57 results about "Extensive data" patented technology

A system and method for comprehensive data utilization and tracking comprising an ontological engine which in some embodiments is configured to create and curate various industry-specific ontologies which can be used to provide deeper context to an enterprise's network traffic and data transmission. The system and method further comprise a tagging and tracking engine configured to inspect network packets, apply a first tag associated with an authentication object, apply a second tag associated with an identified ontology, and track the tagged packets as they traverse the enterprise network, generating data utilization tracking information as the packets move through the network. A scoring engine may leverage the data utilization tracking information in combination with user entity and behavior data to compute a risk score associated with data utilization on the enterprise network.

The invention belongs to the field of artificial intelligence, and particularly relates to a disaster risk early warning algorithm based on multi-modal fusion and a space-time propagation model, which comprises a multi-modal data management module, a cross-modal semantic alignment module, a space-time propagation graph modeling module, a propagation constraint deduction module, a graded early warning generation module and an online closed-loop calibration module. Depth alignment of multi-modal data on a semantic level, dynamic deduction of a disaster propagation process under constraint of a physical mechanism and operable conversion of an early warning result in a business scene are realized, and the whole system takes a unified space-time framework as a base, takes a propagation graph as a link and takes closed-loop calibration as a guarantee, so that the accuracy of the system is improved. The structural defects of extensive data splicing, propagation modeling deficiency and decision chain fracture in a traditional early warning method are fundamentally overcome.

This invention relates to the field of blasting testing technology and discloses a method for quantitatively representing intrusive blasting waves within a double-layer curtain wall space. The method includes establishing an experimental system, creating a basic model of the double-layer curtain wall structure, conducting blasting experiments, verifying the accuracy of the LS-DYNA simulation software's simulation of the experimental content, and parameter analysis. The parameter analysis includes quantitative analysis using blasting wave intensity, intrusion hole height H, and the distance C between the outer and inner curtain walls as variables. Through finite element simulation, combined with extensive data and theoretical analysis, a method for calculating the peak pressure of the intrusive blasting wave inside the structure is derived. This method for quantitatively representing intrusive blasting waves within a double-layer curtain wall space has the advantage of ease of use. It allows for the exploration of the blasting wave pressure inside the structure and the corresponding implementation of structural protection designs, effectively improving the scientific rigor and reference value of internal structural protection designs, and making a significant contribution to the stable management of structural safety.

The invention belongs to the technical field of universal time Transform models, and particularly relates to a universal time Transform model zero sample prediction training method which comprises the following specific steps: S1, data preprocessing and enhancement: firstly collecting and integrating time series data of multiple fields, then performing feature engineering on the time series data, and then enhancing the data; s2, improving a model structure and a training strategy: firstly, adding an adaptive weight adjustment mechanism in an attention module of a universal time Transform model; through the multi-field data fusion and data enhancement technology, types and features of training data are enriched, so that the model can learn wider data modes and laws; therefore, the model can be better generalized when facing zero sample prediction tasks in different fields, the prediction accuracy and stability are improved, and the overfitting problem caused by single data is reduced.

The invention discloses a method for monitoring the driving state of a mobile machine in a thermal regeneration scene. The method comprises the steps of installing a plurality of sensors on the mobile machine; the method comprises the following steps: acquiring sensing data, constructing a driving state anomaly detection model in an ROS data processing system, inputting the sensing data into the driving state anomaly detection model, and performing training and hyper-parameter optimization on the driving state anomaly detection model to obtain an optimized driving state anomaly detection model; and carrying out anomaly detection on the driving state of the mobile machine based on the optimized driving state anomaly detection model, and when an anomaly detection result is abnormal, carrying out visual display and cloud storage on abnormal data, and sending out early warning information. According to the method, the driving state anomaly detection model is constructed and optimized, the sensing data of the mobile machinery and various environmental influence data are combined, accurate detection of the driving state is realized, more deep and comprehensive driving state monitoring is provided in combination with visual display and cloud storage, and wider data sharing and remote cooperation are supported.

A data processing system and method provides automated extraction, translation, load (ETL) operations. A server coupled with the processor executes the instructions and extracts data from various sources, such as cloud storage, external APIs and direct uploads. The system may include a stream mode processing unit to process large data files in manageable blocks, thereby improving efficiency and reducing memory load. It performs integrity checks to ensure data accuracy and consistency. The system configures and applies predefined and custom transitions, which is facilitated by user-friendly interface and API integration. Custom conversion logic is integrated into the process, allowing adaptive data operations to be performed. The converted data is then verified and formatted to be loaded into different destination systems. The ETL process is efficient, extensible and user-friendly, and thus suitable for wide data processing applications.

The invention discloses an urban water flooding risk early warning method based on radar and ground precipitation fusion. The method comprises the steps of terrain analysis and calculation; analyzing a waterlogging area and flooding grid filling, and generating a flooding area watershed topology; rainfall station monitoring data and radar echo map data are integrated, and rainfall forecast grids are integrated; rainfall fusion interpolation is carried out based on actually measured rainfall of a rainfall station and a radar echo map; matching a rainfall grid; extracting an average rainfall sequence of all flooding basin surfaces; extracting an average rainfall sequence of the sub-basin units from the forecast rainfall source; loading and analyzing an input file, constructing an internal data structure and creating a result output grid template; calculating rainfall flooding; performing flooding risk assessment and early warning display; the problems that in the prior art, data information needed in urban water accumulation risk early warning analysis is numerous, and particularly, the accurate acquisition difficulty of urban drainage pipe network data is large are solved; the problem that urban rainstorm intensity and rainstorm volume space distribution cannot be accurately reflected in the prior art is solved.

This invention belongs to the field of equipment life prediction technology, and discloses a method and related apparatus for predicting the remaining life of a photovoltaic inverter. The method includes: acquiring junction temperature data of the IGBT modules in the photovoltaic inverter under test; determining the number of junction temperature thermal cycles of the IGBT modules in the photovoltaic inverter under test based on the junction temperature data; calculating the cumulative damage of the IGBT modules in the photovoltaic inverter under test based on the number of junction temperature thermal cycles; and calculating the remaining life prediction result of the photovoltaic inverter under test based on the cumulative damage of the IGBT modules. This invention can more accurately predict the remaining life of the IGBT modules, and thus more accurately predict the overall remaining life of the photovoltaic inverter. The prediction process does not require extensive data collection and training of the prediction model, effectively reducing the difficulty of life prediction and improving prediction efficiency.

The present application relates to the field of drone control technology and discloses a method, system and medium for cooperative task allocation among multiple drones. The method comprises: S1, defining the problem of cooperative task allocation among multiple drones; S2, solving the optimal control strategy for the problem of cooperative task allocation among multiple drones; S3, solving the dual optimization problem based on the primal-dual theory; S4, solving the convex dual optimization problem using the Q-function and Schur Complementation theory transforms the convex dual optimization problem into a semidefinite programming problem. S5, based on the properties of matrix congruence, transforms the semidefinite programming problem into a model-free semidefinite programming problem. S6, using a solver, obtains the optimal control strategy. S7, by varying the weight coefficients and repeating S2-S6, obtains the optimal energy loss function bound. This application, based on the Q-learning method, requires only a small amount of data collection, not a precise dynamics model or extensive data, to obtain the optimal strategy for multi-UAV cooperative task allocation.

Systems and methods ingest extensive data regarding parties and contextual data to determine correlation between a variety of data types, parameters related thereto, and member service representative (MSR) contact events and corresponding staffing levels. The appropriate level of staffing, based on real-time and historical context to meet demand for calls, emails, messages, and other contact or servicing can be calculated without overstaffing.

A method for achieving super-resolution in localizing electrical fields measured at the head surface with electroencephalography through a generative model of the cerebral cortex that has a very high resolution of cortical surface dipoles constructed from the known properties of human cerebral cortex and adapted to optimize the Bayesian explanation the individual's cortical surface electrical fields. The iterative optimization of the prior (generative) with the posterior (observed) fields with extensive data from extended recordings provides a probabilistic estimation of the individual's functional brain activity that can be used to train artificial neural network approximations of the individual's mental activity.

According to various embodiments described herein, mechanisms are provided for providing a single source for a wide range of data and predictive analyses, integrating data describing corporations, finances, stock performance, competition, educational institutions, and / or job markets, in any suitable combination. Various embodiments provide mechanisms for integrating any or all of such data to generate predictive analysis yield expected corporate and / or investment outcomes. In addition, the system and method described herein are able to create and answer questions in an outcome format that can be used to make financial decisions for investors, business executives, boards of directors, and / or the like. In particular, according to various embodiments, the system and method described herein are able to aggregate data from many different sources, such as for example nationwide jobs data, company information, stock portfolios, and / or educational data, and to provide accurate analytical forecasting based on such aggregated data.

This invention discloses a deep learning-based orthodontic image classification model determination system, relating to the field of image data technology. The system includes a construction module for building an orthodontic data annotation library, comprising multiple tooth overlay models and multiple maxillary / mandibular overlay models, and for annotating orthodontic sample images according to the library. A training module, connected to the construction module, determines the feature information of the annotated orthodontic sample images, selects a deep learning model based on the feature information, and trains the selected deep learning model using the annotated orthodontic sample images to obtain a trained deep learning model. This invention enables data integration and annotation of numerous dental problems through the orthodontic data annotation library, achieving extensive data coverage and ensuring the accuracy and consistency of image annotation. It also enables rapid annotation of oral cavity-related images, improving the efficiency of orthodontic analysis.

This invention discloses an image-based method for detecting and reconstructing irregular strip-shaped objects, comprising the following steps: S1: image acquisition; S2: image preprocessing; S3: optimizing the network model; S4: setting network model parameters; S5: inputting the training set into the optimized network model for training, saving the trained weight file, and using the test set to test the model's segmentation effect on the target; S6: reconstructing branch segments. This method can effectively detect and segment branches with similar background colors, overlaps, and occlusions, without requiring extensive data statistics, and achieves high reconstruction accuracy.

This invention relates to a system and method for inverting formation cavity volume during well gas invasion, belonging to the field of petroleum drilling engineering technology. The system includes a wellbore throttling and circulation pressure control section, a throttling response parameter acquisition section, and multiphase flow simulation software. Using this system, three wellbore throttling response parameters—wellhead back pressure, inlet flow rate, and outlet flow rate—can be acquired. Then, based on a multiphase flow model, bottom hole pressure, gas dissolution rate, and gas invasion flow rate are predicted. Finally, the formation cavity volume is inverted using a reservoir model and gas state equations. This method fully utilizes field-obtainable parameters, eliminating the need for extensive data training, and is simple, efficient, and real-time. Furthermore, the method acquires relevant parameters during pressure control measures, which helps to curb gas invasion development. This invention enables the calculation of formation cavity volume after gas invasion occurs in the wellbore, providing theoretical support for decision-making regarding pressure-controlled drilling and well control measures in such formations.

This invention relates to a method for diagnosing keratoconus by analyzing the geometric properties of Purkinje images. The method involves capturing Purkinje images of the cornea using a low-cost and simple device, such as a smartphone attachment, making it more accessible than current diagnostic tools like slit lamps. By comparing these images to reference images from healthy corneas, significant shifts and distortions are identified as indicators of keratoconus. This non-invasive tool provides an accessible and cost-effective means for early detection and ongoing monitoring. The method's reliability is ensured through extensive data collection from both healthy and keratoconic corneas. This approach offers primary assessments, especially in areas lacking access to advanced medical care, potentially reducing the need for more invasive procedures.

This invention relates to a method, apparatus, electronic device, and medium for calculating landslide thrust, belonging to the field of geotechnical engineering technology. The method includes: acquiring soil structure parameters of a slope and constructing a geotechnical model of the slope; calculating the theoretical value of the landslide thrust by the soil structure parameters; simulating the geotechnical model to obtain simulated values, using the ratio of the simulated value to the theoretical value as a correction coefficient; obtaining a linear relationship between the structure parameters and the theoretical, simulated, and correction coefficients based on different soil structure parameter values; determining a three-dimensional relationship between the soil structure parameters and the correction coefficient based on the linear relationship; acquiring the soil structure parameters and theoretical values ​​of the target slope; determining the target correction coefficient for the target slope; and determining the corrected landslide thrust of the target slope. This invention obtains the correction coefficient through extensive data calculation, thereby obtaining the three-dimensional landslide thrust of the target slope when the theoretical value of the target slope is known.

This invention discloses an NLOS identification method based on the GMM-PSO-EM algorithm. First, the time-delay data is preprocessed and denoised, and the distribution quantity is determined by combining AIC and BIC criteria. Then, the GMM parameters are iteratively updated using the PSO-SA-EM algorithm. After establishing the GMM model, the NLOS probability value within the scene is determined by the area of ​​the region determined by the PDF value. This GMM-PSO-EM algorithm exhibits excellent NLOS identification performance, demonstrating strong effectiveness and robustness in handling NLOS problems. It can solve the problems of high complexity in NLOS identification in complex indoor environments, the need for extensive data labeling, and poor performance with small data volumes. Furthermore, it is relatively simple to implement and has a faster computation speed, making it particularly suitable for identifying NLOS signals during UWB system positioning in complex indoor environments.

This disclosure provides a data reading method, device, and storage medium for a database. The method involves receiving query conditions for the Value column of an LSM-Tree database; retrieving first candidate data from the LSM-Tree database whose Values ​​satisfy the query conditions; constructing a right table based on the Keys of the first candidate data; using the full data of the LSM-Tree database as a left table; performing a semi-join between the left and right tables; obtaining second candidate data from the left table that matches each Key in the right table; and retrieving target data from the second candidate data that satisfies the query conditions. This disclosure implements predicate pushdown in a Merge-On-Read scenario for LSM-Tree databases, avoiding extensive data scanning while balancing write and query performance.

The invention relates to the technical field of vocational education, in particular to a vocational education demand prediction system based on LSTM time sequence prediction and a dynamic adjustment method, and the system comprises a multi-source data collection module, a knowledge graph construction module, an intelligent data processing module, an LSTM-reinforcement learning prediction module, a dynamic adjustment module and an execution feedback module. According to the scheme, firstly, three types of data of industrial economy, post requirements and education and learning are integrated, a knowledge graph containing semantic and quantitative information is constructed, abnormal value recognition and missing value filling are optimized, and data suitability is improved; secondly, capturing time sequence dependence through bidirectional LSTM, optimizing model parameters in combination with knowledge attention and reinforcement learning, and achieving demand prediction; and finally, constructing a resource dynamic coefficient matrix, converting constraints into mathematical conditions to solve an optimal adjustment amount, and feeding back an optimization model and a map in real time, thereby effectively solving the problems of extensive data, prediction deviation and experience-dependent adjustment in the prior art, and realizing efficient matching of vocational education resources and demands.

The invention relates to the technical field of credit scoring, in particular to a real-time credit scoring system based on deep learning, which comprises a data collection module, a data preprocessing module, a deep learning model module and a real-time scoring module, and is characterized in that the data preprocessing module is connected with the data collection module, and the deep learning model module is connected with the data preprocessing module; the real-time scoring module is connected with the deep learning model module; an advanced deep learning algorithm is introduced to process real-time data from a plurality of heterogeneous data sources, such as financial transaction records, social media behaviors, online consumption habits and the like of users, so that a comprehensive, dynamic and real-time updated credit score is generated. According to the system, multiple innovations are carried out in the aspects of the flexibility of an algorithm, the universality of data processing and the real-time performance of risk prediction, and a more comprehensive and efficient credit risk assessment tool is provided for financial institutions.

The present invention provides a distribution line wave velocity measurement and calibration method based on a distribution automation master station. Following a remote control switch operation, the master station sends a command to all traveling wave ranging devices on the switch's line to upload their most recent wave recording. Upon receiving the command, the traveling wave ranging devices upload their most recently cached wave recording, i.e., the wave recording data initiated by the switch operation. After receiving the uploaded wave recording, the master station calculates and updates the wave velocity. This invention provides an active and easy-to-use distribution line wave velocity measurement and calibration method. This method measures and calibrates line wave velocity without relying on faults or requiring extensive data statistics. This method addresses the large errors in distribution line fault location caused by inaccurate wave velocity, improves fault location accuracy, and significantly accelerates distribution line fault handling, significantly reducing property losses, personal safety risks, and power supply reliability losses caused by faults.

The invention discloses a passive and green building coupled indoor environment dynamic regulation and control method, and belongs to the field of green buildings and building automation control. According to the method, environment, energy consumption and user behavior data are collected through a distributed sensor, after normalization and other fusion processing, a time sequence algorithm and building energy consumption are used for simulating and predicting a trend, a multi-target decision matrix is constructed, regulation and control are preferentially carried out through passive assemblies such as a sun-shading system and a phase change material, and the energy consumption and user behavior data are obtained. When the comfort level does not reach the comfort level threshold value, active systems such as an air conditioner and photovoltaic-energy storage are linked for cooperative adjustment, and closed-loop optimization is conducted on a regulation and control strategy through algorithms such as reinforcement learning. The problems that in the prior art, a passive system and an active system are separated, data utilization is extensive, and energy distribution is low in efficiency are solved, and cooperative regulation and control of passive priority and active compensation are achieved. According to the method, the building energy consumption is reduced, the comfort reaching rate is increased, the renewable energy utilization rate is increased, and the method is suitable for intelligent regulation and control of the indoor environment of various green buildings.

According to various embodiments described herein, mechanisms are provided for providing a single source for a wide range of data and predictive analyses, integrating data describing corporations, finances, stock performance, competition, educational institutions, and / or job markets, in any suitable combination. Various embodiments provide mechanisms for integrating any or all of such data to generate predictive analysis yield expected corporate and / or investment outcomes. In addition, the system and method described herein are able to create and answer questions in an outcome format that can be used to make financial decisions for investors, business executives, boards of directors, and / or the like. In particular, according to various embodiments, the system and method described herein are able to aggregate data from many different sources, such as for example nationwide jobs data, company information, stock portfolios, and / or educational data, and to provide accurate analytical forecasting based on such aggregated data.

The system provides real-time safety scores for motor carriers, accurately predicts future safety scores, and allows users to simulate various safety scenarios. Users can input data and perform complex calculations, ensuring accurate and timely updates of safety scores. The system's efficiency lies in accumulating and analyzing extensive data, leading to more precise predictions of future safety scores. The calculated, predicted, or simulated safety scores are presented on diverse devices such as desktop computers, mobile phones, or other compatible devices.

The invention discloses a student physical health data risk assessment method and system. The method comprises the following steps: acquiring physical health data reported by all schools and physical health data generated by spot check and retest; performing data processing and feature extraction on physical health data reported by a school, and establishing a training sample; establishing a physical health data risk assessment dichotomy model based on a Gaussian kernel function, and performing model training by using the obtained training sample to obtain model parameters; and inputting the obtained model parameters into a risk assessment dichotomy model, performing risk prediction on physical health data autonomously reported by a school, and positioning and outputting abnormal data. According to the method, for student groups of different grades and genders, biological differences of different stages of teenager physique development are considered, subdivided data verification is carried out, collaborative anomalies among indexes are considered, and a more accurate and non-extensive data risk inspection mode based on spot check and retest of high-credibility data is provided.

This invention discloses a fault-constrained Dal-UNet stratigraphic surface reconstruction method, applicable to the fields of information processing and oil and gas exploration. Existing deep learning reconstruction algorithms, based on supervised learning, require extensive data and manually labeled training data for pre-training, resulting in high economic and time costs. Data quality directly impacts algorithm performance, and in geological studies with relatively scarce data, supervised deep learning algorithms are prone to overfitting, making them even more difficult to apply directly to stratigraphic surface reconstruction tasks involving complex geological structures with faults. This invention utilizes the Dal-UNet network to capture hidden features from limited and sparse stratigraphic surface data and combines it with a fault-constrained algorithm to reconstruct complex stratigraphic surfaces containing faults, effectively restoring the true topography.

A method for achieving super-resolution in localizing electrical fields measured at the head surface with electroencephalography through a generative model of the cerebral cortex that has a very high resolution of cortical surface dipoles constructed from the known properties of human cerebral cortex and adapted to optimize the Bayesian explanation the individual's cortical surface electrical fields. The iterative optimization of the prior (generative) with the posterior (observed) fields with extensive data from extended recordings provides a probabilistic estimation of the individual's functional brain activity that can be used to train artificial neural network approximations of the individual's mental activity.

The invention discloses a power system data acquisition system which comprises a data acquisition module, a data storage module, a data processing module, a data transmission module, a data analysis module and a data application module. The power system data acquisition system based on the big data has the characteristics of high efficiency, accuracy, real-time performance, stability, safety and the like, can provide powerful support for intelligent upgrading and optimization of a power system, is expansion and upgrading of a traditional data acquisition system, can process wider data sources, and has wide application prospects. And data values are mined by using a big data analysis technology, so that more powerful decision support is provided for safe, economic and efficient operation of a power system.

The invention discloses a compliance report generation system and method, which can monitor data streams in an enterprise operation process, extract compliance-related data based on algorithm rules for analysis, realize automatic storage and collection of compliance evidence data, and prompt and match a rectification scheme when finding compliance potential risk points. The compliance report can be quickly generated as required, the automation degree is high, the data coverage is wide, and the compliance report generation efficiency is high.