234results about How to "Reliable data base" patented technology

The invention discloses a crop growth prediction method based on multi-source data fusion analysis, and the method comprises the steps: carrying out the standardization processing and missing value filling processing of multi-source data, constructing a time window, calculating the correlation between data sources, and generating a current data credibility vector based on historical credibility evaluation data; extracting features by adopting multi-scale decomposition, detecting abnormal points and analyzing abnormal degrees, and constructing an abnormal conduction network to evaluate conduction influence; performing feature importance evaluation and feature fusion; initial weight distribution is calculated, a weight prediction model is constructed, and consistency optimization is carried out on a weight matrix; performing hierarchical prediction by adopting a multi-level prediction model, and performing collaborative integration based on a prediction result and an optimized weight matrix; and calculating a prediction result deviation and carrying out optimization adjustment, and outputting a final prediction result. According to the invention, accurate prediction of the crop growth state is realized, and the reliability and practicability of the prediction result are improved.

The invention relates to the technical field of hydraulic engineering monitoring control, and discloses an automatic monitoring method and system for ecological hydraulic engineering. The method comprises the steps that water body parameters are collected through a sensor and preprocessed to obtain standard monitoring data; performing noise reduction alignment and correlation analysis on the monitoring data to obtain an optimized data set and a feature library; evaluating the water environment state according to the data set and the feature library to obtain an evaluation index and an early warning signal; analyzing the treatment scheme according to the index and the signal to obtain a regulation and control instruction; adjusting equipment operation parameters according to the instruction to obtain operation state data; and performing cycle analysis and strategy optimization based on the state data to obtain regulation and control parameters. Multi-dimensional correlation analysis of water quality parameters is realized, an accurate water environment regulation and control scheme is formulated on the basis, and the pertinence and efficiency of water quality improvement are improved.

The invention relates to the technical field of data processing, and discloses an intelligent bridge reinforcement decision generation method, device and equipment based on damage identification. The method comprises the following steps: carrying out bridge damage identification and evaluation on a bridge state data set to obtain a bridge damage characteristic spectrum; performing intelligent decision analysis on the bridge damage characteristic spectrum, generating a preliminary reinforcement scheme, and obtaining a candidate reinforcement strategy set; performing parametric modeling and multi-scale simulation on the candidate reinforcement strategy set, and performing coupling calculation evaluation on a reinforcement effect to obtain an optimized reinforcement scheme; virtual construction planning is conducted on the optimized reinforcement scheme, a construction process model is generated through an intelligent scheduling algorithm, and a target construction scheme is obtained; and monitoring and feeding back the target construction scheme in real time, and dynamically optimizing the construction process through an adaptive control algorithm to obtain a target intelligent reinforcement implementation scheme. The intelligent bridge reinforcement decision generation efficiency and accuracy based on damage identification are improved.

The invention provides a wind turbine generator array wake flow management method and system for Doppler laser wind measurement, and relates to the technical field of laser, and the method comprises the steps: collecting the real-time wind speed data, wind direction data and turbulence intensity data of each generator in the front and back direction through deploying a Doppler laser radar in a wind turbine generator array; establishing a wake flow influence model containing a wake flow influence coefficient, an attenuation coefficient and a diffusion coefficient based on the data, and calculating to obtain a wake flow influence range, loss power and a recovery distance; according to the method, the wind turbine generator array is cooperatively controlled, and the power generation efficiency of the wind turbine generator array is optimized by adjusting the yaw angle or the pitch angle of the upstream unit and the rotating speed of each unit. The wind speed data acquisition precision of the wind field is improved, the wake flow influence evaluation accuracy is enhanced, and the overall power generation efficiency of the wind field is remarkably improved.

The invention provides an electric control box remote data acquisition system based on the Internet of Things, and relates to the technical field of data processing, and the method comprises the steps: obtaining the operation state data of an electric control box through a sensor, transmitting the operation state data through a wireless signal network, obtaining a data frame set, analyzing the data identifier of each data frame, verifying the integrity of the data, and obtaining the data frame set; determining performance parameters of the wireless signal network, detecting whether an abnormal change occurs in the transmission process, generating a solution when the abnormal change occurs, generating a retransmission signal when packet loss occurs in the transmission process, determining a retransmission queue according to the data identifier, and transmitting the retransmission signal according to the retransmission queue when delay occurs in the transmission process. The priority of a retransmission queue and the distribution of network bandwidth are dynamically adjusted, and when signal attenuation occurs in the transmission process, the execution sequence is adjusted according to the operation record and result of a solution by switching a transmission channel and adjusting transmission power. The transmission process of the operation data of the electric control box can be detected.

The invention discloses a new energy weather forecast method based on numerical weather forecast and AI revision, and particularly relates to the technical field of weather forecast, and the method comprises the steps: obtaining the historical actually measured weather data of a new energy power station, correcting the numerical weather forecast data of multiple sources through employing an artificial intelligence algorithm, and building a correction model; inputting the current numerical weather forecast data into the correction model for correction, extracting the internal structure characteristics of the correction data in combination with a high-dimensional geometric mapping and manifold learning method, and evaluating the matching degree between the manifold distribution of the correction data and the historical data; analyzing and evaluating the data complexity by using multi-scale entropy, and ensuring that the data meets a preset standard; on the basis of matching degree and complexity evaluation, selecting suitable numerical weather forecast data for new energy short-term and medium-term meteorological parameter forecast; the method improves the prediction accuracy and applicability, and is suitable for the generation power prediction and optimization scheduling of the new energy power station.

The invention provides an intelligent bee colony type quality optimization management method and system for multi-source tag data, and relates to the technical field of data tags, and the method comprises the steps: extracting a comprehensive feature vector of a tag through a feature extraction module, carrying out the processing through a deep feature enhancement network, generating a similarity and dependency feature matrix, and constructing a tag quality evaluation model; mapping the label data into bee colony network nodes, performing three-level division, and constructing a node optimization association network; and performing optimization based on the collaborative optimization rule set until the quality improvement value is smaller than a preset threshold value. The label data quality can be effectively improved, the data processing cost is reduced, and the optimization efficiency is improved.

The invention relates to the field of data processing, in particular to a multi-source data quality improving method which is particularly suitable for processing and optimizing load data. Performing feature extraction on the multi-source load data to obtain load data containing a mode and a structure of the data; performing anomaly detection on the load data by applying a local anomaly factor LOF algorithm, and screening out abnormal data; correcting the detected abnormal data through a Pearson correlation analysis method; and based on the feature extraction and anomaly correction results, calculating the weight relationship between the load data features by applying an entropy weight method, and further optimizing the data correction and feature extraction process so as to improve the overall data quality and accuracy. According to the scheme, a reliable data basis is provided for big data analysis and operation state evaluation of the smart power grid, the efficiency and accuracy of data mining and analysis are effectively improved, and a solid guarantee is provided for optimal scheduling and decision support of the power grid.

The invention discloses a glacier jump prediction and data sample enhancement management method, particularly relates to the field of glacier remote sensing monitoring, is used for solving the problem of accurate identification and processing of surface moraine covering type glacier local albedo abnormal effect, and aims at solving the problem of accurate identification and processing of the glacier local albedo abnormal effect by comprehensively analyzing the coupling relationship between spectrum and temperature and the spatial sensitivity of temperature gradient. And accurate identification and processing of the moraine covering glacier local albedo abnormal effect are realized. According to the method, main causes of albedo abnormity can be effectively distinguished, and an abnormal area is corrected in a targeted manner by scientifically selecting an optical and thermal infrared remote sensing fusion mode or an optical and radar remote sensing fusion mode, so that the glacier identification precision and the jump prediction accuracy are remarkably improved. Through multi-level remote sensing data correction and feature extraction, a high-precision data foundation is laid for subsequent analysis; in the process of generating an enhanced sample and optimizing a prediction model, the reliability and response speed of glacier jump prediction are further improved.

The invention discloses a geological mineral exploration device and an exploration method, and particularly relates to the technical field of geological exploration. Mining area data are collected in a plurality of preset time periods, and data analysis is performed in each period, so that whether an early sign of insufficient data quality exists or not is judged; if a quality problem is found, feature analysis is further carried out, and the precision level and the coverage degree of the data are evaluated; dividing a data acquisition period into a high-quality period and a low-quality period according to an evaluation result; for a low-quality acquisition period, resampling or supplementary acquisition is executed, so that the data precision and coverage rate are improved, and the integrity and accuracy of exploration data are ensured; according to the method, through multi-level evaluation of the precision index and the coverage rate index, the quality problem in data collection can be identified in time, multiple times of correction processing are carried out, the precision and the coverage rate of the collected data are remarkably improved, and it is ensured that the collected data can accurately reflect the real geological condition of a mining area.

The invention relates to an adaptive stacked workpiece point cloud data synthesis and instance segmentation method and device, and the method comprises the steps: collecting the point cloud data of a single workpiece, and carrying out the automatic reverse modeling through a feature preserving reconstruction algorithm, so as to obtain a CAD model of the single workpiece; establishing and training a stacked workpiece instance segmentation model comprising a backbone network module, a semantic prediction module, a central point offset prediction module, a binary clustering module, a centripetal offset module and a voting module; and completing the point cloud data and refining edge details through a point cloud data completion and detail feature preserving algorithm, and inputting the processed point cloud data into the trained instance segmentation model to realize accurate instance segmentation of the stacked workpiece point cloud data. According to the invention, through technologies of adaptive point cloud data synthesis, deep learning instance segmentation, point cloud data completion and the like, the quality and segmentation precision of the point cloud data are significantly improved, and the method is suitable for workpiece identification and operation tasks in a complex industrial environment.

The invention belongs to the technical field of Internet, and particularly relates to an energy optimization scheduling method and system, a terminal and a storage medium, and the method comprises the steps: collecting real-time energy data, equipment state information and environment data of energy consumption equipment in a park; processing the real-time energy data, the equipment state information and the environment data by adopting a multi-source data fusion technology to obtain a park data set so as to generate a uniform data input format; obtaining actual demand data of the park, inputting the park data set and the actual demand data of the park into a pre-trained energy model for prediction, and obtaining prediction results of the energy demand and the equipment health state of the park at the next moment; and adjusting the operation mode and parameter of the corresponding equipment based on the prediction result of the energy demand and the equipment health state of the park at the next moment. The method has the effect of improving the energy utilization rate of the park.

The invention provides a method for establishing a precise ammonia injection fuzzy control model based on an LSTM neural network. The method comprises the following steps: S1, analyzing historical data, cleaning, designing a membership function and formulating a fuzzy rule table; s2, constructing a control model by using an LSTM neural network algorithm to obtain an error between a predicted value and an actual value and an error change rate; and S3, comparing a prediction result of the model with an actual output result. According to the invention, an advanced neural network technology and a flexible fuzzy control strategy are fused, so that accurate adjustment of ammonia spraying of the SCR denitration system is realized. A fuzzy expert control system is built, an error, a change rate range of the error and valve opening variation corresponding to different errors and error change rates are obtained by analyzing historical data of ammonia water adjustment, and a fuzzy discourse domain and a fuzzy rule table are designed in combination with a fuzzy control principle and a system working condition. And fuzzy reasoning and fuzzy judgment are carried out on real-time data of the system, and the increment of the ammonia water regulating valve under the current working condition is calculated and regulated.

The invention discloses an automatic conversion analysis method and system based on an implicit common cause fault model, and relates to the technical field of power system fault analysis, and the method comprises the steps: obtaining and marking power equipment, forming a plurality of equipment sets, setting a monitoring period for operation monitoring, and storing data into an operation record library; extracting feature data of the equipment, calculating a correlation index between the equipment according to the category of the equipment, and calculating the correlation between the same category of equipment and different categories of equipment by adopting different strategies; utilizing an implicit common cause fault model to calculate the probability that the reference equipment and other equipment are abnormal at the same time under each abnormal cause, including an independent failure probability, a common cause failure probability and a comprehensive probability; and according to the comparison of the comprehensive probability and a preset threshold value, the monitoring period of the equipment exceeding the threshold value is adjusted, and according to the abnormal reason, the monitoring period of all the associated equipment is also adjusted, so that the effective monitoring and fault prevention of the power system are realized.

The invention discloses a complex water quality parameter prediction method and system based on deep learning, and the method comprises the steps: S1, collecting and preprocessing original water quality parameter data, and obtaining the preprocessed water quality parameter data; s2, extracting a water quality parameter data point cluster from the preprocessed water quality parameter data by adopting an improved DBSCAN algorithm; s3, extracting a feature fusion vector, and extracting a coupling feature vector in combination with the water quality parameter data point cluster; s4, calculating self-adaptive scale and multi-scale water quality parameter characteristics of the gating circulation unit, and then performing water quality parameter prediction; and S5, adding Gaussian noise into the preprocessed water quality parameter data, carrying out simulation calculation, and calculating a pH value predicted value confidence interval, an ammonia nitrogen concentration predicted value confidence interval and a chemical oxygen demand predicted value confidence interval according to a simulation calculation result. The method can solve the problem that a traditional water quality parameter prediction method is difficult to deal with the poor prediction effect caused by coupling association of various water quality parameter types.

The invention, which relates to the technical field of carbon emission calculation, discloses a BIM-based subway engineering muck output and carbon emission calculation system comprising a muck output calculation module, a muck landfill land occupation calculation module, a parameter setting module and a muck carbon emission calculation module. The muck generation amount calculation module is used for automatically obtaining BIM model structure parameter data and automatically calculating the muck generation amount through a subway engineering muck generation amount calculation model; the muck landfill occupied area calculation module is used for automatically calculating the landfill occupied area by combining the landfill proportion, the loosening coefficient and the landfill depth data; the parameter setting module is used for setting various carbon emission factor parameters and energy consumption parameters and laying a data foundation for carbon emission calculation; the muck carbon emission calculation module is used for calculating the muck full-life-cycle carbon emission according to the data of the three modules in combination with a full-life-cycle carbon emission quantitative model; according to the method provided by the invention, the calculation process is optimized, and the calculation period is shortened.

The invention discloses an electric energy information acquisition and management method for an electric power system, and the method is characterized in that the method comprises the steps: S1, obtaining electric quantity, voltage and current data in real time through an electric energy acquisition device, and transmitting the data to a data acquisition system; s2, performing data cleaning, calibration, denoising and data visualization operation on the acquired electric quantity, voltage and current data; s3, performing energy consumption monitoring analysis according to the processed electric energy data, identifying an energy consumption peak period, and executing corresponding energy-saving measures for optimization; and S4, based on an optimization result, performing energy supply prediction by using a neural network. According to the invention, the neural network is introduced for power data acquisition, intelligent prediction of future energy demands is realized, and the problem of energy use optimization based on data driving is solved.

The invention relates to the technical field of production line management, in particular to an automatic production line management method and system based on precise control, and the method comprises the steps: carrying out the searching of product processing procedure data of collected target product text feature data and established different product processing procedure data, and generating target product processing procedure data; acquiring image data corresponding to the processing procedure data of the target product in real time through an image acquisition device, and then performing data preprocessing on the image data to generate real-time image feature data of the target product; and carrying out product image feature matching on the real-time image feature data of the target product and the established qualified image feature data of the target product, judging the operation state of the automatic production line according to a product image feature matching result, and constructing operation management data of the automatic production line and pushing the operation management data to an automatic production line management platform. And intelligent management of the automatic production line is realized.

The invention discloses an intelligent processing method and system for urinary surgery diagnosis and treatment data, and relates to the technical field of medical data. In the data feature extraction process, the method not only covers urinary flow dynamic features such as the average fluid resistance coefficient FRavg and the pressure and flow velocity dynamic integral DPI, but also covers urinary flow dynamic features such as the average fluid resistance coefficient FRavg and the pressure and flow velocity dynamic integral DPI; and the urine conductivity change rate delta UEC, the urine suspended particle volume ratio change rate delta VSP and the functional index urination efficiency Neff of the abnormal characteristic of the urine are comprehensively extracted and analyzed. By further calculating the bladder emptying function index BEI and the urine fluid resistance index FRE, detailed evaluation of the bladder function and the urinary tract patency is achieved. On the basis of the comprehensive diagnosis score Pf1, the system can accurately identify first-level or second-level evacuation abnormity through hierarchical contrastive analysis, and triggers deep analysis when necessary, thereby providing a more comprehensive diagnosis basis for complex cases such as early bladder dysfunction or urinary tract obstruction.

The present invention discloses a photovoltaic microgrid energy management and optimization scheduling system, relates to the technical field of smart grids, and solves the problems of unstable output power, complex control strategy and poor real-time response capability of the existing system; the present invention predicts future photovoltaic power generation and load curves through an intelligent prediction and analysis module, provides a reliable data basis for the optimal scheduling of the system, adjusts the energy distribution between the photovoltaic system, energy storage device and controllable load in real time through a dynamic scheduling optimization module, realizes efficient energy utilization and stable operation of the system, optimizes the output of distributed energy resources and the charging and discharging strategy of the energy storage device through a distributed energy resource control module and an energy storage management and optimization module, and further improves the real-time response capability and operation efficiency of the system; the present invention greatly improves the energy management, prediction accuracy, real-time response capability and safety control capability of the existing photovoltaic microgrid.

The invention relates to the technical field of intelligent assistants, and discloses an intelligent assistant system and an implementation method thereof, the intelligent assistant system comprises a data acquisition unit, a preprocessing unit, a data analysis unit, a knowledge graph construction unit, an intelligent interaction unit and a personalized service unit; multi-source data collection is achieved, web pages, third-party APIs and user behavior data are covered, and richness, timeliness and pertinence of information are guaranteed. Through efficient data preprocessing, the data quality is improved; text content and emotional tendency are accurately mastered through multiple technologies in text analysis; user behavior analysis deeply excavates an association relationship, constructs a portrait and analyzes a trend; the knowledge graph construction effectively organizes knowledge and improves retrieval efficiency, personalized service provides personalized experience based on user portraits and behavior analysis, user satisfaction is improved on the whole, intelligent, convenient and personalized service experience is brought to users, and the method has wide application value and market potential.

The invention provides a precision shaft health state diagnosis method and system based on data twinning, and relates to the technical field of precision shaft health state monitoring, and the method comprises the steps: outputting a fusion health feature of a target precision shaft from a health state model of data twinning, and determining a linear offset during the linear change of the health state of the target precision shaft; determining a health response label of the target precision shaft in the operation state under the variable working condition according to the balance health index, and determining a cross degradation index of the health state of the target precision shaft according to the health response label; determining a health deviation point position of the target precision shaft at each moment during operation, and determining a time sequence health gradient of the full life cycle of the target precision shaft according to the health deviation point position and the linear offset; and performing fusion diagnosis on the health state of the precision shaft according to the cross degradation index and the time sequence health gradient. According to the method, the health state of the precision shaft can be accurately captured in variable working conditions and a complex degradation process, so that the sensitivity of a health diagnosis model to dynamic performance degradation of the precision shaft is improved.

The invention discloses an in-building emergency rescue system and method based on digital twinning modularization and multi-scale simulation, and the system comprises a parameterization equipment model construction module, a parameterization floor environment simulation module, a data real-time collection and monitoring module, a fault monitoring and fault analysis module, and an emergency rescue path intelligent generation module. And a user interaction interface module. The system adopts a modular design, can be independently deployed and can also be flexibly combined so as to meet the requirements of different building structures, floor environments and elevator brands. Meanwhile, by means of the multi-scale simulation technology of a Unity 3D platform, high-precision simulation and visualization of the operation state of the equipment are achieved, operation and maintenance personnel and rescue personnel can visually and accurately know the operation state of the equipment and the floor environment, a precise and visual virtual environment is provided for emergency rescue, and the safety of emergency rescue is improved. And powerful technical support is provided for development and application of a simulation equipment emergency rescue system.

The invention discloses an intelligent sensor acquisition system, which relates to the technical field of sensor acquisition and is realized by adopting a data acquisition module, a data processing module, a comprehensive correction optimization module, a communication module and a sensitivity adjustment execution module. The environment data comprises data related to illumination and temperature collected by a sensor, the collected environment data is preprocessed through the data processing module, and the corrected illumination intensity value and the power demand value are calculated and output through the comprehensive correction optimization module. And inputting the corrected illumination intensity value and the power demand value into a sensitivity adjustment execution module, performing feedback adjustment of multiple times of calculation, outputting an adjusted photosensitive element sensitivity value, and changing and influencing the corrected illumination intensity value and the power demand value and subsequent measurement values based on the feedback adjustment of the photosensitive element sensitivity value. And the collected and calculated data are displayed through the communication module.

The invention relates to the technical field of data asset management, in particular to a data asset management system and method. The method comprises the following steps: acquiring a data asset information set from different data sources in real time and performing tag structure sub-column division to obtain data asset information tag description structured sub-columns, and meanwhile, performing time sequence creation synchronization processing and integrating and storing the data asset information tag description structured sub-columns into a data asset management database; performing use frequency and sensitivity evaluation analysis on each piece of data asset sub-information in the data asset management database, and performing data asset access utility evaluation analysis to obtain data access utility of each piece of data asset sub-information; and performing dynamic access right control and full life cycle management design on each piece of data asset sub-information in the data asset management database, and performing automatic management and report feedback recording to generate a data asset information management report. The transparency, safety and efficiency of data asset management can be improved.

The invention discloses a micro-seismic outrange waveform reconstruction method based on multi-station joint calibration, and the method comprises the steps: firstly obtaining outrange sections and outrange stations corresponding to different micro-seismic events of each station, and then for the same micro-seismic event, selecting a station which does not generate outrange saturation as a reference station, and carrying out the calibration of the outrange waveform. Carrying out time alignment on a seismic phase time window of the reference station and a corresponding time period of the outrange station, and then calculating an equivalent attenuation coefficient corresponding to the micro-seismic event by adopting a multi-station joint inversion mode, so as to avoid the use of missing data; and finally, mapping the complete waveform amplitude of the reference station in the outrange section to the outrange station by using the equivalent attenuation coefficient to realize the real waveform amplitude of the outrange station in the outrange section, so that each station can obtain continuous microseismic waveforms in different microseismic events. Through the method, the physical consistency and availability of the outrange micro-seismic waveform are improved, and a reliable data basis is provided for micro-seismic energy evaluation, risk analysis and a subsequent intelligent early warning model.

This invention discloses a comprehensive management method and system for base station power consumption, belonging to the field of base station power consumption. The method includes: topological mapping of the access relationships of AC / DC equipment within the base station to form a hierarchical power consumption association list; dynamic clustering parsing of the list to generate a time-series evolution chain reflecting state changes and remote control history; constructing a device differentiation identification model based on the time-series evolution chain, determining abnormal metering factors and outputting candidate correction events; labeling the corresponding areas of the candidate events as local anomalies, and generating a correction reference group by fusing multi-source information such as meter readings and sensor data; dynamically iteratively correcting the global metering results based on the correction reference group, and generating intelligent management instructions by combining the corrected load sharing ratio and branch authorization strategy. The system includes corresponding functional modules. This invention can systematically solve the problems of unclear base station power consumption topology, inaccurate metering, and extensive management, achieving high-precision metering and automated intelligent control.

The application discloses a water pump performance curve correction method based on a similarity principle, relates to the technical field of water pump working condition analysis, and comprises the following steps: acquiring a conversion working condition point corresponding to a model pump test working condition point converted to a prototype pump and a theoretical performance curve of the prototype pump; confirming a similarity parabola passing through the field measured working condition point and mapping the similarity parabola to the theoretical performance curve of the prototype pump to obtain an intersection point as a similarity working condition point of the field measured working condition point according to a similarity parabola relationship and field measured working condition point data; obtaining a corresponding point under a theoretical rotating speed according to the similarity relationship and combining the working condition point on the theoretical curve of the prototype pump; replacing the theoretical rotating speed with a measured rotating speed, converting to a corresponding point under a rated rotating speed, and fitting to obtain an actual flow-head curve; and acquiring a theoretical efficiency value and an optimal efficiency value corresponding to the similarity working condition point to obtain an actual flow-efficiency curve, so that the actual performance curve of the prototype pump can be efficiently and accurately corrected.

The invention provides a rock mass structural surface strength prediction method driven by a ground vibration load, and relates to the technical field of rock mass mechanics. The method comprises the following steps: acquiring and integrating geometric and mechanical parameters, seismic oscillation parameters and test and numerical simulation data of a structural plane to form a standardized database; cleaning and standardizing database data, constructing a dynamic attenuation factor, and screening and enhancing key features to obtain initial input features; constructing a physical experience reference model and a machine learning predictor, and training a hybrid prediction model by combining prediction results of the physical experience reference model and the machine learning predictor; verifying the model and quantizing the output uncertainty; inputting the basic data calling model to complete calculation and outputting a result. According to the method, the reliability and adaptability of rock mass structural surface strength prediction are improved, and support is provided for seismic design of geotechnical engineering.

The invention discloses a gene sequencing data management method for whole genome methylation sequencing, and relates to the technical field of gene sequencing data management.The method comprises the steps that original gene sequencing data are obtained and preprocessed, and a regional methylation matrix is generated; calculating a technical difference measure and a phenotype difference measure of each region unit based on the region methylation matrix and the sample metadata, identifying a technical methylation bias domain, and grading the technical methylation bias domain into a steady-state technical methylation bias domain and an event-driven technical methylation bias domain according to the occurrence frequency of the technical methylation bias domain in multiple batches; based on a linear adjacency relation and a co-bias relation, constructing a technical methylation bias super-domain and classifying the technical methylation bias super-domain; performing regional shielding on the methylation bias super-domain of the steady state technology, performing normalization correction on the methylation bias super-domain of the event-driven technology, and generating a corrected regional methylation matrix; calculating a sample quality index based on the corrected matrix and performing quality grading; according to the method, technical bias can be identified and corrected, and the methylation data quality and analysis reliability are improved.