122results about How to "Efficient analysis" patented technology

The invention discloses a quality-graded remote sensing image composite cloud removal method, system and device. The method comprises the following steps: preprocessing an acquired remote sensing image; based on the preprocessed data, thin cloud radiation correction is carried out; based on the data after thin cloud radiation correction, thick cloud detection is carried out; based on the data after thick cloud detection, thick cloud missing reconstruction is carried out; performing atmospheric correction based on the data after the thick cloud missing reconstruction; and performing data quality grading based on the data after atmospheric correction. According to the method, a series of standardized processing is realized on data acquired under different space and time conditions, the influence of atmospheric radiation attenuation is eliminated, and the data are formatted into surface reflection data with consistent observation characteristics, so that the utilization rate of remote sensing images is improved, and effective comparison and analysis of subsequent application research are promoted.

The invention relates to the technical field of braid quality detection in industrial production, in particular to a defect data processing query method and system for braid visual inspection. The method comprises the steps of obtaining braid image data, performing enhancement processing on an image based on a dynamic illumination compensation algorithm, and generating a high-quality image in combination with material characteristics; inputting the enhanced image data into a deep learning defect detection model, detecting and classifying defect areas in the image, and updating the model weight based on defect feature parameters; performing multi-dimensional indexing processing on the detected defect feature data to generate a defect distribution database supporting real-time query; and the defect distribution database and the production process parameter sequence are combined, the causal relationship of defect occurrence is analyzed, process optimization parameters are generated and fed back to the detection module, and self-adaptive updating of model parameters is completed. According to the invention, high precision, high efficiency and closed-loop optimization of braid defect detection are realized, and intelligent support is provided for industrial production quality control.

The invention discloses a tunnel construction quality evaluation system and method. The evaluation system comprises a data acquisition module, a quality evaluation standard system, an early warning and notification module and a data management module. Wherein the data acquisition module is used for acquiring construction vehicle data, construction site sensors and site photographing; the quality evaluation standard system comprises drilling quality evaluation, back break evaluation, primary support thickness evaluation, anchor rod construction quality evaluation, waterproof and drainage pipe quality evaluation, steel bar binding quality evaluation, secondary lining quality evaluation and primary support flatness evaluation; the early warning and notification module integrates early warning signals from all the evaluation modules; and the data management module is responsible for storing data generated by each independent module and executing cross-module data analysis. According to the tunnel construction quality evaluation system, by integrating advanced monitoring equipment, an automatic algorithm and comprehensive data analysis, the quality control effect in the tunnel construction process is remarkably improved.

The invention discloses a method and device for recognizing and analyzing PDF texts and pictures based on OCR and a storage medium, and particularly relates to the field of text and picture processing.The method for recognizing and analyzing PDF texts and pictures based on OCR comprises the following steps that S100, PDF documents are loaded, page analysis is conducted, page elements are extracted, and page layout information is processed; and step S200, for the extracted picture or the PDF page generated by scanning, performing high-precision OCR recognition by using a deep learning model, and converting characters in the image into editable texts. According to the method, efficient and accurate analysis of complex texts and picture contents in the PDF document is realized, the technical scheme not only improves the analysis precision and efficiency and enhances the flexibility and expandability of the system, but also promotes the development of digital and intelligent application of the document, and meanwhile, by saving resources and time cost, the method and the system have good application prospects. And remarkable economic benefits are brought to enterprises and individual users.

The invention discloses an unattended transformer substation inspection method based on image recognition, and relates to the field of intelligent inspection. Visible light and infrared thermal imaging cameras are installed according to the layout of a transformer substation to respectively capture equipment visual information and monitor temperature distribution; and setting an acquisition frequency adjustment strategy according to equipment electrical parameters, a temperature change rate and the like to dynamically acquire data. A generative adversarial network (GAN) is utilized to assist in fault diagnosis, a deep learning model is also established, and a model for classification judgment is obtained through training after equipment state image collection, preprocessing and data set division. And meanwhile, a cloud edge cooperative computing architecture is constructed to efficiently process image data, a real-time interaction feedback mechanism is designed to remotely display related conditions of equipment in real time, the running state of the equipment can be monitored more comprehensively and accurately, and the inspection efficiency, the operation and maintenance management efficiency and the like are improved.

The invention discloses an automatic auditing system and method for road engineering completion settlement. The system comprises a data acquisition and integration module; a data preprocessing module; a settlement index construction module; an intelligent auditing engine; an abnormity early warning and tracing module; a settlement result management module; and a data analysis and optimization module. The auditing method comprises the following steps: S1, collecting and integrating multi-source settlement data; s2, big data preprocessing; s3, constructing a settlement auditing index system; s4, performing intelligent automatic auditing; s5, performing abnormal early warning and tracing; s6, managing settlement results; and S7, performing big data driving optimization. According to the invention, multi-source data intelligent integration, full-process automatic auditing, abnormal accurate tracing and dynamically optimized big data driven settlement auditing can be realized; the method is applied to whole-process management of road engineering completion settlement, and data-driven intelligent auditing and continuous optimization control are realized in the process.

The invention discloses a risk monitoring method, device and equipment for a source load storage network, a medium and a program product, and the method comprises the steps: obtaining accident monitoring data of a sky-ground system, carrying out the preprocessing of the accident monitoring data, obtaining target risk data, constructing a risk knowledge graph of the source load storage network based on the target risk data, and obtaining a risk knowledge graph of the source load storage network; obtaining a fault factor of the source load storage network, analyzing association information between the fault factor and the risk knowledge graph, and constructing a defect spreading model based on the association information and the environment feature information and seasonal feature information of each risk entity, real-time monitoring data, collected by the sky-ground system, of the source load storage network are input into the defect spreading model for risk analysis, risk monitoring is carried out on the source load storage network based on a risk analysis result, space-time analysis is carried out on multi-dimensional data of the sky-ground system, the relationship between faults is fully mined in combination with a knowledge graph, and the risk of the source load storage network is monitored. The accuracy of risk disaster prediction is effectively improved, and stable operation of a power system is ensured.

The invention discloses a characteristic value calculation method based on electrocardio data, belongs to the field of medical data analysis, and provides an electrocardio data analysis and processing method. Comprising the following steps: decomposing different frequency components of an electrocardiosignal by using wavelet transform, and analyzing on different time scales. Wavelet transformation is relatively suitable for detecting QRS wave groups, and due to the fact that the wavelet transformation can highlight transient characteristics of signals on different frequency bands, complex electrocardiosignal processing is more efficient. In addition, wavelet transform is not only suitable for wave crest detection, but also can help to remove signal noise and improve signal definition. The deep learning technology is used for automatically extracting the features from the electrocardiosignals, a feature extractor does not need to be manually designed, related medical professional knowledge and the artificial intelligence technology are combined, wave crests in the electrocardiosignals can be more accurately detected, and meanwhile anomaly detection can be carried out, such as early signs of heart diseases such as sinus arrhythmia and the like.

The invention relates to the technical field of flood prediction, in particular to a non-consistent regional flood frequency analysis method based on the Bayesian theory. The method comprises the following steps: acquiring flood sequence data, meteorological data and drainage basin characteristic data of hydrological stations in a drainage basin; performing regional flood dynamic change analysis on the flood sequence data and the meteorological data to generate regional flood time sequence dynamic change data; on the basis of the regional flood time sequence dynamic change data, hydrological station flood characteristic parameters in the drainage basin are extracted from the drainage basin characteristic data, and hydrological station flood characteristic parameters in the region are obtained; and performing flood distribution parameter regression coefficient prior information derivation on the flood characteristic parameters of the hydrological stations in the region to obtain a maximum likelihood estimation value of a single hydrological station distribution parameter. According to the method, the Bayesian theory, the spatial correlation analysis and the spectral analysis are introduced, so that the accuracy and the applicability of flood frequency analysis based on the Bayesian theory are improved.

The invention relates to an intelligent office process processing method and system based on a large language model, and belongs to the field of artificial intelligence. Comprising the steps of obtaining enterprise documents; sequentially carrying out document analysis, document cutting and key information extraction on the enterprise document; constructing an enterprise knowledge base based on an ES library according to the extracted content; receiving a user question about the office process; a semantic text block and a keyword text block about the user question are recalled in the enterprise knowledge base, and the semantic text block and the keyword text block are spliced by using a large language model to obtain a user question answer of the user question; taking the user question and the answer of the user question as question-answer pairs, and performing office process work order automatic filling of the question-answer pairs by utilizing a large language model according to a preset work order filling rule; and outputting the filled office process work order. According to the invention, comprehensive intellectualization and automation of the office process are realized, the office efficiency is improved, the error rate is reduced, and the user experience is improved.

The invention discloses an automatic test case generation and execution method, which comprises the following steps: obtaining input content uploaded by a user, and analyzing the input content uploaded by the user to obtain structured key data; generating a preliminary test case according to the structured key data and a general rule of a knowledge base, optimizing the preliminary test case according to historical data and a code coverage rate, and generating a test case; compiling a test script according to the test case, and executing the test script; analyzing and processing the test execution result, and generating an execution structure and a test case analysis report; and performing optimization processing on the test execution result and the historical data, and dynamically adjusting a test strategy according to an optimization processing result. The invention further discloses a system for realizing the automatic test case generation and execution method. The test efficiency is improved, the test coverage rate is increased, the execution performance is improved, and automatic continuous testing is supported.

The invention discloses a power generation equipment maintenance knowledge question-answering system and method based on a multi-modal large model. The system comprises an equipment question-answering robot module, a data lake module, a large data model module and the like. A data lake knowledge base is constructed; receiving user demand questions for semantic analysis and classification, and determining data types in the demand questions; according to the data type, selecting an appropriate trained RAG large model to carry out comprehensive processing and reasoning, and generating a question answer; and presenting the question answer to the user as a reply. According to the invention, data intercommunication between equipment knowledge questions and answers and maintenance questions and answers is realized through the data sharing module, so that the accuracy of fault diagnosis and the maintenance efficiency are improved; the application of the multi-modal large model and the data management module provides powerful data support for equipment maintenance; and through the real-time monitoring function of the fault alarm module, the equipment fault can be found in time, the downtime is reduced, and the production efficiency is improved.

The present invention relates to the field of intelligent control technology, specifically to a digital control method and system for the online operation of multiple punching machines, which obtains production line historical data and real-time collection of punching machine operation status data, combines product appearance images and quality inspection parameters, realizes product appearance defect detection through the YOLOv7 model, generates offset quality inspection parameters in combination with quality inspection standards; and generates process abnormality positioning results using the offset quality inspection parameters and appearance defect detection results. The process abnormality positioning results are fused with the punching machine operation status data to form multimodal comprehensive operation feature data for identifying abnormal processes. After the abnormal process is detected, the abnormal propagation path is analyzed through a dynamic working condition adaptive graph neural network model, the current working condition characteristics are extracted, and similarity matching is performed with historical data to extract the best historical reference parameters. The production process adjustment parameters are generated by dynamically correcting the historical reference parameters. The adaptive ability and flexible response ability of the production line to complex working conditions are improved.

The invention relates to the technical field of data processing, and discloses a data processing method and system for soil detection, and the system comprises an acquisition module which comprises a sensor network and a node control unit which are arranged in a detection area; a preprocessing module configured to determine whether there is a missing value; the data complementation module is configured to obtain the position of the missing value when the missing value is judged to exist, and determine an initial missing value according to the weighted average of the soil information on the two adjacent sides of the position of the missing value; the data correction module is configured to correct the initial missing value; and the storage module is configured to store the humidity value, the temperature value, the rainfall data, the plant coverage data, the terrain data and the corrected final missing value and establish a corresponding database. By improving a data processing flow and a correction mechanism, the accuracy and reliability of soil detection data are improved, and powerful technical support is provided for agricultural production and soil management.

The invention relates to the technical field related to data processing, in particular to an automatic labeling method, device and equipment based on a large model and a clustering algorithm. The method comprises the following steps: performing preliminary analysis on an original data set by using a natural language processing technology, and generating an initial label for each piece of data in the original data set; performing clustering analysis on the generated initial labels by applying a clustering algorithm, and grouping the initial labels based on similarity or relevance among the labels to obtain a plurality of initial label groups; selecting the most representative label from each initial label group to replace all labels in the initial label group so as to simplify a label system and reduce redundancy; judging the similarity between any two pieces of data in the original data set; and adjusting the label system based on the similarity.

The application discloses a lining cloth dyeing control method and system based on artificial intelligence, and is used for the control field, and the method comprises the following steps: real-time monitoring of multi-source data in the lining cloth dye vat, capturing the video stream inside the lining cloth dye vat; using a Gaussian mixture variational autoencoder model to extract features from the multi-source data, obtaining multi-source feature data; obtaining the changing moment of the fabric state in the video stream, and extracting the fabric state change feature data according to the observed fabric color change and the heterogeneity of the dye distribution in the video stream; fusion of multi-source feature data and fabric state change feature data; introducing a model predictive control layer, predicting the result to adjust the dyeing parameters in real time; establishing an intelligent feedback mechanism. The Gaussian mixture variational autoencoder model is trained and optimized through the small-batch stochastic gradient descent and back propagation algorithm, important features are learned and captured from the data.

The application discloses a signal processing method based on deep learning and adaptive filtering fusion, comprising the following steps: S1, original signal acquisition and pretreatment; S2, adaptive filtering; S3, constructing a deep neural network model to extract deep features of the filtered signal; S4, signal fusion to eliminate interference; S5, establishing a real-time closed-loop feedback mechanism for dynamic parameter adjustment; S6, end-to-end dynamic optimization process; S7, iterative update and high confidence sample judgment; S8, using the trained adaptive filtering module and deep neural network model to process newly collected signals, and generating reconstructed signals through signal fusion. The application effectively fuses adaptive filtering and deep neural network, realizes accurate suppression of multipath effect, nonlinear noise and transient interference in complex wireless signals, and high-dimensional feature extraction, thereby significantly improving the precision, robustness and real-time performance of signal processing.

The invention discloses a user behavior analysis method and device based on a graph neural network and deep clustering. The method and device are applied to scenes such as social network information transmission analysis and disease transmission prediction. According to the method, social network data are abstracted into a graph structure, node features are extracted through an L-layer auto-encoder, data are reconstructed, the node features and an adjacent matrix are fused through an L-layer graph neural network, network interference is dynamically captured, deep clustering is carried out on graph network output to obtain exposure conditions, and a loss function is defined through a double-feedback network. Global loss function optimization model parameters including a loss function, mean square error loss and a variance item are designed; and finally, the causal effect is estimated by using an inverse probability weighted Hajak estimator. According to the method, dynamic adaptive exposure condition learning is realized, the problems of poor flexibility, low expandability and limited accuracy of a traditional method are solved, and the causal effect estimation precision in a complex network is improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an artificial intelligence-based image processing system, which comprises an image acquisition module, a preprocessing module, a feature extraction module, a deep learning module, a post-processing module and an output module, and is characterized in that the image acquisition module is responsible for receiving to-be-processed image data and transmitting the to-be-processed image data to the preprocessing module; the pre-processing module is used for pre-processing the input image, including de-noising, enhancing, normalizing and the like; the feature extraction module is used for further performing feature analysis and processing on the image based on the features extracted by the deep learning module; the deep learning module is used for repairing, classifying and processing the image subjected to feature extraction by adopting a deep neural network; and the output module is used for outputting the output result of the post-processing module in a proper format for a user to use or further analyze.

The invention provides a multi-thread software and hardware fault analysis method and device applied to a lock step core, and relates to the technical field of software and hardware fault monitoring of an ECU (Electronic Control Unit), and the method comprises the following steps: respectively detecting internal hardware of a multi-core processor and a software process of each thread in a current period; if the internal hardware has the fault in the current period, storing and backing up the first software and hardware information at the first current moment according to a preset fault type, and resetting the internal hardware; if the software process has a fault in the current period, storing and backing up second software and hardware information at a second current moment according to a preset fault type, and resetting the software process; analyzing software and hardware faults of the lock step core according to the first software and hardware information and the second software and hardware information; hardware fault detection and software fault detection are unified in the same time sequence, so that the technical problem that data interference and data pollution are easily caused when fault analysis is carried out through backup data subsequently is solved.

The invention provides a physical examination information and data association analysis system based on a medical laboratory. The physical examination information and data association analysis system comprises a data acquisition unit, a data annotation and arrangement unit, an association analysis unit and a visual display unit. The invention further provides a medical laboratory-based physical examination information and data association analysis method, which comprises the following steps of: acquiring physical examination personnel information and a detection result, and storing the physical examination personnel information and the detection result as original physical examination data; labeling and sorting the original physical examination data to obtain to-be-analyzed physical examination data; establishing a correlation analysis model; calculating and analyzing correlation; and the system displays a correlation analysis result. According to the invention, personnel information and detection data are collected and stored in combination with an existing medical examination laboratory management system, and on the basis of monotonicity and discontinuity characteristics of the personnel information and detection data of physical examination people, visual correlation analysis of personnel and regional personnel information and a detection item system can be realized; and the information is converted into effective and valuable health management suggestions which are simple and easy to understand, so that diseases are prevented.

This invention discloses a method, equipment, and medium for testing the durability of camshafts, relating to the field of durability testing technology. It solves the problem of being unable to test the durability of camshafts under different practical application conditions. The method includes collecting initial parameters of the camshaft used in plug-in hybrid electric vehicles, placing the camshaft in a camshaft test bench and conducting a cyclic wear test, collecting characteristic data of the camshaft after the cyclic wear test, conducting a pressure test on the shaft body and cam tip at the cam mounting point under specific temperature conditions, and comprehensively analyzing the durability of the camshaft based on the results of the cyclic wear test and the pressure test. This invention enables durability testing of camshafts under different practical application conditions.

The invention discloses a multi-modal large model ground penetrating radar disease data intelligent interpretation method and device, and belongs to the computing intelligence and information processing technology. The method is based on a computing device of machine learning, artificial intelligence and a specific mathematical model, and an inference model of specific ground penetrating radar knowledge is fused for information processing. Comprising the following steps: acquiring underground disease data by using multi-frequency ground penetrating radar equipment, and converting the underground disease data into a serialization format adaptive to a multi-modal large model; and then sequentially executing detection, segmentation and image description tasks. Wherein a segmented label mask is generated by adopting a Bayesian optimization method based on a target detection result, and an image description data set is constructed by utilizing a segmentation result; and finally, performing intelligent interpretation on the data by using the multi-modal large model to generate an interpretation report. According to the method, through multi-task collaborative learning, full-process automatic interpretation from disease detection to semantic description is realized, the interpretation efficiency and reliability are improved, and an intelligent basis is provided for road maintenance decision making.

The utility model provides a sterilization glass slide convenient for gram staining and identification, which comprises a glass slide main body, a sample information marking area, a standard bacterium area and a sample bacterium area are sequentially arranged on the front surface of the glass slide main body, and the sterilization glass slide is characterized in that the sample information marking area, the standard bacterium area and the sample bacterium area are sequentially arranged along the length direction of the glass slide main body; the front surface of the glass slide main body is partitioned through partition straight lines coated with the front surface of the glass slide main body, the partition straight lines are arranged in the width direction of the glass slide main body, and the multiple partition straight lines are sequentially distributed at intervals in the length direction of the glass slide main body; separation round lines are respectively arranged in the standard bacterium area and the sample bacterium area; and the thicknesses d of the separation straight lines and the separation round lines are 0.5-2 mm, and the widths D of the separation straight lines and the separation round lines are 0.5-2 mm. According to the utility model, the cross contamination problem in the staining process can be effectively improved, false positive and false negative phenomena are avoided, the accuracy of a staining result is improved, and the operation convenience, the result accuracy and the test efficiency of gram staining and identification of sample bacteria are improved.

The application discloses a park energy management and control method and system based on building energy consumption monitoring, relates to the process control technical field of energy management, and executes edge-end monitoring deployment, constructs a park simulation body through park twinning, determines a target energy structure according to energy management emphasis, executes energy consumption overrun analysis and energy scheduling balanced decision-making, determines an energy management and control strategy and reversely transmits the energy management and control strategy to intelligent monitoring equipment for local management, and is used for solving the technical problems that the prior art cannot make light and flexible analysis and decision-making for diversified park energy scenes, leading to redundant processing modes and insufficient scene adaptability of energy management. The application realizes light and efficient analysis based on energy scenes and precise management of park energy management.

This invention relates to the field of photovoltaic power generation prediction technology, and discloses a photovoltaic power generation system output prediction system and method. The method collects historical power generation data of the target photovoltaic power station, as well as environmental monitoring data including irradiance, ambient temperature, module temperature, and cloud cover. This data is time-aligned to form a synchronized data sequence, from which meteorological and power generation characteristics are extracted. A dynamic correlation model between meteorological and power generation characteristics is constructed, and the instantaneous impact weight of meteorological fluctuations on power generation is calculated. Based on this, sensitive and non-sensitive periods for power generation are defined. High-fluctuation meteorological characteristics are extracted during sensitive periods, and steady-state meteorological characteristics are extracted during non-sensitive periods. The high-fluctuation meteorological characteristics are input into a first prediction network to generate short-term power prediction values, and the steady-state meteorological characteristics are input into a second prediction network to generate long-term power prediction values. The two are then fused to generate a complete output power prediction curve.

This disclosure relates to distributed hardware tracing. A computer-implemented method executed by one or more processors includes: monitoring the execution of program code executed by a first processor component; and monitoring the execution of program code executed by a second processor component. The computing system stores data identifying hardware events in a memory buffer. The stored events occur across processor units including at least first and second processor components. Each hardware event includes an event timestamp and metadata characterizing the event. The system generates a data structure identifying the hardware events. The data structure arranges the events in a time-ordered sequence and associates the events with at least the first or second processor component. The system stores the data structure in a repository on a host device and uses the data structure to analyze the performance of program code executed by the first or second processor component.

The invention relates to the field of biotechnology and dead / living cell detection, in particular to a method for rapidly determining the activity of biological membrane cells in immobilized fermentation. According to the method, a methylene blue and trisodium citrate synergistic chromosome system is optimized, a biofilm EPS matrix is synergistically penetrated by virtue of the dispersion effect of trisodium citrate and the reduction characteristic of methylene blue, and a spectrophotometric method is combined to detect the OD value of a staining solution instead of stained cells, so that the efficient quantitative analysis of the ratio of dead cells to living cells of the immobilized biofilm is realized. The method can complete detection within 200 s, is simple and convenient to operate and low in cost, does not need complex equipment, and is remarkably superior to a traditional fluorescent staining method.

This invention provides a method, apparatus, device, and medium for joint monitoring and analysis of sleep posture and respiratory status, relating to the field of artificial intelligence technology. The method includes: acquiring multi-channel signals; the multi-channel signals are mixed vibration signals of the chest and abdomen during sleep; inputting the multi-channel signals into a pre-trained joint monitoring and analysis model to obtain the joint monitoring and analysis results output by the model; wherein, the joint monitoring and analysis results are the correlation analysis results between sleep posture and OSA (Exhaustive Sleep Acuity). Through the above method, synchronous joint monitoring of sleep posture and OSA is achieved, effectively analyzing the correlation between the two.

The invention discloses a building sudden opening method based on mapping, and the method comprises the following steps: S1, an opening model and a closed model of a building are created respectively, and in the opening model and the closed model, an opening is formed in the windward side of the opening model, and other sizes are the same; s2, performing numerical simulation on the closed model under a set flow field condition until the flow field is stable, and storing flow field data; s3, setting the same flow field condition for the opening model, and mapping the flow field data stored in the step S2 to numerical simulation of the opening model as an initial condition; and S4, carrying out numerical simulation on the opening model to realize a dynamic simulation process of sudden opening of the building. The invention further discloses an internal and external pressure change numerical simulation method under the sudden opening of the building. According to the mapping-based numerical simulation method for the sudden opening of the building and the internal and external pressure change under the sudden opening, dynamic simulation of the sudden opening of the building can be realized, and numerical simulation of the internal and external pressure change under the sudden opening of the building can be realized.