42 results about "Integrative data analysis" patented technology

The present invention relates to the technical field of power system equipment and control. The method of the present invention comprises: installing sensors to acquire data for online real-time monitoring, and constructing a comprehensive condition online monitoring model for comprehensive data analysis; comprehensively evaluating the health condition of a generator on the basis of a comprehensive analysis result, and identifying fault causes; and constructing a generator comprehensive data intelligent monitoring and dynamic early warning model to perform insulation degradation trend analysis and prediction on the generator. In the present invention, by comprehensively monitoring the condition of a turbine generator, various key indicators of the generator are captured in real time, and trends and patterns underlying the data are revealed, providing technical support for accurately evaluating the health condition of the generator; and continuous monitoring for the insulation condition of the generator allows for proactive identification of potential risks, thereby providing decision-making support for preventive maintenance, helping operators take measures promptly, preventing faults, improving the reliability and safety of the generator.

The invention discloses a juicy peach yield prediction method based on comprehensive data analysis, and particularly relates to the technical field of agricultural intelligent perception. The method comprises the following steps: collecting multi-source data of an orchard, and constructing a data set containing meteorological, physiological, remote sensing and soil information; fruit tree physiological response parameters are extracted, and a bimodal diagram structure fusing the spatial adjacency relation and the physiological state similarity is established in combination with the dynamic climate anomaly index; inputting the graph structure into a graph neural network model, extracting spatial-temporal characteristics, dynamically adjusting an edge weight and a propagation layer number, and constructing an adaptive model; performing region division and weighted summarization according to a model output result, and finally obtaining a predicted value of the total yield of the orchard; the method improves the prediction accuracy under the conditions of complex climate and unstable data, and is suitable for refined orchard management.

The invention relates to the technical field of drainage management, in particular to a drainage management system based on data analysis, which comprises a pipe network pressure monitoring module, a rainfall trend analysis module, a drainage facility management module, an underground water dynamic monitoring module and a maintenance priority scheduling module. According to the invention, the operation state of the drainage system is timely predicted and adjusted through comprehensive data analysis, the monitoring capability of pressure, rainfall, pump station state and underground water level is improved, the accurate real-time data processing capability allows the system to rapidly identify potential risk areas, the pump station flow and underground water management are optimized, and the system reliability is improved. The continuous tracking and real-time adjustment mechanism not only reduces the potential of system overload or equipment failure, but also reduces manual inspection errors and operation cost through scientific maintenance scheduling optimization, and meanwhile, can perform early warning at the early risk stage to ensure the stable operation of the drainage system, so that the safety of the drainage system is ensured. Therefore, the efficiency and reliability of urban drainage management are improved.

The invention discloses a cable trench fireproof cover plate performance detection device and method based on multi-band electromagnetic waves. The device comprises a microwave detection module, a terahertz imaging module, a high-precision two-dimensional mechanical scanning platform, a synchronous control and data fusion unit and a comprehensive data analysis platform. The microwave module is used for transmitting and receiving 1-10GHz electromagnetic waves to realize deep structure detection; the terahertz module works at a frequency band of 0.1-3 THz and is used for acquiring near-surface time-domain spectral information. And the scanning platform drives the modules to perform two-dimensional rasterization scanning on the surface of the cover plate. And the synchronous control and data fusion unit coordinates the time sequence action of each module to ensure the time-space consistency of data acquisition. And the comprehensive data analysis platform performs fusion processing on the multi-source data through an inversion and imaging algorithm to generate a comprehensive evaluation report covering internal macroscopic defects, near-surface microdefects and coating thickness. According to the invention, through collaborative integration of microwave and terahertz technologies, multi-scale and integrated nondestructive testing and performance evaluation of the fireproof cover plate from a deep layer to a surface layer are realized.

The invention relates to the technical field of state monitoring, in particular to an interface state monitoring system based on multi-sensor fusion, and the system comprises a multi-sensor acquisition module and sensors which form a monitoring node for acquiring interface terminal signals, and performing channel number matching and interference term filtering on vibration amplitude, temperature value and voltage measurement value to obtain multi-sensor interface parameters. According to the power terminal interface monitoring system, the efficiency and the accuracy of the power terminal interface monitoring system are improved by integrating various sensor technologies and comprehensive data analysis. Potential faults can be quickly recognized in the initial stage of problems through anomaly detection and threshold comparison, early warning time is advanced, and therefore maintenance work is more active instead of passive handling. And load balance and resource scheduling of the monitoring nodes are optimized, and through decision support driven by real-time data, the response speed and the processing capability are improved, the fault processing time and the overall operation and maintenance cost are reduced, and the practicability is relatively high.

The invention discloses a search method based on multi-source information fusion, and relates to the technical field of multi-source information search, and the method comprises the steps: S1, obtaining a user operation instruction based on a redefined shortcut operation, so as to recognize a user demand; s2, acquiring search content of a user, and determining a search way for performing multi-source information search in combination with a natural language processing technology and a large language model; s3, searching based on the determined search way to obtain a corresponding search result; and S4, obtaining user preference information, integrating and analyzing the search result in combination with the user preference information to obtain an integration result, and returning the integration result to the user for viewing according to a set return rule. According to the method, the problem of low information search efficiency of the user in the mobile internet era is solved by providing a quick search way and automatically fusing and returning the multi-source data, meanwhile, a more accurate comprehensive data analysis result can be provided, and the search efficiency and the accuracy of data analysis are greatly improved.

The invention relates to a method for detecting microcracks on the inner wall of a wall enclosure superheater of a boiler. The method comprises the following steps: macroscopically identifying and preliminarily screening a high-risk area; creating an endoscope channel based on the primary suspicious area list and performing primary peeping on the inner wall; carrying out detection surface preparation and acoustic coupling optimization; data acquisition is carried out through accurate arrangement of the small-sound-velocity self-coupling phased array ultrasonic probe; extracting defect signal features and carrying out three-dimensional digital modeling; performing crack depth accurate quantization based on a time of flight diffraction mode; carrying out comprehensive data analysis and evaluating a security level; and integrating detection results, and generating a digital file based on the results. According to the invention, standardization of the detection process and systematization of data management can be realized, the detection efficiency and accuracy are greatly improved, a scientific basis is provided for equipment life prediction and maintenance decision, and the safe operation level of the boiler is effectively improved.

The invention belongs to the technical field of underground ore body monitoring, and particularly relates to an underground mine cross-border mining dynamic monitoring method and system. The method comprises the following steps: (1) constructing a mine digital twinborn base map; (2) arranging a seismic source monitoring network on the basis of the mine digital twinborn base map, and carrying out micro-seismic monitoring and three-dimensional micro-motion exploration; and (3) a multi-dimensional intelligent dynamic monitoring and early warning platform is built through the mine digital twinborn base map and the data acquired by the seismic source monitoring network, and the multi-dimensional intelligent dynamic monitoring and early warning platform is used for realizing real-time pushing and three-dimensional visual display of the monitoring data and synthesizing a data analysis result to carry out border-crossing analysis research and judgment. And early warning information is sent in time for the suspected border-crossing mining event.

The invention belongs to the technical field of soil ecological risk monitoring, and discloses a soil ecological risk assessment method and system based on multi-source data analysis. The system comprises a data acquisition module, a data preprocessing module, a comprehensive data analysis module, a database module, a risk dynamic early warning module and a decision report output module, the data preprocessing module is used for preprocessing a chemical data set to obtain a second chemical data set; the comprehensive data analysis module is used for processing the second chemical data set to obtain a risk prediction value; the risk dynamic early warning module is used for analyzing the risk prediction value and outputting a risk decision report; the decision report output module is used for processing and outputting the risk decision report; in general, the method has the remarkable advantages of being high in data prediction accuracy, good in auxiliary evaluation capability and high in risk decision-making practicability.

The invention relates to the technical field of data processing and digital medical treatment. The invention discloses a content pushing method and device for home-based care, equipment and a medium. The method comprises the following steps: acquiring user basic data, user behavior data and user health data; generating a user portrait according to the user basic data, the user behavior data and the user health data; and according to the user portrait, predicting a user demand, and according to the user demand, adjusting the pushed content. According to the method, the user portrait is generated through the user basic data, the user behavior data and the user health data, and comprehensive data analysis can be performed from multiple dimensions, so that the matching degree between the pushed content and the actual demand of a long user is high. The user demand is predicted through the user portrait, the pushed content is adjusted according to the user demand, the user does not need to search a demand function in numerous contents, the demand content is easier to obtain, the user experience is good, and the use pain point of a long user is solved.

The application discloses a kind of comprehensive data analysis system and method applied to intelligent audit, it is related to big data analysis technical field, the present application is by obtaining audit object whole-link business data, constructs full audit data set by standardization processing;Extract static and dynamic risk characteristics, build the comprehensive rule system containing static and dynamic required verification data type library;Analysis single risk and associated business link causality, generate audit associated link mapping table and ordered risk set;Calculate corresponding risk determination threshold;Real-time monitoring audit business based on threshold, data characteristics are intercepted by sliding window, after audit cycle ends, iteration updates threshold.The system includes data full collection, audit rule construction, associated link analysis, determination threshold calculation, audit threshold iteration module and subordinate unit.The present application realizes full data coverage and real-time monitoring, accurately traces risk transmission, improves the confidence and timeliness of audit compliance judgment.

This invention discloses an automated inspection system for semiconductor packaging wire bonding quality, relating to the field of semiconductor packaging technology. It includes: a multi-frequency signal transmitting module for sequentially transmitting high-frequency, mid-frequency, and low-frequency electrical signals to the wire bonding; a receiving module for receiving the multi-frequency electrical signals transmitted by the wire bonding and acquiring the signal strength of each frequency band in real time; a thermal noise detection module for detecting the thermal noise level in the wire bonding; a time-domain reflectometry module for transmitting high-frequency pulse signals to the wire bonding, detecting the reflection time and waveform characteristics of the signals in the wire bonding, and identifying physical defects in the wire bonding; and a data analysis module for comprehensively analyzing the multi-frequency signal strength, thermal noise level, and time-domain reflectometry waveform characteristics after temperature compensation to determine the wire bonding quality. In the comprehensive data analysis, all detection data are integrated and analyzed to achieve comprehensive inspection of wire bonding quality, significantly improving the accuracy and reliability of the inspection.

The application discloses an automatic zinc oxide solid waste extraction rotary kiln production line control system, and particularly relates to the technical field of automation, which comprises a monitoring area division module, a data monitoring and collecting module, a data preprocessing module, a data analysis module, a comprehensive data analysis module, a judgment and early warning module and a man-machine interaction module. The monitoring area division module is used for arranging monitoring points; the data monitoring and collecting module is used for collecting solid waste extraction first key information values, and the data preprocessing module is used for obtaining solid waste extraction second key information values; the data analysis module is used for obtaining solid waste extraction third key information values; the comprehensive data analysis module is used for obtaining solid waste extraction fourth key information values, i.e. solid waste extraction evaluation indexes; the judgment and early warning module is used for constructing solid waste extraction comprehensive evaluation standard values, judging whether the zinc oxide solid waste extraction meets the standards or not; and the man-machine interaction module is used for providing a visual interface. The state of the zinc oxide solid waste extraction is comprehensively and effectively fed back.

The invention relates to the field of photo-thermal heliostat cleaning control, and discloses a heliostat cleaning vehicle regulation and control method based on photo-thermal mirror field comprehensive data analysis, and the method comprises the following steps: collecting and preprocessing the historical data of a photo-thermal mirror field; a comprehensive cleaning efficiency quantitative evaluation model is established, a comprehensive cleaning efficiency historical measured value is calculated, and therefore the historical average comprehensive cleaning efficiency is obtained; establishing a ternary dynamic coupling model; solving parameters of the ternary dynamic coupling model by adopting a method of combining maximum likelihood estimation and gradient descent to obtain an optimal ternary dynamic coupling model; in the cleaning operation process, collected real-time data are preprocessed and substituted into the optimal ternary dynamic coupling model, a comprehensive cleaning efficiency real-time prediction value is obtained through calculation, and a cleaning operation task is dynamically adjusted; and updating model parameters and early warning the service life of the battery of the cleaning vehicle. According to the method disclosed by the invention, the problem of decision blindness caused by parameter splitting in the prior art is solved, and a quantitative basis is provided for cleaning, operation and maintenance of the precise photo-thermal heliostat.

The application discloses a kind of based on comprehensive data analysis's software user experience evaluation method and system, it is related to human-computer interaction technical field, this method is by embedding behavior perception engine in software operation interface, collect the use frequency of user in task path, jump operation frequency, process standard step number and access user total number, and further unified normalization processing is carried out in background server, constructs normalized behavior dataset, calculates path deviation joint index PDI.In based on path deviation joint index PDI joint consideration path selection information entropy and path jump rate mean two dimensions, whether the use of user path exists concentrated deviation or process jump behavior can be effectively identified, and then whether there is forced guidance, process hidden shortcut in system path design is judged.Compared with the evaluation mode of traditional only relying on click heat map or jump rate, the present method realizes higher dimensional path behavior modeling and deviation identification ability.

The present application belongs to the technical field of underground ore body monitoring, and particularly relates to a dynamic monitoring method and system for cross-border mining in underground mines. The method comprises: (1) constructing a digital twin base map of the mine; (2) laying a seismic source monitoring network on the basis of the digital twin base map of the mine, and performing microseismic monitoring and three-dimensional micro-motion exploration; (3) building a multi-dimensional intelligent dynamic monitoring and early warning platform through the data obtained from the digital twin base map of the mine and the seismic source monitoring network, wherein the multi-dimensional intelligent dynamic monitoring and early warning platform is used for realizing real-time pushing of monitoring data and three-dimensional visual display, and performing cross-border analysis and judgment based on the comprehensive data analysis results, and timely issuing early warning information for suspected cross-border mining events.

The application discloses an intelligent diagnosis auxiliary system based on a medical knowledge graph and belongs to the medical diagnosis field. In order to solve the problem that a traditional medical diagnosis system is difficult to integrate information, a large amount of medical information is wasted, effective auxiliary diagnosis cannot be performed, the diagnosis efficiency is low, effective intelligent triage prediction cannot be performed, the patients are easily caused to have a burden of diagnosis, the hospital is easily caused to have a burden of operation, the work efficiency is low, corresponding diagnosis results cannot be given in time and quickly, and the diagnosis accuracy is influenced. The existing medical data island problem is overcome, the intelligent auxiliary diagnosis of common diseases is realized, the diagnosis efficiency is improved, the doctors are provided with high-credibility auxiliary diagnosis, the intelligent triage prediction is realized, the probability of wrong triage is reduced, the patients are provided with efficient and convenient pre-diagnosis consultation, the burden of the patients and the operation load of the hospital are reduced, the diagnosis accuracy of the diagnosis auxiliary system for comprehensive data analysis of patient information is gradually improved through human error correction.

The invention discloses an intelligent classification control system and an intelligent classification control method for urban garbage recovery, and belongs to the field of garbage recovery. The method comprises the steps of performing classification error garbage weight distribution, performing classification effect analysis according to a classification error garbage weight distribution result, classification data of garbage classification and classification accuracy condition data, and performing classification abnormity early warning according to an obtained classification effect analysis result. The abnormal classification condition is accurately identified, so that the influence of an image identification factor on the judgment accuracy of the abnormal classification condition is avoided, and the identification accuracy and efficiency of the abnormal classification condition are improved.

The invention provides a polar region low-temperature valve sealing performance testing system and method based on multi-physics field coupling, and belongs to the technical field of polar region environment equipment reliability testing. According to the invention, the sealing performance of the valve in a low-temperature environment is quantitatively evaluated by simulating polar extreme climate conditions and combining a multi-physical field coupling test technology. According to the invention, multiple environmental stresses such as polar region low temperature, temperature circulation, pressure fluctuation and surface icing can be accurately simulated, and a high-precision sensing technology and comprehensive data analysis are integrated, so that comprehensive and quantitative evaluation and life prediction of the sealing performance of the valve from a static boundary to dynamic degradation and from a macroscopic phenomenon to a micromechanism are realized; and an effective technical means is provided for reliability design and verification of key valves of polar equipment.

A method, system, and device for predicting the reliability of transmission lines based on SENet and EffNet are disclosed. The method first acquires image data of the target area and constructs a neural network model of the transmission line, and then performs reliability calculation through the neural network model. This invention integrates multi-source data such as images, electrical data, and weather data through comprehensive data analysis methods. It extracts and fuses multi-dimensional features through convolutional layers, fusion modules, and pooling layers of convolutional neural networks to form a comprehensive feature vector. This vector is integrated through fully connected layers and combined with nonlinear transformation and normalization processing to enhance the training efficiency and stability of the model. The feature scale is adjusted through a composite scaling layer. Finally, the reliability prediction of the transmission line is output by the decision layer, which can effectively reflect the real-time status of the transmission line, improve the accuracy of reliability assessment, and solve the problems of neglecting real-time environment and external factors and relying on historical experience in traditional methods.

The present invention relates to a technical field of uncertainty analysis of power systems, and in particular to a quantitative analysis method for uncertainty factors of a grid-connected entity actively connected to a distribution system. By deploying sensor networks and communication networks, real-time data collection and preliminary analysis are achieved; multi-physics imaging technology is applied to perform equipment detection and defect identification to improve the level of equipment safety management; data integration and risk assessment methods are used, combined with user-side power usage data, to conduct a comprehensive risk assessment; risk matrix tools are used to perform comprehensive data analysis and risk rating, and optimize risk sorting and priority division; through time series analysis and simulation, the impact of uncertainty factors on system stability is quantitatively analyzed; and a global sensitivity analysis method is used to evaluate the relative impact of uncertainty factors. The present invention effectively solves the existing technology in the analysis of uncertainty factors in the distribution system, improves the risk assessment of the power grid, and ensures the stable and safe operation of the power system.

The present invention discloses an image data and structured data fusion analysis method based on an artificial intelligence algorithm, which relates to the field of data fusion technology. The method includes calculating the first image feature weight and the second image feature weight of each image feature vector, calculating the comprehensive weight of the image feature, and obtaining the first structural feature and the second structural feature weight of each structured data feature vector, calculating the comprehensive weight of the structural feature, deleting redundant image features or structural features sorted at the end after alignment processing, starting from the image feature with the largest comprehensive weight of the image feature, matching the image feature with the structural feature corresponding to the largest cosine similarity among the cosine similarities of all structured data features, repeating the above steps until all image features are matched. The method can promote cross-domain fusion between image data and structured data, and provide support for comprehensive data analysis and decision-making.

This application discloses an enterprise ESG risk assessment method and an enterprise ESG risk assessment system, which involve the field of comprehensive data analysis technology, realize comprehensive, systematic and dynamic ESG risk assessment, help enterprises better fulfill their social responsibilities, enhance their market competitiveness, and provide strong guarantees for the sustainable development of enterprises. The method includes: responding to an enterprise risk assessment request, determining the target enterprise to be risk assessed; obtaining the supply chain network to be assessed, and querying whether the target enterprise has upstream supplier enterprises in the supply chain network to be assessed; when the query determines that the target enterprise has upstream supplier enterprises, determining the first ESG individual risk value corresponding to the target enterprise, and determining the second ESG individual risk value corresponding to the upstream supplier enterprise; based on the first ESG individual risk value and the second ESG individual risk value, calculating the ESG relationship risk value of the target enterprise, and outputting the ESG relationship risk value.

The present invention discloses a circuit board positioning tool detection system based on artificial intelligence, which specifically relates to the field of artificial intelligence, including a positioning tool detection device preparation module, a circuit board fixed detection area division module, a circuit board appearance data acquisition module, a circuit board appearance data analysis module, a circuit board component data acquisition module, a circuit board component data analysis module, a circuit board solder point data acquisition module, a circuit board solder point data analysis module, a comprehensive data analysis module, and a human-computer interaction module. The present invention divides the circuit board to be tested into equal areas through the detection area division module, collects data on the circuit board appearance, components and solder points that affect the quality of the circuit board through the data acquisition module, processes and analyzes the collected data through the data analysis module, and establishes a circuit board quality evaluation index through the comprehensive data analysis module, compares the circuit board quality index standard value constructed by the human-computer interaction module with the quality evaluation index, judges the quality of the circuit board, and generates a quality inspection report.

The present application discloses an intelligent classification control system and method for urban garbage recycling, which belongs to the field of garbage recycling. The present application assigns weights to misclassified garbage based on the error analysis results of the misclassified garbage, performs classification effect analysis based on the weight assignment results of the misclassified garbage, classification data of the garbage classification, and classification accuracy data, and issues classification anomaly warnings based on the obtained classification effect analysis results. The present application accurately identifies classification anomalies by performing comprehensive data analysis on classification errors in the garbage classification process, avoids the influence of image recognition factors on the accuracy of judging classification anomalies, and improves the recognition accuracy and efficiency of classification anomalies.

The invention discloses a flaxseed variety screening method based on comprehensive data analysis, and particularly relates to the technical field of crop variety screening. Constructing a multi-dimensional data set containing phenotypic characteristic data, grain physicochemical index data and environmental meteorological data; performing multi-scale feature recombination and variable compression on the data set to generate a high-dimensional feature vector; constructing an adaptive weighted scoring model based on environmental response and performance fluctuation indexes, and introducing a graph structure attention mechanism and a robustness parameter adjustment network to improve scoring stability; performing quantile analysis and clustering processing on a scoring result, and simulating and identifying candidate varieties with excellent adaptability in combination with a heterogeneous environment; and finally, screening out a target flaxseed variety with optimal comprehensive performance through a multi-target decision model. The method has remarkable advantages in the aspects of improving the screening efficiency, enhancing the model robustness and adapting to multi-ecological scene popularization.

The invention discloses a software user experience evaluation method and system based on comprehensive data analysis, and relates to the technical field of man-machine interaction. The method comprises the steps that a behavior perception engine is embedded in a software operation interface, and the number of use times, the number of jump operation times, the number of flow standard steps and the total number of access users of a user in a task path are collected; and further performing unified normalization processing on the background server, constructing a normalized behavior data set, and calculating a path deviation joint index PDI. On the basis of a path deviation joint index PDI, two dimensions of a path selection information entropy and a path jump rate mean value are considered in a joint manner, so that whether concentrated deviation or process jump behaviors exist in user path use can be effectively identified, and then whether problems of forced guidance, process hiding shortcut and the like exist in system path design can be judged. Compared with a traditional evaluation mode which only depends on clicking a thermodynamic diagram or a jump rate, the method has the advantage that higher-dimension path behavior modeling and deviation recognition capability are realized.

The invention relates to the field of medical detection, and provides a microbial marker combination for predicting esophageal cancer prognosis and application thereof. According to the present invention, the significant correlation between the microbial spectrum and the esophageal cancer prognosis is found, the comprehensive data analysis of the microbiome abundance, the clinical information and the transcriptome data is performed on the patient to determine the specific 15 microbial markers related to the esophageal cancer prognosis, and the risk scoring model for predicting the esophageal cancer prognosis is established; the microbial model is used for predicting the immunotherapy curative effect and response of esophageal cancer patients and can help a doctor to more accurately judge which patients are more likely to benefit from immunotherapy before treatment, so that a more personalized and effective treatment scheme is formulated for the patients.

The invention discloses a corn ear quality detection method based on comprehensive data analysis, and relates to the technical field of agricultural product quality detection. The method comprises the following steps: pre-selecting a forward sample and a reverse sample from a plurality of corn ear samples to be detected; according to the obtained feature parameters of the forward sample and the reverse sample, utilizing a classification model to obtain a preliminary classification result of the cluster sample; selecting a final classification result from the preliminary classification results of the classification model according to a preset selection condition; in the classification process, pre-selected forward samples and reverse samples are dynamically updated according to feature parameters of the classified samples; retraining the first classification model and the second classification model according to the updated forward sample and reverse sample; according to the method, under the condition of no prior knowledge, various characteristic parameters are integrated, and efficient and accurate detection of the quality of the corn ears is realized.

The invention relates to the field of medical detection, and provides a microbial marker combination for predicting breast cancer prognosis and application thereof. According to the present invention, the significant correlation between the microbial spectrum and the breast cancer prognosis is found, the comprehensive data analysis of the microbiome abundance, the clinical information and the transcriptome data is performed on the patient to determine the specific 22 microbial markers related to the breast cancer prognosis, and the risk scoring model for predicting the breast cancer prognosis is established; the microbial model is used for predicting the immunotherapy curative effect and response of breast cancer patients, and can help a doctor to more accurately judge which patients are more likely to benefit from immunotherapy before treatment, so that a more personalized and effective treatment scheme is formulated for the patients.