21 results about "Computer data analysis" patented technology

The invention provides a computer data analysis system and method based on big data, and relates to the technical field of computer data analysis.The computer data analysis method comprises the steps that firstly, through time axis alignment and standardization processing, it is guaranteed that data can be processed under a unified time frame, and then through sample label distribution and basic feature vector extraction, the data can be processed under the unified time frame; the method comprises the steps of converting original data into standardized features which can be processed by a machine learning model, then capturing static features of the data by calculating a time sequence feature vector, deeply understanding a behavior pattern of the data changing along with time, fusing basic features and time sequence features to construct feature representation with richer information dimensions, and finally, carrying out feature representation on the basis of the basic features and the time sequence features. According to the method, a feature subset with the highest prediction value is screened out by calculating the correlation strength, the calculation complexity and the over-fitting risk brought by high-dimensional data are overcome, finally, a data analysis result is output through a model training and prediction analysis module, and automatic processing from original data to intelligent decision making is achieved.

Embodiments described herein are generally related to computer data analytics, and computer-based methods of providing business intelligence data, and are particularly related to systems and methods for use with enterprise data for profile matching and generating gap scores and upskilling recommendations. In accordance with an embodiment, the system can operate to match a set of position requirements with candidate attributes or skillsets, ranking them on the basis of match scores. The system can be used, for example, to determine a skill gap between the position requirements and candidate attributes, and recommend which skills might be augmented to better address the position requirements.

The application discloses an operator tail volume data intelligent analysis and benefit optimization method, relates to the technical field of computer data analysis, and the operator tail volume data refers to the whole-process business data of the long-tail product of the value-added service, and the package name refers to the unique application identifier of the subscription request initiation, which is extracted from the interface request header. Multiple source data sources are configured, read-write separation and permission distribution are realized, data is pulled and cleaned at a fixed time, package name features are extracted to identify and intercept brushing, multi-dimensional reports are generated, product benefits are evaluated and strategies are optimized, a hierarchical risk control and full-link data traceability system is established. The application realizes unified processing and real-time brushing interception of multi-source tail volume data, improves data accuracy and risk control efficiency, evaluates product benefits in multiple dimensions and generates optimization strategies, supports full-link data traceability, and provides reliable support for fine operation of operators.

The invention relates to the technical field of computer data analysis, in particular to an artificial intelligence blood data analysis system and method, and the system comprises a data collection module, a data synchronization module, a blood data network modeling module, an entropy compression and optimization feature module, and a digital twin modeling module. Through cooperative work of a series of modules such as data acquisition, synchronization, modeling, compression optimization and the like, full-process intelligent processing from blood data acquisition to drug administration time recommendation is realized. According to the method, multi-modal blood data can be efficiently integrated to construct an accurate DBN model to mine the dynamic causal relationship between the data, the high efficiency and accuracy of data processing are guaranteed through an entropy compression and optimization feature module, and a quantum feature vector value state is predicted by using a digital twinborn and an A3C algorithm model; finally, the drug administration time is accurately recommended and sent to the medical data terminal, and the medical data processing efficiency is effectively improved.

The application provides a multi-directional resistance detection type wheel bucket excavator, solves the problem that a conventional wheel bucket excavator is difficult to synchronously acquire detection data of multi-directional resistance of a bucket wheel device, and has the technical scheme that the bucket wheel device is provided with a resistance test system, the resistance test system comprises a test bucket and a first data acquisition device which are installed on the bucket wheel device, the test bucket is fixed on an outer cylindrical surface of the bucket wheel device through four pin shaft sensors, the first data acquisition device comprises a data acquisition module, a wireless signal transmission module for transmitting collected data to a computer data analysis system, a positioning system module and a power supply, and the data acquisition module is connected with the pin shaft sensors through a test cable. The wheel bucket excavator can directly monitor and synchronously acquire three-directional resistance detection data, and the test device has high reliability.

The application relates to the technical field of computer data analysis, and provides a convection gravity wave decoupling method based on a physical information neural network and a server. The method performs joint decoupling analysis on a convection parameter field, a gravity wave activity field and an ozone concentration field of a target atmospheric region, separates the influence of dynamic transportation process and chemical process on the ozone distribution by using a neural network embedded with physical constraints, and determines the relative dominant role of different physical processes under different phases of the intra-seasonal oscillation by time sequence correlation analysis and causal inference, so as to provide an accurate basis for understanding the ozone exchange mechanism near the tropopause.

The application discloses a kind of commodity attention tracking system and method based on user behavior analysis, it is related to computer data analysis technical field, obtain the browsing record of each network account in browsing record sequence, the time proportion of different commodity attribute introduction content that network account browses same commodity is taken as the browsing feature of network account, the average residence time of sharing link in browsing record sequence is calculated, the history record of target user sharing commodity is obtained, the network account of target user sending sharing link in history record is gathered to introduction content, obtain all the browsing record sequence of target contact person, the difference of browsing feature and average residence time are used to evaluate each browsing feature of target contact person, according to the proportion of each commodity attribute introduction content in the introduction content of the sharing commodity to be shared, contact is matched, the contact matched is sorted by average residence time, and the sequence of recommended sharing is obtained.

The present invention discloses a computer data analysis management system based on big data, belonging to the field of data analysis technology. The system of the present invention comprises a database, a data acquisition module, a data analysis module, a real-time file classification storage module, a file cleaning suggestion module and a user management module; the database stores all file information and user behavior data; the data acquisition module extracts the file saving information and deletion records from the database, and performs corresponding processing; the data analysis module analyzes the file information and deletion records, extracts the target file, and calculates the feature vector and user attention; the real-time file classification storage module classifies and stores the real-time files according to the user attention, and dynamically calculates the user attention; the file cleaning suggestion module analyzes the file list and suggests cleaning operations; the user management module receives notification information, performs file management and feeds back the results to the file cleaning suggestion module.

This invention relates to the field of computer data analysis and fusion, specifically to a method for multimodal data fusion based on location-sensitive optimization, comprising the following steps: data collection; data preprocessing; data annotation; extraction of information from the annotated data; calculation of specific vector values ​​using text and image information; substituting the specific vector values ​​into a similarity formula to calculate the similarity between text and image information; fusion of text and image information with high similarity; promotion and matching of information from similar enterprises; and optimization of the text Transformer encoder and image Transformer encoder to better capture key features and information in the image, helping the model effectively fuse these multimodal data to obtain more comprehensive and accurate information.

The present invention provides a text data enhancement method for improving vector retrieval performance, which belongs to the field of computer data analysis. The method first uses a prompt template of a large language model to compress long text data and decompose it into several short texts; during the training process, these short texts will replace the original long texts and be used as training data, thereby saving the size of the video memory occupied by a single piece of information; in order to address the problem of decreased representation ability that may result from shortened text length, the present invention constructs a guidance vector by combining multiple short texts from the same long text, and uses the guidance vector as auxiliary information to guide the encoding process of a single short text. In this way, the present invention can effectively reduce the adverse effects of text shortening on the model's representation ability, thereby improving the training effect and generalization ability of the model while using shorter single pieces of information.

The production facility operation method according to the present application is a production facility operation method for operating a plurality of the same production facilities arranged in a plurality of production sites, including: a data information preparation step of collecting operation data of the production facilities for each production site; a data accumulation step of accumulating the operation data collected in the data information preparation step to a computer arranged at a data accumulation site; a data analysis step of analyzing the operation status of each production facility using the operation data accumulated in the data accumulation step; a data display step of displaying information on the operation status of each production facility analyzed in the data analysis step on a display unit arranged at each production site; and a facility operation step of operating the production facility arranged at a second production site from a first production site with reference to the information displayed by the data display step.

The invention discloses an intelligent monitoring and alarming system for an electric locomotive instrument, and relates to the technical field of monitoring and alarming. A processing unit; a display unit; a voice unit; a power supply module; a clock module; and an interaction module. The intelligent monitoring and alarming device for the electric locomotive instrument comprises a panel, a case, a pressure sensor and upper computer data analysis software, the case is located on the rear side of the panel, and connecting holes are symmetrically formed in the edge of the panel; the front end of the panel is sequentially provided with a power switch, a loudspeaking hole, a display screen, a button and an indicating lamp from left to right. The buttons and the indicating lamps are correspondingly arranged, and a plurality of groups of buttons are arranged; and a TF socket is arranged below the display screen.

The invention discloses a computer data analysis management system based on big data, particularly relates to the technical field of computers, and comprises a science and technology project data acquisition module, a cloud database, a science and technology project association degree screening module, a science and technology project classified storage module, a frequency integration module and a science and technology project search module. The science and technology project classified storage module stores all project information of all science and technology projects; retrieval information of all science and technology projects in a set time period is recorded in the cloud database; the science and technology project data acquisition module is used for collecting retrieval information of all science and technology projects in the cloud database; according to the method, science and technology projects with high retrieval frequency and strong relevance can be better screened out and integrated and compressed, when retrieval personnel perform retrieval, better response can be made and relevance recommendation science and technology project display can be given, information acquisition of the retrieval personnel is facilitated, and the use experience of the retrieval personnel can be greatly improved.

The present invention relates to the field of computer data analysis technology, and in particular to a system and method for artificial intelligence blood data analysis, comprising a data acquisition module, a data synchronization module, a blood data network modeling module, an entropy compression and optimization feature module, and a digital twin modeling module. The present invention achieves intelligent processing of the entire process, from blood data acquisition to recommended medication times, through the collaborative operation of a series of modules, including data acquisition, synchronization, modeling, compression and optimization. This system not only efficiently integrates multimodal blood data to construct an accurate DBN model to explore dynamic causal relationships between data, but also ensures the efficiency and accuracy of data processing through the entropy compression and optimization feature module, and utilizes the digital twin and A3C algorithm model to predict the state of quantum eigenvector values. Ultimately, it accurately recommends medication times and sends them to medical data terminals, effectively improving the efficiency of medical data processing.

The present invention discloses a data analysis method and system for the turning gear result of a hydropower station, which relates to the field of computer data analysis. The method includes obtaining the state data of the turning gear within a preset time and the real-time state detection information of the turning gear; performing data parsing on the state data of the turning gear within the preset time to obtain a first data set, a second data set, and a third data set; solving the first data set and the third data set through a preset first association model to obtain a first processed data set; solving the second data set and the third data set through a preset second association model to obtain a second processed data set; fusing the first processed data set and the second processed data set; and inputting the real-time state detection information of the turning gear into the turning gear state recognition model to obtain the adjustment feature information of the turning gear. This method establishes the data correlation of each part of the unit, reads the relevant data of each part of the unit during turning gear in real time, and achieves the purpose of guiding maintenance and adjustment.

The present application relates to the technical field of bridge maintenance, in particular to a detection method, a detection device and a detection system for bridge repair beam end grouting reinforcement. A sounding pipe is pre-embedded and installed in the grouting cylinder, and the two sides of the grouting cylinder are sealed. A hole is opened on one side of the grouting cylinder for concrete grouting and waiting for the concrete to solidify. The vibration device is started to continuously output vibration effect to the grouting cylinder to simulate the vibration effect of vehicles on the pier. The ultrasonic detection equipment emits ultrasonic waves to detect the concrete in the grouting cylinder. The data detected by the ultrasonic detection equipment is sent to the computer data analysis system. The bridge reinforcement detection is carried out under the same conditions, and the influence of the site vehicles and air temperature and other conditions can be considered relatively. The method has low cost, less time-consuming and less destructive than the detection of the test institution, and the bridge plate needs to be split for detection. The test can be carried out every day, and the quality problem of the reinforced bridge part on the same day is reduced.

The application discloses a computer data analysis processing method based on big data and belongs to the technical field of data processing, and specifically comprises the following steps: collecting all storage data in computer space, screening out storage data greater than a preset byte amount and marking the storage data as pending storage data; dividing the pending storage data into text data and video data, respectively performing deduplication processing on the text data and the video data, and screening out redundant text and video data; obtaining evaluation indexes of the deduplicated pending storage data, wherein the evaluation indexes include but are not limited to the number of days from the last reading to today, reading frequency and byte size, converting the evaluation indexes into maximum indexes, calculating the score F of each pending storage data based on the distance between superior and inferior solutions, and marking a plurality of pending storage data with higher scores as recommended cleaning data; and the application realizes large-scale and rapid analysis of repeated data in the computer storage space.

A puff-by-puff nicotine monitor and detection method for smoking tobacco products. The monitor comprises a puff control unit, a smoking device holder, a smoke capture unit, a detection unit, and a computer data analysis unit. Each unit is connected by a pipeline to form a closed detection system. The instrument and method can instantly obtain the nicotine content in the aerosol of each puff (i.e., the nicotine release per puff, μg·puff). ‑1 、Consumer nicotine exposure rate level μg•min ‑1 ), further data such as cumulative nicotine release, cumulative consumer nicotine exposure, and nicotine utilization efficiency can be obtained. A curve of puff-by-puff nicotine release in aerosol versus puff number can also be plotted, allowing for the assessment of nicotine release stability in smoking tobacco products. This enables in vitro, continuous, online, real-time, and on-site testing of nicotine content in smoking tobacco products, providing technical support and scientific basis for safety risk assessment, quality control, product design, and policy development for smoking tobacco products.

The invention provides an enterprise management level risk dynamic evaluation method and system, relates to the technical field of computer data analysis, and solves the problem of complex risk evaluation requirements in enterprise management. The method comprises the steps of collecting data, related to enterprise risk management, of a web portal through a third-party interface, and calling enterprise system data from a local system; querying enterprise risk feature information worthy of attention at the current time from data of a third-party web portal in a structured query mode; classifying the feature information to obtain enterprise risk categories; extracting related information from the enterprise system data according to the enterprise risk category; analyzing the analysis data, and calculating the risk value of each enterprise risk category according to the occurrence frequency and quantity of the data under each enterprise risk category.

The present invention proposes a comprehensive fault arc protection electrical appliance test system, comprising a host computer data analysis system, a computer control system, a multifunctional data acquisition card, a digital I / O card, a data acquisition module, a stepper motor control system, an electromagnetic switch control system, a switch state feedback system, an arc generator, a cable carbonization device, a cable cutting device, a load device, and a comprehensive test circuit; a carbonized cable used in the comprehensive test circuit is prepared by the arc generator, the cable cutting device, and the arc carbonization device; the computer control system sends pulses to the stepper motor controllers of the arc generator and the cable cutting device via a timing counter on the multifunctional data acquisition card, utilizes motor motion to change the displacement of a blade and an electrode, and provides a carbonized cable and an arc of a desired strength; the test process comprises a high-voltage carbonization stage and a low-voltage test stage. The present invention can accommodate the commonalities and differences between different standard tests, has a high degree of automation, and ensures a safe and reliable test process.

This invention relates to the field of computer data analysis technology, specifically a method and system for intelligent attribution analysis of promotional effects across the entire live streaming journey. It constructs a comprehensive data collection system covering pre-live stream promotion, in-live stream interaction and conversion, and post-live stream repeat purchases and dissemination. Based on a unified user identifier, it achieves cross-platform and cross-device user behavior tracking. A multi-touchpoint attribution model integrating temporal attention mechanisms and causal inference is designed to automatically learn the dynamic contribution weights of different promotional channels and live stream stages in the user conversion path. It quantifies the conversion effects of fine-grained stages such as product explanations, giveaways, and interactive raffles during the live stream. Furthermore, it employs stream computing technology to provide real-time attribution analysis and dynamic optimization suggestions. This invention solves the problems of incomplete attribution paths, rigid models, fragmented data, lack of causality, and insufficient real-time performance in existing technologies, providing comprehensive, accurate, and real-time effect evaluation and decision support for live streaming promotion.