336 results about "Regression analysis" patented technology

The invention provides a digital twin water conservancy hyper-fusion method for coupling multi-source data and an all-in-one machine, and relates to the technical field of intelligent water conservancy. By constructing a unified water conservancy spatio-temporal data link protocol, efficient fusion of multi-source heterogeneous data of hydrology, meteorology, engineering operation and the like is realized, and the problem of water conservancy system data islands is solved; a coupling distributed hydrological model and a hydrodynamic model are established, and real-time accurate simulation of the watershed hydrological process and the river channel hydrodynamic process is achieved; a multi-target dynamic weight reinforcement learning algorithm is adopted to optimize a water conservancy scheduling scheme, model parameters are dynamically corrected in real time through a time sequence error regression analysis model, and closed-loop adaptive optimization is achieved; according to the method, closed-loop cooperation of water conservancy data fusion, real-time accurate simulation, intelligent scheduling optimization and dynamic parameter self-correction is realized, and the real-time performance and accuracy of water conservancy intelligent decision making are remarkably improved.

The invention belongs to the technical field of blasting vibration prediction, and particularly discloses a blasting vibration speed attenuation law prediction method based on regression analysis, and the method comprises the steps: laying monitoring points in multiple directions around a blasting source, synchronously collecting the geological parameters and mass point peak vibration speed of each point, and constructing a segmented propagation path; a single dominant propagation section is identified based on adjacent monitoring point data, independent influence of specific geological conditions on vibration attenuation is quantified by combining single-factor control regression analysis, decoupling modeling of geological factors and attenuation behaviors is realized, and meanwhile, a double-factor dominant propagation section is identified on the basis of quantizing the independent influence of the single geological conditions. And deducting a known effect through residual analysis to separate out an independent contribution of another geological parameter, and finally integrating a multi-factor influence function to construct a comprehensive attenuation model. Progressive modeling from single-factor decoupling to multi-factor collaboration is achieved, and the precision and physical interpretability of blasting vibration prediction in a complex heterogeneous stratum are remarkably improved.

The invention discloses a method for analyzing the uplift bearing capacity of a PHC spiral pipe pile based on numerical simulation. The method comprises the following steps: vertically screwing a pile body into a designed elevation by adopting intelligent rotary excavating equipment provided with a special drill bit, and monitoring the press-in speed, the rotating speed, the torque and the perpendicularity in real time; recording a press-in resistance-depth curve, a torque-depth curve and pile body perpendicularity data; establishing a three-dimensional refined numerical model containing a real spiral blade, and calibrating soil key parameters through a parameter inversion algorithm to obtain a calibrated numerical model; an anti-pulling load is simulated on the calibrated numerical model, and the ultimate anti-pulling bearing capacity and the failure mode are analyzed; based on a multi-working-condition simulation result, a practical calculation formula of the ultimate uplift bearing capacity is obtained through regression analysis; and model inversion is driven through construction data, refined numerical simulation is combined, the uplift bearing capacity prediction precision is improved, and a reliable basis is provided for the uplift design of the PHC spiral pipe pile.

The invention discloses a stomach cancer postoperative survival prediction model construction and verification method based on machine learning, and belongs to the technical field of medical and industrial combination. According to the technical scheme, the method comprises the steps of obtaining clinical data of postoperative patients of gastric cancer, preprocessing the clinical data, and dividing the data into a training set and a verification set; screening out independent risk factors through single-factor and multi-factor Cox proportional risk regression analysis; constructing and training a plurality of prediction models based on the independent risk factors; the model is evaluated through time dependence AUC, C-index, a calibration curve and Kaplan-Meier survival analysis; and selecting the model with the optimal performance as a final prediction model. The method has the beneficial effects that the constructed GBM prediction model remarkably improves the gastric cancer postoperative survival prediction accuracy, the model can effectively process a complex nonlinear relationship in clinical data, a clear and explainable prediction basis is provided by using the SHAP value, and the transparency and clinical credibility of the model are enhanced.

The invention relates to the technical field of intelligent agriculture and green plant protection, and discloses a corn tasseling period pest and disease damage green prevention and control system and method based on environment self-adaption, and the system comprises a prevention and control system which comprises an environment simulation unit, a pest and disease damage simulation database, a prediction analysis module, an early warning decision module and an acquisition unit. According to the method, a nonlinear mapping model of five-dimensional environment vectors (temperature, humidity, illumination intensity, rainfall and wind speed) and pest and disease damage activities is constructed, multiple regression analysis and Monte Carlo simulation are combined, pest and disease damage occurrence probability distribution under different climate scenes is generated, the limitation of a single threshold model is effectively overcome, and the method is suitable for popularization and application. The prediction reliability in a complex environment is obviously enhanced; according to the method, the disease and insect pest outbreak risk can be early warned 72 hours in advance, and the disaster loss is reduced by at least 30%; the dosage of chemical pesticides is reduced by more than 40% through a graded green prevention and control strategy, and the biodiversity of farmland and the ecological health of soil are protected.

The invention relates to the technical field of equipment life prediction, and provides a nuclear power plant equipment predictive evaluation and maintenance method and system based on self-attention, and the method comprises the steps: obtaining multi-source sensing data of equipment through a sliding window mechanism, and obtaining an input time window sequence after preprocessing; reasoning the time window sequence data through a model to obtain a state vector for regression analysis, and generating the residual service life of the equipment; performing visualization processing on the attention matrix to obtain an equipment health state interpretation map of the attention weight, and generating interpretation information; and fusing the obtained residual service life of the equipment and the corresponding interpretation information to generate an equipment maintenance strategy suggestion. According to the method, the sliding window and the Transform model are creatively fused, deep modeling and interpretability analysis of multi-source equipment data are achieved, and the equipment health state atlas is constructed based on an attention mechanism and used for accurate prediction and maintenance strategy generation.

The invention discloses an intelligent prediction method for a fracturing wellhead yield increase effect, and the method comprises the steps: carrying out the fusion collection, standardization and embedded dimension reduction of multi-source real-time and historical data, environment and geological parameters and expert experience, cooperatively extracting high-dimensional features, evaluating the uncertainty of the features through a Bayesian model, and carrying out the weighted input of a reinforcement learning architecture, thereby achieving the intelligent prediction of the yield increase effect of a fracturing wellhead. According to the method, causal reasoning and deviation correction are carried out in combination with physical and business indexes, high consistency of a strategy and a multi-target business index is realized by dynamically adjusting a reward function, and self-adaptive retraining and robust optimization of a model in extreme environments such as high noise and data missing are realized through field evaluation regression analysis of output. According to the method, the adaptability and prediction accuracy of the fracturing yield increase prediction and optimization system to complex working conditions are remarkably improved.

The invention relates to the technical field of regional carbon emission quantitative management, in particular to an accounting method, system, equipment and medium for regional carbon emission statistics, and the method comprises the steps: collecting carbon emission accounting basic data, and building a dynamically updated historical database; performing prediction and trend analysis on the carbon emission through a composite model fusing regression analysis and time sequence analysis on the basis of a historical database; monitoring the change of an energy structure, and dynamically adjusting a carbon emission factor and a weight parameter of the energy structure; and updating the composite model based on the dynamically adjusted weight parameters, and generating an optimized regional carbon emission statistics and prediction result. The method has the beneficial effects that the accuracy and reliability of the input data are ensured by collecting the carbon emission accounting basic data and performing strict data preprocessing. Regional electricity utilization carbon emission is analyzed by utilizing ridge regression and a time sequence algorithm, the prediction accuracy is improved, and a scientific basis is provided for a local government to optimize an energy structure and promote green and low-carbon development.

According to the slope geological disaster risk assessment method and system provided by the invention, the feature information of the slope object can be accurately assessed by obtaining the non-natural vibration data and combining the hidden danger description factor information of the slope object, so that the adjacent slope objects around the target slope object are considered together in the slope object set, and the risk assessment accuracy is improved. Comprehensive analysis of a target slope object is formed through slope hidden danger data features in the slope object set, and then feature extraction of important hidden dangers is carried out on the target slope object set to obtain a description vector of the target slope object set; and according to the description vector regression of the target slope object set, whether the slope object attribute of the target slope object affects the slope geological disaster risk assessment result is analyzed, the slope object attribute is used for representing the attribute affecting the hidden danger of the slope object, and the regression analysis accuracy of the attribute change of the slope object is improved. And the accuracy and confidence of evaluation are ensured.

The invention discloses a thunderstorm gale regression analysis method and system based on multi-stream parameters, and relates to the technical field of data processing. The method comprises the following steps: collecting multi-stream parameters in real time, and constructing a real-time multi-stream parameter sequence; mapping the real-time multi-flow-parameter sequence to a pre-constructed multi-flow-parameter coupling sensitivity field model, and calculating to generate a real-time convection sensitivity field; inputting the real-time convection sensitivity field into the physical consistency constraint model to generate real-time structured convection characteristics; main regression analysis under multi-flow parameter regression is executed, and a main regression result is established; and after the real-time local disturbance index is calculated, disturbance residual compensation of the main regression result is triggered, dynamic weight adjustment is carried out on the main regression result after residual compensation by utilizing seasonal phase characteristics, and a thunderstorm gale regression analysis result is output. The technical problem that thunderstorm gale prediction accuracy is insufficient in the prior art is solved, and the technical effect of improving thunderstorm gale regression analysis result accuracy is achieved.

The application discloses a kind of intelligent anchor rod system early warning method and threshold determination method, belong to anchor rod intelligent early warning field, including S1: monitoring data preprocessing, S2: data trend regression analysis and S3: early warning method and index;Step S1 includes substep, S11: detection and correction of singular point, S12: obtain equidistant data and S13: data denoising smoothing;Step S2 includes substep S21: regression model establishment and S22: determine regression coefficient;Step S3 includes substep, S31: the determination method of limit stress static threshold of each monitoring point, S32: the determination method of stress change rate static threshold, S33: sharp end mutation model, the determination method of section stability threshold and S34: multi-factor joint early warning method.The application is by monitoring data preprocessing, data trend regression analysis and division early warning method and index. According to this, the overall trend and law of stress change of roadway surrounding rock can be accurately analyzed, and the anchor rod early warning system early warning threshold is accurately determined.

The embodiment of the invention provides a mutual inductor state monitoring system and method for an intelligent laboratory standard electric energy meter, and relates to the technical field of electric energy meters. The mutual inductor state monitoring system comprises a main control unit and a data acquisition unit, wherein the data acquisition unit is used for acquiring a signal measurement value and a working temperature of a secondary side of a mutual inductor; the main control unit is configured to execute the following calculation modules: a linear regression calculation module used for performing regression analysis on the signal measurement value acquired by the data acquisition unit so as to estimate a signal true value; the temperature compensation module is used for removing the influence of the working temperature on the real value of the signal based on the standard value matrix; and the state judgment module is used for calculating an abnormal score of the signal true value after the influence of the working temperature is removed based on the isolated tree model, and judging the misalignment state of the mutual inductor based on the abnormal score and a preset misalignment boundary threshold, so that the monitoring precision of the mutual inductor in the standard electric energy meter can be improved.

The invention discloses a damage safety assessment method and system for a building service glass curtain wall, and the method comprises the steps: setting a plurality of damage conditions based on a glass curtain wall standard test piece, and building a function relation between the maximum panel deflection and the inherent frequency through a regression analysis method; calculating the maximum theoretical panel deflection according to specifications, substituting the maximum theoretical panel deflection into the function relationship to obtain a critical inherent frequency, and constructing a safety evaluation model according to the critical inherent frequency; and acquiring the actual inherent frequency of the curtain wall to be tested, and evaluating the damage safety state of the curtain wall according to the model. According to the invention, rapid and efficient safety assessment can be carried out on the active glass curtain wall without damaging the rubber material body, and large-area screening and regular inspection can be realized; field test provides real data, finite element simulation supplements multiple working conditions, and data accuracy and model universality are guaranteed; the obtained inherent frequency reflects the essential attributes of the structure, and the damage detection and evaluation sensitivity is high; according to the constructed evaluation model, the result is direct and reliable through comparison between a safety threshold value and actual measurement.

The invention relates to the technical field of energy storage coordination control, in particular to an energy storage coordination control method and system for wind power generation. The method comprises the following steps: acquiring historical power generation states and environmental data, and drawing a power generation power fluctuation state curve to obtain a fluctuation state filling curve; then, on the basis of a historical environment state, multivariable regression analysis and generated power fluctuation data association are utilized to obtain multivariable power fluctuation associated data, and power generation behavior learning induction is carried out to form generated power incremental behavior data; and finally, constructing a wind power generation prediction model by using the generated power increasing behavior learning data, predicting the future generating capacity based on the model, formulating an energy storage adaptive coordination control strategy, and sending the strategy to a control terminal to execute energy storage coordination control. According to the invention, the energy storage coordination control technology is optimized, so that the energy storage coordination control technology is more accurate.

The invention discloses an ocean mesoscale vortex three-dimensional temperature field inversion method, device and equipment and a medium, and relates to the field of ocean observation and data inversion, and the method comprises the steps: obtaining multi-source ocean observation data; based on the multi-source ocean observation data, performing mesoscale vortex identification, and determining a mesoscale vortex three-dimensional structure; constructing a mesoscale eddy three-dimensional temperature field inversion model by adopting a regression analysis method according to the mesoscale eddy three-dimensional structure based on the multi-source ocean observation data; solving a regression coefficient in the mesoscale eddy three-dimensional temperature field inversion model by adopting a hierarchical least square method; and according to the regression coefficient, the mesoscale eddy three-dimensional temperature field inversion model is adopted to invert a mesoscale eddy three-dimensional temperature field, and dynamic constraints are embedded in the inversion process so as to obtain the temperature of each standard layer of the mesoscale eddy. According to the invention, the precision and reliability of the temperature inversion result of each standard layer of the mesoscale vortex are improved.

The invention discloses a coal type sales planning and sales time collaborative management and control method and system based on a coal enterprise, and relates to the technical field of coal sales management. Multi-source data are integrated through technologies such as a data fusion algorithm, the problems of data dispersion and different formats are solved, high-quality data support is provided for follow-up analysis, the sales trend and price fluctuation are accurately predicted by applying a long-short-term memory neural network and a regression analysis model, enterprises are assisted in optimizing sales strategies, the inventory overstock risk is reduced, and the sales quality is improved. By means of linear programming and reinforcement learning algorithms, sales tasks are dynamically adjusted in combination with real-time data, market response speed and sales efficiency are improved, scientificity and flexibility of sales management of coal enterprises are effectively improved, and market competitiveness is enhanced.

The invention provides a preparation method and device of a composite matrix for sewage treatment and a server, and relates to the technical field of water treatment material.The preparation method comprises the steps that center combination treatment is conducted on a ternary composite matrix for sewage treatment, and a test formula set is generated; obtaining test data corresponding to the test formula set, and taking the removal rate corresponding to each matrix as a response value to obtain target test data; substituting the target test data into a quadratic polynomial regression model of the ternary composite matrix to carry out regression analysis processing to obtain a model predicted value, and carrying out model verification updating processing on the quadratic polynomial regression model by utilizing the model predicted value and the target test data to determine a target quadratic polynomial regression model; and through the target quadratic polynomial regression model, optimizing the ratio of the ternary composite matrix, and determining the target mass ratio of the ternary composite matrix. According to the invention, the pollutant removal rate of synergistic decontamination of various pollutants in the sewage treatment process can be obviously improved.

The invention relates to the technical field of agricultural drought monitoring and precise irrigation, in particular to a crop water stress diagnosis method and system based on sunlight-induced chlorophyll fluorescence, crop canopy oxygen absorption band spectral data is collected through a hyperspectral sensor, and a sunlight-induced chlorophyll fluorescence intensity SIF value is solved by adopting a fluorescence extraction algorithm. The method comprises the following steps: taking an SIF value of a crop canopy without water stress as a reference canopy SIF value, determining a difference between the SIF value of a to-be-detected crop canopy and the reference canopy SIF value, then generating a relative SIF water deficit index SIFWDI by combining a preset formula, establishing a mapping relation between the SIFWDI and a soil water deficit index by utilizing a regression analysis model, and outputting a diagnosis report. Variable irrigation decision making and early warning are triggered according to the stress level, and model parameter self-adaptive optimization is achieved through a feedback mechanism. The system realizes lossless, real-time and high-precision water stress monitoring and irrigation management.

The invention relates to the technical field of data processing, in particular to a process detection method for a manufacturing process of a chicory preparation, which comprises the following steps: acquiring a concentrated solution concentration data sequence and a corresponding chicoric acid yield data sequence in the manufacturing process of the chicory preparation under each ethanol concentration; dividing each concentrated solution concentration data sequence into at least two stage sequences according to data change characteristics in each concentrated solution concentration data sequence; each concentrated solution concentration data sequence is divided into at least two subsequences, and according to the data difference of the subsequences contained in each stage sequence in each concentrated solution concentration data sequence and the fluctuation characteristics of the concentrated solution concentration data in each stage sequence, the concentrated solution concentration data in each stage sequence is obtained; according to the method, the stage influence weight of each stage sequence in each concentrated solution concentration data sequence is obtained according to the concentration data sequence of each concentrated solution and the similarity between each concentrated solution concentration data sequence and the corresponding chicoric acid yield data sequence, a regression analysis model is constructed, and the accuracy of a regression analysis result is improved.

The invention provides a mountain road coal gangue slope ecological restoration method, which comprises the following steps: acquiring real-time monitoring data from a coal gangue slope through a sensor network, including weathering degree and compaction density indexes, and obtaining an initial feature set; according to the initial feature set, carrying out preliminary grouping on the side slopes by adopting a clustering analysis method, and determining a classification standard threshold value; feedback data in the restoration implementation process are obtained and compared with the preliminary groups, and the effect deviation degree is judged; if the effect deviation exceeds a preset threshold value, a classification standard threshold value is updated through a regression analysis model, and an adjusted classification system is obtained; for the adjusted classification system, extracting a corresponding repair scheme template from a database, and generating an optimization scheme version; performing virtual testing on the optimization scheme version through an analog simulation module to obtain a prediction effect index; and collecting subsequent monitoring data according to the final execution parameter, and performing iterative comparison with the prediction effect index to obtain a further adjustment signal.

A calculation method of karst underground reservoir capacity curve based on hydrogeological parameters, including the following steps: hydrogeological exploration, determination of karst underground water reservoirs scope, determination of water storage space, determination of relevant hydrogeological parameters based on hydrogeological tests, and underground reservoir capacity analysis. The invention uses the unit water permeability and the borehole line karst rate that are very easily obtained through the borehole pressure (injection) water test and combines it with a small amount of water pumping test, thereby obtaining a correlation function relationship through regression analysis between the specific yield and the karst rate that are difficult to obtain and the unit water permeability or the permeability coefficient; the underground storage capacity at the corresponding elevation is calculated according to the specific yield or the karst rate and the underground storage capacity curve is obtained, which can improve accuracy and reduce errors.

The invention relates to the technical field of forage grass drying evaluation, and particularly discloses a forage grass drying evaluation method and system based on multi-modal fusion. According to the method, a quality file containing indexes such as the moisture content, the dry matter content and the dryness grade is established for each batch of forage grass, the feed intake, the feed intake behavior and the production performance data of cattle herds are synchronously collected in the feeding period, and a multi-modal association data set is established with batch identifiers and timestamps as association keys; and obtaining response results of the ingestion performance and the production performance corresponding to different drying characteristics by adopting correlation analysis, regression analysis and statistical significance test, and dynamically correcting the forage grass drying evaluation standard according to the response results of the ingestion performance and the production performance. According to the method, the forage grass drying characteristic data is associated with the cattle herd feed intake, the feed intake behavior and the production performance data according to batches and time, the multi-mode associated data set is constructed, and the direct corresponding relation between the forage grass drying state and the actual feed intake performance and the growth effect of the cattle herd is achieved.

The invention discloses a periodontitis dynamic prognosis prediction model construction method and system based on survival analysis, and the method comprises the steps: collecting and preprocessing periodontitis patient data, and constructing a structured multi-modal clinical data set; the method comprises the following steps: randomly dividing a multi-modal clinical data set into a training set and a test set according to patient IDs, only on the training set, carrying out single-factor Cox proportional risk regression analysis on all patient level data and tooth level data together, and screening indexes significantly related to periodontitis progress risks; taking periodontitis progress time as a dependent variable, and using the training set and the screened indexes to construct a time-dependent Cox periodontitis dynamic prognosis prediction model; and introducing an SHAP algorithm to analyze a prediction result of the time-dependent Cox periodontitis dynamic prognosis prediction model. The method can be used for realizing dynamic, accurate and explainable prediction of the periodontitis progress risk.

The invention relates to the technical field of aviation safety, in particular to a short-distance parallel runway collision risk assessment method based on influence of front and side wind, which comprises collision risk assessment, Bayesian parameter estimation, linear regression analysis, dynamic front and side wind fitting and Monte Carlo analog simulation. The problem of small sample parameter estimation is solved by introducing a Bayesian parameter estimation method, crosswind parameters are calculated by combining linear regression analysis, positive crosswind data are fitted by applying Weibull distribution, and an influence rule of positive crosswind and an initial interval on collision probability is quantified through Monte Carlo simulation. According to the short-distance parallel runway collision risk assessment method based on the influence of the positive and crosswind, on the basis of a traditional collision model, a re-flight condition is incorporated into the model, complex scenes such as crosswind and re-flight are considered, a dynamic interval adjustment model is fused, and a new collision risk model is established; the crosswind grading minimum interval standard is provided for airline companies and air traffic control operation management, and the runway safety and the airport operation capacity are improved.

The invention discloses an industrial chain electricity consumption sensitivity feature extraction method, system and device and a storage medium, and the method comprises the steps: obtaining multi-dimensional electricity consumption data, carrying out the feature extraction and dimension reduction of the multi-dimensional electricity consumption data, and mapping an original high-dimensional feature into a low-dimensional feature; carrying out regression analysis on the low-dimensional features, calculating a feature sensitivity coefficient, reversely associating original high-dimensional features, calculating the total influence degree of the original high-dimensional features on power consumption, and screening to obtain key features; based on the characteristic sensitivity coefficient and the key characteristic, the deviation degree of the low-dimensional characteristic and the normal working condition distribution is calculated, abnormity is detected according to the deviation degree, when abnormity is detected, a dynamic optimization mechanism is triggered, extraction of the electricity utilization sensitivity characteristic is achieved, and online learning and dynamic optimization requirements in a super-large scale time sequence scene are supported.

The invention provides a variable-speed vibration signal feature extraction method and system. The method comprises the following steps: acquiring vibration signal data under a variable-speed condition; preprocessing the vibration signal data; carrying out feature extraction on the preprocessed vibration signal data by adopting order analysis; optimizing the feature data by adopting a self-adaptive filtering algorithm; constructing a fault feature physical model, and performing classification or regression analysis on feature data based on a machine learning or deep learning algorithm; and inputting the optimized feature data into the fault feature physical model, and carrying out bearing fault diagnosis. According to the variable-speed vibration signal feature extraction method and system provided by the invention, an equal-angle resampling technology is adopted, the accuracy and consistency of signal analysis are ensured, and in combination with an intelligent feature extraction and mode recognition algorithm, mild, moderate and severe classification evaluation of faults such as bearing wear is realized.

This invention relates to the field of parcel delivery volume prediction technology, and discloses a method for predicting logistics parcel delivery volume based on a multi-level network architecture. The method includes: acquiring raw data and standardizing the data; constructing a multi-level network hierarchy and a multi-dimensional weighted ranking model; displaying the data in multiple dimensions; calculating the confidence interval based on the prediction model and attaching a confidence level label to each predicted value; introducing a user behavior learning model to record user click habits and query preferences, and optimizing the network display order and recommendation logic; using an integrated prediction model to fuse time series analysis, regression analysis, and machine learning prediction methods to form a combined prediction result; establishing a prediction error feedback mechanism to compare the actual delivery volume with the predicted delivery volume and optimize model parameters; and adopting a hierarchical prediction strategy. This invention provides reliable support for logistics delivery planning and resource allocation, reduces operating costs, and improves service quality.

According to various embodiments of the present invention, a regression analysis-based circuit characteristic optimization device for digital circuit design may: receive, from the outside, information about a digital circuit to be designed, and arbitrarily set a characteristic value for at least one transistor included in the digital circuit; generate a circuit characteristic regression model by mathematically modeling the characteristic of a path included in the digital circuit on the basis of the characteristic value of the transistor included in the digital circuit; obtain an initial coefficient of the circuit characteristic regression model by applying the arbitrarily set characteristic value thereto; and obtain the improved characteristic value for the at least one transistor included in the digital circuit on the basis of the circuit characteristic regression model into which the initial coefficient has been incorporated.

This invention relates to the field of intelligent driving technology for vehicles, and provides a method, system, vehicle, and storage medium for missing lane line completion. The method includes the following steps: collecting lane information to form a dataset; importing the dataset into a deep learning network model to train the network, enabling the network to predict the future trajectory of a vehicle based on the historical trajectories of surrounding vehicles; fitting the predicted trajectory of the vehicle and the lane line trajectory output by the network; performing regression analysis and matching the fitted curve of the lane line trajectory near the predicted trajectory to obtain a mathematical model of the fitted curve; obtaining the relationship between the fitted curve of the lane line and the vehicle's driving trajectory through the mathematical model; and completing the lane line based on the fitted lane line trajectory and the vehicle's position. This invention can achieve high-precision lane line completion in scenarios where lane lines are missing or blurred.

An analysis device according to the embodiment includes a creation unit and an output control unit. The creation unit arranges a line segment indicating a point that is a product of an attribute value of a regression model obtained by regression analysis by secure computation and a regression coefficient in a direction corresponding to a sign of the corresponding regression coefficient and creates a nomogram in which the point is plotted at a position corresponding to a target point on the line segment. The output control unit outputs the created nomogram.