1950 results about "Usage data" patented technology

Methods, systems, and computer storage media for providing a data analysis pipeline using a data analysis pipeline engine in a data intelligence system are described. A data analysis pipeline refers to a structured sequence of data processing steps that support transforming raw data into meaningful insights or actionable outcomes. The data analysis pipeline engine is an unsupervised learning pipeline based on clustering, topic modeling, and Large Language Models (LLMs). For example, the data analysis pipeline can use advanced machine learning techniques to automatically categorize emails into semantically similar clusters, enabling the data intelligence system to quickly identify and prioritize potentially high-risk emails for further investigation. The data analysis pipeline employs AI agents for context-aware graph induction relevance assessment. The AI agents employ induction and deduction loops to build and refine a data feature hypergraph (e.g., vulnerability hypergraph) that encompasses identified relevant data providing a holistic view of a contextual landscape.

The invention relates to the technical field of computers, in particular to a task scheduling optimization analysis method and system based on big data. The method comprises the following steps: acquiring task related data, task system resource use data and task execution state data in real time in a corresponding distributed big data processing scene among a server, a storage device and a client; performing big data feature mining analysis and task scheduling clustering analysis by using a Hadoop distributed framework to generate task scheduling cluster groups corresponding to similar resource requirements; performing residual execution duration calculation and task emergency accounting on the task calculation nodes corresponding to the distributed big data processing scene to obtain task node scheduling emergency degrees; and performing scheduling priority determination and task scheduling optimization analysis on the corresponding task calculation nodes to generate a task dynamic optimization scheduling scheme. According to the method, the rationality of distributed task resource scheduling can be improved.

The invention provides a method and system for preparing a high-temperature anti-aging test board for chip testing, and relates to the technical field of data energy storage and resource scheduling, and the method comprises the steps: designing a schematic circuit diagram and a PCB wiring scheme; a test plate is made of a high-temperature resistant material; bare board verification, electrical performance verification, program setting verification, high temperature verification and junction temperature measurement verification are carried out; using data is recorded, ultrasonic cleaning and drying are carried out, and a performance evaluation model is established through a hardware management system to judge whether maintenance or replacement is needed. According to the invention, the accuracy of chip testing in a high-temperature environment is improved, and the service life of the test board is prolonged.

The invention provides an electricity stealing behavior detection method based on feature fusion and a CNN-LSTM hybrid model, and belongs to the technical field of electric power information technology and deep learning. According to the method, after power load data and user behavior characteristics are subjected to data processing and enhancement, a deep learning model is combined with a self-attention mechanism to process user electricity consumption data, user electricity stealing behavior anomaly detection is carried out, and the electricity stealing risk identification accuracy and calculation efficiency can be remarkably improved. According to the invention, power grid enterprises can be helped to efficiently deal with electricity stealing conditions, and comprehensive and accurate identification of electricity stealing behaviors is guaranteed. By learning user historical load data, integrating time sequence characteristics of user loads and optimizing a data abnormity diagnosis and judgment mechanism, the electricity stealing user detection accuracy is improved, and reliable support is provided for power grid enterprise decision making.

The invention provides an intelligent decision-making method and device for cross-modal data, equipment and a storage medium, and the method comprises the steps: carrying out the blockchain storage of collected multi-modal process data, and generating an on-chain hash certificate; performing cross-modal space-time alignment and feature fusion on the multi-modal process data according to the space-time metadata analyzed by the Hash certificate on the chain to generate a process knowledge graph; constructing an interactive teaching model according to the process knowledge graph; determining a multi-dimensional contribution measurement value according to the use data of the interactive teaching model; and based on the multi-dimensional contribution magnitude and the hierarchical smart contract architecture, determining the data access permission through a community voting mechanism. Through implementation of the scheme, the process knowledge graph and the interactive teaching model, the inheritor operation data and the material response data form a causal association model, a traceable decision basis is provided for accurate reproduction of an endangered process, and meanwhile, the intelligent contract can automatically execute a decision to ensure high efficiency and transparency of the decision process.

This document discloses methods and systems for cohort identification. The methods and systems include improved calculations to perform cohort identification and practical applications of the improved calculations. Specifically, the systems and methods described herein may utilize key components that include enhancements of existing cohort clustering techniques with regard to selecting a number of cohort input dimensions, normalizing input data using a logarithm kernel-function, treatment of categorical data with mutually exclusive and not-mutually exclusive values, methods and visualization tool to determine appropriate number of cohorts, methods and visualization tool to compare cohorts extracted from different input dimensions, and methods to quantify the difference in cohorts. Beyond improvements to the cohort clustering techniques, also disclosed are ancillary tools to prepare input data by joining CRM and product usage data and facilitate subsequent automated action via an API to retrieve cohort results.

The invention discloses a resource dynamic allocation method and system under a micro-service architecture, relates to the technical field of resource allocation, and aims to overcome the defect that an existing dynamic resource allocation scheme cannot meet operation requirements of micro-services, and the adopted scheme comprises the following steps: acquiring resource use data in a service operation process in real time; cleaning and filtering the collected data of the data collection module to generate a standardized time sequence data set; for the time sequence data set, analyzing a resource use trend and predicting a future resource demand by adopting a time sequence model, and then generating an elastic capacity expansion and contraction suggestion in combination with a preset service priority rule to provide data support for subsequent resource scheduling; and on the basis of the elastic capacity expansion and contraction suggestion, carrying out actual allocation operation on the resources in combination with a pre-formulated resource allocation strategy. According to the method, the resource use condition of the micro-service is monitored in real time, and a scientific and reasonable dynamic adjustment strategy is combined, so that flexible allocation of resources is realized.

Techniques for managing cloud computing resources hosting burstable performance instances are described. A host computer system of a provider network executes burstable performance compute instances. Compute capacity usage data is obtained from the host computer system, the compute capacity usage data including a first indication of a first compute capacity used by a first burstable performance compute instance. A first weight for the first burstable performance compute instance is calculated, the first weight being inversely related to the first compute capacity. A scheduler of the host computer system is updated with process prioritization weights, the process prioritization weights including a first process prioritization weight that is based at least in part on the first weight. The scheduler allocates compute capacity based on the process prioritization weights.

Disclosed herein is an extended reality system, and associated techniques, whereby personalized usage data of a user in one or more extended reality environments over a period of time can be collected and provided to a predictive model to determine optimal shortcut assignments for presentation to and subsequent use by the user. Determining the optimal shortcut assignments can involve estimating a plurality of interaction times for the user in the one or more extended reality environments, generating an optimized graphical user interface within the one or more extended reality environments with the optimal shortcut assignments reflected in the optimized graphical user interface, and rendering the optimized graphical user interface in the one or more extended reality environments to the user. The system may collect additional personalized usage data and periodically update the optimal shortcut assignments based on ongoing use of the system by the user.

The embodiment of the invention discloses a hard disk fault prediction method and device, electronic equipment and a storage medium, and relates to the technical field of reliability of data storage systems.The method comprises the steps that hard disk data are obtained, data cleaning and feature selection are conducted on the hard disk data to obtain feature data, and the feature data are stored in a database; performing standardization and normalization on the feature data to obtain a data set, using a long-short term memory network as a deep learning model, designing a model structure, introducing a regularization technology and a dropout layer to construct an initial model, training and optimizing the initial model by using the data set to obtain a trained prediction model, and performing prediction on the trained prediction model. And performing state monitoring, fault early warning and maintenance on the hard disk by using the prediction model. According to the method, efficient and accurate hard disk state monitoring, fault early warning and maintenance are achieved through data preprocessing and the LSTM network deep learning model, and the problems that in the prior art, changes of the running state of the hard disk cannot be accurately captured, and data loss and system crash cannot be prevented are solved.

The invention relates to the technical field of task scheduling, in particular to a container computing-oriented dynamic scheduling system and method, and the system comprises a resource state modeling module which obtains CPU, memory and network bandwidth parameters and analyzes multi-node resource characteristics based on resource use data of nodes in a container cluster. According to the invention, through dynamic resource modeling, analysis of probability change of multi-node resources and accurate prediction of resource fluctuation, a scheduling system can rapidly deal with dynamic resource adjustment, real-time resource comparison is combined, refined load optimization and reallocation are executed, global resource balance is enhanced, resource waste and node overload conditions are avoided, and the scheduling efficiency is improved. In the task scheduling process, matching is carried out based on the priority of tasks and resource multi-dimensional data, scheduling intelligence and high efficiency are ensured, loads are monitored in real time, task distribution is flexibly adjusted, the loads are prevented from being concentrated in a few nodes, task migration among the nodes is optimized, the bottleneck is eliminated, and the system stability and the overall performance are remarkably improved.

The invention relates to a decentralized information safe and efficient storage and sharing method supporting data verification. The method comprises the following steps: 1) an initialization stage; 2) generating and uploading a ciphertext; 3) evaluating the size of the uploaded ciphertext; if the size of the ciphertext exceeds 256 KB, the IPFS carries out fragmentation processing on the ciphertext, stores the fragments in node servers distributed globally, and generates a unique address identifier at the same time; 4) splicing into a new plaintext; 5) the data provider selects an encryption strategy and performs attribute encryption on the new plaintext; (6) the data requester submits an access application to the Hyperledger Fabric before using the data; 7) verifying the legality of the data request; 8) acquiring a symmetrically encrypted ciphertext; and 9) decrypting to obtain the privacy data of the patient. According to the invention, the paralysis state of the whole system caused by faults of some nodes can be avoided.

Systems and methods are disclosed herein for compressing a prompt. In an example system, an importance score listing is obtained that includes a score indicative of an importance of a plurality of dataset keywords. From the importance score listing, a keyword importance score is identified for a plurality of keywords in a current text fragment, such as a text fragment to be compressed. A set of placeholders in an abstract prompt template is populated based on the current text fragment. The current text fragment is compressed based on the importance of the plurality of keywords in the current text fragment to generate a compressed text fragment. In an example, the compressed text fragment is included in the prompt for transmission to a computing entity, such as a large language model of a generative question-answering system.

Systems and methods of the disclosure include: identifying, by a cloud resource management system, a cloud resource consumption model associated with one or more cloud resources; generating, by the cloud resource management system, a sequence of instructions implementing a smart contract based on the cloud resource consumption model; sending, to a distributed ledger network, the smart contract; receiving, by the cloud resource management system, cloud resource usage data associated with the one or more cloud resources; and causing, by transmitting a message reflecting the cloud resource usage data to the distributed ledger, the smart contract to be executed.

The invention provides a dynamic spectrum prediction method based on a time sequence knowledge graph, a medium and equipment. The method comprises the following steps: constructing a communication spectrum coordination knowledge graph framework structure fusing static knowledge and dynamic knowledge; constructing a knowledge embedding model based on a cyclic evolution network, and realizing semantic representation of entities and relationships thereof in an electromagnetic spectrum space; through a knowledge fusion method, static knowledge embedding and dynamic knowledge embedding are fused with historical communication node frequency data so as to realize feature interaction of a static knowledge graph and a dynamic knowledge graph, and features of communication nodes in a frequency domain are extracted at the same time; constructing a space-time diagram convolutional neural network model, extracting space correlation characteristics between nodes, capturing a time evolution rule of node attributes, and dynamically predicting communication frequency; according to the method, the characteristics of the communication node frequency are effectively extracted from the communication semantic relationship, and the prediction precision of the communication frequency is remarkably improved.

The purpose is to easily manage abnormal program operation. [Solution] The abnormality management device 1 includes a first learning unit 11 that uses normal data indicating processing resource usage data in which the processing resource usage corresponding to each process ID is normal as training data from among a plurality of processing resource usage data each including the usage of processing resources used in executing a process corresponding to each process ID, and learns parameters of a probability model that outputs a posterior probability that the processing resource usage corresponding to each process ID is normal by maximum likelihood estimation, and a derivation unit 12 that derives a probability distribution of abnormal data indicating processing resource usage data including abnormal processing resource usage based on the posterior probability estimated by the learned probability model, the probability distribution of the normal data, and the prior probability of normality.

The invention discloses an enterprise-level cloud computing resource dynamic allocation and management system and an implementation method thereof, and relates to the technical field of computing resource management. According to the invention, cloud resource use data is collected and processed in real time through the resource state sensing and preprocessing module, and a load change rule is accurately extracted in combination with the load analysis and period detection module, so that intelligence and adaptive adjustment of resource allocation are realized, and the resource utilization efficiency and the service response speed are remarkably improved; meanwhile, by recording an operation log, generating a trust certificate and dynamically adjusting the access authority, the credibility and transparency of the resource allocation process are enhanced by using a block chain technology and a zero-knowledge proof protocol, and data tampering and trust crisis are effectively prevented.

The invention discloses a traffic package page display method and system based on time dimensionality, and the method comprises the steps: obtaining historical traffic package consumption data, historical traffic package available data and historical traffic actual use data under the condition of receiving an access request for a traffic package display page; determining a candidate traffic plan based on the sending time of the access request and the display time of the traffic plan; performing first screening on the candidate traffic packages based on the historical traffic package consumption data and the historical traffic package available data to obtain initial candidate traffic packages; and performing second screening on the initial candidate traffic package based on the historical traffic actual use data to obtain a final candidate traffic package, and displaying the final candidate traffic package on a traffic package display page. Through the technical scheme, the flexibility of a flow packet display mode and the accuracy of a display result can be improved, and the flow packet handling efficiency can be improved.

The current disclosure provides methods and systems that can manage the dynamic-uncertainty induced by resources of decentralized data networks, in order to ensure the stability and sustainability of task-oriented automated operations, such as operations conducted through intelligent agents. In contrast to the state-of-the-art object-based processing, the methods and systems enable object-aware processing, to ensure the establishment of stable and sustainable associations with physical and digital web-objects, while enabling those objects to be processed dynamically with full adaptability in response to contextual and structural alterations in real-time. Thus, the disclosure provides—both human and machine—users with the ability to develop and deploy modular systems capable of engaging any conceivable physical or digital interaction with the resources of a data network, such as dynamically linking and manipulating clusters of complex web-objects to execute complex tasks in complex and dynamic web environments—most importantly—stably and sustainably.

The invention relates to the technical field of Internet of Things data management, in particular to an intelligent terminal identity authentication and data security management method, system and device based on a block chain and a medium, and the method comprises the steps: generating a unique identity label and a public and private key pair of an intelligent terminal device, a fog node and a user, packaging a digital certificate, and writing the digital certificate into a block chain network; the intelligent terminal equipment initiates a registration request, and the blockchain network distributes a hosting fog node to the intelligent terminal equipment after verification is passed; the intelligent terminal device generates data, constructs a data fingerprint, sends the data fingerprint to the block chain network for storage, encrypts the data, and sends the data to the hosting fog node for storage; a user initiates an authentication request, and a temporary access token is generated after verification is passed; and establishing a secure communication channel by using the temporary access token, obtaining the required encrypted data stored by the fog node, decrypting to obtain the required data, and verifying the integrity of the required data by using the data fingerprint. According to the invention, the overall security and reliability of data management of the Internet of Things system can be significantly improved.

The invention relates to the technical field of AI demand intelligent analysis, and discloses an AI-based software demand intelligent analysis and prediction method and system, and the method comprises the steps: collecting the use data of software when a software product is used; demand feedback information is generated by acquiring user feedback; carrying out demand analysis on the use data and the demand feedback information by utilizing a transfer learning strategy; and gaming a value analysis result and resource occupation to generate a final software demand prediction result. While the demand satisfaction degree is ensured, the resource consumption is minimized, and the development efficiency is improved. By constructing a function value evaluation system, the method can balance the function income and implementation cost, provides scientific and automatic decision basis for enterprises, greatly reduces the software iteration period, and improves the user experience.

Systems, methods, and network attached storage nodes for peer-to-peer file sharing using consistent hashing for distributing data among storage nodes. A plurality of network attached storage nodes may be interconnected by a network and configured for peer-to-peer communication without a centralized server. When a node receives a user data file, it divides the file into data chunks and determines hashes for those data chunks. It uses consistent hashing of the chunk hashes and storage node addresses to map the data chunks to other nodes for distributed storage.

Provided in the embodiments of the present disclosure are a resource scheduling method and apparatus. The resource scheduling method comprises: determining historical operating log data and historical resource usage data of a data processing node set which are generated within a preset historical time period; on the basis of the historical operating log data, and by means of using a log prediction model, predicting predicted operating log data of the data processing node set that is generated within a preset future time period; on the basis of the predicted operating log data, and by means of using a resource prediction model, predicting predicted resource usage data of the data processing node set that is required within the preset future time period; on the basis of the historical resource usage data, determining target resource usage data of the data processing node set within the preset future time period; and on the basis of the predicted resource usage data and the target resource usage data, performing resource scheduling on the data processing node set. The resource scheduling method is more reasonable and more intelligent, and does not require human participation, thus resulting in more stable application systems.

The invention provides a charging pile cluster power coordination method, device and equipment and a storage medium, relates to the technical field of charging pile optimization, and is applied to a charging pile power distribution system which comprises a plurality of charging piles. The method comprises the following steps: acquiring current state data, historical use data and position data of each charging pile in different time periods, and determining corresponding priority weight data according to the position data and the historical use data; based on a preset prediction model, obtaining use probability data of the corresponding charging pile according to the historical use data and the corresponding current state data; obtaining corresponding power reference data according to the power grid dispatching plan data, the current state data and the use probability data; and obtaining a power adjustment strategy of each charging pile according to the current state data, the priority weight data and the power reference data based on a deep reinforcement learning model. According to the method, the prediction model and the deep reinforcement learning technology are combined, and intelligent and efficient power coordination is realized.

A method can include rendering a graphical user interface to a display; receiving data; responsive to determining boundary locations for formations in the subsurface environment using the data, automatically rendering the boundary locations in the graphical user interface; responsive to determining extended boundary locations in the subsurface environment for a region beyond an end of the borehole using the boundary locations, automatically rendering the extended boundary locations in the graphical user interface; responsive to determining a target location in the region using the extended boundary locations, automatically rendering the target location in the graphical user interface; and, responsive to generation of a trajectory from an end of the borehole location to the target location, automatically rendering the trajectory in the graphical user interface.

System and methods are provided for achieving data standardization and normalization through an Agnostic Data Format (ADF) architecture. ADFs systems and processes provide a transformative bridge, enabling disparate data sources to converge into a unified and standardized format within the Real-Time Data Mesh (RTDM) framework. This dynamic process utilizes Artificial Intelligence (AI) and Machine Learning (ML) algorithms to interpret and align diverse data attributes. The ADF management system, integrated into a dynamic event-driven architecture, allows vendors to interact with RTDM by translating and standardizing their data. The synchronized data integrates canonically, incorporating real-time updates and collaborative decision-making across the distribution platform. This innovative approach enhances operational efficiency, enables data-driven decision-making, and provides users improved ability to use data within the distribution ecosystem.

A system and associated methods for organizing, representing, finding, discovering, and using data. Embodiments represent information and data in the form of a data structure termed a Feature Graph, that includes nodes and edges, where the edges serve to connect a node to one or more other nodes. A node in a Feature Graph may represent a variable, such as a measurable object, characteristic, or factor of a study. An edge in a Feature Graph may represent a measure of a statistical association between a node and one or more other nodes. Datasets that demonstrate or support the statistical association or measure the associated variable may be accessed through an identifier in a Feature Graph. An application may traverse a Feature Graph and aggregate and process data associated with a set of nodes or edges.

The invention discloses a mobile phone data line performance test and fault prediction method and system, and belongs to the technical field of electronic product detection and fault prediction. The power-on resistance, the voltage stability, the charging current response and the data transmission rate of a target data line are collected, and under the conditions of different bending angles, insertion and extraction cycles and environment temperatures, the data transmission rate of the target data line is calculated; acquiring stress response data, and extracting multi-dimensional stress principal component characteristics; the electrical property and stress data are fused, a performance degradation correlation model is established, and coupling modeling of data line internal structure damage and electrical property degradation is achieved; historical use data are input, analogue simulation is carried out through the model, and residual performance indexes are generated; judging whether a fault risk exists or not according to a set threshold value, and outputting early warning information if the fault risk is lower than the threshold value; meanwhile, a fault position and a mode type are inferred in combination with an internal characteristic spectrum of the model; the method has high prediction precision, strong structural adaptability and good expansibility, and is suitable for quality control and intelligent diagnosis of consumer electronic cable products.

The invention relates to the technical field of computers, in particular to a containerized scene fault prediction and self-healing method and device, equipment and a storage medium. The containerized scene fault prediction and self-healing method comprises the following steps: obtaining resource use data related to a container in real time by using a sliding window, carrying out noise filtering on the resource use data, generating time sequence characteristics, and obtaining preprocessed data; analyzing the preprocessed data by adopting an isolated forest model to obtain an abnormal score, and judging whether a node of the container has a fault risk or not according to the abnormal score; and carrying out corresponding self-healing operation on the fault node according to the fault type by adopting a pre-constructed self-healing strategy. By analyzing the container resource data, the fault nodes and the types of the fault nodes can be effectively identified, the self-healing operation is pertinently executed, the resource utilization rate is effectively increased, and the core service continuity is guaranteed.

The invention relates to the technical field of scientific research instrument management, in particular to an intelligent supervision method and system for a scientific research instrument, and the method comprises the following steps: extracting instrument reservation task information and constructing a timetable, collecting and analyzing control instruction abnormal behaviors to generate risk information, collecting power trend data to recognize an operation state, and carrying out management. And collecting an identity instruction and power information to construct a use record, and analyzing power fluctuation and load degree to generate a maintenance cycle evaluation value. According to the method, dynamic sorting and conflict screening of the reservation requests are realized by combining the task dependency relationship and the priority information, the risk level is evaluated by using the user instruction behavior characteristics, the real-time identification of the operation risk is realized, the accuracy of the operation state identification is enhanced by using the power fluctuation trend analysis, and the user experience is improved. A structured binding relationship of use data is established in combination with the identity and the device number, the information tracing integrity and the data tracing efficiency are improved, and the prejudgment capability of the maintenance period is optimized by using the combined evaluation of the operation load and the power trend.