A method for measuring icing meteorological parameters

Abstract

The present invention discloses a method for measuring icing meteorological parameters, comprising measuring pressure data under various wind speeds and directions using an icing meteorological parameter measuring device, and constructing a first database using the data; training a BP neural network N1 optimized based on a particle swarm algorithm based on the first database to obtain a first artificial neural network that calculates wind speed and direction based on pressure; measuring ice shapes at detection points under various icing conditions using the icing meteorological parameter measuring device to obtain data on effective ice thickness and icing rate, and establishing a second database; training a BP neural network N2 optimized based on a dung beetle algorithm based on the second database to obtain a second artificial neural network that calculates average volume diameter of water droplets and liquid water content of icing meteorological parameters based on ice thickness and wind speed and direction; the method avoids complex optical path construction and can be used for real-time measurement of icing meteorological parameters during engine icing tests and aircraft airworthiness verification flights.

Description

Technical Field

[0001] The present invention relates to the field of aerospace, and in particular to a method for measuring icing meteorological parameters. Background Art

[0002] Existing methods for measuring mean effective droplet diameter (MVD) and liquid water content (LWC) are primarily based on rotating multi-cylinder methods and optical measurement. The rotating multi-cylinder method is a key method for ice wind tunnel benchmark calibration. This method involves mounting multiple cylinders of varying diameters on concentric axes and rotating them at low speed under icing conditions. After a period of time, the ice thickness and density of each cylinder are measured to calculate the incoming flow's LWC and MVD. While this method offers reliable measurement accuracy, it can only be used for calibrating parameters in icing environments and cannot provide real-time measurements. The basic principle of optical measurement is to use a charge coupled device (CCD) to record the fluctuations in the laser scattering and interference fringes of droplets of varying diameters, thereby calculating the average droplet diameter. MVD measurements can be performed using optical instruments such as forward scattering spectrometers (FSSPs), optical particle sizers (OAPs), Malvern particle sizers (MPSAs), and phase Doppler particle analyzers (PDPAs). However, most of these measurement methods cannot meet the requirements of real-time measurement, and the optical equipment that can achieve real-time measurement has great limitations when used. For example, if it is necessary to measure the icing meteorological parameters behind the fan in the engine, it is difficult to arrange the optical path. Summary of the Invention

[0003] In order to solve the above problems, the present invention provides a solution that avoids complex optical path construction and can be used for real-time measurement of icing meteorological parameters during engine icing tests and aircraft airworthiness verification flights.

[0004] In order to achieve the above object, the present invention provides a method for measuring icing meteorological parameters, comprising the following steps:

[0005] Step 1: Using the above-mentioned icing meteorological parameter measurement device to measure data of various wind speeds and wind directions, a first database of incoming wind speeds and wind directions corresponding to pressure is constructed based on the data;

[0006] Step 2: training the BP neural network N1 optimized by the particle swarm algorithm according to the first database to obtain a first artificial neural network that calculates the wind speed and direction of the incoming flow according to the pressure;

[0007] Step 3: Using the above-mentioned icing meteorological parameter measurement device to measure ice shapes at the detection point under various icing conditions, obtain data on effective ice thickness and icing rate, and establish a second database;

[0008] Step 4: Train the BP neural network N2 optimized based on the dung beetle algorithm according to the second database to obtain a second artificial neural network that calculates the average volume diameter of water droplets and liquid water content of icing meteorological parameters according to ice thickness and wind speed and direction.

[0009] Optionally, the icing meteorological parameter measuring device includes: a disc-shaped base, the edge of the base protrudes toward one side to form an open cavity, the center of the cavity is connected to one end of a connecting rod, the other end of the connecting rod is connected to a hemispherical accommodating cavity, a plurality of icing ultrasonic sensors for measuring the thickness of the ice layer are arranged inside the base, and a pressure sensor is arranged in the accommodating cavity.

[0010] Optionally, step 2 includes:

[0011] The input condition of BP neural network N1 is the pressure corresponding to different positions in the first database, and the output is the corresponding incoming wind speed and direction;

[0012] The BP neural network N1 is trained using the particle swarm algorithm;

[0013] During the training process, the particle swarm algorithm is used to optimize the initial threshold and weight matrix of the BP neural network N1 to determine the optimal threshold and weight;

[0014] The trained BP neural network N1 is used to predict the incoming wind speed and direction.

[0015] Optionally, the BP neural network N1 is trained using a particle swarm algorithm, including:

[0016] Randomly generate a cluster and initialize the particles and particle velocities in the cluster;

[0017] According to the individual, the initial weights and thresholds of the BP neural network N1 are obtained. After the BP neural network N1 is trained with the training data, the system output is predicted. The absolute sum of the errors between the predicted output and the expected output is taken as the individual fitness value, the maximum error between the predicted output and the expected output is taken as the individual fitness value, and the sum of the maximum error and the average error between the predicted output and the expected output is taken as the individual fitness value.

[0018] Three fitness functions are used and the optimization accuracy of the algorithm under different fitness functions is compared;

[0019] ;

[0020] ;

[0021] ;

[0022] Where: n is the number of output nodes of the network; is the expected output of the i-th node of the BP neural network; is the predicted output of the i-th node; l is the coefficient;

[0023] In each iteration, particles update their velocity and position through individual extreme values ​​and group extreme values, and the update formula is:

[0024] ;

[0025] ;

[0026] Where: is the inertia weight; ; is the current iteration number; is the velocity of the particle; and is a non-negative constant; and For distribution in A random number between is the position of the particle, is the individual extreme value, is the group extreme value.

[0027] When the particle fitness value is less than the individual extreme value, the new fitness value is replaced by the individual extreme value; when the particle's individual extreme value is less than the population extreme value, the individual extreme value is replaced by the population extreme value. The update formula is:

[0028] ;

[0029] Where: The fitness value of the new particle.

[0030] Optionally, step 3 includes:

[0031] Set multiple freezing conditions based on the relationship between the average volume diameter of water droplets, liquid water content and temperature;

[0032] calculating the ice shape under the corresponding icing conditions according to the icing conditions;

[0033] According to the different ice shapes, appropriate measuring point positions and numbers are selected on the above-mentioned icing meteorological parameter measuring device, the effective ice thickness at the measuring points is obtained, and a second database is established.

[0034] Optionally, the second database is established by the following steps:

[0035] The ice thickness L and the corresponding ice parameters and incoming flow parameter vector Y are obtained by numerical calculation or experiment. The ice thickness L is measured by an ice detection ultrasonic sensor.

[0036] The Kriging algorithm is used to construct a mathematical proxy model. The icing parameters and the incoming flow parameter vector Y include temperature, average volume diameter of water droplets, liquid water content, and incoming flow wind speed and direction.

[0037] The mathematical proxy model takes the temperature, incoming wind speed and direction, and ice thickness L in Y as input, and the average volume diameter of water droplets and liquid water content as output, generating a second database for training the neural network N2.

[0038] Optionally, step 4 includes:

[0039] The input conditions of BP neural network N2 are the effective ice thickness and ambient temperature, wind speed and wind direction in the ice database, and the output conditions are the meteorological parameters of average volume diameter of water droplets and liquid water content;

[0040] The BP neural network N2 is trained using the dung beetle algorithm;

[0041] During the training process, the dung beetle algorithm is used to optimize the initial threshold and weight matrix of the BP neural network N2 to determine the optimal threshold and weight;

[0042] The trained BP neural network N2 is used to predict the meteorological parameters of average volume diameter of water droplets and liquid water content.

[0043] Optionally, the BP neural network N2 is trained using the dung beetle algorithm, including:

[0044] The position of the rolling dung beetle is updated as follows:

[0045] ;

[0046] ;

[0047] Where t represents the current number of iterations, represents the position information of the i-th dung beetle at the t-th iteration, It is a natural coefficient, indicating whether it deviates from the original direction. It is assigned to -1 or 1 according to the probability method. k∈(0,0.2) represents the deflection coefficient, b∈(0,1) represents the constant, and k and b are set to 0.1 and 0.3 respectively. represents the global worst position, Used to simulate light intensity changes;

[0048] The formula for updating the position of the rolling ball dung beetle dance is defined as follows:

[0049] ;

[0050] In the formula ∈[0, π] represents the deflection angle, When it is equal to 0, π / 2 or π, the position of the dung beetle will not be updated;

[0051] The brood ball uses a boundary selection strategy to simulate the female dung beetle's egg-laying area. The egg-laying area is defined as:

[0052] ;

[0053] ;

[0054] Where, represents the current local optimal position, and Represent the lower and upper limits of the spawning area, respectively, R=1-t / T max , T max represents the maximum number of iterations, Lb and Ub represent the lower and upper bounds of the optimization problem respectively;

[0055] During the iteration process, the position of the brooding ball changes dynamically and is defined as:

[0056] ;

[0057] Where, is the position information of the i-th brooding ball at the t-th iteration, b1 and b2 represent two independent random vectors of size 1×D, and D represents the dimension of the optimization problem;

[0058] Establish an optimal foraging area to guide dung beetle larvae to find food and simulate their foraging behavior. The optimal foraging area is defined as:

[0059] ;

[0060] ;

[0061] Where, represents the current local optimal position, and represent the lower and upper limits of the optimal foraging area, respectively;

[0062] The location of the Dung Beetle has been updated as follows:

[0063] ;

[0064] Where, is the position information of the i-th dung beetle at the t-th iteration, C1 represents a random number following the normal distribution, and C2∈(0,1) represents a random vector;

[0065] The Thief Dung Beetle's position is updated as follows:

[0066] ;

[0067] In the formula, g represents a random vector of size 1×D that follows the normal distribution, S represents a constant value, and X b It’s the perfect location for food competition.

[0068] The present invention offers advantages over existing technologies in that it uses a pressure sensor within the device's hemispherical cavity to determine local wind speed and direction. An ultrasonic icing sensor mounted within a disc-shaped base then measures ice thickness. Combined with the local wind speed and direction, this sensor calculates local icing meteorological parameters (average droplet volume diameter and liquid water content) in real time. This device avoids complex optical path construction and can be used for real-time measurement of icing meteorological parameters during engine icing tests and aircraft airworthiness verification flights. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a structural diagram of an icing meteorological parameter measuring device provided by one embodiment of the present invention;

[0070] Figure 2 This is a flow chart of a method for measuring icing meteorological parameters provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0072] Reference Figure 1 This embodiment provides an icing meteorological parameter measuring device, comprising: a disc-shaped base 1, the edge of the base 1 protruding toward one side 2 to form an open cavity, the center of the cavity is connected to one end of a connecting rod 3, the other end of the connecting rod 3 is connected to a hemispherical accommodating cavity 4, a plurality of ice detection ultrasonic sensors 5 for measuring ice thickness are arranged inside the disc-shaped base, and a pressure sensor (not shown in the figure) for measuring wind speed and direction is arranged in the accommodating cavity 4.

[0073] In this embodiment, the base 1 has a diameter of 2 cm and a thickness of 0.5 cm, and the accommodating cavity 4 has a diameter of 0.5 cm.

[0074] Number and layout of pressure sensors and ultrasonic ice detection sensors. Five pressure sensors are installed on the accommodating chamber 4; twenty ultrasonic ice detection sensors are installed on the disc-shaped base. It should be noted that the layout and number of sensors can be adjusted to increase or decrease density based on the required accuracy.

[0075] The working principle of the device is as follows:

[0076] The pressure distribution on the surface of the hemispherical chamber 4 of this device varies under different incoming wind speeds and directions. Therefore, several pressure sensors arranged within the hemispherical chamber can measure the pressure at corresponding locations on the hemispherical surface. By pre-establishing a mathematical model N1 that uses the pressure at a specific surface point as input and the incoming wind speed and direction as output, the incoming wind speed and direction can be calculated in real time based on the surface pressure measured by the pressure sensors. Furthermore, a de-icing device is installed within the hemispherical chamber 4 to prevent ice from forming on the hemispherical surface and affecting the pressure measurement. Under varying wind speeds, directions, and icing conditions, ice formations at the base 1 and protrusion 2 of this device vary. Therefore, several ultrasonic ice detection sensors can be deployed at these locations. A pre-established mathematical model N2 takes ice thickness at a specific surface point and wind conditions (wind speed, direction, and temperature) as inputs, and outputs icing parameters (LWC and MVD). This model calculates the incoming flow parameters (LWC and MVD) in real time based on the local temperature, ice thickness at that specific point measured in real time by the ultrasonic ice sensors, and the incoming flow speed and direction calculated by N1. The edge of the base 1 has a protrusion 2 to one side, creating an open cavity to prevent overflow water loss and the resulting loss of LWC calculation accuracy. This device enables real-time measurement of icing parameters without requiring a complex optical path. Therefore, it can address the challenge of measuring icing parameters within an icing wind tunnel, where optical path measurement is often difficult. Alternatively, it can be mounted on an aircraft surface to measure icing parameters in real time during flight.

[0077] Reference Figure 2 This embodiment also provides a method for measuring icing meteorological parameters, comprising the following steps:

[0078] Step S10: using the icing meteorological parameter measurement device described in the above embodiment to measure data of various wind speeds and directions, and constructing a first database of incoming wind speeds and directions corresponding to pressure based on the data.

[0079] Specifically, the appropriate locations and number of pressure measuring points are selected on the icing meteorological parameter measurement device described in the above embodiment. A mathematical proxy model is constructed using the Kriging algorithm using the pressure vector P at each pressure measuring point and the corresponding atmospheric data vector Y, obtained through numerical calculation or experimentation. The atmospheric data vector Y includes wind speed and direction. The mathematical proxy model uses Y as input and P as output to generate a first data set for training the neural network N1.

[0080] Step S20: Training the BP neural network N1 optimized by the particle swarm algorithm according to the first database to obtain a first artificial neural network that calculates wind speed and direction according to pressure.

[0081] Specifically, the particle swarm algorithm is used to optimize the initial weights and thresholds of the BP neural network, enabling the optimized first artificial neural network to better predict the function output. The elements of the particle swarm algorithm for optimizing the BP neural network include population initialization, fitness function, particle velocity and position updates, and individual and population extreme value updates.

[0082] (1) Population initialization: A cluster is randomly generated, and the particles and particle velocities in the cluster are initialized. Each particle in the cluster consists of four parts: the connection weight between the input layer and the hidden layer, the hidden layer threshold, the connection weight between the hidden layer and the output layer, and the output layer threshold. The particle contains all the weights and thresholds of the neural network. When the network structure is known, a neural network with a certain structure, weights, and thresholds can be constructed.

[0083] (2) Fitness function: The initial weights and thresholds of the BP neural network are obtained according to the individual. The BP neural network is trained with training data and then the system output is predicted. The absolute sum of the error between the predicted output and the expected output is used as the individual fitness value, the maximum error between the predicted output and the expected output is used as the individual fitness value, and the sum of the maximum error and the average error between the predicted output and the expected output is used as the individual fitness value. Three fitness functions are used and the optimization accuracy of the algorithm under different fitness functions is compared.

[0084]

[0085]

[0086]

[0087] Where: n is the number of output nodes of the network; is the expected output of the i-th node of the BP neural network; is the predicted output of the i-th node; l is the coefficient.

[0088] (3) Particle velocity and position update: In each iteration, particles update their velocity and position through individual extreme values ​​and group extreme values. The update formula is:

[0089]

[0090]

[0091] Where: is the inertia weight; ; is the current iteration number; is the velocity of the particle; and is a non-negative constant; and For distribution in A random number between . is the position of the particle, is the individual extreme value, is the group extreme value.

[0092] (4) Update of individual extreme value and population extreme value: When the fitness value of a particle is less than the individual extreme value, the new fitness value is replaced by the individual extreme value; when the individual extreme value of a particle is less than the population extreme value, the individual extreme value is replaced by the population extreme value. The update formula is:

[0093]

[0094] Where: The fitness value of the new particle.

[0095] The trained BP neural network N1 is used to predict the incoming wind speed and wind direction. After the BP neural network N1 is optimized using the particle swarm algorithm, the BP neural network N1 is used to predict the wind speed and wind direction, providing conditions for the subsequent prediction of icing meteorological parameters.

[0096] Step S30: Use the icing meteorological parameter measurement device described in the above embodiment to measure the ice shape at the detection point under various icing conditions, obtain data on effective ice thickness and icing rate, and establish a second database.

[0097] Specifically, different icing conditions are set based on the relationships between the mean volume diameter (LWC) of water droplets and liquid water content (MVD) at the maximum icing state, as well as the incoming flow parameters, as specified in Appendix C of the "China Airworthiness Standard for Transport Aircraft [CCAR-25-R4]." The ice thickness L and the corresponding icing parameters and incoming flow parameter vector Y are obtained through numerical calculations or experiments. Ice thickness L is measured at the detection point. A mathematical surrogate model is constructed using the Kriging algorithm. The icing parameters and incoming flow parameter vector Y include temperature, MVD, LWC, incoming flow velocity, and angle of attack. The mathematical surrogate model uses temperature, incoming flow wind speed and direction, and ice thickness L in Y as inputs, and MVD and LWC as outputs, generating the second dataset for training the neural network N2.

[0098] Step S40: Training the BP neural network N2 optimized by the dung beetle algorithm according to the second database to obtain a second artificial neural network for calculating the average volume diameter of water droplets and liquid water content of icing meteorological parameters according to ice thickness and wind speed and direction.

[0099] Specifically, the input conditions for BP neural network N2 are the effective ice thickness in the ice database and the ambient temperature, wind speed, and wind direction. The output conditions are the meteorological parameters of average water droplet volume diameter and liquid water content. BP neural network N2 is trained using the dung beetle algorithm. It should be noted that the dung beetle population in this algorithm is divided into four components: ball-rolling dung beetles, brood balls, young dung beetles, and thief dung beetles. Ball-rolling dung beetles dynamically adjust their movement direction by simulating natural conditions (such as light and wind direction) to initially explore safe foraging areas. Brood balls are placed in known safe areas to hatch and eventually develop into adults. Young dung beetles, hatched from brood balls, concentrate on local search in the currently optimal foraging area. Thief dung beetles obtain resources by stealing the discoveries of other dung beetles or directly utilizing information about optimal foraging areas.

[0100] (1) Ball-rolling dung beetle: The ball-rolling dung beetle uses the sun as a navigation tool to ensure that the dung ball rolls along a straight path. Natural factors such as light intensity and wind will affect the ball-rolling dung beetle's route. The ball-rolling dung beetle's position is updated as follows:

[0101] ;

[0102] ;

[0103] Where t represents the current number of iterations, represents the position information of the i-th dung beetle at the t-th iteration, It is a natural coefficient, indicating whether it deviates from the original direction. It is assigned to -1 or 1 according to the probability method. k∈(0,0.2) represents the deflection coefficient, b∈(0,1) represents the constant, and k and b are set to 0.1 and 0.3 respectively. represents the global worst position, Used to simulate light intensity changes;

[0104] The formula for updating the position of the rolling ball dung beetle dance is defined as follows:

[0105] ;

[0106] In the formula ∈[0, π] represents the deflection angle, When it is equal to 0, π / 2, or π, the dung beetle's position will not be updated.

[0107] (2) Brooding ball: The brooding ball uses a boundary selection strategy to simulate the egg-laying area of ​​female dung beetles. The egg-laying area is defined as:

[0108] ;

[0109] ;

[0110] Where, represents the current local optimal position, and Represent the lower and upper limits of the spawning area, respectively, R=1-t / T max , T max represents the maximum number of iterations, Lb and Ub represent the lower and upper bounds of the optimization problem respectively;

[0111] During the iteration process, the position of the brooding ball changes dynamically and is defined as:

[0112] ;

[0113] Where, is the position information of the i-th brooding ball at the t-th iteration, b1 and b2 represent two independent random vectors of size 1×D, and D represents the dimension of the optimization problem.

[0114] (3) Young dung beetles: Establish an optimal foraging area to guide dung beetle larvae to find food and simulate their foraging behavior. The optimal foraging area is defined as:

[0115] ;

[0116] ;

[0117] Where, represents the current local optimal position, and represent the lower and upper limits of the optimal foraging area, respectively;

[0118] The location of the Dung Beetle has been updated as follows:

[0119] ;

[0120] Where, is the position information of the i-th dung beetle at the t-th iteration, C1 represents a random number following the normal distribution, and C2∈(0,1) represents a random vector.

[0121] (4) Thief Dung Beetle: The location of the thief dung beetle is updated as follows:

[0122] ;

[0123] In the formula, g represents a random vector of size 1×D that follows the normal distribution, S represents a constant value, and X b It is the best position for food competition.

[0124] Finally, the trained BP neural network N2 is used to predict the meteorological parameters of average volume diameter of water droplets and liquid water content.

[0125] This measurement method trains neural network N1 using pressure data provided by the front end of the measuring device. It then combines predicted wind speed and direction with ice thickness data from the back end of the device to train neural network N2. The measuring device enables real-time measurement of icing parameters. Experimental measurements show that the MVD measurement error is less than 3μm, demonstrating that this method not only measures both incoming airflow and meteorological parameters using the same device, but also does so with high accuracy.

[0126] The above describes, with reference to the accompanying drawings, an exemplary flowchart for implementing a method for measuring icing meteorological parameters according to an embodiment of the present invention. It should be noted that the numerous details included in the above description are merely illustrative of the present invention and are not intended to limit the present invention. In other embodiments of the present invention, the method may have more, fewer, or different steps, and the order, inclusion, and functional relationships between the steps may differ from those described and illustrated.

Claims

1. A method for measuring icing meteorological parameters, characterized in that: The following steps are involved: Step 1: Using an icing meteorological parameter measurement device to measure pressure data at various wind speeds and directions, a first database of incoming wind speeds and directions corresponding to pressure is constructed using the data; Step 2: Training the BP neural network N1 optimized by the particle swarm algorithm according to the first database to obtain a first artificial neural network for calculating wind speed and direction according to pressure; Step 3: Using an icing meteorological parameter measuring device to measure ice shapes at detection points under various icing conditions, obtain data on effective ice thickness and icing rate, and establish a second database; Step 4: Train the BP neural network N2 optimized based on the dung beetle algorithm according to the second database to obtain a second artificial neural network that calculates the average volume diameter of water droplets and liquid water content of icing meteorological parameters according to ice thickness and wind speed and direction.

2. The measuring method according to claim 1, wherein The icing meteorological parameter measuring device includes: a disc-shaped base, the edge of the base protrudes toward one side to form an open cavity, the center of the cavity is connected to one end of a connecting rod, and the other end of the connecting rod is connected to a hemispherical accommodating cavity. A plurality of icing ultrasonic sensors for measuring the thickness of the ice layer are arranged inside the base, and a pressure sensor is arranged in the accommodating cavity.

3. The measuring method according to claim 1, wherein The step 2 includes: The input condition of BP neural network N1 is the pressure corresponding to different positions in the first database, and the output is the corresponding incoming wind speed and direction; The BP neural network N1 is trained using the particle swarm algorithm; During the training process, the particle swarm algorithm is used to optimize the initial threshold and weight matrix of the BP neural network N1 to determine the optimal threshold and weight; The trained BP neural network N1 is used to predict the incoming wind speed and direction.

4. The measuring method according to claim 3, characterized in that The BP neural network N1 is trained using the particle swarm algorithm, including: Randomly generate a cluster and initialize the particles and particle velocities in the cluster; According to the individual, the initial weights and thresholds of the BP neural network N1 are obtained. After the BP neural network N1 is trained with the training data, the system output is predicted. The absolute sum of the errors between the predicted output and the expected output is taken as the individual fitness value, the maximum error between the predicted output and the expected output is taken as the individual fitness value, and the sum of the maximum error and the average error between the predicted output and the expected output is taken as the individual fitness value. Three fitness functions are used and the optimization accuracy of the algorithm under different fitness functions is compared; ； ； ； Where: n is the number of output nodes of the network; is the expected output of the i-th node of the BP neural network; is the predicted output of the i-th node; l is the coefficient; In each iteration, particles update their velocity and position through individual extreme values ​​and group extreme values, and the update formula is: ； ； Where: is the inertia weight; ; is the current iteration number; is the velocity of the particle; and is a non-negative constant; and For distribution in A random number between is the position of the particle, is the individual extreme value, is the group extreme value; when the particle fitness value is less than the individual extreme value, the new fitness value is replaced by the individual extreme value; if the individual extreme value of the particle is less than the population extreme value, the individual extreme value is replaced by the population extreme value, and the update formula is: ； Where: The fitness value of the new particle.

5. The measuring method according to claim 2, characterized in that The step 3 comprises: Multiple icing conditions are set based on the average volume diameter of water droplets, the relationship between liquid water content and temperature, and the incoming wind speed and direction; calculating the ice shape under the corresponding icing conditions according to the icing conditions; According to the different ice shapes, appropriate measuring point positions and numbers are selected on the icing meteorological parameter measuring device, the effective ice thickness at the measuring points is obtained, and a second database is established.

6. The measuring method according to claim 5, characterized in that The second database is established by the following steps: The ice thickness L and the corresponding ice parameters and incoming flow parameter vector Y are obtained by numerical calculation or experiment. The ice thickness L is measured by an ice detection ultrasonic sensor. The Kriging algorithm is used to construct a mathematical proxy model. The icing parameters and the incoming flow parameter vector Y include temperature, average volume diameter of water droplets, liquid water content, and incoming flow wind speed and direction. The mathematical proxy model takes the temperature, incoming flow velocity, angle of attack and ice thickness L in Y as input and the average volume diameter of water droplets and liquid water content as output to generate a second database for training the neural network N2.

7. The measuring method according to claim 2, characterized in that The step 4 comprises: The input conditions of BP neural network N2 are the effective ice thickness and ambient temperature, wind speed and wind direction in the ice database, and the output conditions are the meteorological parameters of average volume diameter of water droplets and liquid water content; The BP neural network N2 is trained using the dung beetle algorithm; During the training process, the dung beetle algorithm is used to optimize the initial threshold and weight matrix of the BP neural network N2 to determine the optimal threshold and weight; The trained BP neural network N2 is used to predict the meteorological parameters of average volume diameter of water droplets and liquid water content.

8. The measuring method according to claim 7, characterized in that The BP neural network N2 is trained using the dung beetle algorithm, including: The position of the rolling dung beetle is updated as follows: ； ； Where t represents the current number of iterations, represents the position information of the i-th dung beetle at the t-th iteration, It is a natural coefficient, indicating whether it deviates from the original direction. It is assigned to -1 or 1 according to the probability method. k∈(0,0.2) represents the deflection coefficient, b∈(0,1) represents the constant, and k and b are set to 0.1 and 0.3 respectively. represents the global worst position, Used to simulate light intensity changes; The formula for updating the position of the rolling ball dung beetle dance is defined as follows: ； In the formula ∈[0, π] represents the deflection angle, When it is equal to 0, π / 2 or π, the position of the dung beetle will not be updated; The brood ball uses a boundary selection strategy to simulate the female dung beetle's egg-laying area. The egg-laying area is defined as: ； ； Where, represents the current local optimal position, and Represent the lower and upper limits of the spawning area, respectively, R=1-t / T max , T max represents the maximum number of iterations, Lb and Ub represent the lower and upper bounds of the optimization problem respectively; During the iteration process, the position of the brooding ball changes dynamically and is defined as: ； Where, is the position information of the i-th brooding ball at the t-th iteration, b1 and b2 represent two independent random vectors of size 1×D, and D represents the dimension of the optimization problem; Establish an optimal foraging area to guide dung beetle larvae to find food and simulate their foraging behavior. The optimal foraging area is defined as: ； ； Where, represents the current local optimal position, and represent the lower and upper limits of the optimal foraging area, respectively; The location of the Dung Beetle has been updated as follows: ； Where, is the position information of the i-th dung beetle at the t-th iteration, C1 represents a random number following the normal distribution, and C2∈(0,1) represents a random vector; The Thief Dung Beetle's position is updated as follows: ； In the formula, g represents a random vector of size 1×D that follows the normal distribution, S represents a constant value, and X b It is the best position for food competition.

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