254 results about "Linear correlation" patented technology

The invention provides a prediction method and system for a rock stratum fracture disaster in tunnel engineering, and belongs to the field of tunnel engineering. Comprising the steps of multi-modal monitoring data acquisition and synchronization, data preprocessing and feature calculation, multi-modal information entropy evolution analysis, catastrophe point detection and precursor identification, risk assessment and early warning output and the like. Multiple types of sensors are arranged at key positions of a tunnel, an edge computing gateway is accessed through an industrial-grade field bus, and space-time reference unification of multi-source heterogeneous data is achieved; carrying out preprocessing and feature calculation on the collected original data; performing sliding window probability distribution modeling on each modal feature, calculating an information theory index, and analyzing non-linear relevance among the multi-modal features; judging whether a catastrophe precursor event exists or not by adopting multiple methods; and finally, based on a catastrophe point detection result, performing risk assessment on the tunnel region by using a multi-dimensional risk quantification model, and performing early warning output on high-risk and emergency states.

The invention discloses a vehicle-mounted monitoring system based on deep learning, particularly relates to the technical field of vehicle-mounted multi-mode cooperative sensing, and is used for solving the problems of space-time progressive deviation accumulation and high-speed scene target state estimation distortion caused by hardware synchronization limitation in existing vision and millimeter wave radar fusion. According to the method, high-precision alignment and self-adaptive correction of multi-modal data are realized through dynamic space-time baseline compensation and a closed-loop feedback optimization mechanism; a dynamic compensation coefficient is generated based on sensor space-time distribution mode prediction, and a nonlinear correlation model of a multi-modal data stream is constructed to eliminate sampling frequency difference and transmission fluctuation interference; visual motion artifacts and radar positioning jump noise are separated through a cross-modal feature decoupling technology, and high-confidence feature fragments are extracted in combination with historical trajectory screening and real-time residual analysis; and a closed-loop architecture fusing weight distribution and compensation parameter reverse correction is established, and dynamic calibration and error traceability of multi-source data collaborative sensing are realized.

The present disclosure provides a method for remote sensing blue-green wave band ratio logarithmic water depth retrieval of wavelet spline instantaneous tidal height correction, and belongs to the field of remote sensing water depth retrieval. Aiming at water depth retrieval precision reduced by blue-green light and a tidal height in a remote sensing water depth retrieval process, the model fully considers a linear correlation between an attenuation ratio of the blue-green light in water and a depth, the smoothness of the tidal height, and the characteristics of consistent convergence, first-order continuous derivation and second-order continuous derivation of the tidal height with time change, and constructs the method for remote sensing blue-green wave band ratio logarithmic water depth retrieval of wavelet spline instantaneous tidal height correction, and according to Molokai experiment verification, compared with an early model, the invention improves the remote sensing water depth retrieval precision.

The invention provides a marine meteorological data quality control method based on a cross-parameter correlation network, which belongs to the technical field of marine meteorology, and comprises the following steps: constructing a cross-parameter correlation network model comprising an air temperature and air pressure correlation rule, a humidity and air temperature linear correlation rule and a wind speed and air pressure gradient extraction correlation rule; a sliding window algorithm is adopted to calculate a correlation coefficient in real time and identify correlation abnormity, a multivariate abnormity detection mechanism of four dimensions of parameter threshold overrun, spatio-temporal change rate abnormity, probability density distribution offset and correlation verification failure is established, a fuzzy comprehensive evaluation method is adopted to calculate a comprehensive abnormity index, and data abnormity is judged. And the authenticity of the abnormal data is confirmed in combination with a multi-sensor cross validation mechanism, and finally the abnormal data is restored by adopting a virtual sensor data reconstruction algorithm based on correlation network reverse calculation, so that the technical problem of poor abnormal data detection effect in the prior art is solved.

The invention discloses an electric power resource scheduling method and device suitable for extreme weather, terminal equipment and a storage medium, and belongs to the technical field of electric power scheduling, and the method adopts nonparametric kernel density estimation to construct a wind and light output joint probability distribution function representing output characteristics of wind power output and photovoltaic output in extreme weather. When flexibility demand analysis is carried out, the advantages of nonlinear correlation between variables and tail risks can be analyzed by means of a joint distribution probability function, wind power prediction errors and photovoltaic prediction errors are accurately analyzed, and then adjustment demand risks of net load flexibility demands are quantified in combination with convolution operation. The problems that the joint probability distribution characteristics of wind and light output in extreme weather cannot be accurately described and the regulation demand risk cannot be ignored in the current flexibility demand evaluation method depending on the normal distribution independent assumption are solved. And the power supply reliability and the operation stability of the power system in extreme weather can be ensured by the formulated power dispatching plan.

The invention relates to a method for detecting pyridine in air, a test paper sheet is placed in an air environment to be detected, the color change of the test paper sheet is observed, the pyridine concentration is qualitatively judged or quantitatively judged according to the color change of the test paper sheet, and the test paper sheet is made of a filter paper sheet and a zinc-based organic-inorganic hybrid halide material. According to the method, qualitative or semi-quantitative judgment of the pyridine concentration in the air can be performed by adopting a test paper sheet and a color card or a linear correlation model standard curve graph, so that whether the pyridine content in the air exceeds the standard or not or the exceeding amplitude can be monitored, and the method has the advantages of convenience in operation and more convenience in detection.

The invention discloses a multi-source heterogeneous data processing method and system based on a test chip, and the method comprises the steps: employing a distributed message queue to access a multi-protocol data stream in real time, collecting the multi-source heterogeneous data related to a chip test, and carrying out the normalization preprocessing; performing cross-domain spatio-temporal feature alignment and mapping on the preprocessed multi-source heterogeneous data, constructing a hybrid incidence matrix based on linear correlation degree and nonlinear correlation degree, extracting three-dimensional joint features, and generating a dynamic metadata tag; generating nodes and edges by using the incidence matrix and the extracted three-dimensional joint features, constructing a parameter association graph structure, performing graph embedding training and generating an index structure, positioning Top-K candidates based on the index structure by using query features and labels, and sorting and returning a result according to a comprehensive distance; the problem that cross-domain association analysis is difficult to carry out due to the fact that the format difference of multi-source heterogeneous data generated by existing chip testing is large is solved, and the precision and efficiency of multi-source heterogeneous data processing are improved.

The invention provides a chronic disease risk early warning system and method based on artificial intelligence. The method comprises the following steps: acquiring historical monitoring information of a diabetic patient; determining development trends of different disease course stages through historical monitoring information, and performing trend evolution based on all the development trends to obtain evolution characteristics of each disease course stage; determining the risk contribution degree of each health index to the chronic disease risk according to the linear correlation among different health indexes, and determining a risk monitoring model of the target patient through all the risk contribution degrees; performing confidence adjustment on the chronic disease risk in the risk monitoring model according to each evolution feature, and further obtaining a confidence risk value of the current disease course stage of the target patient; and carrying out risk prompting on the target patient based on the confidence risk value. By adopting the scheme of the invention, real-time modeling can be carried out on the dynamic change of the course of disease of the patient under the background of massive health data.

The invention discloses a circulating water culture pond fish school biomass evaluation method, which comprises the following steps of: firstly, acquiring a sonar image corresponding to each circle of rotary scanning of a circulating water culture pond by sonar through steps S1 and S2, and extracting a spatial three-dimensional coordinate of each identified cultured fish in each sonar image based on image identification; filtering noise points generated due to repeated identification in the identified cultured fish coordinate points through the step S3, and counting the number of fish schools corresponding to each sonar image according to the actual cultured fish coordinate points obtained through point cloud merging, and then obtaining a linear correlation curve Si (hi) of the effective area Si of the pool wall and the sampling depth hi through the step S4, and finally, performing integration on the two curves according to intervals through the step S5, performing layered calculation on the number of the fish schools in each interval, and performing accumulation to obtain an estimated value P of the fish school biomass in the circulating water culture pond, so that the measurement accuracy of the fish school biomass in the circulating water culture pond can be improved.

The invention discloses a model processing method and device, equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, in the method, singular value decomposition is carried out on a network weight matrix of a hybrid expert model, and in an obtained right singular vector matrix, columns with singular values smaller than a threshold value are screened to serve as a null-space basis matrix. In this way, the null-space basis matrix can represent the activation mode direction which contributes little to the activation of the expert network. Furthermore, a sub-network weight matrix is constructed based on each column of the null-space basis matrix and the network weight matrix. Therefore, according to the maximum singular value and the minimum singular value of the sub-network weight matrix, the closeness degree of linear correlation between each column of the network weight matrix and the null-space base matrix can be determined, then the invalid feature sensitivity of each expert network can be determined, and according to the invalid feature sensitivity, the invalid feature sensitivity of each expert network can be determined. And cutting network parameters of the hybrid expert model. In this way, the model parameter quantity can be reduced.

The invention discloses a system and a method for producing a glue injection industrial brush by using an injection molding machine, and relates to the technical field of industrial brush processing, a multi-parameter coupling index is generated according to acquired working parameters, and the working parameters, the multi-parameter coupling index and a batch identifier in a corresponding time period are bound; the method comprises the following steps: performing multi-angle imaging on brushes, adding mark information sections, performing preprocessing, extracting candidate areas which may have defects, after detecting a plurality of defect areas, constructing a morphological synergy index to quantify the overall defect degree, and obtaining defect data of each brush; after a comprehensive index structure is constructed, an injection molding process feature vector is constructed, kernel mapping and an AWCCA method are utilized to calculate nonlinear correlation and local sensitivity between each working parameter and a form cooperation index, a working parameter interval is formulated, and a parameter modification suggestion is generated; in the production process, parameters are continuously corrected in a small-stride mode, the defect degree of the brush and the stability of the injection molding process are balanced, and more flexible regulation and control are achieved.

The invention discloses an attention mechanism and deep learning-based electric tug energy consumption prediction method, which comprises the following steps of: acquiring ship power battery parameters, propulsion system parameters, navigation state parameters and navigation environment parameters in real time, and performing data preprocessing; the tug working conditions are classified and recognized, working condition recognition results are coded, and a data set for energy consumption prediction is constructed; and a pure electric tug energy consumption prediction model based on the LSTM-Transform hybrid architecture is constructed, the constructed prediction model is trained and tested, and finally energy consumption prediction per minute and energy consumption prediction per sea are realized. According to the method, the attention mechanism of Transform is combined with a deep learning method to fully mine the time sequence characteristics and non-linear correlation of the electric tug energy consumption data, the prediction precision is improved by adaptively adjusting model parameters, and reliable data support can be provided for energy optimization scheduling and cruising ability evaluation of the electric tug; and the economy and safety of ship operation are effectively improved.

The invention discloses a coal seam roof pre-grouting optimization method based on data driving. The coal seam roof pre-grouting optimization method comprises the following steps: S1, fine exploration and original information extraction of a geologic body in a research area; s2, construction information extraction; s3, performing grouting project classification by adopting a DBSCAN clustering algorithm; s4, establishing a quantitative relation model based on a classification result; the method is suitable for the technical field of coal water prevention and control, multi-source geological exploration data and dynamic grouting engineering parameters are fused, a geological-engineering feature coupling model is established, a nonlinear association rule of a geological structure and grouting response is quantitatively analyzed based on a machine learning algorithm, and the method has the advantages of being high in practicability and high in reliability. And a multi-objective collaborative optimization system is developed, and intelligent matching of grouting parameters, accurate prediction of the construction effect and dynamic optimization of an engineering scheme are achieved. Finally, the technical purposes of improving the historical data utilization rate, enhancing the grouting and water plugging success rate and reducing the engineering cost are achieved, and scientific decision support is provided for coal mine water disaster prevention and control.

The invention provides a gas leakage detection method and circuit, and the method comprises the steps: obtaining a plurality of calibration electric signals of calibration concentrations of target gas through employing a gas concentration sensor, and forming a concentration electric signal calibration table; determining the linear correlation between the gas concentration of the target gas and the detection electric signal based on a concentration electric signal calibration table; obtaining a leakage threshold value corresponding to the gas alarm concentration by combining the concentration electric signal calibration table and the linear correlation; a gas concentration sensor is adopted to obtain the real-time detection concentration of target gas in real time, and when the real-time detection concentration reaches a leakage threshold value, an alarm signal is sent out. The method has no destructive influence on the detection environment and no material loss, and is simple in structure, stable in performance, convenient to use, stable in signal and long in service life by detecting the concentration and leakage state of the target gas, and the corresponding detection circuit is small in structure and convenient to mount and dismount.

The invention provides a shale oil reservoir dessert intelligent identification method and system, relates to the field of shale oil reservoirs, and aims to obtain original logging data and extract curve deep space-time coding features by fusing a hybrid model of a convolutional neural network and a recurrent neural network. Then, multi-source logging features are dynamically fused through a cross-curve feature fusion algorithm, and spatial compression is carried out through feature principal component analysis; thirdly, implicit features strongly related to the geological signs of the sweet spots are screened through semantic level selection based on feature principal components, sweet spot probability prediction is achieved based on a lightweight classification model, an intelligent recognition closed loop without manual intervention is formed, and the dependence of a traditional method on expert experience is broken through; in this way, non-linear correlation in the logging curve is automatically mined through deep learning, and the interpretability and generalization ability of complex reservoir dessert recognition are improved.

The invention discloses a ship maintenance inspection robot and a use method, and relates to the technical field of ship maintenance inspection robots. Through mutual cooperation of the telescopic springs and the anti-collision plates, it is conveniently guaranteed that in the collision process of the robot, impact on a robot body is reduced, the use safety of the robot is improved, and then the function of protecting the robot and ship equipment is achieved; a linear relation model of obstacle length, width, walkway width and passing time is constructed to quantify the influence of the obstacle on the passing efficiency, and the limitation that only the route length is used as a single optimization index is avoided; the actual passing time of each route is calculated based on historical inspection data, the consumed time of the optimal route and the consumed time of the to-be-selected route are dynamically compared, real-time adjustment of the route priority is achieved, and the inspection efficiency in a complex environment is improved; and the distribution rule of abnormal positions is judged by using a linear correlation coefficient, and if abnormal points are linearly arranged, the corrosion possibility is eliminated, so that the misjudgment rate is reduced.

The invention relates to the technical field of application of a neural network to interval prediction of regional power system load, and provides a method for reasonably and accurately predicting and estimating power load which is a key element of a novel power system, so that safe and stable operation of the power system is ensured, and efficient supply and demand balance is realized. According to the interval prediction method based on an adaptive cooperative myxomycete algorithm and parallel prediction, firstly, an improved grey correlation degree analysis IGRA is adopted to screen climate, holiday and festival factors, and factors having high non-linear correlation with a load sequence are identified; then, a neural network model of time convolutional neural network-deep learning is built; carrying out parameter optimization on the TCN-Transformer model by adopting an adaptive cooperative mucus algorithm (ACSMA); and utilizing the trained TCN-Transform to carry out interval prediction on the load of the regional power system. The method is mainly applied to power system load prediction management occasions.

The invention discloses an ICP-MS (Inductively Coupled Plasma Mass Spectrometry) detection method for phosphorus in trichlorosilane, and belongs to the field of analysis of high-purity electronic chemicals. According to the invention, a low-temperature matrix digestion technology is adopted to quantitatively form a SiFcomplex and inhibit silicon volatilization loss; according to the matrix matching calibration system, a calibration curve is constructed by adopting a weighted least square method, so that matrix interference is effectively corrected; a detection technology screening strategy is carried out by combining a cascade strategy of high-resolution inductively coupled plasma mass spectrometry, so that the interference of a complex matrix is successfully avoided. Experiments show that when the method is used for detecting phosphorus, the linear correlation coefficient R is greater than 0.999, the detection limit is as low as 0.02 ng / g, and the method accords with ISO 17025 and semiconductor-grade material quality control standards, and is suitable for accurate quantitative analysis of ng / g-grade phosphorus elements in high-purity trichlorosilane.

The invention discloses an enterprise budget dynamic analysis and optimization method based on a multi-modal generative adversarial network and variational Bayesian optimization, and belongs to the technical field of enterprise financial intelligent management. Enterprise subitem budget and actual overhead data are input, and subitems and total difference are calculated to construct a difference matrix; a bidirectional LSTM layer of a variational auto-encoder (VAE) is used to extract time sequence features, a mean value and a variance of hidden variables are generated through a full connection layer, and an evidence lower bound loss function is optimized to realize dimension reduction coding of high-dimensional data; and constructing a generative adversarial network (GAN), and adopting a variational Bayesian optimizer to iteratively search an optimal strategy considering the rate of return on investment and the risk score based on a Gaussian process proxy model and an expectation improvement criterion. The method can effectively capture the non-linear correlation between budget subjects, dynamically generates an optimization strategy conforming to business constraints, improves the model generalization ability in a small sample scene through a self-evolution mechanism, and is suitable for budget management of enterprises in multiple industries.

The present invention relates to a method and system for accurate unit load prediction of a power grid based on deep learning. The method includes the following steps: acquiring operation monitoring parameters of power consumption equipment and equipment environment parameters in a power grid area; extracting dynamic voltage characteristics from the operation monitoring parameters of power consumption equipment in the power grid area to generate voltage fluctuation trend characteristic data and multi-level load curve characteristics; performing non-linear correlation analysis on the voltage fluctuation trend characteristic data and multi-level load curve characteristics according to the equipment environment parameters to generate load-environment deep non-linear correlation data; identifying power consumption equipment in the power grid area to obtain power consumption equipment nodes in the power grid area; performing accurate unit clustering division on the power consumption equipment nodes in the power grid area based on the load-environment deep non-linear correlation data to construct a power grid unit load characteristic network; and generating accurate unit types based on multiple accurate power grid units. The present invention realizes accurate power grid load prediction.

The present invention discloses a method for optimizing the layout of multi-modal vibration sensors on a wind tunnel tail strut, including: establishing a finite element model of the tail strut including the wind tunnel test model and the balance, performing modal analysis to determine the vibration modal orders to be monitored; arranging n vibration sensors along the axis direction of the strut. According to the vibration modal orders to be monitored, the tail strut is excited by a bandwidth random signal, and the vibration modal frequencies of the corresponding tail strut structure are extracted; the tail strut is excited by a sine wave of each modal vibration frequency respectively, the amplitude ratio sequence of the vibration sensors relative to the excitation signal is recorded, and the amplitude ratio sequences of multiple orders of modes are assembled to form a mode shape matrix; the sensor layout optimization criterion is used as the fitness function for the position layout of the sensors, and the optimal installation position layout of the sensors is solved. The present invention uses the optimization criterion to maximize the observation ability of the vibration modal set and minimize the linear correlation between the modal vectors, realizing the optimal position layout of the sensors.

The invention relates to the technical field of data retrieval, in particular to a QT basin logging data processing system and method, and the system comprises a well section structure analysis module, a lithologic sequence reconstruction module, a reservoir physical property integration module, a sealing cover response extraction module and a combined optimal sequence generation module. According to the method, adjacent standard deviations and variation coefficients are calculated through gamma, density, resistivity and interval transit time, parameter sudden change intensity is quantified, effectiveness of a stratum interface is calibrated, keyword matching is replaced based on resistivity, gamma and density difference vector cosine similarity, and the problem of non-linear correlation misjudgment of lithologic parameters is solved; calculating the product of the porosity difference value and the permeability through a sliding window, recognizing the heterogeneous characteristics of the reservoir, extracting the second derivative peak value of gas logging data to construct an argillaceous rock capping effectiveness model, accumulating and integrating the reservoir stability, the fluorescence intensity and the sealing index, generating a new reservoir cap optimal sequence map, and achieving exploration priority sorting.

The invention relates to the technical field of asphalt pavement performance evaluation, in particular to an asphalt pavement technical condition evaluation method based on relative entropy information fusion, and the method comprises the steps: S1, building two parallel probability distribution paths; s2, inputting the average maintenance interval time theta into a Bayesian dynamic linear model, and adjusting a PQI distribution mean value mu in real time through a prediction step and an updating step; s3, non-linear correlation between subitem indexes and the PQI is established, a logarithm-logarithm regression model is adopted for a truncated normal path, and a logarithm regression model of a scale parameter lambda is adopted for a Weibull path; s4, respectively calculating the KL divergence of the distribution functions under the two paths; and S5, solving the minimum value of the target function h by adopting an SLSQP algorithm, and iteratively optimizing a regression coefficient beta-beta through gradient descent by taking a least square analytical solution as an initial value. According to the method, the relative entropy information fusion technology is introduced, and a double-probability model of truncated normal distribution and Weibull distribution is combined, so that a dynamic and accurate evaluation system is constructed, and the reliability and the practical value of an evaluation result are improved.

The invention provides a rocket aircraft flow field correction method based on multi-fidelity data fusion, and the method comprises the following steps: 1, collecting low-fidelity data, and carrying out the construction of a low-fidelity data set: simulating the change process of an unsteady flow field around an object in a finite time step through a Reynolds time-average simulation method or a large vortex simulation method; 2, data correlation analysis is carried out, wherein a linear relation model yH = rho (x) yL + delta (x) of a non-viscous flow field yL and a viscous flow field yH of the rocket aircraft is established; step 3, neural network architecture design: constructing a low-fidelity data approximation network NNL; 4, performing hyper-parameter learning and optimization; defining a loss function; 5, performing data acquisition and preprocessing: performing high-fidelity viscous flow field data acquisition, and preprocessing the acquired high-fidelity and low-fidelity flow field data; and step 6, model training and verification.

The invention relates to the technical field of electric digital data processing, in particular to a real-time monitoring method and system for sludge solidification stirring equipment, and the method comprises the steps: obtaining multi-dimensional parameter data of the sludge solidification stirring equipment at a plurality of historical time nodes; constructing the parameter data of the same dimension into a parameter sequence in a set time window; acquiring the local fluctuation degree of the parameter data of any time node in the parameter sequence to which the parameter data belong, and the linear correlation degree between the parameter data and other parameter sequences; determining the parameter data greater than a linear correlation degree threshold as strong cooperation parameter data; and respectively calculating first abnormal scores of the parameter data and the strong cooperation parameter data of any time node by using an improved local outlier factor algorithm, averaging the first abnormal scores to obtain a second abnormal score, and responding to the parameter data of which the second abnormal score is greater than a set threshold value as abnormal parameter data. The problem that an existing algorithm is not high in detection precision is solved.

The invention provides a multi-state-quantity integrated high-voltage cable distributed online monitoring method and system, and belongs to the field of high-voltage cable monitoring, and the method comprises the steps: S1, deploying monitoring nodes, and constructing a distributed monitoring network; s2, acquiring multi-dimensional state data of the high-voltage cable, and performing preprocessing and feature extraction to form a state vector; s3, calculating linear correlation among the state vectors, calculating nonlinear correlation intensity by adopting a kernel correlation analysis method, and performing weighted cooperative calculation; s4, calculating a connection weight between nodes according to the monitored network topology, and executing a distributed consistency protocol; and S5, establishing a normal state reference vector and a fault state reference vector based on historical data, and calculating a comprehensive health degree evaluation value by adopting a relative distance method. According to the method, collaborative decision-making of multi-dimensional state vectors is realized by using a distributed consistency protocol, the comprehensive assessment capability of the overall health state of the high-voltage cable is formed, and the accuracy and reliability of fault identification are effectively improved.

This invention discloses a downconversion fluorescence thermometry method based on bilinear mode, implemented using a hardware system. The hardware system includes a 280nm light-emitting diode, two convex lenses, and a Tb... 3+ :Cs2Na 0.9 Ag 0.1 The system comprises InCl6 temperature-sensing material, a grating spectrometer, an oscilloscope, a signal generator, a computer, and a thermostat. The specific implementation steps are: signal excitation and acquisition; temperature calibration; actual temperature measurement; and high measurement accuracy: VPR mode linear correlation coefficient R² ≥ 0.998, FL mode linear correlation coefficient R² ≥ 0.992, avoiding measurement errors introduced by the exponential bias or correction term of traditional FIR technology, thus fundamentally improving temperature measurement accuracy.

The present invention provides a method and device for quantitatively evaluating the fault sealing performance. The method includes: receiving the fault sealing characterization parameters of a target work area; quantitatively evaluating the fault sealing performance in the target work area according to the fault sealing characterization parameters and a pre-generated sealing performance evaluation model, where the sealing performance evaluation model is obtained by training and optimizing an initial model generated based on the fault sealing characterization parameters. The method and device for quantitatively evaluating the fault sealing performance provided by the present invention utilize the concept of machine learning, adopt the decision tree algorithm, and establish a quantitative evaluation model of fault sealing based on a non-linear correlation on the basis of comprehensively considering multiple factors affecting fault sealing, filling the blank of the quantitative calculation method for fault sealing, and the results are accurate and effective, thereby providing a more scientific, objective, more applicable and more extensive evaluation method for the quantitative evaluation of fault sealing.

The invention relates to the technical field of atmospheric boundary layer height prediction, and discloses a multi-meteorological parameter coupling-oriented atmospheric boundary layer height prediction method, which comprises the following steps of: acquiring meteorological data, and preprocessing the meteorological data; analyzing linear correlation between meteorological parameters and pollution parameters in the meteorological data and the height of an atmospheric boundary layer, and screening thermal characteristics, dynamic characteristics and pollution characteristics from the linear correlation as a multivariate characteristic set; constructing a prediction model based on a bidirectional long-short-term memory network and an attention mechanism, and performing automatic optimization on model hyper-parameters by adopting a Bayesian optimization method to obtain an optimized prediction model; and predicting the height of the atmospheric boundary layer by using the optimized prediction model. According to the invention, high-precision and high-reliability atmospheric boundary layer height prediction data support is provided for short-term pollution early warning and environment management.

The invention relates to the technical field of model construction, in particular to a displacement fluid and shale rock imbibition law numerical simulation method and system. According to the method, by quantitatively analyzing the linear correlation degree of granularity and experimental capillary constant distribution and the disturbance degree of local feature points, regularization parameters of a regression algorithm are dynamically adjusted, and on the basis of accurately-calibrated capillary constants, a contact angle is determined by combining permeability characteristic data of probe liquid, so that the permeability of the probe liquid is determined. According to the method, shale surface free energy is obtained through surface energy component theoretical model decoupling, the interface action state of displacement fluid, shale and crude oil is reflected through a three-phase contact angle determined through a three-phase equilibrium relation, and the adhesion work needed by shale oil stripping is obtained. According to the method, the high-precision fitting model of the granularity and the capillary constant is established, and the surface energy component theory is combined, so that more accurate calculation of the shale oil stripping adhesion work is realized, and a reliable numerical simulation basis is provided for improving the shale oil recovery ratio.