34 results about "Computer data analysis" patented technology

The invention relates to the field of computer data analysis and fusion, in particular to a position sensitive optimization-based multi-modal data fusion method, which comprises the following steps of: collecting data; preprocessing the data; labeling the processed data; the marked data information is extracted; calculating a specific vector value through the text information and the image information; substituting the specific vector value into a similarity formula, and calculating the similarity between the text information and the image information; fusing the information with high similarity between the text information and the image information; information promotion and matching of enterprises of the same type are realized; by optimizing the text Transform encoder and the image Transform encoder, key features and information in the image can be better captured, and a model is helped to effectively fuse the multi-modal data so as to obtain more comprehensive and accurate information.

The invention discloses a computer data analysis management system based on big data, and belongs to the technical field of data analysis. The system comprises a database, a data acquisition module, a data analysis module, a real-time file classified 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 is used for extracting saved information and deleted records of the file from the database and carrying out corresponding processing; the data analysis module analyzes file information and deletes records, extracts target files and calculates feature vectors and user attention; the real-time file classified storage module is used for classifying and storing the real-time files according to the attention of the user and dynamically calculating the attention of the user; the file cleaning suggestion module analyzes the file list and suggests cleaning operation; and the user management module receives the notification information, performs file management and feeds back a result to the file cleaning suggestion module.

The invention provides a text data enhancement method for improving vector retrieval performance, and belongs to the field of computer data analysis. The method comprises the following steps: firstly, compressing long text data by using a prompt template of a large language model, and decomposing the long text data into a plurality of short texts; in the training process, the short texts replace the original long texts to be used as training data, so that the video memory size occupied by a single piece of information is saved; in order to cope with the problem that the representation capability is reduced possibly due to the fact that the text length is shortened, a guide vector is constructed by combining a plurality of short texts from the same long text, and the guide vector is used as auxiliary information to guide the encoding process of a single short text. In this way, the adverse effect of text shortening on the model representation ability can be effectively reduced, and therefore the training effect and generalization ability of the model are improved on the premise that shorter single information is used.

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 invention provides a TTE analyzer and a data analysis method. The method comprises the following steps: S1, initializing a main controller; s2, the upper computer data analysis software platform sends a communication link heartbeat command to the main controller, and the main controller sends an ACK message to the upper computer data analysis software platform after receiving the command; s3, the upper computer data analysis software platform sends a TTE data card port Link state query command to the main controller to obtain a Link state, and sends a TTE data card port Link state message to the upper computer data analysis software platform; s4, the upper computer data analysis software platform sends a command of starting to capture TTE data to the main controller, and the main controller analyzes the port number; s5, the upper computer data analysis software platform sends a TTE data uploading command to the main controller, and the main controller analyzes the port number; and S6, after the upper computer data analysis software platform receives the data, analyzing and analyzing TTE network data. According to the invention, monitoring, diagnosis and troubleshooting can be carried out on TTE network equipment, and protocol verification and real-time traffic capture are supported.

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 invention discloses a commodity attention tracking system and method based on user behavior analysis, and relates to the technical field of computer data analysis. Taking a time ratio of browsing different commodity attribute introduction contents of the same commodity by the network account as a browsing feature of the network account, calculating average residence time of a sharing link in the browsing record sequence, and obtaining a historical record of sharing the commodity by the target user, the method comprises the following steps: collecting network accounts of a sharing link sent by a target user in a historical record to introduction content, obtaining all browsing record sequences of a target contact, and evaluating various browsing characteristics of the target contact according to the difference of the browsing characteristics and the average residence time; and matching the contact persons according to the proportion of the introduction content of each commodity attribute in the introduction content of the to-be-shared commodity, and sorting the matched contact persons through the average residence time to obtain a recommendation sharing sequence.

The present application provides a forest lightning fire monitoring method and system based on artificial intelligence, which relates to the field of computer data intelligent analysis technology. The method includes: obtaining a sensor monitoring data group for a forest area; extracting the atmospheric electric field time series characteristics corresponding to the atmospheric electric field instrument data based on a long short-term memory network module; constructing a lightning graph structure, updating the node characteristics of each node in the graph structure based on a graph neural network to construct a lightning feature matrix; processing regional monitoring images based on a convolutional neural network to extract an image feature matrix; inputting the atmospheric electric field time series characteristics, the lightning feature matrix, and the image feature matrix into a lightning fire recognition model to determine whether there is a lightning fire risk in the forest area, and the lightning fire recognition model adopts a multimodal fusion deep learning model. As a result, the comprehensive analysis of lightning events and the dynamic monitoring of fire conditions are enhanced, and the efficiency of fire risk identification and early warning and the speed of emergency response are greatly 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.

The invention discloses a multi-mainboard host health monitoring management system and a control method thereof, and the system comprises a lower computer data collection unit, a lower computer data analysis and processing unit, an upper computer heterogeneous integration unit, a user display interface, and a remote processing unit. Wherein the lower computer data acquisition unit is used for data communication with a computer mainboard component; the lower computer data analyzing and processing unit is used for analyzing and operating the acquired data; and the upper computer heterogeneous integration unit combines and arranges multiple groups of data of the lower computer based on a multi-source heterogeneous information processing technology, and uniformly sends the data to the remote unit. According to the multi-mainboard host health monitoring management system and the control method thereof, by configuring a computer BIOS strategy, higher health management authority is achieved, the CPU load frequency and the GPU response speed can be rapidly allocated, deep health management such as DDR4 read-write pressure testing can be carried out, and therefore multi-mainboard computer health detection and response are effectively achieved.

The invention provides an index configuration method based on a database, belongs to the field of computer data analysis, and selects different generation modes to generate indexes based on databases which are connected in a system. Firstly, a library table mode is adopted, a basic data table is selected, connection is established according to requirements, and a user can flexibly select required statistical dimensions and statistical modes, so that corresponding indexes are generated. Secondly, a user can compile corresponding SQL statements according to needs, and directly select dimensions and statistical modes which need to be counted; finally, the invention further provides a calculation formula library established in the system, a user can select a proper formula according to specific requirements, and corresponding dimension indexes are appointed for parameters of the formula. The method not only improves the efficiency of index generation, but also ensures the consistency and accuracy of calculation.

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 invention discloses a data analysis method and system for a barring result of a hydropower station, and relates to the field of computer data analysis, and the method comprises the steps: obtaining the state data of a barring in a preset time and the real-time state detection information of the barring; performing data analysis on the state data of the turning gear in 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 correlation model to obtain a first processing data set; solving the second data set and the third data set through a preset second correlation model to obtain a second processing data set; fusing the first processing data set and the second processing data set; and inputting the real-time state detection information of the jigger into the jigger state recognition model to obtain the adjustment feature information of the jigger. According to the method, data relevance of all parts of the unit is established, relevant data of all the parts of the unit during turning are read in real time, and the purpose of guiding maintenance and adjustment is achieved.

The invention discloses a computer data analysis processing method based on big data, and belongs to the technical field of data processing, and the method specifically comprises the following steps: collecting all storage data in a computer space, screening out storage data greater than a preset byte quantity, and marking the storage data as undetermined storage data; dividing the to-be-stored data into text data and video data, performing duplicate removal processing on the text data and the video data, and screening out repeated redundant text and video data; the evaluation indexes include but not limited to the number of days from last reading to today, the reading frequency and the byte size, the evaluation indexes are converted into extremely large indexes, the score F of each piece of to-be-stored data is calculated based on a superior and inferior solution distance method, and the score F of each piece of to-be-stored data is calculated; marking a plurality of pieces of undetermined storage data with top scores as recommended cleaning data; according to the method, large-scale rapid analysis of the duplicated data of the computer storage space is realized.

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.

This application relates to the technical field of connectivity communication, and provides an apparatus and method for testing a connected vehicle's communication distance. The testing apparatus includes a support member, and a first antenna is provided on the support member and connected to a signal transceiver. The signal transceiver is configured to send signals to a tested vehicle or receive signals sent by the tested vehicle. An output terminal of the signal transceiver is connected to an upper computer. Data analysis software is installed on the upper computer, and used for recording a test distance and a number of data packets and calculating a packet loss rate, such that a maximum communication distance is obtained according to the test distance and the corresponding packet loss rate.

The present invention belongs to the field of computer data analysis, and provides a traffic prediction method based on graph transfer learning, which can efficiently transfer knowledge from a source domain with sufficient training samples to a target domain with insufficient training samples, and improve the prediction performance of a traffic prediction model based on a graph convolutional neural network in a small sample situation. The method of the present invention can help improve the prediction accuracy of a traffic prediction model based on graph data in a small sample situation. The method of the present invention introduces spatial clustering and temporal clustering regularization terms, balances spatiotemporal pattern matching with prediction accuracy, and completes the matching of the spatiotemporal pattern similarity of nodes in the target domain and the source domain in a data-driven manner, thereby achieving the effect of reducing negative transfer, and greatly reducing the computational overhead of previous methods in the spatiotemporal pattern matching process.

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.