524 results about "Data platform" patented technology

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.

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.

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.

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.

A data platform monitors a compute environment by performing multi-stage heuristic analysis of event data representing a plurality of events occurring within the environment. The platform utilizes multiple event analyzers, each configured according to a distinct analysis heuristic, to evaluate different subsets of the event data and generate corresponding output signals. A higher-level event analyzer applies a further heuristic to the multiple output signals to generate a composite alert signal, indicating whether the combination of analyzed events collectively represents a security intrusion or other anomalous condition of sufficient severity to warrant alerting. Based on the composite alert signal, the platform performs an alert-based operation, such as generating a user-facing alert, initiating an automated mitigation, or updating a contextual model of system behavior. By combining the analytical outputs of heterogeneous heuristics, the disclosed architecture enhances the accuracy and contextual relevance of automated intrusion detection within complex computing environments.

The invention provides a non-deep traffic analysis streaming matching search analysis method, which comprises the following steps of: constructing a deep data packet detection architecture on the basis of a data platform development kit (DPDK); aiming at the non-encrypted traffic, establishing a multi-mode recognition mechanism combining a regular expression and a feature bit stream mode; aiming at the encrypted traffic, constructing an encrypted traffic feature library according to the statistical features, the protocol features and the behavior features; real-time risk detection of network traffic is realized by adopting a streaming matching search technology; and constructing an intelligent decision and response mechanism, and integrating flow analysis and identification results. According to the non-deep traffic analysis streaming matching search analysis method provided by the invention, real-time analysis, risk identification and supervision of network non-encrypted traffic and encrypted traffic are realized by taking a data platform development kit (DPDK) as a basis and combining a high-performance streaming regular expression engine and a finite-state machine principle; the method can be widely applied to scenes of network communication supervision, data security protection, malicious traffic monitoring and the like.

Data platforms described herein are configured to monitor a compute environment and to use machine learning models for managing security threat alerts for the compute environment. Such a data platform may identify, based on event data indicative of events occurring in the compute environment, a set of detected security threats present within the compute environment. Using a machine learning model trained based on previous event data indicative of events that occurred previously, the data platform may reduce the set of detected security threats to form a subset of prioritized security threats. The data platform may then provide security threat alerts for the compute environment in a manner that emphasizes alerts associated with prioritized security threats over alerts associated with other detected security threats of the set of detected security threats. Corresponding methods, systems, and products are also disclosed.

The invention provides a real vehicle battery health state estimation method based on data driving, and relates to the technical field of new energy vehicle batteries, the method comprises the following steps: dividing new energy vehicle big data platform data to obtain charging and discharging stage data; performing feature extraction on the data in the charging and discharging stages to obtain initial health features; performing optimization processing on the preliminary health features to obtain health features; training a battery health state estimation model by using the health features to obtain a trained battery health state estimation model; and analyzing the new energy vehicle big data platform data by using the trained battery health state estimation model to obtain a real vehicle battery health state estimation result, and completing the health state estimation of the real vehicle battery. The method solves the problems that the current real vehicle feature extraction is limited by the charge and discharge depth, the discharge data is not fully utilized, and the battery health state estimation precision is low.

The illustrative embodiments provide a data platform, system and method for utilizing carbon credits through blockchain-based tokenization. The platform receives carbon utilization data corresponding to at least one carbon credit and executes a smart contract on a blockchain network based on the received data. Execution of the smart contract generates one or more blockchain tokens that embed metadata including a hash of the carbon utilization data and a unique security identifier. The platform performs electronic transactions for the tokens by propagating transactions for distributed consensus, verifying token authenticity through comparisons with distributed-ledger records, or generating immutable electronic records indicating tracking information for the carbon credit. Records of the electronic transactions are stored in a distributed ledger as immutable entries linking token activity to corresponding carbon utilization data.

One aspect of the present disclosure relates to a distributed energy data platform process, and more particularly, to a distributed energy data platform process capable of increasing efficiency of energy resources and ensuring flexibility in management by configuring an AIoT process based on a distributed energy data platform. The distributed energy data platform process includes a data collection unit for collecting IoT sensor data of an IoT sensor installed in an energy-consuming device in real time; a database for loading the IoT sensor data collected in real time from the data collection unit; a visualization unit for interacting with the database so as to visualize the IoT sensor data to monitor the IoT sensor data in real time; and a training unit for configuring an artificial intelligence life cycle operation for the IoT sensor data.

The described system aims to reduce or eliminate inaccuracies and hallucinations in responses generated by a machine learning model when processing user queries. The data platform parses and categorizes the text within data files to create structured textual representations. The user submits multiple prompts which are collectively assessed to refine and modify the initial queries.The modified query is used to identifying segments of data files that are most relevant to the query. These relevant portions are then compiled into a Retrieval-Augmented Generation (RAG) context block. This RAG context block is fed into a prompt response machine learning model, which processes the enriched information to generate a well-informed and accurate response to the user's query. Finally, this response is displayed back to the user through the chat interface, completing a cycle that enhances the reliability and relevance of machine-generated answers.

The invention provides an intelligent inspection system based on a robot and a camera, and relates to the field of intelligent manufacturing, the system adopts a four-layer architecture including a sensing layer, a transmission layer, a platform layer and an application layer, the sensing layer is composed of a fixed camera and an intelligent inspection robot; the transmission layer adopts optical fibers and 5G hybrid networking; the platform layer is integrated with a distributed server cluster, a data platform and an AI inference engine; the application layer provides a Web and mobile terminal management interface. The core of the system adopts a plurality of AI algorithms, including an instrument reading recognition algorithm based on Hough transform and CRNN, a leakage detection algorithm combined with background difference and an optical flow method, and a multi-device collaborative scheduling algorithm based on reinforcement learning, and constructs a digital twinborn inspection scene of virtual-real mapping. According to the invention, automatic and intelligent inspection and visual monitoring of power plant instruments, pipelines, power distribution rooms and other areas are realized, the inspection efficiency and accuracy are effectively improved, and the labor cost and the safety risk are reduced.

The invention belongs to the technical field of data processing, and discloses an active intelligent operation and maintenance monitoring method for a data center, which comprises the following steps: step 1, component modeling and topology configuration; 2, carrying out distributed health detection and data acquisition; 3, performing real-time health assessment and anomaly detection; 4, performing intelligent alarm and root cause analysis; 5, unified operation and maintenance and closed-loop control are carried out; and step 6, dynamically optimizing the intelligent operation and maintenance strategy. According to the method, a monitoring object and a dependency relationship are clarified through component modeling and topological configuration, and specific scenes, such as multi-mode acquisition, active detection, index pulling, log analysis, coverage message queue theme accumulation and database connection pool exhaustion, of distributed health detection are combined. The quantitative health score is calculated based on the preset scoring model, and by combining with the dynamic baseline learned by the ARIMA or LSTM algorithm, the module abnormity can be actively detected in different periods and weekly updating, and the problems of fault discovery lagging and incomplete monitoring coverage are solved.

The invention discloses a wharf risk assessment and management method based on artificial intelligence, particularly relates to the field of wharf risk assessment and management, and is used for solving the problems that an existing wharf is difficult to quantify and delay influence on a supply chain network level, and prospective intervention cannot be performed on supply chain risks. According to the method, a comprehensive data platform of wharf operation and supply chain cargo linkage is constructed, and the bottleneck time point of a future delivery window is analyzed and predicted based on the ship arrival density, the operation bearing capacity and the stockpiling structure; searching a historical supply chain interruption event and extracting corresponding cargo and node information; tracking a conduction path of an interruption event in the supply chain network, quantifying the influence of a bottleneck on a downstream node, and establishing a supply chain risk influence model; generating a supply chain risk influence map in combination with future bottleneck prediction; and finally, a composite risk portrait is constructed, and targeted risk intervention is carried out on the wharf and the supply chain node. And intelligent prediction and management of wharf and supply chain full-chain risks are realized.

Scoring of events in an edge-based data platform, including: detecting an event associated with a plurality of entities and an event type; calculating, based on a prevalence of each entity in events of the event type and a classification of each entity, a risk score for the event; and generating an alert based on the risk score for the event.

Embodiments described herein comprise an advanced Software as a Service (SaaS) platform addressing challenges including trust, governance, quality, data accuracy, and transparency, ensuring full visibility of enterprise data warehouse data pipelines. These embodiments seamlessly integrate with cloud-based and legacy data platforms, enhancing visibility through monitoring data quality, tracking metadata changes, and generating exceptions for review. Data quality management allows for custom script creation to validate pipelines and business rules. Cloud-native connectors facilitate integration with one or more cloud-based services streamlining data delivery processes. Personalized notifications aid administrators in responsive action, while data profiling capabilities identify quality issues and provide distribution insights. Actionable data intelligence for privacy, security, and governance is enabled through data classification. Automation features encompass data quality checks, metadata monitoring, profiling, and classification, orchestrated by a flexible scheduler. The architecture leverages a scalable serverless design with independent microservices, dynamically scaling based on demand.

The invention discloses a dynamic adaptive operation, maintenance and metering optimization method for an electric energy metering box, and the method comprises the steps: collecting electrical parameters, environmental parameters and box body state parameters in real time, constructing a three-dimensional data set through preprocessing, and carrying out the local storage and preliminary analysis through edge calculation; the condensation occurrence probability is calculated based on the environmental parameters, and self-adaptive regulation and control are triggered and a ventilation structure is linked; abnormal features are extracted, abnormity is recognized in a rule judgment and trend analysis double-engine mode, and graded early warning is carried out; performing comprehensive state scoring in combination with the three-dimensional data set, and matching a differentiated inspection mechanism; presetting a metering error reference value of a temperature and humidity interval, calculating a compensation value in real time and remotely correcting parameters of the electric energy meter; and establishing a shared data platform of the power supply and the power consumption, and realizing full-process data tracing and automatic report generation. According to the method, the condensation prevention and control accuracy is improved, the abnormal false alarm rate is reduced, the operation and maintenance resource allocation is optimized, the metering accuracy is improved, and the data collaboration and tracing capability is enhanced.

Provided is a method for providing a user interface pattern recommendation service. In the method: (a) an administrator terminal registers app information; (b) a user terminal selects the app information and inputs a question to request a chatbot query; (c) a platform server refers to a platform DB and transmits key data including an UI pattern, a user preference, and a selection history via an information inquiry API; (d) an AI model within the platform server receives user input data, analyzes the data in real time using RAG technology and a self-trained model, and recommends a UI pattern preferred by a user; (e) a chatbot response module within a chatbot server receives the chatbot query; (f) the chatbot response module generates a chatbot response by referring to a chatbot DB; and (g) the chatbot response API receives the generated chatbot response and transmits the response to the user terminal.

Techniques for efficient and secure key rotation for backup storage are described. An example method includes generating, by a data platform implemented by a computing system, a first encrypted key and a second encrypted key, where the data platform stores one or more encrypted blocks encrypted using a first encryption key of the first encrypted key and a second encryption key from the second encrypted key, the first encrypted key and the second encrypted key are encrypted with a first system key; replacing the first system key by determining a second system key; and encrypting the first encryption key with the second system key to generate a key that replaces the first encryption, and encrypting the second encryption key to generate a key that replaces the second encryption.

The invention provides a source database storage process migration method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the grammar analysis of an original storage process in a source database, and generating a corresponding abstract syntax tree; based on a preset rewriting rule set, performing semantic equivalent transformation on the flow control syntax structure in the abstract syntax tree to generate a transformed abstract syntax tree; and generating a target script in a target execution environment from the converted abstract syntax tree, executing the target script in a script running engine, dynamically generating a standard SQL statement corresponding to the business logic defined by the original storage process, and submitting the standard SQL statement to one or more target data platforms for execution. And the business logic of the original storage process is reproduced. By introducing the target script as the intermediate execution layer, the flexibility of the migration scheme is greatly improved, so that the migration scheme is suitable for various big data platforms without the native storage process capability.

The invention is applicable to the technical field of data center cabinets, and provides a modular micro data center integrated cabinet of a big data platform, which comprises an integrated cabinet body, a cabinet inner frame is additionally arranged on the inner wall of the integrated cabinet body, and the cabinet inner frame comprises a plurality of air guide pipes and a plurality of unit cabinet inner frames. The air guide pipe is used for transferring air flow in the unit cabinet inner frame; the unit cabinet inner frame comprises a plurality of air guide plates, each air guide plate comprises a plurality of unit air guide assemblies, the two sides of each air guide plate are each provided with an air guide adjusting part, and the air guide adjusting part on one side of each air guide plate can exhaust air for one or more of the unit air guide assemblies. The air guide adjusting piece on the other side of the air guide plate can supply cold air to one or more of the multiple unit air guide assemblies. The cold air inlet point can be dynamically adjusted for a local high-heat-density element, precise regulation and control cooling for a local electronic element heat source is achieved, and energy consumption waste and hot spot accumulation are avoided.

This disclosure relates to methods, devices, and computer-readable media for developing, configuring, building, deploying, packaging and / or testing native applications for a cloud data platform. A device is configured to receive an input and determine whether the input is a first input or a second input. For a given functional component of a cloud data platform native application, the computing device is configured to prepare, if the input is the first input, the functional component to be run in a first mode of execution, said first mode comprising remote cloud-based execution as part of a packaged cloud data platform native application. The computing device is configured to prepare, if the input is the second input, the functional component to be run in a second mode of execution, wherein said second mode comprises either local execution on the computing device; or remote cloud-based execution as a standalone cloud data platform functional component.

The invention belongs to the field of data security, and particularly relates to a time sequence encrypted storage and expired crushing method for monitoring data of external equipment, which comprises the following steps: acquiring a monitoring data stream of the external equipment, segmenting the monitoring data stream according to a preset time window, and deriving a unique encryption key for each segment; encrypting and storing the corresponding window data by using the key to obtain a time sequence encrypted data block; when the conjoint analysis request is responded, a specified window secret key is distributed to a calculation party according to a preset authorization strategy, so that the calculation party executes secure multi-party calculation on the window data block; meanwhile, the life cycle of the data block is monitored in real time, data is automatically erased when the data block expires, a corresponding secret key is destroyed, and a shredded verifiable evidence is generated and uploaded to a storage chain constructed by combining a root secret key of the general data platform and secret keys of all calculation parties with a block chain; according to the application, data storage security and conjoint analysis compliance are guaranteed, the risk of residual keys is avoided, the traceability of crushing operation is ensured, and the credibility of cooperation of multiple participants is improved.

The illustrative embodiments provide a system, method, device, and platform for managing carbon credits. Information corresponding to carbon credits with carbon emissions are received. A smart contract is executed on a data platform of a blockchain network based at least on the received information. The smart contract specifies one or more parameters for transactions corresponding to the carbon credits. One or more tokens for performing transactions in accordance with the smart contract are generated utilizing the data platform. Transaction for the one or more tokens are performed in accordance with the smart contract. The transactions include at least verifying the authenticity of the one or more tokens. Records of the transactions for the tokens a stored in a distributed ledger associated with the blockchain network.

An illustrative method for performing an identification of secrets misuse may include accessing runtime workload data associated with one or more keys configured to be accessed by a workload deployed in a compute environment, detecting, based on the runtime workload data, an anomaly associated with the one or more keys, and determining, based on the anomaly, that secrets misuse has possibly occurred within the compute environment.

Various embodiments relate generally to data science and data analysis, computer software and systems, to provide a platform to facilitate updating compatible distributed data files, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate correlation of event data via analysis of electronic messages, including executable instructions and content, etc., via a cross-stream data processor application configured to, for example, update or modify one or more compatible distributed data files automatically. In some examples, a method may include activating APIs to receive via a message throughput data pipe different data streams, extracting features from data using the APIs, identifying event-related data across data sources, correlating the event-related data to form data representing an even, classifying event-related data into a state classification, determining compatible data at data sources, identifying compatible data, and transmitting integration data to integrate with a data source.

A building system can operate to receive an indication from a user device of a user to query a digital twin of one or more buildings, the digital twin including entities including buildings, equipment, spaces, or data of the one or more buildings, the equipment, or the spaces, the digital twin including relationships between the entities. The building system can operate to receive building data from the digital twin by querying the digital twin based on the indication received from the user device. The building system can operate to generate an analytics model based on the building data, wherein the analytics model is trained based on the building data and deploy the analytics model to operate based on data of the one or more buildings and generate one or more analytic results based on the data of the one or more buildings.

The invention provides an MES (Manufacturing Execution System)-oriented arc-shaped battery manufacturing process data acquisition method and system, and relates to the technical field of manufacturing execution system data acquisition. The method comprises the following steps: firstly, constructing a hierarchical model of a production line, a workshop section, a station, equipment, an acquisition channel and a data field, enabling the hierarchical model to correspond to a manufacturing execution system object, establishing a global unique identifier and time and space marks, forming a unified data item description table, and implementing field acquisition, unified time service and standardized preprocessing; bending features are extracted, mapping entries are generated with process states and quality results, the mapping entries are uploaded to a manufacturing execution system and a data platform, abnormal supplementary transmission and monomer-level tracing are supported, and the data consistency and tracing capability of the arc battery manufacturing process are improved.

The invention discloses an intelligent segmented water injection system and method for coal seam gas extraction drilling based on stress response. An underground PLC (Programmable Logic Controller) and a pressure regulating pump station comprise a PLC, a pressure regulating electric pump, an electronic pulsation regulating valve and a pressure feedback sensor, and are arranged in an underground control roadway or a position close to a drilling hole; the air bag type multi-section hole sealing mechanism is composed of a plurality of phi73 double-channel expansion type air bag units, and each hole sealing unit is independently controlled by a hydraulic system and installed in the axial direction of a drill hole. The matched double-channel water injection drill rod and pulse spray head assembly comprises a hollow double-channel steel water injection drill rod, a flexible water conveying inner pipe and directional pulse spray heads located in all hole sealing sections. The ground water supply and proportional mixing wetting agent system comprises a ground water supply system, a three-way proportional mixer and a dosing pump; the coal stress and fracture structure comprehensive monitoring module is used for collecting key response information in the water injection process; the ground remote control and data analysis platform comprises an underground control cabinet and a ground data platform; the gas extraction efficiency can be improved.

The invention provides an electronic old-age card data closed-loop processing method and system based on an intelligent card. According to the method, biological characteristic information of a user is collected through a mobile terminal application, identity information is submitted, a government affair data platform interface is called to carry out remote identity verification, and encrypted identity element data of old people is generated; an APDU instruction stream is dynamically generated based on the JavaCard specification, and encrypted identity element data is written into a secure storage area of the smart card through a machine-card communication channel; reading encrypted data of the smart card in a business scene through NFC card reading equipment, generating a triple verification record in combination with a merchant identifier and a transaction timestamp, and feeding back a verification result to a merchant terminal; and synchronizing the triple verification record to a government affair data platform to form a traceable subsidy data chain for the government to approve subsidy issuing. According to the invention, remote safe writing and non-contact automatic verification of the identity information of the old people are realized, and the service convenience and the data credibility are improved.