262 results about "Multiple linear regression model" patented technology

The invention relates to the technical field of water treatment intelligent control, in particular to an AI-based intelligent urban water purification and sewage treatment process optimization system and method, and the method comprises the steps: obtaining related sensor data information, and monitoring and recording the change of key data in real time; establishing a water quality change prediction model by using a multiple linear regression model, wherein the water quality change prediction model predicts the water quality change according to the key data change acquired in real time; the dynamic agent AI model adjusts the dosage, and the water treatment chemical / biological reaction kinetics AI model predicts the pollutant degradation condition; a local lightweight AI model optimizer and a multi-target genetic algorithm are combined with energy consumption, medicament cost, processing efficiency and carbon emission to obtain an optimal control model; the optimal control model optimizes the operation parameters of the sewage treatment process according to the pollutant degradation condition. According to the method, the operation parameters of the sewage treatment process are optimized through the optimal control model, the energy consumption, the agent cost and the carbon emission are reduced, and the treatment efficiency is improved.

The invention discloses a switch cabinet discharge detection method and system, and relates to the technical field of power equipment state monitoring and fault diagnose.The light intensity attenuation rate and the vibration frequency are collected through a photonic crystal fiber, the concentration of decomposed gas is detected in combination with a Raman spectrum, and a space-time joint feature matrix is generated; performing space weight distribution on the space-time joint feature matrix based on the switch cabinet structure, and inputting the space weight to an RBF-SVM (Radial Basis Function-Support Vector Machine) to generate discharge type probability distribution; based on a high-frequency energy parameter and a gas decomposition concentration parameter in the spatio-temporal joint characteristic matrix, inputting the parameters into a multiple linear regression model, calculating an insulation aging index, and judging an insulation degradation grade according to a threshold value; the photonic crystal fiber is used for collecting the light intensity attenuation rate and the vibration frequency, the Raman spectrum is combined for detecting the decomposition gas concentration to generate the space-time joint characteristic matrix, synchronous monitoring of various physical and chemical parameters in the switch cabinet is achieved, and the accuracy and comprehensiveness of recognition of different types of discharge phenomena are improved.

The invention discloses a roadbed settlement prediction method and system for a permafrost region. The roadbed settlement prediction method comprises the steps of obtaining roadbed historical data corresponding to the permafrost region; intercepting data of a preset fixed length and settlement monitoring data corresponding to the data of the preset fixed length at a future moment, and constructing a permafrost region roadbed settlement sample database; building a base model; training the base model through the established database to obtain a trained base model; outputting a preliminary prediction result through the trained base model; the prediction result of the base model is used as the input of a meta-model through a stacking model, the meta-model is a multiple linear regression model, the prediction results of multiple prediction units of the base model are integrated, and the roadbed settlement corresponding to the final permafrost region future moment is output. According to the method, prediction results of a plurality of models are fused, so that the final model is more robust for abnormal values and noise data, and the stability of training prediction of the model for large fluctuation of roadbed settlement data in the frozen soil region is improved.

The invention relates to electroencephalogram signal acquisition, in particular to an electroencephalogram signal acquisition and monitoring system, which comprises a mobile terminal, a signal acquisition device, an EEG (electroencephalogram) signal, an fNIRS hemodynamic signal and IMU (inertial measurement unit) motion data, a physical information neural network method of a self-adaptive sampling strategy is adopted to identify a BCG vascular pulse pseudo-film segment in an EEG signal by using an fNIRS hemodynamic signal, and then a multiple linear regression model of motion artifacts is established based on the fNIRS hemodynamic signal and IMU motion data so as to identify a motion pseudo-film segment in the EEG signal. Performing interpolation restoration on the detected pseudo-film segments to eliminate artifacts in the EEG electroencephalogram signals, and drawing and displaying an electroencephalogram in real time according to the EEG electroencephalogram signals after the artifacts are eliminated; according to the technical scheme provided by the invention, the defect that BCG vascular pulsation artifacts and motion artifacts in the EEG signals are difficult to effectively eliminate can be effectively overcome.

The invention discloses a disastrous big wave occurrence probability and wave height combined prediction method, device and equipment, a medium and a product, and relates to the technical field of meteorological ocean forecasting. The method comprises the following steps: acquiring an effective wave height measured value and environmental factor data of a to-be-predicted region in a historical time period; key environment factors are screened through spatial correlation analysis and linear fitting verification, a first training data set and a second training data set are constructed, and a multiple linear regression model and a logic regression model are trained respectively to obtain a wave height prediction model and a big wave occurrence probability prediction model; and inputting the key environmental factors of the current time period of the to-be-predicted region into the model to obtain the predicted occurrence probability of the disastrous waves and the predicted value of the significant wave height in the predicted time period. According to the method, the key environmental factors are screened and the two models are used for respective prediction, the influence of the various environmental factors on the big waves is considered, the occurrence probability and the wave height of the disastrous big waves can be predicted more accurately, and the ocean safety is effectively guaranteed.

The invention discloses a heart tension index dynamic calculation and risk assessment method based on HRV, and the method comprises the steps: carrying out the filtering and denoising of an electrocardiosignal through a Butterworth filter, and obtaining a filtered electrocardiosignal; calculating interval data of adjacent wave crests; processing the interval data according to fast Fourier transform to obtain frequency domain features, and combining the interval data to obtain time domain features; performing normalization processing on the time domain and frequency domain features based on a maximum and minimum normalization method; calculating a cardiac tension index according to the normalized features and a pre-trained multiple linear regression model; matching a pre-constructed reference database according to the individual age, gender and body mass index to obtain a reference cardiac tension index; calculating a cardiovascular risk score in combination with the normalized frequency domain features and a reference cardiac tension index; and repeating the above steps regularly, smoothing through a moving average method based on continuous multiple cardiovascular risk scores, and analyzing the trend to send out cardiovascular risk early warning information.

The invention discloses an LED energy storage street lamp management method and system combined with Internet of Things perception, relates to the technical field of Internet of Things and intelligent illumination, and solves the problem that it is difficult to use the Internet of Things to monitor, collect and process various data in real time so as to predict maximum brightness demand values of street lamps in different time periods. The influence of shelters on the illumination intensity of the street lamp is difficult to analyze; the battery state and the energy storage efficiency of the LED energy storage street lamp are difficult to evaluate; a charging and discharging control strategy is difficult to generate; the energy consumption under the charge and discharge control instruction is difficult to optimize by using digital twinning, and the optimized charge and discharge control instruction is difficult to execute. Various sensing data are collected and processed through the Internet of Things, the brightness requirement of the street lamp is predicted by using a multiple linear regression model, a dynamic shielding coefficient is analyzed, and the battery state and energy storage efficiency of the LED energy storage street lamp are evaluated, so that an intelligent charging and discharging control strategy and a digital twin model simulation energy consumption optimization strategy are generated; and the energy consumption of the LED energy storage street lamp is reduced.

The invention provides a multi-tree forest aboveground biomass remote sensing estimation method and system and a storage medium, and the method comprises the steps: obtaining multi-source remote sensing data based on a target region, including airborne LiDAR point cloud, ground survey data and DEM data; constructing a terrain heterogeneity index DGTHI in combination with DEM data, and quantifying terrain complexity through weight fusion of standardized terrain factors; extracting forest characteristic parameters and calculating the overground biomass of the single tree; performing classification modeling on the sampling region based on DGTHI, respectively establishing multiple linear regression models for different tree species, and optimizing feature parameter selection; and evaluating the precision of the model through cross validation, and popularizing the model to a county scale to perform overground biomass space inversion and mapping. According to the method, terrain heterogeneity classification and tree species specificity modeling are fused, so that the estimation precision of the forest biomass in the complex terrain region is remarkably improved, and a technical support is provided for regional carbon sink monitoring and sustainable forestry management.

The invention discloses a pillow regulation and control method self-adaptive to cervical vertebra flexion and shoulder width and application of the pillow regulation and control method to a pillow, and relates to the technical field of sleeping posture regulation. The method comprises the steps of registering a three-dimensional contour point cloud through an ICP algorithm, calculating a neck curvature value, establishing a multiple linear regression model in combination with a pressure gradient change curve and a pressure peak offset, and realizing quantitative correlation between pressure data and a cervical vertebra inclination angle; based on the curvature change amplitude and shoulder breadth classification, a personalized model is constructed; and through attitude monitoring and pressure feedback, a real-time optimization instruction is generated. A target displacement error is set, and correction is stopped after the bending degree reaches the standard continuously for three times; pre-storing a medical-level support parameter library; multi-user switching is achieved through pressure feature and head contour double verification, and physical compression emergency stop is set. According to the method, through multi-source data fusion, scientific model construction and dynamic closed-loop control, the personalization and safety of pillow supporting are improved, and the method is suitable for people with sub-health cervical vertebrae and multi-user scenes.

The invention discloses a clinical examination equipment remote calibration method and system based on edge calculation, and relates to the field of instrument calibration, the system is composed of a plurality of functional modules, and the system comprises: a multi-modal data acquisition module for acquiring historical calibration data of absorbance, including fluorescence images, cell fluorescence signal intensity and morphological characteristics; the edge calculation module is used for establishing a multiple linear regression model according to historical calibration data of the absorbance and predicting an attenuation curve of the optical detection unit; on the basis of a federated learning framework, a fluorescence signal intensity compensation matrix is optimized by utilizing shared parameters among edge nodes, and light path gain parameters are automatically adjusted; performing outlier analysis on each batch of detection data through a built-in Westgard rule engine, and triggering automatic redetection of morphological characteristics; analyzing absorbance time sequence data in combination with a 1D-CNN model, and identifying reagent pollution and optical assembly aging; and the drifting calibration module adopts a PID controller to dynamically adjust the power of the laser.

The invention relates to the technical field of exploration and development of water flooded layers, in particular to a sandy conglomerate water flooded layer porosity determination method based on an XRD (X-Ray Diffraction) logging technology, which comprises the following steps of: performing correlation analysis on the content of each mineral in mineral content data and the actually measured porosity of the same group; screening out mineral types of which the determination coefficient with the actually measured porosity is greater than a determination coefficient set value; performing multiple linear regression fitting on the content of each mineral type obtained by screening and the corresponding actually measured porosity of the same group to obtain an optimal multiple linear regression model; and for the rock debris sample of the non-coring section of the reservoir stratum of the glutenite water flooded layer, obtaining the porosity calculated value of the rock debris sample through the optimal multiple linear regression model. The porosity data of the rock debris sample of the non-coring section of the reservoir stratum of the glutenite water-flooded layer can be obtained, and the porosity data of the oil reservoir water-flooded layer of the whole well can be obtained by combining the porosity data of the coring section of the rock debris sample, so that the porosity data of the whole oil reservoir water-flooded layer can be obtained.

The invention provides a soil comprehensive nutrient evaluation method and device based on a satellite remote sensing image, equipment and a medium. The method comprises the following steps: acquiring a Sentinel-2 multispectral image, cultivated land vector boundary data and actually measured soil nutrient data of a target area; establishing a multiple linear regression model by utilizing the wave band reflectivity of the Sentinel-2 multispectral image and actually measured soil nutrient data, generating a region-level nutrient content grid map, and extracting a nutrient content grid map of soil in a cultivated land range of a research region; classifying the grid map by adopting a spatial constraint natural breakpoint method clustering target function; a three-digit decimal code is adopted to represent a grading result, a soil comprehensive nutrient spatial distribution diagram is generated, coordinated color matching is set, various areas and proportions of soil comprehensive nutrients are counted, and fertilization suggestions are given, so that the defects of single sensing dimension and disjunction of decision support during remote sensing monitoring of the soil nutrients are overcome; and a closed loop serving precision agricultural practice is difficult to form.

The invention discloses a method for detecting tissue evolution in a heating process after quenching of tool steel based on saturation magnetization, resistivity and hardness, and relates to the technical field of metal material detection.The method comprises the steps that heating parameters are set for different types of tool steel, and an actual tempering process is simulated; then, the saturation magnetization intensity, the resistivity and the hardness are measured through professional equipment, and data are obtained through accurate measurement and scientific calculation; and then, sorting the data to establish a database, analyzing by using a multiple linear regression model, and judging an organization evolution stage according to a preset result range. According to the method, the structure evolution of the tool steel can be accurately and comprehensively monitored, the detection accuracy and scientificity are improved, powerful support is provided for quality control and process optimization in the tool steel production process, and the tool steel product performance and production efficiency are effectively improved.

The invention discloses a coal bed gas typical reservoir gas content evaluation method based on well logging information, and relates to the technical field of coal bed gas evaluation. According to the coal bed gas typical reservoir gas content evaluation method based on the logging information, logging data and core data of a set area are obtained and preprocessed; performing screening processing based on the logging data to obtain a screening set of the set area; based on the core data, constructing a lithology indication matrix and physical property constraint characteristics of the set area; constructing a constrained multiple linear regression model of the set area based on the screening set of the set area, the lithology indication matrix and the physical property constraint characteristics; according to the method, the target logging data of the to-be-evaluated target layer section are obtained, analysis is carried out in combination with the logging coefficient set, and the coal seam gas content value of the to-be-evaluated target layer section is obtained, so that the gas content of coal seam gas is evaluated more accurately; and the accuracy and reliability of the evaluation result are improved.

The invention discloses a six-dimensional force sensor calibration method based on an intelligent algorithm and ensemble learning, which improves calibration precision and system adaptability by combining data anomaly detection, the intelligent algorithm and the ensemble learning. The calibration method comprises the following steps: S1, building a six-dimensional force sensor calibration system; s2, loading and unloading experiments of force and torque are carried out on the six-dimensional force sensor on the standard calibration table, and multi-channel analog signal data output by the six-dimensional force sensor are obtained; s3, repeating the operation in the step S2 for a plurality of times; s4, data anomaly detection; s5, performing preliminary calibration by adopting a multiple linear regression model to obtain a preliminary decoupling matrix of the six-dimensional force sensor; s6, calculating error data after multiple linear regression calibration, and taking the error data as input characteristics of subsequent error compensation; s7, performing error compensation based on the residual neural network; and S8, determining calibration precision and outputting a final model.

The invention discloses a coal preparation plant production personnel positioning method and system based on video analysis, and the method comprises the steps: processing a video stream through employing an improved OfficientDet model, and obtaining an initial target personnel detection frame; processing the initial target person detection frame by using an improved FairMOT model and a dynamic motion model to obtain a continuous tracking trajectory of the target person; processing the continuous tracking trajectory of the target person by using a multiple linear regression model, predicting the position of the next frame, and allocating weights by using an entropy weight method to obtain a predicted motion trajectory; and processing the continuous tracking trajectory of the target person by using a ResNet-50 model to obtain a depth feature, performing identity confirmation of the target person based on the feature, and associating the identity with the predicted motion trajectory to complete person positioning. According to the method, tracking errors caused by environmental changes are reduced, and effective application in different environments is ensured.

The invention relates to the technical field of fault diagnosis of intelligent electronic equipment, and particularly discloses a fault diagnosis method and system for intelligent electronic equipment, and the method comprises the steps: collecting temperature data and operation response time in real time through a high-precision temperature sensor disposed on the surface of the equipment and an external high-precision timer; a temperature anomaly feature value and an operation response time anomaly feature value are respectively calculated by adopting fast Fourier transform and an autoregression integral moving average model, and are constructed into comprehensive feature vectors, and based on the feature vectors, whether the intelligent electronic equipment has a fault state is judged by using a random forest model. And the probability that the equipment breaks down in a period of time in the future is predicted through the multiple linear regression model, and for non-fault intelligent electronic equipment, the system formulates a personalized dynamic maintenance plan according to a prediction result, so that the accuracy and high efficiency of maintenance work are ensured.

The invention relates to the technical field of environment analysis, in particular to a building construction environment monitoring system based on big data analysis, which comprises a wind direction correlation analysis module, a flying dust data prediction and evaluation module and a flying dust detector anomaly monitoring module. The wind speed threshold value is determined, clues can be provided for determining the area corresponding to the flying dust data abnormity in combination with the wind direction information to a certain extent, although the positioning difficulty is increased due to flying dust drifting along with wind, the analysis of the module is beneficial to narrowing the abnormal area range, and a direction is provided for follow-up precise flying dust treatment; the flying dust data prediction and evaluation module can accurately predict the flying dust data influenced by the wind speed and determine the flying dust data generation area when establishing the multiple linear regression model, the flying dust data generation area can be determined through accurate prediction, a flying dust source can be quickly found, and management personnel can conveniently take dust prevention and falling measures in a targeted mode.

The invention discloses a method for increasing the sidewall turn-up height in a building process of an all-steel radial tire, which comprises the following steps of: acquiring multi-source data such as temperature, pressure and angle in a turn-up process through an infrared temperature sensor, a pressure sensor and the like; and constructing a multiple linear regression model, a machine learning model and a hybrid model fused with the physical principle, analyzing the relationship between the process parameters and the anti-package height, and performing verification and optimization. According to model output parameters, a servo motor is used for accurately controlling the downward pressing angle and pressure of a reverse wrapping pressing roller, and production is executed in cooperation with an auxiliary supporting ring and a sectional type reverse wrapping technology; meanwhile, the turn-up height is detected in real time through an intelligent monitoring system, and parameters are automatically adjusted when the turn-up height does not reach the standard. According to the method, precise regulation and control of the tire sidewall turn-up height are achieved, compared with a traditional process, the product percent of pass is increased to 95% or above from 85%, the production efficiency is improved by 20%-30%, the tire structure stability is effectively enhanced, the rolling resistance and the tire burst risk are reduced, and intelligent upgrading of tire manufacturing is promoted.

The invention relates to the field of coal mine safety engineering, and provides a coal mine gas control extraction effect dynamic evaluation and closed-loop control method, which comprises the following steps: S1, based on a basic threshold value in an extraction design scheme, periodically and dynamically calibrating the threshold value through a multiple linear regression model, and generating a dynamic threshold value matrix of a core index; s2, collecting multi-dimensional data to construct a fusion data matrix; s3, a dynamic multi-scale convolutional neural network model is constructed and trained, and the model comprises a variable-length filter generator which is used for adaptively generating a variable-length filter according to the input data so as to extract multi-scale time sequence features; outputting a standard state judgment result of the current extraction effect and a core index prediction value of a future preset time period in parallel; and S4, starting a three-level linkage feedback management and control flow coordinated by the AI model and the expert rule base according to the standard reaching state judgment result and the predicted value output in the step S3. Accurate evaluation and short-term prediction of core indexes such as the extraction flow, the gas concentration and the attenuation coefficient are achieved.

The invention provides an ABS remaining distance judgment method and system based on vehicle conditions and self-adaptive estimation, and belongs to the technical field of automobile braking safety. Comprising the steps that multiple braking influence parameters under the actual vehicle condition are collected and calculated in real time, and a multiple linear regression model is constructed to preliminarily estimate the residual braking distance; a self-adaptive correction algorithm is adopted, and a corresponding correction factor is introduced according to the driving state for correction, so that a corrected residual braking distance estimated value is obtained; and optimizing the corrected residual braking distance estimation value by adopting a Kalman filtering algorithm so as to obtain a final ABS residual distance judgment result. According to the method, the estimation mode of the residual braking distance of the ABS can be adaptively adjusted according to the actual vehicle condition and the driving state of the vehicle, so that the judgment accuracy is improved, and more reliable data support is provided for safe driving of the vehicle.

The invention relates to the technical field of ambient air quality forecasting, in particular to an O3 pollution forecasting method and system based on multiple linear regression. The O3 pollution forecasting method based on multiple linear regression comprises the following steps: S1, acquiring data; s2, preprocessing the data, and constructing an O3 pollution case library; s3, typing the O3 pollution cases according to the meteorological elements; s4, screening proper meteorological elements as forecasting factors for each type of O3 pollution case; s5, establishing a step-by-step multiple linear regression model of meteorological data and pollutant concentration for each type of O3 pollution case, and evaluating a regression effect; and S6, obtaining a future day-by-day weather factor forecast result of the high-resolution atmospheric chemical numerical model, and predicting a future O3 pollution condition by using a multiple linear regression model. According to the O3 pollution event forecasting method provided by the invention, the calculation method is simple, the accuracy of the output forecasting result is high, the O3 pollution forecasting effect is effectively improved, an open research framework is provided for O3 pollution forecasting, and powerful support is provided for related researches such as forecasting and evaluation of O3 combined pollution.

The invention provides a wind generating set power curve prediction method and device, and relates to the technical field of wind generating sets, and the method comprises the steps: firstly collecting the actual power of a wind generating set, the rotating speed of a generator and environment data, and providing a basis for subsequent analysis; then, the power generation data at the previous moment serve as input, the actual power at the next moment serves as a label, a multiple linear regression model is trained, the influence of the current moment is focused, the previous accumulated influence is easily ignored, then the power generation data at the previous moments serve as input, and the actual power at the next moment serves as the label; the training of the long and short-term memory network model focuses on the influence of time series data and is suitable for dynamically changing data. And the rotating speed, wind speed, temperature and humidity data of the generator are analyzed, a fluctuation adjustment coefficient is obtained, the fluctuation adjustment coefficient is applied to correct the actual power predicted by the two models at the current moment, and the corrected actual power is applied to the power curve of the wind generating set.

The invention provides a hot blast stove system energy-saving control method based on multi-parameter cooperative control. Firstly, parameters such as waste gas temperature, furnace top temperature, waste gas oxygen content and coal gas flow are collected through sensors, real-time data are input into a cooperative control module, an optimal waste gas temperature rise curve is fitted through a pre-trained deep neural network model, and an initial opening instruction of an air and coal gas regulating valve is calculated in combination with a multiple linear regression model. And then a parameter self-adaptive PID controller is used for dynamically adjusting parameters according to the real-time temperature deviation, a final control instruction is generated and executed, and effective regulation and control of waste gas and the vault temperature are achieved. The system can intelligently decide to increase or decrease the air or gas amount, optimize the combustion efficiency, reduce the gas consumption and prolong the service life of equipment. The method breaks through the limitation that control only depends on the optimal air-fuel ratio traditionally, a gas energy-saving scheme based on the waste gas temperature rise curve is provided, the method is suitable for a hot blast stove system in the metallurgical industry, and it is expected that gas consumption can be reduced by 3%-15%.

The invention provides a method for predicting the flammability upper limit volume percentage of a pure compound. The method can be used for predicting a mathematical model of the flammability upper limit volume percentage of the pure compound which is composed of 12 or less elements of hydrogen, carbon, nitrogen, oxygen, sulfur, fluorine, chlorine, bromine, iodine, silicon, phosphorus, arsenic and the like and has the atom number of 25 or less (excluding hydrogen) with high accuracy. Wherein the model is obtained as a universal quantitative structure-property relationship model by determining an optimal model from a plurality of multiple linear regression models by means of a step-by-step selection method, and the model takes some of the various molecular descriptors as independent variables, takes flammability upper limit volume percent as a dependent variable, and takes the flammability upper limit volume percent as a dependent variable. The value of the molecular descriptor included in the model can be received and input in a short time, and the flammability upper limit volume percentage can be output, so that only the specific value of the molecular descriptor included in the model is known. And the flammability upper limit volume percentage of the compound purely formed by the molecule can be predicted for any molecule meeting the requirements of the invention.

The invention discloses a fan blade strain monitoring method based on optical fiber sensing, and relates to the technical field of fan blade monitoring, and the method comprises the steps: collecting a vibration signal in a blade operation process, and carrying out the denoising and time-frequency analysis processing of the vibration signal; calculating a plurality of time-frequency parameters, carrying out frequency band division on the vibration signal by using wavelet packet transformation, and extracting a frequency index to obtain a signal characteristic parameter; establishing a multiple linear regression model between the blade strain and the signal characteristic parameters, constructing a health assessment model in combination with a stress analysis theory, and outputting a health assessment result; and evaluating the residual service life of the blade by combining the signal characteristic parameters with a health evaluation result according to a credibility analysis algorithm and a load subitem coefficient so as to carry out real-time monitoring on a strain state. The optical fiber sensors are arranged in the target monitoring area of the fan blade in the axial direction and the circumferential direction, the measurement principle of the optical time domain reflection technology is combined, and the spatial resolution and the measurement precision of strain monitoring are improved.

The invention discloses an ecological system service dominant factor identification method. The method comprises the following steps: 1) establishing a basic ecological system service influence factor database; 2) selecting key evaluation indexes for evaluating the target area and quantifying the key evaluation indexes; 3) constructing a multiple linear regression model, and introducing a nonlinear term correction model; 4) performing primary screening on influence factors in the correction model based on a variance expansion factor VIF; 5) further screening the influence factors by using optimal subset feature screening, and establishing a corresponding influence factor index system; and 6) carrying out local regression analysis by adopting an improved multi-scale geographically weighted regression MGWR model, and identifying the ecosystem service dominant factor according to an obtained regression coefficient value. The method disclosed by the invention not only can reveal the dominant factors of ecosystem services in different areas and on different scales, but also ensures that the identified dominant factors have statistical significance through significance test on the regression coefficient, and enhances the credibility and practicability of an analysis result.

The invention discloses a power load probability prediction method and system based on a neural network quantile regression model and multiple linear regression, and belongs to the technical field of power system load prediction. The method comprises the following steps: obtaining standardized data by using a longitudinal data analysis method; identifying key influence factors of the power load in the standardized data based on a Pearson correlation analysis method, and constructing a factor analysis model to quantify influence weights of the key influence factors on the power load; constructing a neural network quantile regression model based on seasonal trend decomposition to fit the key influence factors with different influence weights to obtain a quantile prediction result; based on the quantile prediction result, estimating a continuous probability distribution curve of a load common factor by adopting a non-parametric kernel density technology to obtain an interval prediction result; and constructing a multiple linear regression model to predict the load scale change, and adjusting the interval prediction result. According to the invention, the precision and calculation efficiency of load prediction are effectively improved.

The embodiment of the invention discloses an air quality improvement evaluation method and device based on assistance intensity and a medium. The method comprises the following steps: acquiring the atmosphere supervision assistance intensity and various meteorological indexes of a to-be-evaluated area, and the pollutant concentration in the air around an enterprise before and after atmosphere supervision assistance; establishing a multiple linear regression model by taking the assistance strength, each meteorological index and the pollutant concentration before atmospheric assistance as independent variables and taking the pollutant concentration after atmospheric assistance as a dependent variable; taking data of each region in the to-be-evaluated region as a unit sample, and carrying out bivariate spatial autocorrelation analysis on assistance strength and pollutant concentration improvement before and after assistance; and according to the local space self-correlation characteristics of the assistance intensity and pollutant concentration improvement of each region and the regression coefficient of the assistance intensity, performing evaluation and assistance strategy recommendation on the air quality improvement effect of enterprises in each region. According to the embodiment of the invention, atmosphere supervision assistance intensity data is fused, and refined evaluation of the air improvement effect is realized.

The invention discloses an intelligent pipeline data monitoring method and related equipment, and the method comprises the steps: collecting the multi-source original data of a target pipeline through a distributed sensor and intelligent inspection equipment; preprocessing the multi-source original data to obtain preprocessed data; performing feature extraction operation on the preprocessed data to obtain target feature data; acquiring historical operation data of the target pipeline; according to the historical operation data, constructing a target multiple linear regression model; constructing a target long-short-term memory network model according to the target feature data; and performing anomaly detection on the target pipeline according to the target multiple linear regression model and the target long-short-term memory network model to obtain a detection result. The method can improve the precision of pipeline data monitoring, and can be widely applied to the technical field of pipeline engineering.