1119 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 accessing, by a data platform, an alert generation policy associated with an entity that deploys containers in a cloud environment, the alert generation policy modifiable by the entity and specifying criteria for providing alerts associated with one or more vulnerabilities associated with the containers; detecting, by the data platform based on a scan of a container included in the containers, a vulnerability associated with the container; determining, by the data platform, an attribute of the vulnerability; and generating, by the data platform when the attribute meets the criteria specified in the alert generation policy, an alert associated with the vulnerability.

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.

A disclosed data platform may be configured to access a custom policy developed by a particular client entity that uses the data platform. The custom policy may invoke a query that targets a target dataset ingested from a cloud environment and stored in a data store. The data platform may determine that the custom policy is likely to be of value to one or more other client entities, besides the particular client entity, that also use the data platform. In response to the determining that the custom policy is likely to be of value to the one or more other client entities, the data platform may generate a community policy based on the custom policy. The community policy may be available for use by the one or more other client entities. Corresponding methods, systems, and products are also disclosed.

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.

The invention relates to an SQL (Structured Query Language) optimization interaction method and device based on a deep learning framework large model. The method comprises the steps that a statement construction model used for generating and optimizing SQL statements is deployed in a target system; in response to a natural language demand input by a user in the target system, identifying key elements in the natural language demand, and verifying whether the key elements exist in an enterprise data platform connected with the target system in combination with the enterprise data dictionary; inputting the target key elements passing the verification into a statement construction model, and generating a target SQL statement and a corresponding performance optimization suggestion; converting the target SQL statement into a service execution step according to the execution logic, and generating service description information; displaying the target SQL statement and the corresponding generation progress to a user in real time in an interactive interface; and displaying performance optimization suggestions and / or business description information matched with the target SQL to a user in an interactive interface to assist the user in realizing SQL operation related to enterprise business.

An illustrative method includes tracking, by a data platform configured to monitor a compute environment, a plurality of identity transitions that occur over time with respect to an entity, wherein each of the identity transitions includes a transition by the entity from being associated with one identity to being associated with another identity, the one identity and the another identity having different permission sets with respect to resources within the compute environment; determining, by the data platform while performing the tracking, that an attribute of the plurality of identity transitions satisfies a predetermined criterion; and performing, by the data platform based on the attribute of the plurality of transitions satisfying the predetermined criterion, a remedial action associated with the entity.

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 discloses a vertical retaining structure displacement automatic monitoring device and a monitoring method thereof, and relates to the technical field of displacement automatic monitoring, and the device comprises a data acquisition unit which is used for configuring monitoring nodes, obtaining sensor data monitored at the top of a retaining wall, and preprocessing the sensor data to obtain effective inclination angle data; the displacement conversion unit is used for recognizing and optimizing the attitude angle by using a dynamic Kalman filtering algorithm, and calculating a displacement value of the vertical soil retaining structure by combining the vertical distance from the bottom to the top of the soil retaining wall; the monitoring and early warning unit is used for uploading the displacement value of the vertical retaining structure to a remote data platform, constructing a monitoring network, analyzing and predicting the deformation trend of the retaining wall and triggering early warning according to an analysis result; and the battery management unit is used for providing battery state monitoring and management. The displacement of the top of the retaining wall can be accurately sensed and monitored through the inclination angle data, and comprehensive and accurate monitoring is ensured by adopting an attitude solver and a dynamic Kalman filtering algorithm.

The invention relates to a food safety risk analysis method and system based on big data. The method comprises the steps of collecting food full-life-cycle key data through Internet of Things equipment and a data platform, and completing data format standardization to form a structured data set; key risk factors are extracted through the structured data set, a risk factor weight matrix is constructed, and a primary risk value of the food sample is calculated; establishing a time sequence prediction model by using the primary risk value and historical time sequence data, calculating a risk change trend, and predicting a future risk fluctuation condition; calculating a food risk grade according to the primary risk value and the risk change trend, and generating a corresponding risk classification label; performing comparative analysis on the risk classification label and an actual supervision result, calculating a model deviation, and optimizing a risk calculation and trend prediction model based on a deviation result; the optimized analysis results are integrated, the risk level and the change trend are displayed through a graphical interface, and intelligent early warning prompts and intervention decision suggestions are provided.

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 discloses a data acquisition method and device for an energy management system, equipment and a medium. The energy management system is used for managing multiple power plants. The method comprises the following steps: creating a to-be-constructed big data platform which comprises a data acquisition layer, a data processing layer and a data storage layer; deploying a multi-protocol adapter cluster on a data acquisition layer; constructing a data standardization pipeline in a data processing layer, and generating a multi-level security label in combination with a data source security level, a service association degree and an updating frequency; a hierarchical storage framework is designed in a data storage layer and used for constructing a redundant index table crossing storage nodes and a data blood relationship graph based on a graph database; and deploying a dynamic security gateway between the data acquisition layer and the data processing layer, dynamically selecting a corresponding encryption algorithm according to the data security label, and completing the construction of a big data platform, so as to complete the data acquisition of the plurality of power plants based on the big data platform. And high-reliability, traceable and safe data support is provided for cooperative management of multiple power plants.

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.