109 results about "Weather factor" patented technology

A double-path ultra-short-term wind power prediction method based on numerical weather forecast and multi-order time sequence dynamic gating fusion comprises the following steps: acquiring wind power generation historical data and numerical weather forecast data of a wind power plant, and screening weather factors highly related to wind power by using an MIC; the CEEMDAN is adopted to decompose the power sequence into a plurality of intrinsic mode functions (IMF); a dual-path prediction architecture is constructed, one path adopts xLSTM to predict an intrinsic mode function (IMF), all subsequences are superposed, and a prediction result is obtained; in the other path, the XGBoost is combined with key meteorological characteristics of an intrinsic mode function (IMF) and a numerical weather forecast (NWP) for prediction, and all the subsequences are superposed to obtain a prediction result; the method comprises the following steps: designing an MT-DGFusion module through an enhanced attention and dynamic gating network; and fusing the dual-path prediction results through an MT-DGFusion module to obtain a final prediction result. According to the method, double breakthrough of prediction precision and stability is realized, and a new technical path is provided for a complex time sequence prediction task.

The invention discloses a short-term power load prediction method, and provides a CNN-BiLSTM-Attention power load short-term prediction model optimized based on an improved crested porcupine optimization algorithm in order to excavate hidden information in power load data and improve the precision of short-term load prediction. The method comprises the following steps: firstly, taking historical load data as the input of a CNN-BiLSTM-Attention network in combination with the influence of weather factors; then, the VMD decomposes the original signal, and extracts information in the data to improve the data quality; meanwhile, CPO is improved through Circle chaotic mapping, spiral fluctuation search and an adaptive t variation strategy, then hyper-parameters of the CNN-BiLSTM-Attention network are optimized by using ICPO, and different sequence prediction values are superposed to obtain a final prediction result.

The invention discloses an enhanced GAN extreme scene generation method and system considering weather factors, and relates to the technical field of scene generation in new energy power system planning and operation, and the method comprises the steps: extracting key factors from multi-time-delay meteorological data, forming an extreme label, converting the extreme label into a generation condition, and achieving the output through two-stage confrontation generation, wherein a scarce sample is stably amplified by WGAN-GP, a joint curve is generated according to conditions, and related structures, climbing and power boundary constraints are synchronously embedded to ensure statistical and physical consistency. According to the method, the authenticity of the distribution tail, the cross-variable correlation and the time structure can be considered when the sample is scarce. Extreme situation deduction of a novel power system can be supported while extreme situation coverage and credibility are improved, and an existing new energy scene analysis and planning process is integrated.

An integrated energy system short-term load prediction method based on an improved multi-task model includes the steps that in the input stage, the MIC is adopted to analyze the correlation degree of multi-element loads and weather factors, and STL is used for decomposing a load sequence to construct a component data set; in the modeling stage, the CNN is used as a feature extraction network, the spatial coupling relation of an input sequence is directly learned, the general representation of the multi-element load is learned by applying a hierarchical progressive routing strategy, and specific parameter information of different tasks is separated; in the output stage, modeling is conducted on time sequence characteristics through combination of LSTM, multi-element load prediction is achieved, aiming at the complex nonlinear relation between loads, a CPLE joint prediction model is established through combination of the characteristics of CNN and LSTM networks and a PLE multi-task learning method, and while multiple networks are adopted to learn the space-time coupling characteristics between the multi-element loads, the multi-element load prediction efficiency is improved. And different load specific knowledge is highlighted by using a progressive routing mode.

The invention discloses a method for estimating subsidence of ground around a subway under construction by considering weather factors. Selecting a ground subsidence research area around the subway under construction, and obtaining ground subsidence data and weather data in the research area; a settlement estimation subarea module is built based on a time sequence clustering algorithm, monitoring points in a research area are divided into a plurality of settlement estimation subareas according to settlement rule similarity, and the influence of data heterogeneity of different areas is reduced; a graph neural network is adopted to construct a rule capturing module, and the spatial topological relation between monitoring points in the settlement estimation subarea is analyzed; a weather factor module is built through a gating circulation unit neural network, time sequence data corresponding to weather factors are processed, and the influence of the time sequence data on land subsidence is captured; and fusing the settlement spatial-temporal characteristics and the weather characteristics to obtain a land settlement estimated value of the research area. Compared with an existing estimation method, the land subsidence estimation method can obtain more accurate regional future land subsidence data, and reliable data support is provided for subway construction.

The invention discloses a power transmission line icing prediction method, system and device based on satellite data assimilation and a medium, and relates to the technical field of ice and snow monitoring, and the method comprises the steps: obtaining satellite microwave radiance observation data, fusing the observation data and a numerical mode background field through data assimilation, generating grid meteorological data, and carrying out the prediction of the icing of a power transmission line. The method comprises the following steps: processing gridding meteorological data by using a spatial-temporal feature joint modeling technology, reconstructing meteorological data of continuous time steps into a spatial-temporal coupled four-dimensional tensor, synchronously extracting spatial-temporal dimension dynamic evolution features through a three-dimensional convolution kernel, and fusing historical states and current features based on a gating mechanism to obtain a spatial-temporal feature fusion model; and outputting a spatial distribution prediction result of the icing thickness of the power transmission corridor. According to the method, the high-precision meteorological field is obtained, the time-space dynamic characteristics of icing evolution are synchronously extracted, the key meteorological factors are screened in combination with an improved gating mechanism and attention, and accurate closed-loop simulation of power transmission corridor icing is realized based on iterative prediction of physical constraints, so that the accuracy and reliability of icing early warning are remarkably improved.

The invention discloses an urban atmospheric pollution real-time monitoring method and system. The method comprises the following steps: acquiring pollutant concentration, weather and geographic position data through multi-source monitoring equipment; performing data fusion by adopting space-time Kriging interpolation and wavelet threshold denoising to generate a high-quality urban pollution distribution initial field; a machine learning model of an encoder-decoder structure is used for prediction, an encoder is a convolutional long-short term memory network, a decoder is a dynamic graph convolutional network fused with meteorological factors, and a dynamic pollution situation map is generated; polluted area identification is carried out based on a wind field streamline, and accurate tracing is carried out through combination of reverse simulation of a Lagrange particle diffusion model and a control variable method. The whole process optimization of pollution monitoring from data fusion, accurate prediction to quantitative traceability is realized, and the accuracy and timeliness of urban atmospheric pollution supervision are remarkably improved.

The invention discloses an automatic driving vehicle driving behavior compliance simulation test method, which comprises the following steps of S1, decomposing a natural language traffic regulation into an initial premise and a constraint condition, digitalizing the initial premise and the constraint condition by utilizing measurement sequential logic, and generating a mathematical formula which can be analyzed by a machine; s2, constructing a simulation scene according to the initial premise; s3, accessing the tested automatic driving algorithm to the self-vehicle in the simulation scene, and collecting vehicle state and environment data in real time; and S4, dynamically adjusting a violation detection threshold based on the constraint condition and the current weather and road conditions, and outputting a detection result. The method is different from a traditional real vehicle testing method, and various possible scenes including extreme weather, road conditions and the like can be covered more comprehensively; different from a traditional violation detection method, the method considers the influence of road and weather factors on violation detection conditions for different test scenes, and is higher in universality.

The invention relates to a digital intelligent trajectory prediction method and system based on logistics transportation order tracking. The method comprises the steps of collecting multi-source transportation data including vehicle GPS data, AI telephone interaction data, ETC passing record data and freight station transfer operation node data; performing space-time alignment, data cleaning and map matching based on road network information on the multi-source transportation data; utilizing the processed multi-source data to generate a continuous digital intelligent track through an interpolation algorithm and a path deduction method; calculating the latest predicted position of the vehicle through a prediction algorithm model based on the digital intelligent track in combination with real-time traffic information and a historical driving mode; residual paths, real-time road conditions, weather factors and driver behavior modes are comprehensively considered, and the predicted arrival time is calculated through a dynamic ETA model. According to the invention, the GPS signal blind area problem is effectively solved, the whole-course blind-area-free tracking and high-precision arrival time prediction are realized, and the transparency and intelligent management level of logistics transportation are greatly improved.

The short-term load forecasting method based on SARIMA-random forest combination model comprises the following steps: grouping the original load data by using a sliding window, decomposing the to-be-tested week-before-next day data set of each group to obtain a trend item, a seasonal item and a residual item; establishing a SARIMA model, predicting the trend item to obtain a preliminary prediction result and a residual; clustering weather factors to obtain similar days, grouping to construct a weather-residual data set and establishing a random forest regression model, learning the influence of the weather factors on the residual, and selecting model parameters by using a grid search method; combining the prediction results of the model, and comparing the influence of weather clustering and residual training on the load prediction accuracy. The method can accurately predict the next day load under the condition that the historical load and weather factors of the to-be-tested day are known, and improves the prediction accuracy.

This invention relates to a JP complete power distribution cabinet, belonging to the technical field of power distribution cabinets. It includes a housing with a pre-reserved cavity on its inner side. A temperature control platform is mounted on the inner side of the cavity. Several mounting brackets are arranged on one side of the inner side of the temperature control platform. Several temperature-conducting plates are arranged on the side of the temperature control platform closest to the inner side of the cavity. The temperature-conducting plates are wavy and composed of several semi-circular rings. This invention solves the problem that cooling efficiency is easily affected by weather factors, and that while vents allow air to pass through, liquid and contaminant particles can easily enter with the airflow. In this invention, by using a temperature control platform, the wavy shape of the temperature-conducting plates increases their surface area, thereby increasing the efficiency of absorbing heat energy inside the cavity and the heat release efficiency inside the solution cavity. The calcium chloride solution inside the friction wheel has the characteristics of high specific heat capacity and low freezing point.

The invention relates to the technical field of solar photovoltaic panel cleaning, and discloses an online solar panel obstacle avoidance cleaning machine which comprises two solar photovoltaic panels, an obstacle is arranged between the two solar photovoltaic panels, and two symmetrically-distributed cleaning brushes are arranged on the solar photovoltaic panels in a close contact mode. The robot further comprises a rotating mechanism fixed to the ends, away from each other, of the two cleaning brushes, and the rotating mechanism is used for adjusting the positions of the cleaning brushes when the cleaning brushes approach an obstacle. Under the action of the designed rotating mechanism, the reciprocating driving mechanism and the triggering mechanism, obstacle avoidance of the cleaning brushes is achieved in a pure mechanical rotating mode; compared with the prior art, the obstacle avoidance device can automatically trigger the obstacle avoidance action at a specified position, does not need extra power supply when executing the obstacle avoidance work, reduces extra power supply cost, is lower in maintenance cost, and is less affected by external weather factors.

The application discloses a complex environment water level monitoring system and method based on heterogeneous light field cooperation, which is characterized in that: a water gauge image is segmented from a water gauge point cloud and a joint external parameter; the water gauge image is further segmented after image enhancement, and the tilted water gauge is corrected; the intensity of the water gauge point cloud is accumulated, and the water level line is obtained through the maximum gradient method; the corrected water gauge image is detected by using a target detection algorithm to detect digital characters, the character recognition algorithm is used to perform OCR recognition on the detected digital characters, and the water level value is calculated according to the digital characters, the water level line and the water gauge image. Through the combination of laser radar and visible light field, the reflection characteristics of different materials are utilized, so that the water level in the complex environment of a river channel, a reservoir and a coast can be monitored in real time. The interference of floating objects and weather factors on water level detection is effectively avoided, and the accuracy and robustness of water level monitoring are significantly improved.

The application discloses a rain weather multi-view feedforward Gaussian reconstruction method and system based on weather factor gating, and the method comprises the following steps: acquiring rain weather context images under different views and camera parameters thereof; inputting the rain weather context images into a weather factor decomposition network to generate predicted weather factors; constructing a matching reliability graph for representing the corresponding reliability degree across views according to the predicted weather factors; extracting multi-scale features of the rain weather context images; generating gated features according to the multi-scale features, the matching reliability graph and reliability gating; performing multi-view aggregation based on the gated features to generate fusion representation; inputting the fusion representation into a three-dimensional reconstruction network for feedforward prediction to obtain a three-dimensional Gaussian set; inputting the three-dimensional Gaussian set into a differentiable renderer to render a clean predicted image of a target view in combination with target view camera parameters; acquiring clean context images of multiple views to construct rain weather training samples; and constructing a loss function to train the weather factor decomposition network and the three-dimensional reconstruction network.

A refueling prediction mode in a microgrid power system may include inputting historical load and weather date of the microgrid power system to a forecasting block and determining forecasted site load and weather factors for the microgrid power system. The forecasted site load and weather factors may be used along with fuel efficiency curves and asset fuel levels for power assets of the microgrid power system, along with other information, to determine an asset dispatch schedule and a refueling timeline for the power assets. Asset dispatch commands based on the asset dispatch schedule and an actual site load may be output to the power assets to meet the load demand on the microgrid power system. A sustained reliability mode may use the information plus real-time fuel costs for the power assets to determine an asset dispatch schedule for longest microgrid sustenance for the power assets.

The invention discloses an image correction method and device, a storage medium and electronic equipment. The method relates to the field of artificial intelligence and big data, and comprises the following steps: obtaining a target image acquired by a target camera at a target moment and a reference image acquired at a reference moment, the target moment being later than the reference moment; according to an image offset scene between the target image and the reference image, a target offset correction mode for the target camera is determined, and the image offset scene is one of a scene generating image offset along with time accumulation and a scene generating image offset due to weather factors or human factors; determining offset correction information based on the target offset correction mode, the target image and the reference image; and performing image correction on the target image based on the offset correction information. Through the method and the device, the problem of low correction accuracy when the image shot by the camera is corrected under the condition that the pose of the camera is changed in the prior art is solved.

This invention provides a method, system, computer equipment, and storage medium for predicting passenger flow at rail transit stations. The method extracts directed origin-destination (OD) pairs from the entry and exit records of an automated fare collection system using a fully directed network; determines the travel time for non-transfer and single-transfer journeys; determines the real-time location of passengers based on the OD pairs and travel time; determines the passenger flow at each station within a target time period based on the passengers' real-time locations; acquires and processes weather factors; constructs a CNN-BiLSTM-Attention passenger flow prediction model; and uses the passenger flow at each station within the target time period and weather factors as input variables to predict the passenger flow at each station using the CNN-BiLSTM-Attention passenger flow prediction model. This invention considers the influence of weather factors, time dependence, and spatial characteristics, enhancing the focus on key features and improving the accuracy of station passenger flow prediction.

The invention belongs to the technical field of power systems, and relates to a reliability evaluation method and system for an extreme meteorological new energy high-permeability power system. The method comprises the following steps: judging extreme meteorology, and constructing a new energy output and meteorological factor coupling model under the extreme meteorology; establishing a system operation model considering extreme meteorology, and obtaining system operation state parameters and power distribution parameters; calculating a short-time index reliability score and an ultra-short-time index reliability score of the system under each extreme weather and different time length scales; and fusing the reliability scores to obtain a comprehensive reliability score. The method can accurately describe the instantaneous impact and short-term influence of the extreme weather on the operation of the new energy high-permeability power system, and realizes the quantitative analysis of the reliability of the system; the reliability indexes of the system are divided into a short-time type and an ultra-short-time type, the power shortage risks under different time scales can be captured respectively, and more refined and dynamic reliability evaluation is provided for system operation under extreme weather.

The invention discloses a wind measurement data correction method based on flow field simulation, and the method comprises the following steps: obtaining the current topographic data and historical target data of a target mountainous area; identifying key weather factors according to historical target data; constructing a quantitative relation model of the key weather factors and the surface form change; obtaining a topographic data prediction value of the target mountainous area; constructing a prediction digital elevation model according to the topographic data prediction value; obtaining corrected wind speed prediction data; comparing the corrected wind speed prediction data with the actually measured wind speed data of the field anemometer tower, and calculating the deviation; and constructing a space correction function based on the deviation, and applying the correction function to subsequent wind resource evaluation or fan layout optimization to realize dynamic correction of the wind measurement data. The fan flow field prediction method solves the problems that fan flow field prediction precision has a large error and the prediction result is not high in accuracy.

The application provides a photovoltaic output probability prediction method and related devices. The method comprises: obtaining historical weather factor data, predicted weather factor data and historical photovoltaic output data of a region to be predicted; obtaining a photovoltaic output point prediction value according to the data and a neural network point prediction model; calculating a correlation coefficient and a collinearity intensity between the photovoltaic output and the weather factor, the photovoltaic output point prediction value by using the historical weather factor data, the historical photovoltaic output data and the photovoltaic output point prediction value; determining a target weather variable from the weather factor according to the correlation coefficient and the collinearity intensity; constructing a vine Copula model by using the target weather variable, the photovoltaic output and the photovoltaic output point prediction value; and determining a photovoltaic output confidence interval according to the vine Copula model, the predicted weather factor data, the photovoltaic output point prediction value and a preset confidence level, thereby improving the accuracy of the photovoltaic output probability prediction result.

The utility model relates to the technical field of condenser shells, and provides an outdoor condenser shell which is characterized in that an edge groove is formed in the top of a bearing seat, and after a shell part is placed in the edge groove, a screwing cap head can be screwed by an external screwing tool, so that a rotating shaft located on one side of the screwing cap head rotates in the edge groove; at the moment, the sliding seat which is in threaded connection with the rotating shaft and is located at the threaded ring synchronously moves and drives the clamping plate and the rack edge to be pushed towards the tooth groove to complete meshing and clamping, and compared with a comparison file, the clamping work of the shell part on the top of the bearing seat can be rapidly completed through the design; the dismounting work of the shell part at the top of the bearing seat can be completed by reversely screwing the screwing cap head, and meanwhile, the tooth groove and the rack edge are both arranged in the inner groove of the shell part, so that the situation that the whole device is rusted and corroded due to the influence of weather factors such as rainwater when being used outdoors can be avoided.

The application discloses a vehicle type classification detection method based on a single geomagnetic sensor, and mainly solves the problems of delay of acquired vehicle type classification information caused by complex existing vehicle type classification detection algorithm, and precision of acquired vehicle type information caused by that data collection of the sensor is easily affected by environmental factors. The application places a geomagnetic sensor on a lane line on one side of a lane, adopts the single geomagnetic sensor to collect a magnetic field disturbance value of a vehicle, extracts a magnetic field feature in the magnetic field disturbance value of the vehicle, generates a SoftMax function model by using the screened effective magnetic field feature, and realizes vehicle type judgment of a small vehicle, a medium vehicle and a large vehicle. The vehicle type classification detection method based on the single geomagnetic sensor used by the application has low calculation complexity, reduces calculation pressure of the geomagnetic sensor, is convenient to install, has low cost, is not easily affected by weather factors, and can be used for road intelligentization.

A lamp includes a controller, a plurality of light-emitting devices and a plurality of current conversion modules. The controller is configured to output a dimming signal, which is adjusted according to at least one weather factor. The current conversion modules are coupled to the light-emitting devices and configured to generate a current signal according to the dimming signal to control the light-emitting devices, wherein the dimming signal is related to a visible light including a red light or an infrared light emitting characteristic of the light-emitting devices.

The invention belongs to the technical field of runway intrusion prevention, and discloses an airport runway intrusion prevention, detection and alarm method and system. The system comprises a runway intrusion detection module which is used for capturing the change of the inductance value in a ground induction coil loop through a detector and judging the position of an aircraft; the field module is used for calculating the speed and acceleration of the aircraft, estimating the time when the aircraft reaches the intersection of each runway and taxiway, carrying out local alarm, and sending the position of the aircraft and alarm information to the upper computer monitoring module for monitoring in a wireless communication mode; and the upper computer monitoring module is used for being connected with the field module in a wireless communication mode, providing clock calibration for the field module, and receiving runway conflict prevention and alarm information acquired and processed by the field module at the same time, so that air traffic management can be carried out by air control personnel. The system employs a ground induction coil mode to detect the operation conditions of aircrafts and vehicles, and has the advantages of low cost and small influence of weather factors.

The invention discloses a new energy power modeling and consumption capability evaluation method considering a public meteorological factor. The method comprises the following steps: 1) establishing a day-degree Markov model of wind power and photovoltaic; 2) respectively constructing a Markov similarity matrix of wind power and a Markov similarity matrix of photovoltaic power; 3) respectively establishing wind power and photovoltaic weather feature type sequences; 4) establishing a public meteorological factor vector, and training a BP neural network by taking the public meteorological factor vector as input and wind power and photovoltaic weather feature types as output; 5) clustering the public meteorological factor vectors; 6) inputting the public meteorological factor clustering center into a BP neural network to obtain a typical wind power and photovoltaic weather feature type combination and wind power and photovoltaic typical day power; and 7) based on wind power and photovoltaic typical day-degree power, evaluating the new energy consumption capability considering the wind-solar power correlation. According to the method, the internal correlation between the wind power and the photovoltaic power can be mined, and the new energy consumption capability can be evaluated more accurately.

The invention belongs to the technical field of dispatching distribution, particularly relates to a wet and dirty runway downwind boundary and influence calculation method based on efficiency and operation cost, and aims to solve the problem that the prior art lacks a technical method for judging and calculating downwind take-off and landing operation requirements, operation boundaries and operation load shedding influences of a wet runway and a polluted runway of a field. The anti-weather factor disturbance capability of airport control operation is relatively low; and the operation safety risk is relatively high when the airport frequently switches the runway operation direction. The method comprises the following steps: collecting historical operation related data of an airport as input data; the downwind take-off and landing operation requirements of wet runways and polluted runways of a computer yard are calculated; determining downwind take-off and landing operation boundaries of an airport wet runway and a polluted runway; the average everyday wet runway and polluted runway downwind running flight take-off performance load shedding of the airport based on the running boundary and the hourly capacity is obtained. According to the invention, the blank in the prior art is filled, the weather factor disturbance resistance of airport control operation is improved, and the overall operation safety level of the airport is improved to the greatest extent.

The invention discloses an air supply system of a tobacco warehouse, and relates to the technical field of air conditioning of the tobacco warehouse, the air supply system comprises a shell assembly, two sets of air exhaust pipelines are arranged at the bottom of the front face of the shell assembly, and electromagnetic valves are arranged on the two sets of air exhaust pipelines; the output ends of the two sets of air suction pipelines are jointly connected with negative pressure suction equipment through a three-way pipe, two sets of power assemblies are arranged on the top and in the shell assembly, cleaning mechanisms are arranged at the bottoms of the outer sides of the power assemblies, and filtering mechanisms are arranged on the outer sides of the power assemblies. Two groups of shielding mechanisms are arranged on the inner side of the filtering mechanism; and the power assembly drives the adjacent shielding mechanism, so that the adjacent shielding mechanism locally shields the filtering mechanism. In the cleaning process, equipment does not need to be shut down, the automation degree is high, the cleaning process is not affected by weather factors while manpower is saved, and higher cleaning efficiency is achieved while manpower is saved.

The invention relates to a photovoltaic power station intelligent control method based on a large model and deep learning, which belongs to the field of photovoltaic solar energy intelligent control, and is characterized in that through crawling news policies and 9km * 9km grid weather information, meteorological policy interpretation and influence analysis based on the large model, and missing value prediction and complementation of weather data, the intelligent control of a photovoltaic power station is realized. The accurate acquisition of the weather data is realized; meanwhile, by means of an illumination intensity prediction method for predicting and complementing unknown data through known data and a sunrise and sunset information and sunshine information coding method, weather factors can be better analyzed and controlled, and accurate photovoltaic solar panel power generation control is achieved; besides, according to a weather time sequence prediction method and an intelligent decision-making method based on deep learning, a model is learned in an unsupervised manner by using historical data, and intelligent decision-making is carried out by using a cost function, so that intelligent control of the photovoltaic solar panel is realized; and finally, a result display and alarm method can accurately store and display a predicted value, and early warning is carried out according to an alarm condition.

The invention discloses a starry sky appreciation suitability forecasting method. The method comprises the following steps: determining a forecasting area, a forecasting time period and a forecasting time number which need to be subjected to starry sky appreciation suitability forecasting; determining a night time period corresponding to each regional grid; determining a forecasting time point for each area grid according to the forecasting time period, the forecasting time and the determined night time period; obtaining a starry sky viewing suitability evaluation value of each regional grid at each forecasting time point through a starry sky viewing suitability forecasting algorithm; and according to the star sky viewing suitability evaluation value, completing star sky viewing suitability forecasting for the forecasting area day by day and forecasting time by day. According to the method, the influence of the quantity index, the quality index, the disastrous weather factor and the hotspot astronomical image addition is integrated, the starry sky viewing suitability evaluation value for reflecting the starry sky viewing suitability level is given, the completed forecasting result is objective and accurate, and the method is suitable for popularization.

The invention relates to a concrete stirring device which comprises a base, a tank is fixedly connected to the base, an upper bearing is arranged on the bottom wall of the tank, a stirring shaft is supported by the upper bearing, the stirring shaft is fixedly sleeved with a plurality of stirring paddles, each stirring paddle is provided with a plurality of blades, the longitudinal section contour of each blade is triangular, and the top wall of each blade inclines downwards from back to front. The bottom walls of the paddles incline upwards from back to front, the tank body is connected with an air-liquid entraining header pipe through a plurality of branch pipes, each branch pipe is connected with a flow control valve, a fixed cover body is arranged on the upper portion of an inner cavity of the tank body, and a gap is reserved between the circumferential edge of the fixed cover body and the inner wall of the tank body. According to the concrete preparation method of the concrete stirring device, errors caused by weather factors can be eliminated, and therefore it is ensured that the fluidity of the prepared concrete meets the design requirement.