165results about How to "Guarantee data quality" patented technology

The invention relates to a health monitoring and fault prediction technology of intelligent equipment, in particular to an equipment fault prediction method based on health degree analysis of the intelligent equipment. According to the method, multiple sensors are embedded in the intelligent device, data of the operation state of the device are continuously collected, and primary processing and abnormal value elimination are carried out through an edge computing node. The processed data is transmitted to a cloud server, and machine learning models such as a deep neural network, a Bayesian classifier and a support vector machine are utilized to analyze the equipment health degree trend, and a potential fault mode is identified. And intelligently generating a fault prediction report according to an analysis result, and notifying an operation and maintenance team to take measures through an operation and maintenance management system. In addition, the system further comprises a historical fault database, a scoring system, an online review group and other functional modules, the accuracy and reliability of prediction are ensured, and the equipment maintenance efficiency and safety are improved.

The embodiment of the invention discloses a bridge bolt monitoring image recognition method and system based on deep learning, and belongs to the technical field of bridge image processing. The method comprises the following steps: acquiring a multi-angle monitoring image set of a bridge bolt part through an image acquisition device, and preprocessing the monitoring image set to generate a standardized target image sequence; positioning a bolt monitoring area for each image frame in the target image sequence based on a pre-trained positioning model, and generating an area coordinate set containing bolt position information; inputting the region coordinate set into a state recognition model, extracting structural state features of each bolt monitoring region, and generating a looseness degree prediction result of the bolt part according to the structural state features; according to a matching result of the looseness degree prediction result and a preset safety threshold range, a bolt state abnormity alarm signal is output; and associating the bolt state abnormity alarm signal to the corresponding monitoring image frame, and generating a monitoring report containing a visual mark.

The invention relates to the technical field of machine learning, in particular to a heating fault prediction method and system for a high-voltage circuit breaker and an isolating switch of the high-voltage circuit breaker. The method comprises the following steps: carrying out multi-source data acquisition on a high-voltage isolation switch and an associated high-voltage circuit breaker through an SCADA system, and carrying out data preprocessing to obtain a preprocessed data set; performing operation feature correlation analysis on the preprocessed data set to obtain a correlation operation feature set; performing dynamic association network construction on the association operation feature set to obtain a dynamic association network graph; acquiring historical fault data; performing propagation risk path search according to the historical fault data and the dynamic association network diagram to obtain a candidate risk path set; and performing path propagation probability calculation on the candidate risk path set to obtain a path probability data set. According to the method, the influence of the associated high-voltage circuit breaker on the heating risk of the isolating switch is effectively identified, so that the accuracy of fault prediction and the timeliness of early warning are improved, and a more reliable guarantee is provided for safe and stable operation of a power system.

The invention discloses a building construction safety risk assessment method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing a neural network model, and recognizing an unsafe behavior mode of a worker; receiving a data stream from a construction site in real time, dynamically adjusting risk assessment parameters according to a current construction state and environment change, and predicting potential safety risks; according to the prediction result, generating corresponding prevention measure suggestions; key indexes and abnormal conditions are displayed through a monitoring interface, and a detailed analysis report is generated; according to the invention, through dynamic assessment and timely response to the safety risk of the construction site, the risk assessment parameters can be rapidly adjusted according to the environmental change, the potential risk can be predicted, and the prevention measures and suggestions can be provided, so that the occurrence probability of safety accidents is effectively reduced, and the construction efficiency is improved.

The invention relates to the technical field of environment monitoring and early warning, in particular to a road and bridge pavement environment monitoring and early warning method and system. The method comprises the following steps: arranging acquisition equipment for acquiring pavement environment data, wherein the pavement environment data comprises pavement ice and snow state data; the collected road surface environment data are preprocessed; training an environment monitoring and early warning model based on the road historical environment data to obtain a trained environment monitoring and early warning model, and inputting the preprocessed environment data into the environment monitoring and early warning model for monitoring and early warning; comparing a monitoring and early warning analysis result with a preset threshold value, and determining whether early warning needs to be sent out according to a comparison result; meanwhile, when ice and accumulated snow are detected to appear on the road surface, the position and the size of the ice and the accumulated snow are marked in visual equipment. The system comprises a data acquisition module, a model training module and an early warning judgment module. According to the invention, a comprehensive and efficient road and bridge pavement environment monitoring and early warning system is formed.

The invention relates to the technical field of brain function imaging, in particular to a target crowd recognition method and device based on neural blood flow feature multi-level fusion, and the method comprises the steps: collecting brain nerve blood flow signals of normal and target crowds by using synchronous EEG-fNIRS equipment, preprocessing and extracting single-mode features, and obtaining a single-mode feature set; a multi-frequency local nerve and blood vessel coupling analysis method is used for calculating nerve and blood vessel coupling strength characteristics, a space co-positioning method is used for unifying nerve and blood flow modal space information, the position precision is improved, and data layer fusion is achieved; and integrating and analyzing different modal feature space relationships by using a Yeo7 brain template to realize feature layer fusion, and finally training various machine learning classifiers by using significant difference features to realize identification of a target population. Therefore, the problems that in the prior art, macroscopic signal correlation is paid more attention to, so that analysis of the synergistic effect of cross-modal space information in a specific brain region is deficient, and deep understanding of a neurovascular function mechanism and brain region role revelation are restricted are solved.

The embodiment of the invention discloses a salinity time sequence prediction method and device, and the method comprises the steps: obtaining a historical observation data set, carrying out the preprocessing of the historical observation data set, and carrying out the normalization processing of the preprocessed historical observation data set; constructing a prediction model architecture, and inputting the historical observation data set into the prediction model architecture for training and testing to generate a prediction model; and collecting real-time salinity observation data, and inputting the real-time salinity observation data into the prediction model to output a salinity prediction result. A large amount of historical observation data is collected to construct and test the model, salinity observation data is collected in real time and input into the model to output a prediction result in real time, the problems that data sets are insufficient and hydrological elements are lacked in time sequence prediction in industrial application are solved, the prediction model is higher in precision, and the prediction efficiency is improved. Due to the data quality control process of preprocessing the historical observation data, the data quality is ensured, the problem of low data quality is solved, and reliable salinity time sequence prediction is provided.

The invention relates to the technical field of geological disaster prediction, in particular to a field geological exploration monitoring system based on remote sensing data, which comprises a data acquisition module, a data preprocessing module, a multi-source data fusion module, a deep learning model module and an early warning module. The data acquisition module, the data preprocessing module, the multi-source data fusion module, the deep learning model module and the early warning module are connected in sequence. Through sequential connection of the modules, the system realizes closed loop of data from acquisition, analysis and early warning, and ensures integrity, real-time performance and accuracy of the data. The data preprocessing module eliminates the interference of source data while ensuring the data quality, and the fusion module improves the synergistic effect of multi-source data through an intelligent algorithm. The high-precision prediction of the deep learning model module ensures the timeliness and effectiveness of disaster early warning. The early warning module responds in time to help related departments to do disaster prevention and reduction work, and risks caused by geological disasters are effectively reduced.

The invention discloses a short-term photovoltaic power generation power prediction method adaptive to weather change, which comprises the following steps of: firstly, acquiring meteorological data around a photovoltaic panel and power data of a photovoltaic module, preprocessing the data, and dividing a training set and a test set according to a proportion; according to the method, a Bi LSTM-Attention model based on ISSA optimization is constructed, photovoltaic power generation power prediction is realized by capturing time sequence characteristics and historical information of input data, meanwhile, a mean square error between a predicted value and a true value is calculated to continuously fit the predicted value, prediction model parameters can be updated, model self-learning is realized, and the prediction efficiency is improved. And the accuracy of the short-term photovoltaic power prediction value of the prediction model is further improved. According to the method, the short-term prediction precision of the photovoltaic power generation power under different weather conditions is improved, so that a photovoltaic grid-connected system is helped to respond to the control parameter design of the power grid stability requirement, and the support capability of photovoltaic power generation on the power grid is improved.

The invention discloses a battery health state assessment method based on real-time electric quantity detection, which relates to the technical field of battery management and comprises the following steps: acquiring state data of a battery in a charging and discharging process, and forming a time sequence data set; calculating a battery charge state value according to the time sequence data set, and comparing the battery charge state value with a preset health state threshold value to generate an initial health assessment result; performing segmentation processing on the time sequence data set, and extracting a voltage change rate and a current integral value in each time window to obtain a characteristic parameter set; inputting the characteristic parameter set into a battery capacity prediction model, and outputting a battery residual capacity value; and according to the initial health assessment result and the battery residual capacity value, combining a temperature compensation coefficient to generate a final battery health state assessment report. According to the invention, through a comprehensive data acquisition system and multiple algorithms, a comprehensive health state assessment report is generated in combination with the temperature compensation coefficient, and accurate assessment and early warning of the battery health state are realized.

The invention relates to the technical field of mobile communication, in particular to an automatic optimization method for a mobile base station network, which comprises the steps of collecting data of network equipment, user equipment and other related sources, cleaning a collected data set to ensure data quality, extracting related characteristics in the cleaned data, selecting a deep learning model for training, and optimizing the mobile base station network. A training model trained in advance is utilized to analyze potential problems of a base station network to obtain an optimization decision, network parameters are automatically adjusted according to the optimization decision based on an automatic script, the adjusted network parameters are monitored in real time, comparison before and after optimization of the base station network is carried out, and the network optimization effect is ensured. Therefore, the problem that according to an existing mobile base station network optimization method, due to the fact that an automatic planning tool and manual data analysis are needed in the optimization process, the process is tedious, time consumption is long, and the optimization efficiency is low is solved.

The invention discloses a sensor dynamic sampling method based on environmental fluctuation perception and CARAFE optimization, which comprises the following steps: S1, an environmental perception module calculates a standard deviation sigma of window data through original data collected by a sensor node and judges whether the standard deviation sigma exceeds a dynamic threshold value theta, so that the fluctuation condition of environmental data is monitored; s2, the dynamic sampling controller switches the sampling frequency according to different states of the fluctuation condition of the environment, and the energy consumption of the system is reduced while the data quality is ensured through down-sampling processing of content awareness and an intelligent batch transmission strategy. According to the sensor dynamic sampling method based on environmental fluctuation perception and CARAFE optimization disclosed by the invention, the sampling frequency can be adaptively adjusted, and feature optimization is carried out on the sampled data through a content-aware CARAFE operator, so that the precision and energy efficiency of data acquisition are improved.

The invention discloses a diagnostic drug dose key information extraction system based on a multi-agent architecture, which comprises a diagnostic drug dose key information extraction system and a diagnostic drug dose key information extraction system, the diagnostic drug dose key information extraction system comprises a data acquisition and preprocessing module, a natural language processing and information extraction module, a medical knowledge graph construction module, a diagnosis and drug dose recommendation module, a user interaction and feedback module and a safety management and privacy protection module. According to the system, key information about diagnosis and drug dosage can be automatically extracted, analyzed and processed from complex medical data, the system provides accurate and personalized diagnosis and drug dosage suggestions for patients through cooperative work of a plurality of intelligent modules, and meanwhile, the safety and privacy of the data are ensured.

The invention provides an elevator risk early warning method based on local patch and enhanced position coding, which comprises the following steps of: firstly, collecting original data of an elevator of the internet of things, and converting the original data into multivariable time sequence characteristics after cleaning the original data; secondly, performing patch operation on the multivariate time sequence, namely, firstly dividing each input univariate time sequence into patches to serve as input tokens of Transformer, so that local semantic information is reserved, and a longer review window can be obtained under the condition that the number of the input tokens is the same; after relative position coding is carried out on the token, the token is input into a Transform model, and in the model, a self-attention mechanism and a convolution pooling layer are adopted to carry out extraction and intersection on patches, so that the feature relation between the patches is concerned, and meanwhile, the time feature relation in the patches is concerned; performing fusion operation on the global time sequence characteristics and the in-patch time sequence characteristics, and then handing over to a full connection layer for prediction to output a prediction result; during model training, the model is adjusted through an optimization algorithm to improve the performance.

The invention discloses a multi-beam backscattering intensity jump correction method, which takes a multi-beam measurement strip as a unit, automatically detects backscattering intensity jump generated by factors such as equipment parameter setting through a Gaussian weighting function, and selects a moving window to perform difference compensation by using a weighted average value. And carrying out iterative detection on the repaired intensity data and repairing the jump point until the jump point converges to 0. An example calculation result shows that the method can effectively detect and repair the intensity jump in the multi-beam strip, improve the processing efficiency and quality of the backscattering intensity data of the multi-beam strip, and improve the analysis accuracy of the submarine landform of the multi-beam intensity data.

The invention discloses a power plant data management method and system based on an SIS system, and relates to the technical field of data management, and the method comprises the steps: obtaining power plant operation data, and carrying out the unified formatting processing of the power plant operation data; preprocessing the formatted data, and removing noise and invalid data; performing quality verification on the preprocessed data, and checking data integrity and consistency; classifying, marking and storing the data subjected to quality verification into a database; and extracting and analyzing data from the database according to operation requirements of the power plant, and generating a decision report. According to the power plant data management method based on the SIS system, through unified formatting and time alignment processing, data format difference and time disorder between different devices are eliminated, and consistency and accuracy of data are ensured. And noise elimination, interpolation processing and anomaly detection are adopted, so that the data quality is guaranteed, and the reliability of an analysis result is improved. And aiming at data with different access frequencies.

The invention discloses a data management system based on a monitoring video stream, belongs to the field of data analysis, and aims to solve the problems of camera connection acquisition, image quality optimization, pedestrian related information analysis and data storage retrieval and backup in video monitoring. The system comprises a data acquisition module, a target detection module and a data storage module. The data acquisition module is used for identifying and connecting a video monitoring camera, performing video stream data acquisition, adjusting an acquisition frame rate by estimating a data size, and evaluating and optimizing image quality to obtain a detection image data set; the target detection module is used for carrying out feature extraction, classification and marking on pedestrians in the detection image data set, recording pedestrian trajectory information, carrying out real-time tracking and updating, identifying a behavior mode according to the trajectory information, dividing a gathering area and obtaining the pedestrian flow; and the data storage module is used for classifying and labeling the data, constructing an index structure to guarantee efficient retrieval, dividing data grades according to multi-dimensional features, and dynamically adjusting a backup time interval.

The invention discloses an electric power monitoring system based on artificial intelligence, relates to the technical field of electric power monitoring, and solves the technical problem of low fault detection efficiency of an existing electric power system. Comprising the following steps: collecting resident electricity consumption and real-time data of power equipment through a data collection module, and preprocessing to obtain power data; the monitoring prediction module judges whether the power equipment is abnormal or not according to the power data, if yes, an early warning signal is generated, a reason is analyzed, abnormal data is marked as a sequence, the sequence is input into the power monitoring model to obtain an abnormal label, and then the abnormal reason is matched; predicting future electricity consumption according to the historical electricity consumption; and the early warning feedback module sends an early warning signal and an abnormal reason to a manager so as to dispatch a technician to check and maintain, and the user interaction interface displays related information and provides functions of checking historical records and the like, so that accurate fault detection and intelligent prediction of power consumption can be realized, and the operation efficiency and safety of a power system are improved.

The invention discloses a fabricated curtain wall construction method, system and equipment based on a BIM technology and a storage medium. The method comprises the following steps: performing virtual paying-off on a building main body structure to obtain paying-off data; acquiring curtain wall information of the fabricated curtain wall; performing hoisting path simulation on the fabricated curtain wall according to the curtain wall information and the pay-off data, and adjusting the simulated hoisting path in a BIM model including the building main body structure to obtain an adjusted BIM model; the assembled curtain wall is temporarily fixed through the adjusted BIM model; performing error detection on the temporarily fixed fabricated curtain wall to obtain an error report, performing position adjustment on the temporarily fixed fabricated curtain wall according to the error report, and stopping adjustment when an error value is within a preset range to complete installation. By implementing the technical scheme provided by the invention, the construction precision of the fabricated curtain wall is improved.

The invention provides an insulating part defect identification method based on a synthetic aperture ultrasonic imaging technology, which belongs to the technical field of ultrasonic defect identification, and comprises the following steps: step 01, setting parameters of an ultrasonic transducer to enable the ultrasonic transducer to emit ultrasonic waves, and attaching the ultrasonic transducer to the surface of a detected insulating part; 02, connecting the ultrasonic sensor with an acquisition card, and acquiring real-time echo data by using acquisition software; step 03, importing the data into data processing software for data processing, and performing defect imaging by applying an imaging algorithm; and step 04, performing defect analysis and positioning on the formed ultrasonic image. According to the method, the accuracy, reliability and practicability of defect detection of the insulating part are remarkably improved, and more effective technical support is provided for quality control in the industrial field. Due to the automatic and intelligent characteristics of the system, the detection efficiency is greatly improved, the uncertainty caused by human factors is reduced, and the system has remarkable practical value and popularization significance.

The invention provides a rural infrastructure three-dimensional modeling method and system based on laser point cloud, and the method comprises the steps: collecting point cloud data at a rural infrastructure site through employing a standing laser scanner, and obtaining the space information of the infrastructure; preprocessing the collected point cloud data; the point cloud data is automatically segmented and processed by using a track extraction technology; performing circle fitting and automatic fine denoising on the segmented point cloud data; correcting the scanning intensity value of the point cloud based on a distance-intensity model; extracting and smoothing the central axis of the rural infrastructure, and providing a basis for the construction of a three-dimensional model; a lining plane image of the rural infrastructure is formed through a three-dimensional point cloud technology, and a basis is provided for texture mapping; rapid browsing and storage of the three-dimensional texture model of the rural infrastructure are realized, and subsequent use and analysis are facilitated. According to the invention, the acquisition and preprocessing process of the point cloud data can be optimized, the data quality is ensured, and the modeling efficiency and precision are improved.

The invention provides a temperature rise test method of a low-voltage switchgear assembly and related equipment, and relates to the technical field of throttling devices. By receiving equipment scale data and outputting related data, a basis is provided for subsequent processing. And then, a series of sub-steps are executed circularly, so that the intelligentization of temperature monitoring is realized. When the temperature exceeds the threshold value, the system can improve the data acquisition frequency, and it is ensured that the temperature change at the critical moment is monitored more densely. Meanwhile, by analyzing the temperature rise trend and identifying the potential fault area, the system can find abnormal conditions in time. In addition, by comparing the current temperature rise trend with the historical temperature rise trend, the system can judge whether cooling measures need to be executed or not. And finally, through temperature stability analysis, the system can adjust a data acquisition strategy according to actual conditions. The dynamic adjustment method can effectively cope with the temperature change characteristics of different scales of equipment, and the detection efficiency and accuracy of temperature abnormity are improved.

The invention discloses a foreign matter type identification method and system for power channel safety monitoring. The method comprises the following steps: acquiring a to-be-identified image for power channel safety monitoring; the image to be recognized is preprocessed; inputting the preprocessed to-be-recognized image into a pre-trained foreign matter type recognition model for recognition, and determining a recognition result; when it is determined that the foreign matter type with the hidden danger exists in the recognition result, the risk generated by the foreign matter is evaluated, and a target evaluation value is obtained; when it is determined that the assessment value is greater than or equal to a preset risk assessment threshold value, sending out an early warning prompt; the foreign matter type in the power channel can be timely and accurately identified, risk assessment is carried out on the foreign matter type with hidden danger, an early warning prompt is given out when the risk assessment value reaches or exceeds the threshold value, related personnel can be timely notified to carry out processing, and occurrence and expansion of potential danger are avoided.

The invention provides an optical network multi-modal data fusion and alarm identification method and device, and relates to the technical field of data processing, and the method comprises the steps: collecting multi-modal alarm data from an optical network, and inputting the multi-modal data into a multi-modal Transform model; the multi-modal Transform model fuses the data of different modals by using position embedding, cross-modal attention and self-attention mechanisms to obtain fused multi-modal data; and inputting the fused multi-modal data into a DNN model, and outputting an alarm identification result of the optical network.

The embodiment of the invention discloses a vehicle information processing method and device and a medium, and the method comprises the steps: responding to a received configuration file issued by a cloud, generating a scene triggering model based on the configuration file, and determining a target signal related to the scene triggering model; processing real-time bus data of the vehicle according to the scene triggering model and the target signal to obtain a processing result; and uploading the processing result to a cloud. The purpose that the vehicle end can efficiently and flexibly change the data acquisition strategy according to the requirements of the cloud end is achieved, and therefore the requirements of vehicle full-life-cycle data application such as vehicle development testing, mass production operation, user experience improvement and after-sales troubleshooting can be met.

The invention discloses an enterprise stock equity atlas construction method based on multi-dimensional data verification and a storage medium, and relates to the technical field of knowledge atlas.The enterprise stock equity atlas construction method comprises the steps that a text is extracted from obtained document data, layout analysis and table structure reconstruction are conducted on the extracted text, and the text obtained after format conversion is input into a large language model; extracting the enterprise entity information and stock equity relationship in the text; constructing a preliminary equity atlas; detecting a loop in the preliminary equity atlas, performing benefit community identification on the equity atlas after contradiction resolution, and outputting a healthy equity atlas; enterprise entity information is extracted in real time, credibility verification is carried out, and the information is stored in a database. A set of complete automation technology chain is formed from data acquisition and OCR table reconstruction to LLM extraction, map construction, contradiction resolution, benefit community recognition and health map comparison, and the manual operation cost is greatly reduced.

The invention discloses a multi-source heterogeneous data processing method and system based on a domestic intelligent chip. The method comprises the steps of data collection, data preprocessing, intelligent analysis, data visualization and data storage and management. Compared with the prior art, the method has the advantages that the data collection capability is efficient; comprehensive data preprocessing is carried out; the intelligent analysis capability is strong; visual data visualization is realized; and safe data storage and management are realized.

The invention discloses a space surveying and mapping calibration method and system based on synchronous positioning drawing, and relates to the technical field of space surveying and mapping, the space surveying and mapping calibration method based on synchronous positioning drawing comprises the following steps: obtaining environment characteristic data; identifying a space environment and a key landmark; based on a stereoscopic vision technology, obtaining a space environment map according to the identified space environment and the key landmarks; adjusting the motion track of the detection equipment in the space environment by using an optimization algorithm; and calibrating the space environment map. The dynamic change in the space environment is monitored in real time, and the influence of the change on the map is evaluated, so that the environment change can be automatically judged and dealt with, the map is ensured to be always kept in the latest state, the space environment map can be automatically calibrated and updated, the need of manual intervention is reduced, and the working efficiency is improved. And the efficiency and timeliness of map maintenance are improved.

The invention discloses an unmanned aerial vehicle multispectral image real-time radiation correction method and equipment, and relates to the field of unmanned aerial vehicle multispectral image preprocessing. The method comprises the following steps: calculating a ratio of a brightness average value of a standard reflection reference plate area on each wave band to a brightness value of a wavelength corresponding to each wave band in a solar radiation spectrum at the same time; principal component analysis is carried out on all the ratios, principal components serve as independent variables, the actual reflectivity of the standard reflection reference plate serves as dependent variables, a real-time radiation correction model of multiple regression is constructed, and the optimal principal component number is determined; and carrying a multispectral camera and a spectrograph by using an unmanned aerial vehicle, inputting the ratio of the number of optimal principal components into a real-time radiation correction model of multiple regression according to a real-time multispectral image of the field vegetation and a solar radiation spectrum, and outputting the reflectivity of the field vegetation after real-time radiation correction. According to the invention, real-time radiation correction can be carried out on the multi-spectral image of the unmanned aerial vehicle, and the data consistency of the multi-spectral image is improved.

The present invention relates to the technical field of electronic digital data processing, and specifically discloses a hierarchical federated learning method of adaptive compression coding, the method comprising: hierarchical federated learning architecture construction, edge server matching, model generation performance judgment, compression rate matching and hierarchical federated learning architecture judgment, firstly, a hierarchical federated learning architecture is constructed so that each layer can make full use of its computing resources, an edge server corresponding to a terminal device is obtained by matching, each terminal device is used to generate an environmental data training model, a corresponding compression rate is obtained according to the network transmission performance of the edge server, the edge server receives the environmental data training model of each terminal device and performs adaptive compression coding and edge aggregation in sequence according to the corresponding compression rate, a cloud server receives the edge aggregation result and performs global aggregation, and finally judges whether the cloud server to which the hierarchical federated learning architecture belongs uploads the corresponding result of the global aggregation to an environmental resource scheduling cloud platform.