577 results about "Atmospheric pressure" patented technology

The invention discloses a vehicle idling start and stop control system and method. The control system judges whether a vehicle has idling start and stop conditions by monitoring the water temperatureof an engine, the capacity of a storage battery, a safety belt and other information, judges whether the vehicle has conditions of stopping the engine by monitoring the rotating speed of the engine, the speed of the vehicle, the brake air pressure and other conditions, and judges whether the vehicle has conditions of starting the engine by monitoring the gear of a gearbox, the speed of the vehicle, a hand brake and other information. When the relevant conditions are met, start and stop work signals are sent to the engine though a CAN bus. In order to guarantee the reliability of frequent startand stop of the engine, the system strengthens flywheel gear rings of a starter and the engine. In order to ensure that the battery does not lose power, a storage battery capacity sensor is added tothe system on the basis of increasing the capacity of the storage battery, and the idling start and stop are allowed when the state of charge (SOC) of the storage battery satisfies a safe starting threshold. The idling start and stop control system has advantages of low cost and high reliability, and is especially suitable for commercial vehicles.

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

The invention discloses a constant-temperature and constant-humidity test box temperature control system based on a multistage PID algorithm and a control method thereof. The constant-temperature and constant-humidity test box temperature control system comprises an environmental parameter detection module, an in-box parameter detection module and a multistage PID controller, wherein the environmental parameter detection module is used for collecting temperature, humidity and air pressure data outside a test box in real time; the in-box parameter detection module is used for collecting multi-dimensional environmental parameters inside the test box; when the failure of any sensor is detected, starting a fault self-diagnosis module, replacing the data of the failure sensor with a data interpolation method of a side sensor, and recording the deviation compensation amount at the same time. A multi-stage PID control architecture is adopted, and feedforward compensation, parameter adaptive optimization and fault redundancy strategies are combined, so that high-precision and anti-interference control of the temperature and humidity of the test box is realized.

The invention discloses a high-precision flow online monitoring device and method for a siltation section, and belongs to the technical field of hydrological monitoring. The device comprises a sensing terminal, an edge terminal and a mounting mechanism, the sensing terminal is integrated with an upper and lower section layered speed measurement module, an underwater distance measurement module, a first absolute pressure type water level measurement module and an attitude and temperature sensor, and vertical section flow velocity data is obtained by receiving water body reflection signals in a time-sharing manner; and the dynamic deposition section area is corrected in real time in combination with ultrasonic ranging. And the edge terminal compensates water level measurement data by using atmospheric pressure, and calculates the flow through fusion of a telemetering module. The installation mechanism supports fixed or telescopic installation, and the height of the installation mechanism is automatically adjusted according to the distance between a deposition layer and equipment. The method comprises the steps of dynamically correcting the section, calculating the flow through layered integration and compensating errors of multiple sensors, the problems of large area deviation, high operation and maintenance cost and the like caused by sedimentation in a traditional flow measurement technology are solved, and high-precision online monitoring under the high-sand-content complex flow state is achieved.

The invention discloses an ozone concentration forecasting method based on peak value perception and influence factor analysis, which utilizes continuous online observation data and meteorological observation data of multi-site pollutants, processes abnormal and missing data, and combines with boundary layer height, radiation and sea level air pressure provided by an ECMWF meteorological forecasting product to forecast ozone concentration. Building a high-quality Yunnan region large database for supporting deep learning according to hour-scale meteorological numerical simulation data of vertical wind speed, and building an ozone forecasting model based on influence factor analysis and peak value perception; capturing a global dependency relationship among historical meteorology, pollutant data and an ozone long-time sequence and a nonlinear relationship between ozone and an influence factor, so that the model can better understand context information and a related mechanism influencing ozone generation; the defect of long-time sequence dependence of a traditional deep learning method is overcome, the influence of influence factors on ozone concentration prediction is ignored, and the accuracy of ozone prediction is improved.

The invention discloses a physical constraint fused multi-scale extreme gale event AI identification method and device and a medium, and relates to the technical field of atmospheric science and artificial intelligence. According to the method, a novel multi-scale extreme gale event identification model is constructed by integrating multi-scale meteorological data. The model effectively captures extreme gale features under different scales through a multi-scale feature extraction module, a feature fusion sharing module and a multi-task output module. In addition, a physical constraint loss design is introduced into the model to ensure that outputs such as wind speed and air pressure conform to meteorological dynamics rules. Through deep learning analysis of meteorological data and combination of physical mechanism understanding, different types of extreme gale events such as hurricane, thunderstorm gale and tornado can be effectively distinguished, and reliable data support and scientific basis are provided for meteorological prediction and emergency response.

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 a typhoon wave height prediction method based on a deep learning MOE-Transform model, and the method comprises the following specific steps: collecting historical typhoon data of a research region, and constructing a virtual typhoon data set in combination with a central pressure difference formula; processing the historical and virtual typhoon data sets through a Holland typhoon empirical model and a storm growth relation to obtain a wind field, an air pressure field and a significant wave height field; correcting the field data by using an error model on the basis of the ERA5 data set to form a typhoon space-time fusion database containing meteorological data, the significant wave height field and typhoon data; and training and testing the MOE-Transform model to obtain a typhoon wave significant wave height prediction model for typhoon wave height prediction. According to the method, the multi-task adaptability and generalization ability are improved, and the precision and timeliness of typhoon wave height prediction are improved.

The embodiment of the invention discloses a water-gas interface greenhouse gas flux automatic monitoring system and a distribution prediction method. The system comprises a wind speed sensor and an air pressure and temperature sensor which are respectively used for collecting the wind speed of air at a monitoring point, the atmospheric pressure and the Kelvin air temperature; the infrared sensor is used for collecting the content of CO2, CH4 and N2O in the air of the monitoring point; the acidity sensor and the temperature sensor are respectively used for collecting the pH value and the centigrade water temperature of monitoring point water; the carbon dioxide sensor is used for collecting the concentration of bicarbonate in water at a monitoring point; the methane sensor and the nitrous oxide sensor are respectively used for collecting the contents of greenhouse gases CH4 and N2O dissolved in the monitoring point water; and the calculation module is used for calculating the flux of the greenhouse gas on the water-gas interface. According to the embodiment of the invention, the water-gas interface flux of various greenhouse gases under actual conditions can be accurately monitored.

The invention relates to the field of surveying and mapping science and technology, in particular to an engineering surveying and mapping method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: deploying an unmanned aerial vehicle on a predetermined flight route, carrying out the coverage flight of a designated area, collecting the temperature, humidity, wind speed and air pressure data in the area, and transmitting the data back to a ground station through wireless transmission; and obtaining meteorological observation data. According to the invention, through real-time meteorological observation data, the instantaneity of data and the practicability of application are significantly improved, an executor is allowed to quickly respond to meteorological changes and timely adjust flight and surveying and mapping plans, recent meteorological changes are analyzed and predicted through a time sequence, and the flight plan can be further flexibly adjusted. Through the introduction of a prediction and adjustment mechanism, the precision of engineering surveying and mapping is improved, the safety of operation is also improved, and flight under extreme meteorological conditions can be avoided.

Disclosed in the present invention are a successive approximation four-redundancy atmospheric parameter voting system and method. The method comprises the following steps: measuring the current total pressure and static pressure of an aircraft, and measuring some or all of the current total temperatures of the aircraft; collecting the total pressure and the static pressure, and collecting some or all of the total temperatures, so as to obtain four sets of atmospheric data parameters; determining the validity of each atmospheric data parameter in each set of atmospheric data parameters, performing atmospheric parameter voting according to a successive approximation four-redundancy voting algorithm, and calculating an atmospheric parameter voting value; using a fault tree analysis (FTA) method to perform system security analysis, and identifying bottom events in a fault tree; calculating a failure rate of each bottom event in the fault tree; and calculating the probability of pressure altitude / airspeed output error data. The present invention solves the problem of it being impossible for an original algorithm to eliminate abnormal data during four-redundancy parameter voting, thus affecting the reliability and integrity of voted atmospheric parameters, not being conducive to flight control, and even generating design defects endangering flight safety.

The invention relates to the technical field of pipeline noise elimination, in particular to a precision filter sealing performance detection method and device.The method comprises the steps that in the pressurization and pressure maintaining process of different temperature grades, air pressure is collected, and the real air pressure sequence, the standard air pressure sequence, the pressurization time period and the pressure maintaining time period of the same temperature grade are determined; determining the temperature influence degree of the precision filter; leakage sub-time periods are divided from the pressurization time period and the pressure maintaining time period, and the intermittent leakage degree corresponding to the same temperature grade is determined according to the duration of all the leakage sub-time periods divided according to the same temperature grade and the duration of the pressurization time period and the duration of the pressure maintaining time period; and combining the difference between all the temperature grades and the actual temperature for detecting the sealing performance of the precision filter and the temperature influence degree of the precision filter to finish the sealing performance detection of the precision filter. The precision filter sealing performance detection accuracy can be improved.

The invention relates to the technical field of three-dimensional real-time wind speed measurement, and discloses a large-span roof three-dimensional ultrasonic anemometer and a measurement method thereof, and the method comprises the steps: building a three-dimensional coordinate system through laser ranging, and analyzing and compensating installation errors; an initial calibration coefficient is generated through a self-inspection and optimization algorithm, and the initial calibration coefficient is compared and analyzed with a standard instrument for initial calibration; ultrasonic pulse emission is controlled, full-path scanning and redundancy measurement of all axes are completed, signal quality is improved through multi-modal signal processing detection, and abnormal values are eliminated through weighted median values; the sound velocity is corrected based on real-time temperature data, the influence of humidity and air pressure on the sound velocity is adjusted through a nonlinear regression model, and a Reynolds stress correction term is introduced to compensate for sound wave path bending in an eddy current field; a three-dimensional turbulence intensity distribution diagram is constructed by fusing ultrasonic and LiDAR data, meteorological parameters are calculated, an extreme weather event is early warned by using a machine learning model, a visual three-dimensional wind speed field is provided through a cloud platform, and a potential risk area is identified.

Disclosed in the present application are a method, system and apparatus for temporarily replacing a faulty wind LiDAR. The system comprises: a detection module, used for analyzing output data of a radar, judging, on the basis of the analysis result, whether a LiDAR has had a fault, and if a fault has occurred, outputting logic 0, otherwise, outputting logic 1; a data prediction module, used for predicting five types of meteorological information the wind speed, wind direction, temperature, atmospheric pressure and humidity of wind farm on the basis of historical data and meteorological source data; and a data switching module, used for switching a data source from a simulation system data source to a wind LiDAR apparatus data source when the output logic changes from 0 to 1, switching the data source from the wind LiDAR apparatus data source to the simulation system data source when the logic value changes from 1 to 0, and maintaining an original state when the logic value remains unchanged. The present application can temporarily replace faulty wind LiDARs, thereby reducing the negative effects of wind LiDAR faults on assessment of wind farm output.

The invention belongs to the technical field of airborne atmospheric detector equipment, and particularly relates to an airborne cloud water content detection system, which comprises a constant-temperature hot wire sensor, a cloud water content detection module, a cloud water content detection module and a cloud water content detection module, the pressure sensor is used for measuring atmospheric pressure; the temperature sensor is used for measuring the temperature in the cloud; the humidity sensor is used for measuring the humidity in the cloud; the data processing module is used for receiving the data of the sensor and processing and analyzing the data through a specific algorithm to determine the cloud water content; the data communication module is used for outputting the measured data and the calculated cloud water content data to a computer or other acquisition detector equipment through the Ethernet or a serial port; and the heating control module is used for keeping the detector in a preset standard working temperature range so as to ensure the measurement accuracy and the reliability of detector equipment, and has the effect of finely detecting the airborne cloud water content.

The invention discloses a real-time rainfall prediction method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining influence factors of real-time rainfall; wherein the influence factors comprise temperature, humidity, air pressure, wind speed, dew point, cloud amount, cloud layer type, cloud top temperature, water vapor content, cloud coverage rate and terrain type; and inputting the influence factors of the real-time rainfall into a rainfall prediction model, so that the rainfall prediction model generates predicted real-time rainfall according to the influence factors of the real-time rainfall. According to the invention, the accuracy and response speed of rainfall prediction can be improved, and the capability of dealing with sudden weather events is enhanced.

The invention discloses a PM2.5 forecasting method based on weather circulation and a stability index, and relates to the technical field of air pollution prevention and control, and the method comprises the steps: carrying out the calculation according to the monitoring data of a target region, obtaining a sea level air pressure distance flat field of the target region, carrying out the clustering, and obtaining a weather type; calculating the circulation index value of each meteorological station, and determining the circulation type of the target area; calculating an atmospheric stability index of the target area, and determining a K index interval of the target area; according to the PM2.5 concentration time sequence of each monitoring point position in the target area, establishing a PM2.5 concentration historical data set by matching weather typing, circulation typing and a K index interval of corresponding time; and according to meteorological observation data of a future time period, calculating a weather type, a circulation type and a K index interval of the future time period, and searching a PM2.5 concentration historical data set to obtain a PM2.5 concentration prediction result. The prediction time can be shortened, and the interpretability of the prediction result can be improved.

The invention discloses a low-altitude meteorological detection system, which comprises a lift-off detection subsystem and a ground receiving subsystem, the lift-off detection subsystem and the ground receiving subsystem carry out data transmission in a wireless communication mode, and a plurality of sensors are integrated on the lift-off detection subsystem. The system comprises a lift-off detection subsystem for acquiring temperature and humidity, air pressure, positioning and attitude information and transmitting the information to a ground receiving subsystem, and the ground receiving subsystem receives data transmitted by the lift-off detection subsystem and provides a visual man-machine interaction platform to realize real-time man-machine interaction. The device integrates three core advantages of reliability, flexibility and safety, ensures that the device can still work stably in an extremely severe environment without fear of wind and rain, shows strong environmental adaptability and durability, is light in overall weight, small in size, convenient to carry and rapid to deploy, can easily cope with individual soldier operation or team cooperation, and has a wide application prospect. And flexibility is realized.

The invention discloses a design optimization method for an oscillating water column wave energy device, and belongs to the technical field of ocean wave energy utilization, and the method comprises the following specific steps: S1, determining modeling parameters of a to-be-optimized device, and carrying out the parametric modeling of the wave energy device, so as to obtain the parametric modeling of the wave energy device; s2, selecting an air chamber width in the modeling parameters as a target optimization parameter, and setting other modeling parameters as constants, S3, inputting parameterized modeling in fluid mechanics software, calculating an air pressure value P1 (t) and a gas flow velocity value V (t) of the corresponding wave energy device, and forming a discrete point group; s4, fitting the discrete point group, and constructing an agent model; and S5, carrying out final optimization design by using the proxy model. By adopting the method, a multi-parameter and multi-target optimization scene can be met, and a new method is provided for design optimization of a wave energy device.

The invention discloses a control method for defrosting of a parallel flow evaporator, which comprises the following steps of: constructing a multi-dimensional state sensing network, constructing a three-dimensional state sensing matrix by utilizing a distributed temperature sensor array on the surface of an evaporator coil, an air inlet humidity sensor and an inlet and outlet differential pressure type pressure sensor, and acquiring information such as temperature distribution, environment humidity and air pressure difference; through dynamic threshold self-adaptive judgment, based on an environment temperature field, the historical temperature of an evaporator and an icing critical temperature field calculated by a time sequence prediction model, the defrosting triggering condition is judged by combining humidity and an air pressure difference change gradient field; gradient type energy injection defrosting is adopted, in the first-stage defrosting process, the refrigerant flow is adjusted through a proportional electromagnetic valve to conduct directional defrosting, and in the second-stage defrosting process, a compressor is closed, a fin gap shape memory alloy heating network is activated, and latent heat of a phase change material is released to conduct uniform defrosting. And performing multi-physical field cooperative regulation and control, constructing a coupling model, and adjusting the heating power and pulse modulation duty ratio sequence.

The invention relates to the technical field of meteorological observation, in particular to a parameter measurement method and device based on a dual-band mode, equipment and a storage medium. Generating a dual-band excitation signal according to the aligned sine wave signal; analyzing the echo signal according to a preset gain coefficient, a time difference method and a preset attitude compensation formula to obtain a reconstructed quasi-unipolar pulse, an actual wind speed, a horizontal wind direction angle, a pitch angle, a three-dimensional wind speed vector, a roll angle and a real wind direction; acquiring real-time air pressure, and correcting a preset standard state sound velocity according to a preset sound velocity correction formula and the real-time air pressure to obtain a corrected sound velocity; according to the scheme, a dual-band signal attached to a medium is generated, after efficient transmission analysis, high-resolution pulses and multi-dimensional wind field parameters are obtained by means of gain, a time difference method, attitude compensation and the like, the sound velocity is corrected in combination with air pressure, comprehensive, accurate and rapid updating of data are considered, and the measurement reliability is improved.

The invention provides an active and passive combined microwave remote sensing detection method for earth surface air pressure and air pressure profile, which comprises the following steps: by combining a satellite-borne / airborne millimeter wave differential absorption radar and a satellite-borne / airborne microwave radiometer and utilizing oxygen differential absorption measurement, obtaining accurate land / ocean surface air pressure data; and the air pressure profile is further obtained by combining temperature and humidity profile data measured by a satellite-borne / airborne microwave radiometer. Compared with the prior art, the method has the advantages that active and passive microwaves are adopted for combined detection, detection information obtained by a microwave radiometer is complemented, calculation of the surface pressure is enhanced, and the surface pressure inversion precision is improved; according to the method, the surface pressure value can be obtained, and the atmospheric pressure profile can also be obtained. According to the method, accurate surface pressure detection can be realized, and the atmospheric pressure profile can be further deduced by combining the temperature profile and the humidity profile observed by the microwave radiometer.

The invention belongs to the technical field of underwater measurement, and discloses a test probe underwater depth automatic positioning method and system, and the method comprises the steps: collecting an absolute water pressure value of a current underwater environment, and carrying out the calculation of a hydrostatic pressure value through combining with water surface reference air pressure data; meanwhile, acoustic signals from the water surface base station are received, response is returned, and the round-trip propagation time of sound waves in the water body is measured and obtained; collecting water body temperature data and salinity change data, estimating density information of the current water body, and converting a hydrostatic pressure value into a pressure depth value; calculating an average sound wave propagation speed by adopting an empirical sound velocity model, and calculating an acoustic depth value of the probe in combination with back-and-forth propagation time of sound waves in the water body; the depth positioning precision and stability of the underwater probe in a complex environment are effectively improved, and high-reliability and high-precision underwater depth automatic positioning is realized.

The invention relates to a near space non-uniform atmospheric refraction angle calculation method, which comprises the following steps: acquiring a deflection angle of light in the atmosphere, and obtaining a refraction angle model; determining atmospheric density change, obtaining a Boltzmann statistical model, and further obtaining a Boltzmann energy distribution model; determining molecular kinetic energy and potential energy, substituting the molecular kinetic energy and potential energy into a Boltzmann energy distribution model to obtain the atmospheric molecular number density away from the geocenter, and determining the change rule of the atmospheric molecular number density along with the altitude and the atmospheric temperature according to the atmospheric molecular number density away from the geocenter; acquiring a transmission path from the observation height to the top of the atmospheric layer, and acquiring the atmospheric temperature and atmospheric pressure at the observation height by combining the change rule of the atmospheric molecular number density along with the altitude and the atmospheric temperature; and inputting the atmospheric temperature and the atmospheric pressure at the observation height into an Elden refractive index model to obtain a refractive index value under a corresponding radiation condition, and inputting the refractive index value into a refraction angle model to obtain a refraction angle.

The invention discloses a wind field downscaling method based on physical constraint and artificial intelligence, and relates to the technical field of meteorological numerical forecasting, and the method comprises the steps: obtaining an input data set which comprises topographic data, low-resolution forecasting data and observation data; feature extraction and data processing are carried out on the input data set to obtain a training data set, the training data set is utilized to train a neural network model fused with the attention mechanism, and a loss function of the neural network model comprises physical constraint terms. According to the method, feature information of the terrain and the air pressure field is brought into model input, feature extraction is carried out on the air pressure field through principal component analysis, and the adaptability of the model to different terrain and weather backgrounds is improved.

The invention discloses a sea fog visibility prediction method based on an SLSTM-SVM model, and aims to overcome the defects of an existing sea fog visibility prediction method, the sea fog visibility prediction method comprises the following steps: firstly carrying out smooth stabilization processing on an LSTM network to obtain an SLSTM, and then replacing a softmax layer in the SLSTM with an SVM classifier to construct a model. Four types of hydrological data such as rainfall and water level and five types of meteorological data such as air temperature and air pressure form a sequence input model, an SVM classifier predicts and outputs a value through SLSTM layer smoothing and stabilization processing and data deep mining, parameters are updated through back propagation, and iterative training is carried out until errors reach the standard. Meanwhile, an autocorrelation function is adopted to evaluate data stability, and a Z-score method is utilized to normalize the data. The method can quickly converge the network weight to a globally optimal solution, is high in prediction accuracy, and has great application potential in long-term sea fog visibility prediction.

The invention provides a wind speed prediction method based on a valley fan, and relates to the field of wind speed monitoring, and the method comprises the steps: data collection: collecting historical meteorological data of a valley, the historical meteorological data comprises air pressure, temperature, humidity, wind direction, wind speed and solar radiation data, and collecting data at the same time interval; the beneficial effects of the invention are that the method is specially designed for the unique wind regime characteristics of the valley region, comprehensively considers the topographic data and various meteorological factors of the valley, such as air pressure, temperature, humidity, wind direction and the like, and employs advanced data processing and feature extraction technologies, thereby achieving the real-time monitoring of the valley region. According to the method, the complex change rule of the wind speed of the valley fan can be more accurately captured through the effective learning of the LSTM on the time sequence features and the CNN on the spatial features in the deep learning model, compared with a traditional prediction method, the prediction error is remarkably reduced, and more reliable data support is provided for the stable operation and efficient management of a wind power plant.

The invention relates to the technical field of artificial intelligence and aviation technology crossing, and discloses a multi-environment aircraft trajectory prediction method based on a space-time self-attention mechanism, and the method comprises the steps: obtaining historical trajectory data of a target aircraft and synchronous meteorological data, namely wind speed, wind direction and air pressure; s2, extracting time sequence features of the historical trajectory through a time-space self-attention network (TSAN), wherein the time sequence features specifically comprise local time sequence feature extraction by adopting a time convolution block; according to the multi-environment aircraft trajectory prediction method based on the space-time self-attention mechanism, the prediction precision is remarkably improved, on a TrajAir high-dynamic data set, the average displacement error (ADE) is reduced by 27.7% to 0.68 m, and the final displacement error (FDE) is reduced by 26.7% to 1.37 m. The FDE is reduced to 1.29 m (39.7%) from 2.14 m in severe convection weather, and the problem of track abrupt change caused by wind direction jump of a traditional model is effectively solved.

The invention relates to a quick simulation method and system for micro-scale meteorological elements in a complex terrain area. The method comprises the following steps: calculating underlying surface information of a to-be-simulated area; based on the underlying surface information, utilizing a terrain parameter weighted regression model to generate an air temperature, air pressure and relative humidity grid point meteorological element background field; generating a wind direction and wind speed grid point background field by using inverse distance weight interpolation; based on the grid point meteorological element background field, performing preliminary error correction by using STMAS multi-grid variation to obtain a grid point meteorological element initial value; generating a three-dimensional meteorological initial value according to the grid point meteorological element initial value; inputting the three-dimensional wind field initial value into the wind field rapid diagnosis model to obtain a corrected three-dimensional fine grid point wind field; and on the basis of the corrected three-dimensional fine grid point wind field and other three-dimensional meteorological element initial values, simulating to obtain fine grid point air temperature and humidity through an integral micro-scale mode. The problems that a current meteorological element rapid simulation method is not suitable for a complex terrain region and the simulation error is large are solved.

The invention discloses a space grid method for interconversion of geodetic height and barometric height, and belongs to the technical field of aviation navigation and geographic information systems. According to the method, the mutual conversion of geodetic height and barometric height is realized through establishing a global space-time grid reference system, establishing a space-time grid data base, converting barometric height to geodetic height, inversely converting the geodetic height to the barometric height, and carrying out reference conversion, data fusion and grid compression between the barometric heights. According to the method, the vertical availability of the height estimation value obtained through air pressure measurement within the height range within the stratosphere can be improved, the method is suitable for most weather conditions, the height estimation value with the precision reaching the ten-meter level can be achieved, and compared with an existing ISA-based method, the precision is higher, the application range is wider, and the application range is wider. The method can effectively improve the reliability of flight control systems of various aircrafts, and has wide application prospects in urban air traffic operation.