925 results about "Data platform" patented technology

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.

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.

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.

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.

The invention relates to the technical field of metadata generation, and discloses a data platform metadata automatic generation method based on a large model, which comprises the following steps of: firstly, performing unified standardization and preliminary grammatical analysis on an original SQL (Structured Query Language) code to obtain a structured intermediate representation; on the basis, preliminary blood relationship analysis based on rules is carried out, and simple column references are quickly identified and processed. For complex expressions which are difficult to accurately analyze by a traditional method, code snippets and context information of the complex expressions are accurately extracted and submitted to a large language model for deep semantic understanding and complex blood relationship analysis. And finally, integrating the complex consanguinity analyzed by the large model with the initial consanguinity list to form a comprehensive and accurate field-level consanguinity, and further generating complete data platform metadata. In this way, the defect that a traditional analysis tool understands complex semantics is effectively overcome, and the accuracy and integrity of metadata generation are remarkably improved.

The invention relates to the technical field of optical measurement and geometric position measurement, in particular to a remote multipoint monitoring system and method for roadbed surface settlement, and the system comprises a plurality of distributed monitoring nodes and a remote data platform; the monitoring node comprises a heterogeneous sensor group and an edge processing module which is connected with the sensor group and is used for carrying out local preprocessing and fusion on multi-source data; the wireless communication module is used for sending the data to a remote data platform; the power supply and maintenance module provides electric energy and supports node cleaning and environment adaptation; the remote data platform comprises a data receiving and storage unit, a data fusion and analysis unit, an early warning unit and a visual user interface. According to the invention, a multi-sensor fusion intelligent monitoring network is constructed, all-weather and high-precision remote monitoring of urban road subgrade settlement and associated crack deformation thereof is realized, settlement trend prediction and early warning are provided through AI analysis, and the initiative and safety guarantee level of urban road maintenance are improved.

An illustrative data platform is disclosed that may monitor a compute asset within a compute environment. This monitoring may include receiving registry data collected, by an agent deployed to the compute asset, from a registry space of the compute asset. Based on the registry data collected by the agent, the data platform may determine that a change within the registry space of the compute asset is associated with a security threat to the compute asset. Accordingly, based on the determining that the change within the registry space is associated with the security threat, the data platform may perform an action configured to facilitate remediation of the security threat. Corresponding methods, systems, and products are also disclosed.

Data platforms described herein are configured to monitor a compute environment and facilitate interactive analysis of multifaceted security threats within the compute environment. Such a data platform may determine that one or more assets within the compute environment are possibly being targeted by a multifaceted security threat and present an interactive user interface. The user interface may be configured to display an identifier indicative of the multifaceted security threat, a set of selectable evidence items each associated with a different facet of the multifaceted security threat and assessed based on the monitoring of the compute environment, and a presentation pane for displaying information. As such, the data platform may detect a selection of a particular evidence item from the set of selectable evidence items and, in response to the selection, populate the presentation pane with information related to the particular evidence item. Corresponding methods, systems, and products are also disclosed.

An illustrative method includes accessing, by a data platform configured to monitor a compute environment, permissions data representative of a set of permissions that specify how an entity is entitled to interact with resources within the compute environment; collecting, by the data platform, runtime workload data representative of an actual interaction by the entity with the resources over a period of time; and performing, by the data platform based on the runtime workload data, an operation associated with modifying the set of permissions for the entity.

An illustrative agent deployed in a compute environment monitored by a data platform is disclosed. The agent may access kernel data generated by a kernel of an operating system used within the compute environment. Based on the kernel data, the agent may detect a launch of a new container entity of a set of container entities deployed to the compute environment and managed by a container runtime executing on the operating system. Based on the detecting of the launch of the new container entity, the agent may then provide, to the data platform, agent data that indicates the launch of the new container entity. Corresponding methods, systems, and products are also disclosed.

An illustrative method includes identifying, based on static workload data associated with a compute environment, one or more attack paths from a network to one or more datasets associated with an entity, accessing runtime workload data associated with the compute environment, and performing, based on the runtime workload data, a risk mitigation operation associated with the one or more attack paths.

An illustrative method for querying multiple datasets may include generating, based on models each associated with and defining attributes of a different datasets stored in a plurality of data stores, a semantic layer defining relationships between the models and that provides a centralized application programming interface (API) for exposing the datasets by way of a common query language, receiving, by way of the centralized API, a query request for information that depends on data included in multiple datasets included in the plurality of datasets, querying, based on the query request and the relationships between the models defined by the semantic layer, the multiple datasets, and presenting, based on the querying, a query result representative of the information that depends on the data included in the multiple datasets.

A containerized agent that is deployed to a compute node of a compute environment and that executes in both user space and kernel space of the compute node is disclosed. The containerized agent collects data associated with a first container entity that is deployed to the compute node and that executes only in the user space of the compute node such that the first container entity is isolated from other entities executing in the user space of the compute node. The containerized agent also provides the collected data associated with the first container entity to a data platform that is monitoring the cloud compute environment using the containerized agent. Corresponding methods, systems, and products are also disclosed.

The invention belongs to the technical field of network test equipment, and discloses network response test equipment and a test method thereof, and is characterized in that the network response test equipment comprises the following modules: a distributed multi-mode dynamic sensing probe used for capturing service flow characteristics and microsecond-level performance indexes of a network link in real time; the extensible data middle table is in data connection with the probe and is used for establishing a unified spatio-temporal index for cross-level performance data from a physical layer to an application layer; and the causal atlas analysis engine based on artificial intelligence is used for positioning a network fault root cause according to the cross-level performance data. According to the invention, an evaluation standard is automatically calibrated according to historical business load characteristics through an extensible data-in-station dynamic baseline threshold mechanism, so that a test environment completely fits complex scenes such as video burst frame transmission, database synchronization ACK, high-frequency transaction chain and the like, the simulation distortion of a traditional tool to business traffic burst and multi-protocol mixed characteristics is eliminated, and the test efficiency is improved. And the test result is ensured to accurately map the long-term stability requirement.

The invention provides an SMT patch correction system linked with AOI and a method thereof. The SMT patch correction system comprises a stokehole AOI detection module, a stokehole AOI detection module, a multi-dimensional sensing module, an intelligent data center module, an AI decision optimization module and a process simulation verification module. According to the method, the AOI detection data in front of and behind the furnace are fused through the intelligent data center module, the mapping relation of knowledge graph associated elements, the technology and offset data is constructed, a linkage closed loop is formed, and offset source tracing and full-process dynamic deviation correction are achieved; environmental parameters such as temperature, vibration and suction nozzle pressure are collected in real time through the multi-dimensional sensing module, and the environmental parameters are standardized through an intelligent data platform and then are included into correction logic of the AI decision optimization module, so that dynamic compensation of the influence of environmental factors on the mounting precision is realized; a deep learning and reinforcement learning cooperation strategy is adopted through the AI decision optimization module, parameters are dynamically optimized and corrected in combination with thermal deformation simulation and other multi-source information of the process simulation verification module, and the adaptability to different element types and production scenes is improved.

The invention provides a multi-dimensional multi-spatio-temporal data base plate construction method and system, and the method comprises the steps: carrying out the standardization processing of obtained original multi-source heterogeneous data, and generating a standardized data set; performing multi-level space fusion and dynamic time weighting processing based on the standardized data set to generate a multi-level fusion spatio-temporal data set; performing space-time grid aggregation and adaptive node screening on the multi-level fusion space-time data set to generate an optimized space-time node set; based on the optimized space-time node set, dynamic association is constructed, and an association model is generated and comprises event rules, topological relations and dynamic weights; and generating a multi-dimensional multi-spatiotemporal data base plate based on the optimized spatiotemporal node set and the association model. By adopting the method, the data base plate which can cover global features and has real-time response capability can be constructed, and the decision-making precision and efficiency of scenes such as water conservancy disaster prevention and control and environment monitoring can be improved.

The invention relates to the technical field of Internet, and discloses an Internet hotspot data mining system and method based on artificial intelligence, and the system comprises a data collection module, a semantic understanding module, a dynamic clustering module, a popularity evaluation module, a visual output module and a feedback optimization module. By setting a multi-source calibration unit, when cross-platform hotspot data acquisition is carried out, standardized processing of multi-source heterogeneous data acquisition is ensured by establishing a dynamic semantic feature library and configuring adaptive weight parameters for different data platforms; meanwhile, by monitoring the semantic offset of the collected data in real time, characterization deviation generated during cross-platform data collection can be detected and eliminated, the accuracy of hotspot clustering similarity calculation is guaranteed, hotspot recognition errors are reduced, and by deploying a semantic tracking engine, when hotspot event propagation analysis is carried out, the analysis accuracy is improved. The deviation degree of event core elements is calculated by constructing spatio-temporal feature vectors, and whether topic semantics are migrated or not is judged in real time.

The invention relates to the technical field of distributed energy storage monitoring, and discloses a distributed energy storage monitoring system based on an Internet of Things data platform. The system comprises a multi-dimensional state evaluation module, a load response strategy generator, a collaborative optimization decision module and a dynamic control instruction compiler. The multi-dimensional state evaluation module obtains real-time Internet of Things monitoring data of the energy storage unit and extracts dynamic operation characteristics, and the data comprises a voltage fluctuation curve, temperature gradient distribution and the number of charge and discharge cycles; the load response strategy generator identifies the operation mode through a charging and discharging mode classification algorithm, and generates a state vector set containing operation mode identification and load capacity score mapping; the collaborative optimization decision-making module generates a fusion result containing node energy interaction characteristics and capacity balance coefficients based on the set; and the dynamic control instruction compiler inputs the fusion result into a strategy optimization model, and outputs a control instruction element containing a load priority time sequence instruction and a parameter self-correction coefficient.

An illustrative data platform may receive, from an agent configuration deployed in a cloud environment and configured to monitor compute assets in the cloud environment, first data periodically collected by the agent configuration at a first collection frequency and second data periodically collected by the agent configuration at a second collection frequency, identify, based on the first data, an anomaly associated with one or more compute assets included in the compute assets, and direct, based on the identifying the anomaly, the agent configuration to perform an on-demand collection of the second data.

The invention discloses a dam leakage monitoring system and method based on optical fiber sensing and time-space fusion. The dam leakage monitoring system comprises an optical fiber sensing system, a data processing and analyzing system, a dam leakage data management system and a real-time monitoring platform. According to the invention, the distributed optical fiber realizes continuous strain and temperature monitoring of the whole length of a dam, a leakage area can be roughly positioned, an FBG sensor is deployed at a key position, local tiny deformation is accurately monitored, a cooperative mode is formed by combining wide-area screening of the distributed optical fiber and key diagnosis of the FBG, and the monitoring efficiency is improved; the all-fiber passive design is adopted, the environmental adaptability is high, the anti-interference performance and the long-term stability are high, frequent maintenance is not needed after the fiber is embedded into the structure, and the operation and maintenance cost is reduced; finally, the leakage risk is displayed through a trend curve of a visual data platform, alarm pushing and emergency plan functions are automatically triggered, the closed-loop time of monitoring, decision making and response is shortened, and the real-time performance and decision-making force are improved.

The invention relates to the technical field of data correction, in particular to an engineering monitoring data correction method based on an internet of things data platform, which comprises the following steps: determining the distribution number of monitoring points in each local area according to obtained building foundation pit point clouds of a current period and a historical period; acquiring a monitoring data sequence of a plurality of dimensions corresponding to the monitoring points with the distribution number of the monitoring points, and further determining an abnormal index of each dimension at each moment; determining an abnormal state index of each monitoring point according to the abnormal index and the monitoring data, and screening out a plurality of real abnormal moments based on the abnormal state indexes; according to the abnormal state index and the area of each monitoring point in each real abnormal region at each real abnormal moment, determining a soil body damage index of each real abnormal region at the current moment; and the monitoring data is corrected according to the soil damage index, so that the correction effect of the engineering monitoring data is effectively improved, and the monitoring data with higher numerical accuracy is obtained.

Data platforms described herein are configured to monitor a compute environment and generate threat intelligence data based on cross-customer data. Such a data platform may access a plurality of customer datasets collected from a plurality of compute environments, aggregate the plurality of customer datasets into an aggregate dataset, and generate, based on the aggregate dataset, one or more indicators indicative of one or more security threats against one or more compute assets within the plurality of compute environments. The data platform may then detect an occurrence of the one or more indicators within a particular compute environment and perform, based on the occurrence of the one or more indicators, a security response operation with respect to the particular compute environment.

The invention relates to the technical field of urban planning, and particularly provides an intelligent site selection method and system based on a spatio-temporal data platform, and the method comprises the steps: collecting multi-source spatio-temporal data of key industry categories of a target region, constructing an enterprise demand feature set based on the multi-source spatio-temporal data, and building a quantitative demand standard based on the feature set; constructing an industrial ecology adaptation model based on the current industrial basic data of the target area, calculating the matching degree between the feature set and the industrial ecology of the target area through the industrial ecology adaptation model, and outputting candidate parks according to the matching degree by taking industrial ecology clustering as a target; establishing a multi-element adaptation relationship between the target enterprise and the candidate parks based on the quantitative demand standard, performing matching verification through a multi-dimensional compliance verification unit, and removing parks which do not meet the constraint condition of the quantitative demand standard; and based on the verification result, outputting a site selection scheme, finally realizing high matching of enterprise demands and public resources, improving site selection efficiency and quality, and assisting sustainable development of regional industries.

An illustrative method includes accessing data representative of a first role associated with a set of permissions with respect to resources within the compute environment and specifying a group of identities assigned to the first role, determining that a first subgroup of one or more identities included the group of identities only uses a first subset of permissions included in the set of permissions to access the resources within the compute environment without using a second subset of permissions, and performing, based on the determining that the first subgroup of one or more identities only uses the first subset of permissions, an operation to reduce permissions usable by the first subgroup of one or more identities.

Techniques are described for techniques for an actionable artificial intelligence bot based on data security correlations. An example method comprises determining, by a data platform implemented by a computing system, a plurality of tags for a snapshot executed by the data platform, detecting, by the data platform, an indication of a security breach relating to the snapshot, processing, by the data platform and using a machine learning model, a plurality of attributes of the security breach and the plurality of tags to identify a potential compromise of the snapshot, processing, by the data platform and using a large language model, at least the plurality of attributes to generate an actionable prompt including a natural language description of at least one security response, and outputting, by the data platform, the actionable prompt.

The invention provides a fire identification model construction method and device. The method comprises the following steps: acquiring multi-source fire monitoring data from different data platforms; aiming at each identification model group, taking all the environment monitoring variables as the input of the identification model group, taking the fire characteristic variables corresponding to the identification model group as the output of the identification model group, and training each fire identification sub-model in the identification model group; the recognition result output by each fire recognition sub-model is compared with a real fire record, and the initial weight of each fire recognition sub-model is determined according to the comparison result; and performing weighted integration on the plurality of fire identification sub-models in the identification model group based on the initial weight, and constructing a fire identification model corresponding to the identification model group so as to construct a comprehensive fire identification model based on different types of fire identification models. By adopting the fire identification model construction method and device, the accuracy and stability of fire identification are improved.

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, determining individual risk scores for each attack path indicating a level that each attack path could be exploited to access the one or more datasets, and determining, based on the individual risk scores, an absolute risk score for the entity indicating a status of a security posture for the entity. A risk mitigation operation associated with the entity is performed based on the absolute risk score.

The invention relates to the technical field of data governance, and provides a data platform metadata annotation completion method, system and equipment, and the method comprises the steps: collecting metadata basic annotations in a multi-source heterogeneous database through a standard application program interface; collecting a logic model definition document and a data standard specification document, and constructing a local knowledge base through vectorization processing; when a user triggers annotation completion, calling a local knowledge base through a large language model, and generating metadata intelligent annotations; and returning the collected basic annotation of the metadata and the generated intelligent annotation of the metadata to the user as annotation completion results. According to the data platform metadata annotation completion method, system and equipment, metadata collection normalization, multi-source knowledge fusion, knowledge base construction and intelligent annotation completion can be achieved, cost reduction and efficiency improvement are achieved, the metadata annotation quality is remarkably improved, and the use threshold is lowered.

Data platforms described herein are configured to monitor a compute environment and provide a user interface. Such a data platform may direct an application to process, on a user interface computing thread, user input to a graphical user interface view that is rendered based on data received from a data platform, to detect, on the user interface computing thread, a set of instructions associated with a visualization of a logical graph, to determine that the set of instructions satisfies one or more criteria, to execute, based on the set of instructions satisfying the one or more criteria, the set of instructions on a set of additional computing threads, and to render the visualization of the logical graph in the graphical user interface view based on the processing of the user input and the executing of the set of instructions. Corresponding methods, systems, and products are also disclosed.