop process of node state acquisition, task allocation model construction, dynamic adjustment and feedback optimization, efficient matching of a task request and a computing node is realized. The method can adapt to dynamic changes in a cloud computing environment, the resource utilization rate is remarkably improved, and task processing delay is effectively reduced.

Systems and methods for customer environment integration with anomaly detection systems are disclosed. The methods include receiving cloud computing environment data for the cloud computing environment; generating, based on the received cloud computing environment data, a set of commands for generating cloud computing infrastructure for anomaly detection, the set of commands including one or more commands for generating a first set of anomaly detection infrastructure elements within the cloud computing environment and for generating a second set of anomaly detection infrastructure elements at the anomaly detection system; and initiating an execution of the set of commands.

The invention discloses a multi-source data fusion method and system based on cloud computing, and particularly relates to the technical field of multi-source data fusion, and the method comprises the steps: obtaining the real-time information of multi-source data, generating a health score and a label through a health evaluation model, and marking a data state and an abnormality type; performing format conversion and semantic enhancement processing on the health data to generate structured data; based on a knowledge graph reasoning technology, extracting a logical relationship to generate a fusion rule, and calculating a rule weight and confidence in combination with a health label, a data source confidence and a semantic feature; a fusion result of conflict data is judged through a semantic consistency index, if the fusion result does not conform to expectation, historical data and rules are loaded according to a verification model, a verification score is calculated, an optimization process is triggered after the verification score is compared with a threshold value, health assessment model parameters, a semantic embedding model and fusion rule logic are optimized, and fusion accuracy and reliability are improved.

The invention discloses a resource scheduling method, system and device in an edge cloud computing platform and a medium, and relates to the field of resource scheduling. In the method, a corresponding digital twinborn model is constructed for each edge node according to a resource state and an environmental parameter, a resource change condition in a future preset time is predicted according to the digital twinborn model, and a global resource state map is generated according to the resource change condition; obtaining a task demand of each task, and determining an execution position and a priority of each task according to the task demand and the global resource state map; generating a global allocation scheme through a federated learning algorithm according to the global resource state map, the execution position and the priority; and acquiring a resource allocation state in real time, determining the difference between the resource allocation state and the global allocation scheme, and correcting the global allocation scheme based on the difference through a hierarchical collaboration mechanism. By implementing the technical scheme provided by the invention, the effects of optimizing resource allocation efficiency, reducing delay and energy consumption and improving task processing performance are achieved.

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 discloses a big data distributed storage and parallel processing collaboration method based on cloud computing. The method comprises the following steps: sensing data stream characteristics in real time through a self-adaptive dynamic partitioning engine, dynamically adjusting a partitioning strategy and generating a metadata label; constructing a node selection model through a comprehensive evaluation algorithm, selecting storage nodes to form an optimal storage cluster, and dynamically adjusting a resource matching weight coefficient based on a load state through a load optimization module; decomposing a data processing task into parallel subtask units, and constructing a dual-objective optimization model; triggering a dynamic rebalance mechanism through a distributed monitoring agent in combination with a hierarchical early warning strategy; and constructing a multi-level cache system to optimize a data access path, outputting a final result, pushing the final result to the user terminal, and updating the knowledge base. Through collaborative optimization of dynamic partitioning, multi-dimensional resource scheduling, elastic scaling and intelligent caching technologies, the resource utilization rate, the load balancing capacity and the stability in a high-concurrency scene of the system are remarkably improved.

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.

The invention relates to the technical field of data mining, in particular to a data security management system and method based on cloud computing. The method comprises the following steps: acquiring real-time vehicle driving data, and performing homomorphic modular exponentiation dangerous case sensing fragmentation encryption to obtain dangerous case sensing encrypted fragmentation data; acquiring an Internet of Vehicles data stream, and implementing zero-trust adaptive data stream encryption to obtain a zero-trust authentication credential; implementing federal learning privacy feature mapping on the dangerous case sensing encrypted fragmented data to obtain an anonymized encrypted feature mapping model; performing Merkle tree aggregation on the anonymized encrypted feature mapping model to obtain a block chain global dangerous case fingerprint; extracting a threat behavior quantization vector according to the block chain global dangerous case fingerprint and the anonymization encryption feature mapping model; and carrying out transmission strategy optimization according to the threat behavior quantization vector and the zero-trust authentication credential to obtain a global dynamic transmission strategy matrix. According to the invention, the safety of vehicle data uploading and the overall uploading efficiency can be improved.

The invention discloses a cross-domain computing task processing method, a program product, equipment and a medium, and relates to the technical field of cloud computing. The method comprises the following steps: determining a computing node for executing a cross-domain computing task in a cross-domain collaborative scene, and generating an identity certificate bound with the computing node based on a hardware credible state of the node; collecting security policies of a plurality of management domains for conflict detection and decision, and generating a conflict-free session policy; scheduling the cross-domain computing task to a target computing node meeting a preset credible requirement, and issuing a revocable dynamic session token; and recording the operation information of the cross-domain calculation task in the whole life cycle as an audit event, and generating a chained audit log which is linked by the hash value and is digitally signed through a preset verifiable audit interface. By means of the technical scheme, it can be ensured that the whole life cycle of any computing task meets the closed-loop safety requirements of identity credibility, permission controllability, execution propriability and behavior traceability in the complex distributed computing environment.

The invention discloses a cloud computing storage, calculation and training resource allocation method based on reinforcement learning and dynamic resource scheduling. The method comprises the following steps: S1, establishing a unified resource scheduling framework; s2, constructing a state space and an action space; s3, generating a state representation for predicting future resource demands; s4, initializing a reward function of key performance index weighted comprehensive evaluation; s5, generating an initial storage resource, computing resource and training resource allocation strategy; s6, continuously adjusting a reward function of the reinforcement learning model based on the self-supervised learning feedback signal; s7, dynamically adjusting a distribution scheme of the storage resources, the computing resources and the training resources through quick response of the resource scheduling framework; s8, optimizing resource allocation among a plurality of cloud computing nodes or platforms; s9, automatically identifying different stages of the current task through a self-supervised learning model; and S10, finally generating an optimal allocation scheme. According to the staged resource scheduling method, the condition of overload or insufficiency of resources is effectively avoided, and the task execution time is greatly shortened.

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.

The invention relates to the technical field of big data, in particular to a cloud computing-based tumor early screening data sharing platform construction method and system. The sharing platform construction method comprises the following steps: carrying out association modeling on multi-dimensional early screening data by adopting a knowledge graph technology; encrypting the sensitive field of the metadata index database, and synchronously generating an irreversible anonymous identifier to replace the original identifier; partitioning and arranging the metadata index database in a distributed storage system corresponding to a cloud platform according to tumor types, and performing cross-region redundant backup by adopting an erasure code technology; and constructing an access control smart contract based on a block chain technology, verifying the qualification of a requester through a digital identity authentication module, and after verifying the validity of a digital certificate of the requester, dynamically decrypting and transmitting a data copy conforming to an authority level. Through organic fusion of the knowledge graph, the encryption technology, distributed storage and the block chain, a safe, efficient and reliable tumor early screening data sharing platform is constructed.

The invention discloses a cloud computing resource optimization method based on intelligent scheduling, and belongs to the technical field of cloud computing resource processing. The method comprises the steps of obtaining real-time operation data of target data in a data optimization detection range, collecting historical resource scheduling records and task execution logs, and constructing a multi-dimensional resource state data set; according to the method, multi-objective optimization, simulation verification and reinforcement learning feedback in the step S5 are carried out, a perception-prediction-scheduling-monitoring-optimization closed-loop mechanism is constructed, the resource utilization rate, the response time and the energy consumption cost of a multi-objective optimization function are balanced, and a particle swarm optimization algorithm is combined with simulation verification to generate a global optimal strategy; and reinforcement learning dynamically adjusts model parameters by taking the execution deviation as a reward signal, continuously updates a resource perception dimension and a prediction model, realizes continuous iterative upgrade of a resource optimization effect, and performs optimization processing on cloud computing resource optimization based on intelligent scheduling.

The invention relates to the technical field of cloud computing, and discloses a resource scheduling method, device and equipment, and the method comprises the steps: generating a multi-dimensional data topological graph of cloud environment monitoring data, decomposing a to-be-scheduled job set into quantum computing unit sets which can be processed in parallel, and adding priority labels for the quantum computing unit sets through a quantum heuristic algorithm, according to priority labels and a multi-dimensional data topological graph, physical resources of a cloud environment are divided into non-uniform resource fragments matched with task requirements, based on the non-uniform resource fragments, resource scheduling is carried out through an improved hybrid particle swarm optimization algorithm, and weighting and fitness evaluation and resource constraint correction are introduced. According to the resource scheduling method and device, the multi-dimensional data topological graph is generated to comprehensively present the resource state and association, then the to-be-scheduled job set is decomposed into the quantum computing unit sets, the priority labels are added, and then the non-uniform resource fragments are divided for resource scheduling, so that the resource adaptation accuracy is improved, and the scheduling efficiency and the resource utilization rate are improved.

The invention discloses a dynamic scheduling method for a cloud computing resource pool in the technical field of cloud computing. Comprising the following steps: S1, analyzing resource demands; s2, performing performance prediction on the resource pool through load prediction; s3, predicting task demand resources; s4, resource allocation decision making; s5, task scheduling; and S6, resource monitoring and feedback. According to the invention, load prediction is carried out through the long-short-term memory network, an adaptive mechanism is introduced, and a load prediction model is updated along with continuous change of data, so that the accuracy is improved; the resource demand of the task is predicted through a machine learning algorithm, a reasonable resource allocation decision is made according to the state of the current resource pool and the resource demand predicted by the task, the utilization rate of the resource is improved, stable operation calculation of the task is guaranteed, efficient task allocation and scheduling are achieved, resource utilization maximization is guaranteed, and the resource utilization rate is increased. And the change of the system load is effectively responded.

The embodiment of the invention provides a task scheduling method, system and device based on cloud computing and a storage medium, and belongs to the technical field of computers. According to the method, task priorities are initialized according to task resource requirements, task types and task timeliness requirements, task scheduling strategies are determined according to task metadata and computing resource information, the task metadata comprise the task priorities and the task resource requirements, and the task scheduling strategies are used for representing task scheduling sequences and task allocation objects; task scheduling is carried out according to the task scheduling strategy, load fluctuation information and task demand change information of each computing node are obtained, the task scheduling strategy is adjusted according to the load fluctuation information and the task demand change information, and the step of task scheduling according to the task scheduling strategy is repeatedly executed, so that the task scheduling efficiency is improved in the task execution process. Task scheduling and allocation are adaptively adjusted based on load changes and task demand changes, and the task execution efficiency and the system resource utilization rate are improved.

The invention discloses a fine-grained access control method and system based on risk identification, and belongs to the technical field of information security. According to the method, user subject attributes, behavior attributes and system environment attribute information are collected in real time, a standardized decision matrix is constructed, an interval type-2 fuzzy set (IT2FS) is used for conducting fuzzy modeling on the attributes, and an upper membership matrix and a lower membership matrix are generated. And calculating the dynamic weight of the attribute index in combination with a CRITIC method, introducing a time decay factor to dynamically correct a risk score through an improved TOPSIS method, calculating the Euclidean distance between an access request and a positive / negative ideal solution, and generating a normalized risk closeness degree. And based on the risk score and a preset threshold value, dynamically matching a hierarchical permission strategy, and adopting a static rule and a priority coverage mechanism to eliminate permission conflicts. According to the method, multi-dimensional risk assessment and dynamic weight adjustment are fused, the problems of insufficient real-time performance, subjective weight dependence and weak uncertainty processing capability in a traditional method are solved, the accuracy and security of access control are remarkably improved, and the method is suitable for scenes with high security requirements such as cloud computing and finance.

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.

The invention discloses a virtual machine scheduling method in a distributed environment based on deep reinforcement learning, and belongs to the technical field of cloud computing resource scheduling. According to the method, the defects of a traditional method in multi-objective optimization and mixed action space collaborative decision-making are overcome by constructing a mixed action space joint decision-making mechanism. The method specifically comprises the following steps: establishing a mixed action space containing discrete node selection and continuous resource allocation, filtering invalid nodes by adopting a dynamic mask mechanism, and ensuring resource ratio constraint through projection gradient descent; designing a hierarchical reward function to realize multi-target dynamic balancing, and dynamically adjusting the priorities of energy consumption, load balancing and SLA guarantee based on an adaptive weight strategy; a multi-agent collaborative framework is provided, cross-node topological dependence is captured by using a graph attention network, and dynamic fusion of spatio-temporal characteristics is realized through cross attention in combination with LSTM coding time sequence load characteristics; a course learning strategy and a priority experience playback mechanism are introduced to improve training efficiency and strategy robustness.

The invention relates to a heterogeneous network engineering-oriented adaptive protocol conversion gateway system and a control method, and belongs to the technical field of Internet of Things, and the system comprises a protocol identification module which is used for monitoring a data stream in real time and identifying the type of a protocol used in current communication; the adaptive protocol selection module is used for selecting the most suitable protocol according to the network state and the data flow type; the protocol conversion module is used for data format conversion between different protocols and mapping of header information; the network resource optimization module is used for dynamically adjusting the transmission mode of the protocol according to the current network state; and the data caching and traffic scheduling module is used for scheduling the cache and the traffic when different protocols are switched. According to the method, the problem of compatibility among different network protocols is solved, the data transmission efficiency and the utilization of network resources are optimized, and the method can adapt to different application scenes and is widely applied to environments needing cross-protocol communication, such as the Internet of Things, a smart power grid and cloud computing.

The invention relates to the technical field of automatic deployment of cloud computing platforms, in particular to a self-adaptive deployment method and system oriented to a credential heterogeneous environment. Aiming at three technical bottlenecks of low multi-CPU architecture adaptation efficiency, frequent software dependence conflicts and complex security baseline configuration in a localization process, a heterogeneous computing resource intelligent scheduling engine and a dynamic security policy generation mechanism are innovatively provided. The method comprises the following steps: constructing a heterogeneous resource portrait through a hardware feature automatic identification technology, and realizing component installation sequence optimization based on a DAG dependency relationship analysis algorithm and Kahn topological sorting; and creating an adaptive network security policy, and sensing the firewall state of the target system in real time through a probe. Compared with a traditional deployment mode, the method supports cross-architecture compatibility, solves the problem of dependency conflicts, improves deployment efficiency, and guarantees consistency and safety of the system.

The invention relates to the technical field of cloud computing and artificial intelligence, in particular to a disaster recovery drill and performance evaluation system and method for multi-cluster distributed training. In order to solve the problem of disaster recovery and performance evaluation in multi-cluster distributed training, the method comprises the steps of environment analysis and modeling, fault injection and drill control, cross-cluster scheduling and resource monitoring, performance data acquisition and index aggregation, training result evaluation and tuning closed loop and the like. Multi-cluster environment elements are identified, a fault and index mapping relation is constructed, faults are injected by using a fault injection tool, and task scheduling, resource monitoring and performance data acquisition and analysis are realized by means of a multi-cluster management and monitoring tool, so that an adjusting and optimizing strategy is formed, and closed-loop management is realized. According to the method, faults can be dynamically simulated in a multi-cluster environment, the training performance is monitored, the disaster recovery and recovery efficiency is evaluated, the training efficiency and robustness of the system are improved, and the requirements for high availability and high performance in the fields of finance, automatic driving and the like are met.

The invention discloses an IT asset fault propagation prediction method and system based on dynamic evolution of a knowledge graph, and relates to the technical field of cloud computing and large-scale IT operation and maintenance management. Through an asynchronous message bus and a logic clock, the knowledge graph is updated immediately when resources are abnormal and a scheduling event occurs; the knowledge graph uniformly integrates physical connection, logic dependence and multi-copy redundancy, so that the cross-machine-room asset relationship is clear at a glance. And then, based on a weighted logistic regression model, node features and relation weights in the knowledge graph are fused, the node fault probability is accurately calculated, the limitation of traditional single-dimensional analysis is solved, self-healing operation is supported, end-to-end intelligent operation and maintenance from fault detection to prediction and early warning to closed-loop self-healing are realized, and the fault detection efficiency is improved. The problems that in a cross-machine-room and multi-live-site environment, resource topology is split, real-time state and alarm information cannot be fused with an asset dependence model, and large-scale real-time deployment of a traditional single-dimensional fault analysis and high-complexity prediction algorithm is difficult are effectively solved.

The invention discloses an ear-nose-throat nursing scheme intelligent recommendation system based on cloud computing, and relates to the technical field of big data analysis. The intelligent recommendation platform is in communication connection with a data acquisition and preprocessing module, a knowledge graph construction module, a patient portrait construction module, a nursing scheme recommendation module, an evaluation and adjustment module and a user interaction and feedback module. According to the intelligent recommendation system for the ear-nose-throat nursing scheme based on cloud computing, deep analysis and mining are carried out on real-time physiological data, ear-nose-throat related symptoms, wound healing conditions and function recovery progress of patients by utilizing the powerful data processing capability of a cloud computing platform, individual differences and rehabilitation requirements of the patients are accurately recognized, and the intelligent recommendation system for the ear-nose-throat nursing scheme based on cloud computing is provided for the patients. And personalized nursing schemes are customized for the patients, so that the pertinence and effectiveness of nursing are improved, and the prolonged rehabilitation time and the discomfort of the patients caused by the improper schemes are greatly reduced.

A distributed generative artificial intelligence (AI) reasoning and action platform that utilizes a cloud-based computing architecture for neuro-symbolic reasoning. The platform comprises systems for distributed computation, curation, marketplace integration, and context management. A distributed computational graph (DCG) orchestrates complex workflows for building and deploying generative AI models, incorporating expert judgment and external data sources. A context computing system aggregates contextual data, while a curation system provides curated responses from trained models. Marketplaces offer data, algorithms, and expert judgment for purchase or integration. The platform enables enterprises to construct user-defined workflows and incorporate trained models into their business processes, leveraging enterprise-specific knowledge. The platform facilitates flexible and scalable integration of machine learning models into software applications, supported by a dynamic and adaptive DCG architecture.