CN-122016151-A - Static pressure error calibration method and device based on density distribution clustering algorithm

CN122016151ACN 122016151 ACN122016151 ACN 122016151ACN-122016151-A

Abstract

The invention provides a static pressure error calibration method and device based on a density distribution clustering algorithm, the method comprises the steps of obtaining a static pressure error calibration coefficient vector Cp S according to flight data, carrying out correlation analysis on each element in the Cp S vector and the corresponding flight data, determining factors affecting Cp S , establishing a mapping relation matrix, carrying out normalization and density clustering analysis on the mapping relation matrix, finding out a plurality of clustering center data, carrying out scattered point interpolation fitting on the clustering center data step by step according to an envelope range to obtain a final static pressure error calibration coefficient mapping matrix, and carrying out static pressure error calibration on an airborne product by using the static pressure error calibration coefficient mapping matrix, thereby improving static pressure calibration efficiency and accuracy.

Inventors

LIU JIE
WANG XINYAN
HU ZHIGANG

Assignees

太原航空仪表有限公司

Dates

Publication Date: 20260512
Application Date: 20251227

Claims (8)

1. The static pressure error calibration method based on the density distribution clustering algorithm is characterized by comprising the following steps of: Step one, acquiring a static pressure error calibration coefficient vector Cp S according to flight data; performing correlation analysis on each element in the Cp S vector and corresponding flight data, determining factors influencing Cp S , and establishing a mapping relation matrix; Step three, carrying out standardization processing on all dimension data in the mapping relation matrix according to weights or data dimensions to obtain a normalized matrix; performing density cluster analysis on the normalized matrix to obtain high-density distribution data points; Acquiring clustering center data of each high-density distribution point; Step six, carrying out scattered point interpolation fitting on the clustering center data step by step according to the envelope range to obtain a final static pressure error calibration coefficient mapping matrix, wherein the static pressure error calibration coefficient mapping matrix is used for carrying out static pressure error calibration on airborne products.
2. The static pressure error calibration method based on a density distribution clustering algorithm according to claim 1, wherein the performing correlation analysis on each element in the Cp S vector and the corresponding flight data thereof comprises: and carrying out correlation analysis on each element in the Cp S vector and Mach numbers, attack angles and sideslip angles in corresponding flight data.
3. The static pressure error calibration method based on a density distribution clustering algorithm according to claim 1, wherein the acquiring of the static pressure error calibration coefficient vector Cp S from the flight data comprises: Using the formula Obtaining a static pressure error calibration coefficient vector Cp S ; Wherein, the Is the free flow static pressure, p is the measured static pressure, and q c is the dynamic pressure.
4. The static pressure error calibration method based on a density distribution clustering algorithm according to claim 1, wherein performing density clustering analysis on the normalized matrix to obtain high density distribution data points comprises: And carrying out density cluster analysis on the normalized matrix, and removing outliers to obtain high-density distribution data points.
5. The static pressure error calibration device based on the density distribution clustering algorithm is characterized by comprising: The static pressure error calibration coefficient vector acquisition module is used for acquiring a static pressure error calibration coefficient vector Cp S according to flight data; The mapping relation matrix acquisition module is used for carrying out correlation analysis on each element in the Cp S vector and the corresponding flight data, determining factors influencing Cp S and establishing a mapping relation matrix; the normalized matrix acquisition module is used for carrying out normalization processing on all dimension data in the mapping relation matrix according to the weight or the data dimension to obtain a normalized matrix; the clustering module is used for carrying out density clustering analysis on the normalized matrix to obtain high-density distribution data points; The cluster center acquisition module is used for acquiring cluster center data of each high-density distribution point; The static pressure error calibration coefficient mapping matrix acquisition module is used for gradually carrying out scattered point interpolation fitting on the clustering center data according to the envelope range to obtain a final static pressure error calibration coefficient mapping matrix, and the static pressure error calibration coefficient mapping matrix is used for carrying out static pressure error calibration on the airborne products.
6. The static pressure error calibration device based on a density distribution clustering algorithm according to claim 5, wherein the mapping relation matrix acquisition module is specifically configured to: and carrying out correlation analysis on each element in the Cp S vector and Mach numbers, attack angles and sideslip angles in corresponding flight data.
7. The static pressure error calibration device based on a density distribution clustering algorithm according to claim 5, wherein the static pressure error calibration coefficient vector obtaining module is specifically configured to: Using the formula Obtaining a static pressure error calibration coefficient vector Cp S ; Wherein, the Is the free flow static pressure, p is the measured static pressure, and q c is the dynamic pressure.
8. The static pressure error calibration device based on a density distribution clustering algorithm according to claim 5, wherein the clustering module is specifically configured to: And carrying out density cluster analysis on the normalized matrix, and removing outliers to obtain high-density distribution data points.

Description

Static pressure error calibration method and device based on density distribution clustering algorithm Technical Field The invention belongs to the technical field of atmospheric static pressure error calibration, and particularly relates to a static pressure error calibration method and device based on a density distribution clustering algorithm. Background The atmosphere data system plays an important role in the flight of the aircraft, is crucial to successfully completing the flight mission, and has important influence on the flight quality, navigation and safety of the aircraft. Among them, accurate measurement of aircraft airspeed and barometric altitude is critical to safe and efficient operation of the aircraft. For example, accurate measurement of airspeed is necessary to avoid runaway at low speeds (stall condition) and to prevent exceeding aerodynamic and structural limitations of the aircraft at high speeds, while accurate measurement of barometric altitude is necessary to ensure avoidance of ground obstructions and maintenance of a prescribed minimum vertical separation on the aircraft. In the flight process of the aircraft, under the conditions of different configurations, mach numbers, attack angles and sideslip angles, the static pressure obtained by measurement has errors due to the influence of the flow field of the aircraft, so that the static pressure is deviated from the static pressure of the free flow field. The barometric pressure altitude is typically calculated using the actual static pressure in the atmospheric data system, and the indicated airspeed is calculated from the difference between the total pressure and the static pressure. If the static pressure cannot be accurately measured, the flying height cannot be truly reflected, meanwhile, errors are introduced into the indicated airspeed data, and finally the flying quality and safety of the aircraft are affected, so that the measured static pressure is very necessary to calibrate. The core of static pressure calibration is to establish an error mapping relation between a measured static pressure and a free flow real static pressure, and generally, the difference value between a standard static pressure (such as a static pressure output by a standard airspeed head) and a static pressure measured by a machine body and relevant data such as Mach, attack angle and the like need to be analyzed to form a static pressure calibration coefficient. And the atmospheric data computer calculates static pressure calibration coefficient values in real time in a table look-up interpolation or curve fitting mode according to the current Mach, attack angle and other data, and further calibrates the measured static pressure to finally obtain the real static pressure of the free flow. In practical application, in order to obtain accurate static pressure calibration coefficients, professional engineers are required to screen test flight data, identify the test flight data one by one, and reject unstable flight, for example, if the roll angle change rate is large in interval, abnormal static pressure value jump points of a sensor or icing conditions of static pressure holes with environmental interference are selected, data which can represent current flight characteristics are selected and substituted into a static pressure calibration coefficient calculation method to obtain final effective static pressure calibration coefficients, and a segmentation interpolation method or a curve fitting method is carried out after data screening. Meanwhile, the process is that professional engineers manually label data, and the judgment standards of different engineers on effective data are different, so that a certain deviation exists in the result. Disclosure of Invention The invention provides a static pressure error calibration method and device based on a density distribution clustering algorithm, which improve static pressure calibration efficiency and accuracy. The first aspect of the invention provides a static pressure error calibration method based on a density distribution clustering algorithm, which comprises the following steps: Step one, acquiring a static pressure error calibration coefficient vector Cp S according to flight data; performing correlation analysis on each element in the Cp S vector and corresponding flight data, determining factors influencing Cp S, and establishing a mapping relation matrix; Step three, carrying out standardization processing on all dimension data in the mapping relation matrix according to weights or data dimensions to obtain a normalized matrix; performing density cluster analysis on the normalized matrix to obtain high-density distribution data points; Acquiring clustering center data of each high-density distribution point; Step six, carrying out scattered point interpolation fitting on the clustering center data step by step according to the envelope range to obtain a final static pressure error calibration